This is a story about stories. Who gets to tell them? What shapes them? Do they help or hinder us? This is the most significant story of our time. It’s about that word – ‘significance’.

We too often think of the news, the media, our information diet, as moving from controlled, old, censored – in the age of Kings, dictators, and the mainstream media (MSM) – towards a freer, more independent, more rational, media.

We think the internet has freed information – citizen journalism, truth to power, a new era. But what’s fascinating is that people have thought that since the dawn of the media. That in being part of a new way of doing journalism, people were on the side of good, the side of progress, the side of history. It’s part of a larger assumption – that we inevitably move from controlled to freer societies.

How many of these faces do you know? What’s happening, when the radically infinite globe-spanning and interest-diverse possibilities of the internet seem to coalesce around recognisable figures with common interests that almost seem to be friends?

Eerily, this isn’t new. Maybe history does repeat itself.

This is why to understand this new era – whatever you want to call it – new media, alternative media, new elites – we have to understand old media, legacy media, corporate  media, mainstream media. Where did they come from? What motivated them? How did they begin to fall? What patterns can we learn? Understanding one might give us some clues to the future of the other.

Is this really new media vs old elites? Are the MSM decent, noble, fourth estate journalists holding the powerful to account? Or are they stooges? Puppets? Too close to power? Too self-interested?

We’re going through a historical shift. I think it’s important to understand this moment in the longest view possible.

The media are a set of institutions which represent, in many ways, public opinion. They shape the world. And now, that happens as much through Rogan as through the BBC, as much through Daily Wire as through National Review, as much through Jordan Peterson as through CBC.

All of these people are institutions that have ideas about what’s significant in the world, what to talk about, how to talk about it. And those sets of ideas have always changed – from the beginning of the press, through to radio, television, to today.

What is the truth? Who gets closer to it? Tucker Carlson or the NYT? Russell Brand or The Telegraph? Me or another channel? What I want to describe is how the truth gets shaped in the first place. Because the truth is a complex irreducible thing. There are so many issues. Which ones get picked as significant? In what ways? I want to lay out a way of judging them – not as right or wrong – but as how they’re made, why, so that we can be critical ourselves.

Contents:

• Becoming Mainstream
• The Television Revolution
• Manufacturing (PC) Consent
• The Internet’s First Media
• The Ideology of the New Elites
• New Elites, Assemble!
• Who’s Right? Who’s Biased?
• Sensationalism
• Free Speech
• Beware the Guru
• The Market Always Wins
• A Better Media (What To Do?!)

Becoming Mainstream

We think of the media as a vast established set of loosely similar institutions, but a look at the history of the media illustrates how these institutions have changed over time. To understand, say, a US newspaper in the early 19th century or a Youtube channel in the 21st, we have to understand the relevant context, relationships, ideas, norms, laws, cultures, technology, and economic circumstances – all of which shape the information in very specific ways.

And despite this constellational context varying from place to place, period to period, there are some identifiable strands that run through.

Since Johannes Gutenberg invented the printing press in Germany in 1440, individuals and groups sought to mobilise this radical powerful technology for different purposes.

This revolution changed the world. It changed religion, giving people a chance to read for themselves, it gave a boost to national languages over Latin, to state bureaucracy, it weakened the church and made the renaissance and the Enlightenment possible, it aided commerce and exploration and created imagined national communities with shared identities – it gives credence to Marshall McLuhan’s famous phrase that it’s not so much what’s said, but the medium itself that’s the message.

Printers boomed everywhere. In London, across the 16th century the numbers went from 1 or 2, to 100. But censorship was the norm. The Vatican granted licences, the French government granted monopolies, the Tudors gave out licences for monopolies on different types of news. Arrests, executions, and control were the water within which printers swam. The first real newspapers were printed at the beginning of the 17th century.

We could even go back further. But as the historian John Nerone argues, the ‘media’ really became interesting when the state started to lose control of its grip on news, creating an ostensibly separate ‘fourth estate.’ This began happening during the English Civil War.

During the war, censorship collapsed and printing flourished. Then during its final act – the Glorious Revolution of 1689 – parliament passed the Bill of Rights that prevented the monarch from infringing on parliament’s freedom to speak.

By the middle of the 18th century, around 13 million were reading newspapers across Britain alone. This was the age of discovery, of science, of Enlightenment, of globalisation, and arguments for freedom of speech grew out of arguments for religious toleration from philosophers like John Locke.

The American Revolution and the US Constitution’s first amendment made the press a truly independent force for the first time. Slowly, a confident, separate, and increasingly powerful ‘fourth estate’ emerged across America and Europe.

But this initial media was really dominated by pamphleteering rather than reporting – partnerships between printers and philosophers, politicians and public figures. Pamphleteering was at the root of the drive towards American independence.

By the French Revolution and the early 19th century, there was an admirable diversity of opinion – federalists, anarchists, communists, utopian socialists, theologians, liberals, monarchists, conservatives all debated the nature of what the best society would like through Europe and America-wide networks of correspondence, books and pamphlets.

I say admirable because this was truly diverse and truly influential. To take one example, Thomas Paine’s The Rights of Man (1791) sold 200,000 copies in its first few years. Many others had a similar readership. The population of Britain at the time was just 10m. That 1 in 50 bought copies (bearing in mind most couldn’t read and books would be read aloud to groups and passed around) demonstrates the extent of passionate and engaged, widespread and diverse debate. The US population was only 2.5 million at the time.

Hundreds of newspapers were published at the beginning of the 19th century in Britain alone, despite the government trying to crack down on radical dissent. The British government passed notorious stamp acts, taxing cheap publications out of existence. Across the 18th century taxes on printing rose by 800%.

These have been called the ‘taxes on knowledge’ and had an affect on the type of news printed. That original diversity started to be quite literally stamped out.

This original diversity of opinion was slowly transformed into the large media corporations we know today. This happened for several reasons.

Initially, it was cheap to start a newspaper. Hand presses could only print 500-5000 copies at a time, so there was a limit on the reach and size. This meant it was quite easy to start a newspaper. In the UK, the Northern Star – a big newspaper that advocated for parliamentary reform and democracy in Britain – was started with donations from the public. As we’ve seen, cheap, simple pamphlets were everywhere.

Then, in the 1810s, the steam powered printing press was invented, making it cheaper to print larger runs at greater cost but cheaper cost per print. These new machines could print 4000 impressions per hour, meaning that for the first time a national daily newspaper could be printed and distributed. But it was too costly to be affordable to all. That meant including advertising.

Advertisers went with middle-class bourgeois newspapers for obvious reasons. One advertising executive wrote at the time that certain publications should be avoided because, ‘their readers are not purchasers, and any money thrown upon them is so much thrown away’.

These ‘bourgeois’ newspapers quickly became larger institutions that made use of technology like the telegraph, railway, reporters, linograph images, electricity, industrialisation, then photography and new printing techniques – all of which made them more efficient and eye-catching.

All of this made it expensive and difficult to compete with the large newspapers who also spent time and money lobbying parliament to reduce taxes on them so that they could, as one editor said ‘instruct the masses’ and ‘put the unions down.’

They included more ‘human interest’ stories, sensationalism, consumerism, ballads, murder mysteries, and folk tales that were easy to read and entertaining.

This was a reasonably simple formula: commercialisation + industrialisation + populism = sales. Any working class press just couldn’t keep up. It was no longer cheap and easy to start a paper that might be successful.

The Sunday Express in Britain, for example, launched in 1918 and spent £2m and had to acquire a circulation of 250,000 before it even broke even.

In the US, media moguls Joseph Pulitzer and William Randolph Hearst got into a competition investing in more expensive journalists, more technology, more sensationalist headlines, and more scary and fake news stories – what came to be called ‘yellow journalism’. Front page headlines with words like GUILTY, GLORY, TREACHORY, and SLAUGHTER became the norm.

In both the UK and US, crime, sexual violence, and sensationalist topics – murders, elopers, robbery – all became more profitable to report on. In 1886, murder stories made up 50% of the pages of London’s Lloyds Weekly, despite the rate in violent crime decreasing across the century.

This didn’t matter to publishers. Sales did. Their newspapers increasingly contained ‘entertaining’ titbits like ‘What does the queen eat? Why don’t Jews ride bicycles? What’s the color of the prime minister’s socks? Stories about a man-woman discovered in Birmingham and whether dogs can commit murder.’

Critics began to complain that the press was pandering to the worst in its reader’s tastes. Norman Angell labelled them ‘the worst of all the menaces to modern democracy.’ The Tory Prime Minister Stanley Baldwin said that press lords had ‘power without responsibility.’ He said they were ‘engines of propaganda for their constantly changing policies, desires, personal wishes, personal likes and dislikes.’

By the 20th century commissions were being setup to investigate their monopoly powers, a press council was setup in the UK that aimed to act as the industry’s ‘conscience’.

As powerful influential businessmen with advertising interests and a fear of an organised working class they even, in some cases, became cheerleaders for fascism. Lord Rothermere supported the BUF, his Mirror had headlines like ‘Hurrah for the blackshirts’ and ‘Give the blackshirts a helping hand.’

Even larger working class papers like The Daily Herald in Britain struggled to compete. In 1956 it was the fourth biggest newspaper in the country. And the most popular amongst working class readers. Despite this it only had a 3.5% share of advertising across the industry. Who would want to advertise to people who couldn’t afford products? Its fortunes declined, the paper was sold and became the tabloid The Sun.

This is the story of the press. From diversity to populism. From many smaller publications to a few corporate ones. To an interest in political ideas to an interest in entertaining ones. But the media had to at least give the appearance of being politically decent. Which is why they were drawn to attention grabbing, exaggerated, sensationalist moral panics.

In 1972, the criminologist Stanley Cohen noted while surveying the history of the press that during moral panics, ‘A condition, episode, person or group of persons emerges to become defined as a threat to societal values and interests; its nature is presented in a stylized and stereotypical fashion by the mass media’.

What seemed obvious and urgent to many, was that the press should be focusing on what was important – improving people’s lives, holding the powerful to account, searching out the issues and threat and dangers that affected the most people, their health, their bank accounts, their homes.

In the 70s, the sociologist Stuart Hall and his colleagues argued that the moral panic was a way for the ruling class to distract away from the real problems facing British society. Oil shocks, poverty, business leaving Britain, nuclear war – there are so many important things to focus on, yet the front pages of the press focused their attention on superficial scare stories.

The tabloid newspapers targeted everything from sexuality, family values, hooligans and gangs, paedophilia, aids, pornography, drugs, abortion, video games, violent films, witches, the youth of today – all of which in some way argued that the fabric of society was being eroded by an element within, and reporting that societal collapse through stories of good vs evil, emotion and drama, threat-entertainment with popular appeal.

As sociologist Kenneth Thompson writes, ‘Whilst professional groups with an interest in making claims for more resources, ranging from social workers and teachers to the police and probation officers, are often prepared to provide evidence of a crisis, sections of the mass media, subjected to market pressures, have responded by presenting dramatic narratives with a strong moral content. The result has been an almost bewildering succession of moral panics.’

These moral panics began as far back as witch trials, but in the 19th century adorned the front pages and spread fears of garrotting, for example, in London. Again, violent crime was decreasing, but in the late 19th century, a glance at the front pages of the press would have led a reader to believe that there was a pandemical threat. Harsh, reactionary, ill-conceived legislation was even passed by parliament. Historians now describe this as a classic ‘moral panic.’ Your biggest concern at the time wouldn’t be being garrotted but the factory you worked in.

By the twentieth century, there were moral panics about jazz, the beatniks, hippies, gay lifestyles, aids, and the rave scene.

Take one more recent example that Thompson discusses in his book. In the 90s, the British media ran with a panic over ecstasy. The death of one girl at a rave was covered ad Infinium.

‘It could be your child,’ the Daily Mail wrote. Today wrote: ‘Leah’s Last Words: She named Ecstasy pill pusher then pleaded “Help me mum, help me”’. MPs called for clubs to be closed. Reading the press at the time you’d be led to believe the rave scene was a demonic nightmare. Studies have shown how to the contrary, raves were safe, friendly, egalitarian spaces. Others pointed out that while any drug could be dangerous, it wasn’t ecstasy as much as the combination with high intensity dancing. In the US, only two ecstasy deaths had ever been reported.

But it was the sort of story the press loved. A hidden danger, limbically appealing, a counterculture, a threat to society, to family values. As Thompson points out, counterculture groups like raves, hippies, or LGBTQ or trans people, supposedly reject mainstream cultural values. He writes, ‘It is when these values seem to be being flouted that the media are likely to resort to discursive strategies that amplify the threat and generate a moral panic about the risks to the moral and social order, not just to the young people themselves.’

As the Observer newspaper warned in 1996, ‘Beware moral crusades. It is true that the British are alarmed and frightened by social fragmentation and growing violence. It is also true that the moral compasses by which to steer are increasingly uncertain. That does not mean the answer is a crusade led by party politicians or conservative newspapers — down that route leads a Dutch auction in repression. Worse, the real dynamics of social breakdown are left unaddressed.’

The moral panic is the likely consequence of a market-driven commercial populist press. Unable or unwilling to focus on economic issues – by the structure of ownership or by advertiser pressure – the press are drawn to stories that will boost sales by emphasising emotion, by selecting facts based on sensationalism, by exaggerating and distorting reality – a ‘discourse of the edges’ that ignored real substantive issues.

Ultimately, there was a transatlantic exchange. In his history of journalist, Martin Conboy writes there was an ‘Americanization of the British press between 1830 and 1914. Gossip, display advertising, sports news, human interest, fast stories transmitted by telegraph, cheap and increasingly visual newspapers, summary leads and front page news were all introduced in England in the 1890s.’

Ultimately, what we see is a history from complexity to simplicity, long pamphlets to quick summaries, nuance to populist appeal.

The Television Revolution

Something similar happened with television. This new powerful medium was never as diverse as the original press – the fifties were a famously conformist period – but in the early days of broadcasting, some tried to carve out a more ethical role for the media.

This was the corporate media at its peak. The BBC dominated radio and television in the UK, and almost everyone read a newspaper. In 1950 the total readership of daily newspapers in the US was 54 million. That was between one and two newspapers for every household across the country. Walter Cronkite anchored CBS for almost 20 years and was regularly voted the most trusted man in America.

Radio and television though were also conformist for another reason. There were a limited number of airwaves and so the FCC had to mandate that to acquire a broadcast license, some programmes had to be in the public interest.

To the early television broadcasters – CBS and NBS – news was unprofitable. People preferred entertainment. However, the FCC forced them to spend some money on news and documentaries.

Some took this responsibility seriously. Ed Murrow produced documentaries like See it Now that tried to shine a light on serious topics. It essentially invented the documentary and took aim at topics like the Red Scare, the Korean War, and Oppenheimer’s protests against nuclear weapons. But See it Now was cancelled after its advertisers dropped out and CBS became the focus of political pressure. Despite the show being popular, the head of CBS said the controversy was a ‘constant stomach ache’.

As with the press, commercial and economic pressures forced out a potential plurality of ethical discussion.

A softer approach was taken by shows like the Today Show which aimed to be a populist birds eye view of the day that, as one producer said, should distract people from the long day they had ahead of them.

Like the press, the trend was towards popular appeal, bigger audiences, and away from difficult topics. The same happened at the BBC, as the more serious programmes and John Reith’s hope that the BBC would ‘inform, educate, and entertain’, in that order, gave way to entertainment first. To many the Reith approach was elitist, but to him, entertainment was meant to be the dessert and now it was the main course.

In his history, Ponce de Leon writes: ‘television’s pioneering, wide-open phase was over. In the future, news and public-affairs programming like See It Now would struggle to find a place on network TV’.

Many bemoaned the media landscape. After See it Now was cancelled, Murrow said that the TV was a depressing spectacle of ‘decadence, escapism, and insulation from the realities of the world in which we live’. He continued: ‘This instrument can teach. It can illuminate; yes, it can even inspire. But it can do so only to the extent that human beings are determined to use it to those ends. Otherwise, it’s nothing but wires and lights in a box’.

Head of the FCC Newton Minow argued that television had become a ‘vast wasteland’.

But this was only critique. To some, the news that was being broadcast was elitist and snobbish anyway. It was urban and coastal and Washington or London-centric and looked down on ordinary people. Television, critics began to argue, had become part of the powerful establishment elite.

To a young Roger Ailes, working in television, the NYTs and CBSs of America liked to tell the rest of the country they were racist, sexist, and needed social security programs. He and the head of Coors beer, Joseph Coors, dreamed of a real conservative media, one that didn’t hold back.

At the same time, cable and satellite made the FCC regulation on licences obsolete, as anyone could make use of the expanded bandwidth. If anyone could broadcast, what was the purpose of the fairness doctrine, that forced the few stations to give opposing view points airtime? By the 80s, Reagan repealed the regulation and a new range of stations proliferated. ESPN, Nickelodeon, CNN, Rush Limbaugh – specialist channels and stations, partisan politics, and more populism. Unlike the early press, starting a television station was extraordinarily expensive, and relied on big business and advertising even more.

But like the newspapers before, Ailes and Murdoch in particular knew that the trick to popular news wasn’t just the news, it was all the trimmings – crime, gossip, good vs bad storylines, good-looking presenters, chemistry, sound and flashy visual effects, sensationalism, and moral panics.

De Leon writes, ‘In previous decades, most well-educated Americans, including many of the corporate elite, would have rejected market populism as a cynical and potentially dangerous excuse to exploit the public’s poor taste and most primitive yearnings. In this view, merely satisfying consumer demand without considering what you were selling was unseemly and amoral’.

By the 90s, Dan Rather said in a speech, ‘They’ve got us putting more and more fuzz and wuzz on the air, cop-shop stuff, so as to compete not with other news programs but with entertainment programs, including those posing as news programs’.

The OJ Simpson trial, America’s Talking, and A Current Affair all relied on new techniques inherited from the press – gossip, storylines, celebrity, flashy text and images – and Murdoch brought all of this together in the launch of Fox News in 1996.

In 2010, looking back, journalist Ted Koppel wrote: ‘The commercial success of both Fox News and MSNBC is a source of nonpartisan sadness for me. While I can appreciate the financial logic of drowning television viewers in a flood of opinions designed to confirm their own biases, the trend is not good for the republic… Beginning, perhaps, from the reasonable perspective that absolute objectivity is unattainable, Fox News and MSNBC no longer even attempt it.’

And at the same time, the internet was slowly beginning to creep into our homes, adding to the disillusion with traditional media.

Nerone writes that, ‘By the 1980s, then, and certainly by the 1990s, the professional press had come to seem a vulnerable institution. The people didn’t trust it. The powers that be were able to manipulate it. Journalism no longer seemed the institution of public intelligence that it wanted to be.’

The complaints in some sense seemed contradictory, though; on the one hand the media was a ‘vast wasteland’ of populist nonsense, on the other they were manufacturing political consent. And even here there was a disagreement. To someone like Ailes that consent was liberal, to Noam Chomsky, it was capitalist propaganda.

Manufacturing (PC) Consent

Most think that the critique of the media goes back to Chomsky and Herman’s book, but as far back as America’s founding, newspaper editor Hezekiah Niles was noticing that the press was moving closer to the political parties and were ‘manufacturing public opinion’.

He complained how the press arranged to, ‘act together as if with the soul of one man, subservient to gangs of managers, dividing the spoils of victory, of which these editors also liberally partake – more than one hundred and fifteen of them being rewarded with offices, or fat jobs of printing, &c. This is a new state of things’.

As the media commercialised, industrialised, and grew into gargantuan conglomerates, moguls like Hearst and Pulitzer expanded into new mediums – radio, film, then television.

As they did, many questioned how much these tentacled institutions represented public opinion. The most famous intellectual of the early 20th century, Walter Lippman, criticised the idea of ‘public opinion’ itself, lamenting how the public could be manipulated with propaganda, before propaganda was a dirty word. He saw the propaganda spread during the First World War, and presciently worried about the future. He called the picture painted by the press a ‘pseudoenvironment.’

The novelist Upton Sinclaire wrote an influential book in 1919 called The Brass Check in which he criticised the ‘yellow journalism’ of the period. He wrote, ‘In every newspaper-office in America the same struggle between the business-office and the news-department is going on all the time’.

He quoted the editor of the San Fransico Star, who had been quoted as saying, ‘You wish to know my “confidential opinion as to the honesty of the Associated Press.” My opinion, not confidential, is that it is the damndest, meanest monopoly on the face of the earth – the wet-nurse for all other monopolies. It lies by day, it lies by night, and it lies for the very lust of lying. Its news-gatherers, I sincerely believe, only obey orders’.

There was a feeling that the media conglomerates were the same large corporations dominating the gilded age of America – railroad barons, oil barons, and now press barons.

Nerone describes how the press responded by taking a more active, responsible, and ethical role in its own affairs, promising to be better, essentially becoming their own regulators.

He writes: ‘The motion picture industry obviously would do anything to make money, including glamorizing crime and transgressive sexuality. In contrast, the press took on the responsibility of informing the public to reinforce morality and public order. Adopting this exalted position meant that the press had to repress its own dark side. The superego of the press would be public affairs reporting. It hoped that its performance in this high-value enterprise would obscure or excuse its id: crime reporting, celebrity gossip, advertising, and trivialities like sports and amusements, where the bulk of its income was earned.’

What this meant was a bit more serious journalism. This happened in many countries at the beginning of the 20th century. Professionalisation meant starting journalism courses, an education in ethics, codes of conduct, regulation, more training – Pulitzer was an advocate of journalist courses in universities – arguing that students should study a bit of everything before entering into the workforce.

The criticisms of the press were enough for the US government to pass the 1912 Newspaper Publicity Act – ownership now had to be published, and content funded by advertisers had to made transparent. The act read, ‘editorial or other reading material… for the publication of which money or other valuable consideration is paid… shall be plainly marked as ‘advertisement’.

Some believed that the press could be a force for good. Nerone points out that the idea of objectivity in journalism didn’t really exist as an idea prior to the 1920s. The first appearance of ‘objectivity’ and ‘journalism’ in the NYT archive appears in 1924.

Journalists until then had what’s been called a ‘naive realism’ – reporting the facts of what happened but not much else, without any pushback, analysis, or investigation into whether what they’d been told by a source was the truth.

This had changed somewhat with the rise of muckraking, when journalists like Ida Tarbell had investigated the corruption, price-rigging and predatory tactics of monopolies like Rockefeller’s Standard Oil. Seeing the popularity of these sorts of investigations, they became more common.

When up against corporate power in an age of propaganda, marketing, advertising and PR, just reporting the naive facts was no longer enough. It would take some work to uncover the truth.

However, during the Cold War, it was hard to argue that capitalism itself was an issue. Exposés tended to focus on political intrigue, sensationalism, as we’ve seen got even more popular, and anti-communism and McCarthyism was the dominant mood.

Marxists and academics may have argued to varying degrees that the press were part of the capitalist superstructure, legitimising the social system they were a part of and benefited from, but for the most part these arguments were confined to the halls of academia rather than written about in the wider public sphere.

But the argument was there. At the beginning of the 20th century Antonio Gramsci had argued from his imprisonment in Fascist Italy that capitalist hegemony is perpetuated by the ruling class through culture. The Overton window, or what Henrik Ekengren Oscarsson has called the “opinion corridor”, sets the tone of the conversation, subtly directs it, perpetuates it. Oscarsson called the opinion corridor the “the buffer zone where you can still voice your opinion without immediately having to receive a diagnosis of your mental condition”.

Thomas Bates writes that, ‘intellectuals succeed in creating hegemony to the extent that they extend the worldview of the rulers to the ruled, and thereby secure the “free” consent of the masses to the law and order of the land.’

It took until the end of the 20th century for this view to approach the mainstream.

In 1988, Noam Chomsky and Edward Herman published Manufacturing Consent, arguing that the news was essentially propaganda for ‘powerful societal interests that control and finance them’.

They didn’t do this through blunt intervention, but by ‘the selection of right thinking personnel and by the editors’ and working journalists’ internalization of priorities and definitions of newsworthiness that conform to the institution’s policy’.

The big news conglomerates like Time Warner and Viacom kept dissenting voices at the margins, picked the right experts, and filtered out critical topics.

Chomsky said: ‘they are way up at the top of the power structure of the private economy which is a very tyrannical structure. Corporations are basically tyrannies, hierarchic, controlled from above. If you don’t like what they are doing you get out. The major media are just part of that system. What about their institutional setting? Well, that’s more or less the same. What they interact with and relate to is other major power centers – the government, other corporations, or the universities. Because the media are a doctrinal system they interact closely with the universities.’

The news corporations distract with sensationalism, side with Western crusades and ‘worthy’ victims, selectively using language, and are aggressively anti-communist.

Chomsky and Hermann laid out five filters through which information passed. The first is that the size, profit, and ownership of the mass media by itself filters out certain views and incentivised others. Market views are more acceptable than non-market views. The revolving door between politicians and media executives, between corporations and the state power.

The second filter is that the driving incentive is, ultimately, advertising and profit – the customer is the advertiser as much as the reader. They point to an NBC documentary on environmental issues that couldn’t get made because of a lack of advertisers.

The third filter is that they are dependent on a finite number of sources that are embedded in institutions like the White House or police departments or trade groups or embassies. The Pentagon, for example, spends billions on PR, the US Chamber of Commerce – a pro business lobby – spent $65 million in the year they were writing, and today that figure is over $200 million.

These groups, they write, ‘provide the media organizations with facilities in which to gather; they give journalists advance copies of speeches and forthcoming reports; they schedule press conferences at hours well-geared to news deadlines; they write press releases in usable language; and they carefully organize their press conferences and “photo opportunity” sessions.’

Fourth, that media is bombarded with what they called flak – ‘letters, telegrams, phone calls, petitions, lawsuits, speeches and bills before Congress, and other modes of complaint, threat, and punitive action’ – which nudges views away from criticisms of special interests.

And fifth, anti-communism is the ultimate dominant ideology. They write: ‘This ideology helps mobilize the populace against an enemy, and because the concept is fuzzy it can be used against anybody advocating policies that threaten property interests or support accommodation with Communist states and radicalism. It therefore helps fragment the left and labor movements and serves as a political-control mechanism.’

Ultimately, ‘The filters narrow the range of news that passes through the gates, and even more sharply limit what can become “big news”.’

But Chomsky’s wasn’t the only critique of the media, nor the most influential. As conservative talk radio shows like Limbaugh’s and Fox News grew, Murdoch, Ailes – the founders of Fox – and the wider conservative critique was that yes, the media were manufacturing consent, but liberal consent. Ailes called CNN the Clinton News Network.

So while the left were criticising the media for being propagandists for capitalism, the right were criticising them for having a socially liberal agenda on race, gender, and social security. That the media were ‘politically correct’ and wanted to tell you how to think, what to say, and who to support.

In 2004, the novelist Dorris Lessing called political correctness, “the most powerful mental tyranny in what we call the free world”.

In his history, Geoffrey Hughes says that, ‘linguistically it started as a basically idealistic, decent-minded, but slightly Puritanical intervention to sanitize the language by suppressing some of its uglier prejudicial features’. It meant not using certain words, or “It means showing respect to all,” or “It means accepting and promoting diversity.”

Where had this mental tyranny – to use Lessing’s phrase – emerged from? Some argued it came from the campuses protesting about race relations, gay rights, and feminism.

Lessing saw it as inspired by Mao’s Little Red Book, towing the party line, being politically in the right. She wrote that ‘Political Correctness is the natural continuum of the party line. What we are seeing once again is a self-appointed group of vigilantes imposing their views on others. It is a heritage of communism, but they don’t seem to see this’.

Hughes saw it as different because, ‘unlike previous forms of orthodoxy, both religious and political, it is not imposed by some recognized authority like the Papacy, the Politburo, or the Crown, but is a form of semantic engineering and censorship not derivable from one recognized or definable source, but a variety.’

But PC was nothing new: the Victorians’ idea of ‘being proper’, the French Revolutionaries’ battles of language, the Puritans – in fact, all societies have their forms of cultural and linguistic persuasions.

It was only a new form of cultural persuasion by a more active, engaged, socially liberal media. And rather than springing from one powerful little red book, the impulse more likely arose out of the cultural, linguistic, and postmodern turn in universities that more closely examined the power of language and culture in shaping people’s views.

Others argued the entire thing was made up. Clare Short wrote in the Guardian in 1995: ‘Political Correctness is a concept invented by hard-rightwing forces to defend their right to be racist, to treat women in a degrading way and to be truly vile about gay people. They invent these people who are Politically Correct, with a rigid, monstrous attitude to life so they can attack them. But we have all had to learn to modify our language. That’s all part of being a human being.’

What’s more interesting for the shift towards the internet is how both of these critiques arose around the same time and have both carried over into this new era.

The question posed by Chomsky and Ailes was really: can we see a monolithic ideology despite the appearance of diversity? Or do people see what they want to see? Are people driven by their own biases in interpreting the media as much as the media is driven by their own? Because by this point, as journalist Sandrine Boudana writes, ‘Journalism long ago abandoned the idea of seeking only neutrality and objectivity in pursuit of creating a more committed journalism, which makes it more difficult to differentiate between opinion and bias.’

This question – who is biased and who is right, and how these opposing critiques fed internet culture – is something we’ll return to. For now it’s worth pointing out that in fact, the critiques aren’t mutually exclusive. The media could be, to generalise, elitist, urban, socially and culturally liberal, close to politicians, driven by market forces and advertising, and biased by all of them, all at the same time.

But as we move into a new era it’s important to keep that dominant trend in mind, from both the early press and early television, that diversity, ethics, working class ideas, maybe high-mindedness, gets overwhelmed by the powerful forces of capital, of technology, of flashy frontpages and expensive studios, good looking presenters, and sensationalist, catchy, populist storylines.

The Internet’s First Media

On the surface, the internet is defined by pluralism, diversity, possibility. Anyone can post anything, anyone can start a podcast, anyone can build a YouTube channel, post on forums, on TikTok and Instagram.

Why then does it seem like this diverse digital landscape has coalesced slowly around specific individuals, groups, and talking points? If you’re interested in politics online, you’re unlikely to get through the day without seeing Joe Rogan, Jordan Peterson, or a Weinstein brother use the word woke.

The early years of the internet was much more like those early years of newspapers. There was a great diversity of ideas, a lot of techno-optimism, a strange unwieldy plurality of ideas. An early book on the internet – Clay Shirky’s Here Comes Everybody – illustrates the outlook of the early years – the subtitle was the power of organising without organisations.

This optimism in digital progress in some way confirmed that whiggish view of journalism – that the media, throughout history, gets freer and freer. Censorship, control and tyranny inevitably give way to free speech, the march of reason, a free press.

But as we’ve seen, and as many historians now argue, that is an old myth; a lazy, naive, triumphalist one. That original plurality in the press was centralised by commercial and industrial conglomerates. And the twentieth century proved that, in many countries, the media can go the direction of authoritarian control – towards Pravda or ministries of propaganda – rather than inevitably towards freedom. Early idealistic pioneers making programs like See it Now can be elbowed out of studios for lack of advertisers, and difficult stories can be replaced by ones with populist appeal. Could the internet be going the same way?

The internet is still, of course, much more diverse than any other medium. Costs are significantly reduced, accessibility increased. You can find videos and podcasts and reels on pretty much anything. Yet despite this, a kind of cohesive culture forms. A constellation of talking points, guests, ideas, and groups. What drives this? Human nature? Social dynamics? Economics? Culture? Politics? Let’s take a look at how this shift towards a cohesive culture happened.

Before around 2017, there were many alternative media outlets online beginning to make a name for themselves. The Drudge Report and Breitbart were loosely libertarian nationalist websites with the same kind of views as Fox News and Rodger Ailes. In 2010, Andrew Breitbart said he was “committed to the destruction of the old media guard.”

On the left, the Young Turks moved from radio to the web in 2006. The British left wing blog Another Angry Voice started in 2010. Joe Rogan started his podcast in 2009.

But while there were channels, blogs, and podcasts growing in prominence, the nascency of the internet put most on an equal footing. Plurality reigned. The internet was a DIY, amateur, botched together jumble of mouths all doing different things.

But around 2016 a shift began. This was the year of Trump and Brexit, both rebellions against the elite establishment of which the mainstream media was a part.

This was the year of Pizzagate, a year after Gamergate. It was the year of the Charlottesville rally and a similar march in Gothenburg, Sweden, which according to the organisers was the second most streamed video on YouTube around the world that day. It was the year that Eric Weinstein officially baptised the Intellectual Dark Web (IDW) as a group rebelling against the establishment status quo. It was a year, in short, of a revolt.

In that year, Vox reported that Infowars – Alex Jones’ conservative conspiracy-laden talk show – was getting 10 million visits a month, more than most mainstream media websites at the time.

Infowars themselves said that, ‘Government and the mainstream media have lost all credibility, leaving opportunity for the alternative media to swoop in and expose the truth, waking up people across the globe.’

Alternative Für Deutschland (AfD) – a Eurosceptic anti-immigration party in Germany – called the media the “Pinocchio-press”.

Two things were happening: certain topics, ideas, groups, and individuals were becoming dominant, and second, most of them were defined by their distrust, critique, or outright condemnation of the traditional media. They were all, to use a loose term, anti-establishment.

In his book on right-wing alternative media, professor Kristoffer Holt describes the process by which alternative media become anti-system media: ‘Alternative news media can publish different voices (alternative content creators) trying to influence public opinion according to an agenda that is perceived by their promoters and/or audiences as underrepresented, ostracized or otherwise marginalized in main stream news media. Alternative accounts and interpretations of political and social events (alternative news content), rely on alternative publishing routines via alternative media organizations and/or through channels outside and unsupported by the major networks and newspapers in an alternative media system.’

What’s interesting though, is how the plurality or diversity or independence turns into something relational. The new media or alternative media defined themselves in part by what they’re not.

He writes, ‘the alternative quality of any news medium is derived from claims to its counter-or complementary position to certain hegemony, since this must be construed as the organizing principle behind alternative media enterprises.’

To these critics, the MSM were defined in the same way Ailes defined them: as urban, elitist, snobbish, socially liberal, globalists. They were feminists, pro-immigration, anti- white.

As Holt writes, ‘The claim is that hegemonic mainstream media withhold or thwart the reporting on information that can be sensitive in light of a politically correct agenda.’

In picking talking points or ideas or guests, they aren’t independent in the sense that they pick them completely freely, but they’re picked through the lens of being ‘anti-system.’

This is not to make any moral judgement about the position, about any specific claim or opinion being right or wrong, only to note how it began to emerge. It is, of course, significant that the IDW, Pizzagate, Brexit, Trump, Gamergate, and the rise of Jordan Peterson, Bret Weinstein’s suing Evergreen and resigning, happened at around the same time. Despite the diversity of opinion on many topics, there was conformity on a central one – they were all, in some way, anti-system, anti-establishment, anti-mainstream media.

The IDW moment was notable because they seemed to able to define themselves by something they had in common despite claiming to have significant disagreements on other issues.

Holt writes of the IDW that, ‘what they have in common, according to their own descriptions (and the famous article by Weiss in NYT), is that they see themselves as “renegades” who have been ousted from mainstream platforms as a consequence of stating uncomfortable facts and opinions.’

Diversity was starting to come very loosely together, but only in opposition – not via specific common ideas but by what they defined themselves against.

The Ideology of the New Elites

Today, Joe Rogan has over 14 million listeners. Jordan Peterson videos get millions of views per video. The Daily Wire revealed in 2022 that it had 600,000 subscribers. Ben Shapiro has 7 million subscribers and each video gets watched hundreds of thousands of times. Lex Fridman has 4 million subscribers and gets millions of views per video.

But in some senses, the death of the MSM has been largely exaggerated. Where as CNN, Fox News, and the BBC ‘viewing’ figures, for example, are in decline, it’s because more people visit their websites rather than watch television. The decline of the MSM depends on the organisation and the metric. BBC News website figures are increasing. In April 2021, it had 1b visits. Musk pointed to the decline in website views on Bloomberg as evidence of the demise of legacy media. However, by other metrics – profit or Instagram followers, for example, Bloomberg is actually growing. And according to PressGazette, the Daily Wire’s traffic is declining more than Bloomberg. And Brietbart is down by 87%.

Similarly, the NYT website visits are growing, with around half a billion visits per month. Other traditional organisations like People, USA today, Forbes, Newsweek, and Politico are doing quite well, with all of their figures rising. In the UK, the old newspapers are declining, but there is some change; the Telegraph is seeing a month on month increase at the moment. Some like the Financial Times are growing. It’s a complex story with many ‘down’ by traditional measures but ‘up’ by new measures like subs, followers, and, importantly, profit. So while the Washington Post is not doing well, Newsmax is surging.

What is true is that trust in old media is at an all time low. However, with more choice, more narratives, diverse opinion, and more accountability, is this surprising?

Two trends are at least notable – the death knell of Mainstream Media is yet to be rung. No-one else is close to the sorts of views the BBC or NYT get. On the hand, the figures of Rogan and the appeal, book sales, and reach of someone like Peterson are at least very significant. These are, after all, individuals not organisations, and their influence is undeniable. Numbers aside, they are a cultural force. They are a new type of elite figure.

These new elite figures that have grown up on the internet – Peterson, Rogan, Shapiro, Russell Brand, the Weinsteins, Dave Rubin, Lex Fridman, Tim Pool, Triggernometry, and many others – all, in some part, are driven by their opposition to old media. That is, first and foremost, the base cultural water they swim in.

Culture is a strange thing. In some sense it’s like a common tree that we pluck language, ideas, art, jokes, music, or hobbies from. It’s a constellation – it shifts and moves but is loosely identifiable.

If culture is a tree, and anti-mainstream media is the trunk of the new elite, what sorts of other branches will likely grow from it? Branches are loose. They are not all the same. Some snap off. Some change. But they are loosely, often there.

The first is populism.

The idea that an establishment media system has failed lends itself to populism, which is less about who’s right or wrong, what’s driven the failures, or what to do them about, and more about the framing of an issue as being one defined by the people vs the elites.

Populism is a difficult term. On the surface, it’s just an appeal to what’s popular, what the people want, what ordinary Americans or average Brits are saying. The French philosopher Pierre-Andre Taguieff described populism as “the appeal to the people, at the same time as demos and as ethnos, against the elites, and against the foreigners”.

Populism frames in terms of an us and a them. Political scientists Casse Mudde defines populism as appealing to a ‘pure people’ vs ‘corrupt elite’, for example.

This is why populism often uses language like the ‘heartland’ or middle America – average, ordinary, hardworking, honest, people, just trying to get along. And the elites in the swamp are lazy, corrupt, enemies of the people.

The problem with populism is there is rarely an us and a them. The so-called ‘people’ are always fractured, have a diverse set of views, and vary from group to group, time to time.

To say the MSM is corrupt and ordinary people just want honest reporting is a populist statement. It might be true in certain instances, but it’s a generalisation that becomes quite meaningless when we ask what corruption means, which journalists we are talking about, and which ‘people’ we’re referencing.

However, the populist frame is enticing to a new elite figure outside the mainstream media. Look at how Russell Brand and Jordan Peterson talk about farmers. The farmers are pure, salt of the earth, decent, hardworking and honest. They’re against the elite globalists.

The reality is that, first, there are many different farmers – left, right, poor, rich, corporate, family, struggling, profitable. And the issues are usually not farmers vs the elite, but different interest groups’ influence on regulation, climate change, subsidies, and other issues.

To populists, instead, it all gets subsumed under the framing of ordinary vs elite. Ordinary is attractive. It appeals to more people. If you are statistically speaking an ordinary person, why shouldn’t I address you – the millions – against the elite. It’s a rhetorical numbers game because the people always outnumber the elite. If I can frame an issue that way, it’s going to be appealing to more people. There is a strong linguistic magnetic incentive to talk in this way.

Peterson consistently rallies against elites at universities, professional psychological associations and bodies, woke institutions and media – his Twitter timeline is full of condemnation of the elite. While Peterson, Brand, Rubin, and the Weinsteins are all themselves elites.

I’m not pointing to any instance of being right or wrong about any particular issue. Only how the conversation is framed. Anti-media establishment very often becomes populism.

The second tenet is anti-wokeism.

Why do almost every one of these figures tend to define themselves in terms of, or at least often refer back to, anti-wokeism?

Anti-wokeism seems to be the natural result of being anti-establishment, outside the mainstream, and populist. After all, it’s the liberal elites that are woke. Like the PC moment, urbane, middle-class, educated academics, politicians, and journalists want to tell the ordinary people how to think, what to believe, and who to vote for. They like to impose their ethical worldview on the rest of the world. They are, in short, snobs.

The critique here is very much a continuation of Roger Ailes and Fox News. Anyone anti-system naturally doesn’t like to conform. So anti-wokeism is natural to disgruntled academics like the Weinsteins and Peterson.

Someone like Critical Drinker can pop up on many of these channels because his film reviews follow this same anti-establishment, populist, anti-woke pattern – Hollywood is woke, the elites are ruining movies, and people just want X from their films.

The third tenet is freedom of speech.

Any system of authority imposes its rules, norms, ideas on the society and people it seeks to convince, propagandise, educate, inform, control – whatever you want to call it. This happens to varying degrees. Sometimes it can be good – as in education or maybe regulating the fringes of speech in, say, regulating pharmaceutical advertising. Sometimes it can be bad in the form of tyranny and propaganda or even just slight overreach.

But being anti-establishment naturally lends itself to being very pro-freedom of speech, in its different guises.

Holt writes, for example, that, ‘What unites [the IDW] is not primarily a common political or ideological agenda, but rather a sense that academic and intellectual freedom is seriously under threat because universities and the media are so influenced by left-wing identity politics and political correctness.’

When figures like Rogan, Peterson, the Weinsteins, or Shapiro get together, they might disagree on many things, but those things are less likely to come up. What they’re united by is their opposition to the establishment, the idea that wokeism has gotten out of hand and the government or the media are censorious – in other words, freedom of speech is fundamental.

Rogan says, for example, that he voted for Bernie Sanders, wants higher taxes, but that’s unlikely to come up when he talks to Musk, Peterson or James Lindsay – freedom of speech and wokeism will.

What’s of note is not that they might disagree on issues, but that they agree enough on certain issues to have that conversation around them and it not get acrimonious. This of course isn’t always the case, but it is the norm.

Their discussion is tailored to the person and revolves around the new elite talking points. Anti-establishment, anti-wokeism, freedom of speech. Tucker Carlson and Russell Brand’s conversations are a masterclass in this dynamic.

But we could broaden this out from freedom of speech to freedom more broadly. Vaccine hesitancy, for example, is common to all of these figures and is driven by the same distrust in the establishment.

Again, none of this is a moral judgement – we all shape our topics of conversation depending on who we’re with and where we are and what shared interests we have – we’re just trying to work out the logic of how these conversations are formed.

The fourth tenet is the mix of popular and political cultures.

For much of history, especially in Europe, there was a now pretentious idea of high and low culture. Theatre and politics and grand tours for the aristocracy and drinking and ballads and sports for the working class.

Politicians would keep outsiders at bay with complex cultural practices demarcating what’s proper. References to opera or Sophocles in debates about policy, for example.

The 20th century modern and postmodern artistic and literary movements were known for mixing high and low culture. Andy Warhol using consumer images in art, novels and films more about everyday life. There’s an entire fascinating literature on this.

Desperate to distinguish themselves from pompous metropolitan elites, new elite figures have a tendency to draw from popular culture and present themselves as ordinary. Carlson for example presents from a shed as if he’s a regular American in his garden.

Often, clearly with Carlson, it’s a cynical ploy. Politicians do it all the time – they’re desperate to appear normal.

But with internet culture it’s often genuine too. Bill Maher smoking weed in his basement on his podcast. Rogan alternates between serious ‘intellectual’ conversations and getting drunk with comedians and doing MMA shows.

What’s interesting, I think, is how this new expression of everyday experience on the internet – vlogging, chatting with friends, doing normal things outside of television studios – gets coopted and used by new elite figures.

Comedy is often naturally anti-establishment, so Peterson, Carlson, and Robert F. Kennedy can fit quite comfortably on someone like Theo Von’s podcast. In fact, RFK talks to a lot of comedians. The Triggernometry hosts, Russell Brand and anti-woke UK pundit Andrew Doyle – the voice behind Titiana McGrath – are all comedians.

Comedy is the perfect vehicle for conspiracy theories about vaccines, the World Economic Forum, the elites who are all covering up secrets. Alex Jones and Rogan can jump naturally from UFOs to vaccines. For Graham Hancock, the critique of archaeology as a discipline is fed through an entertaining narrative of a ‘lost civilization’. The memeification of politics turns complex issues into shareable soundbites.

These tenets – this constellation, these branches – act as incentives, impulses, the cultural water of the new elites; they’re branches that keep the tree together, acting as a social glue, and it’s around them that new elite social groups start to form.

New Elites, Assemble!

There is a large literature of studies on social groups. Social groups are, of course, a fundamental part of human life, and a group needs some principles in common. Clubs form around shared interests, political parties around ideas, friendship groups around hobbies or shared humour, media organisations out of a set of beliefs.

Being part of a group – whether that group is geographic or cultural – a Midwesterner, a banker, a leftist, an impressionist artist – provides a set of cultural norms, social expectations, dominant ideas, informal rules and methods – provide a grounding for identity. Being part of a group – officially or informally – is rewarding. Being in some groups confers status and social capital, connections, and a platform.

Bret Weinstein would not have been so known to us if he didn’t have a group affinity with other new elite figures like Peterson, Rogan, and Alex Jones.

There is a powerful incentive to agree with Joe Rogan on his podcast, to get an invite to dinner with him, to perform at his comedy club, to get him to put you in touch with Jordan Peterson.

These are the same incentives that playout in the mainstream media, as Chomsky and Herman pointed out. What’s interesting is how they’re also playing out in the transition from a diverse internet to a more homogenous internet.

Many studies show how people in groups mimic the behaviour of others in the group, conform their beliefs to the group to get accepted, and tend to point out the problems with other groups while ignoring their own.

In one famous study on conformity in 1951, psychologist Stanley Schachter studied a group discussing a trial. He found that most of the communication was directed towards bringing dissident voices into line. Furthermore, when asked to rate each person, the dissident was voted as most disliked.

Soloman Asch’s influential experiment showed participants lines of slightly different lengths. Each person had to call out whether each line was longer or shorter than the others. But Asch included actors who called out the wrong answer. When all of the actors in a group said a shorter line was the longest, the participant tended to conform to the group. Only a quarter never conformed. And 5% of people conformed 12 out of 12 times. While three quarters did at least once.

A similar experiment was conducted with pictures of a lineup. If other actors in the group gave the wrong answer, the participants were more likely to conform and follow.

Studies like this show not that people want to conform to fit in – although that is often true – but that they do so often without even knowing it. The social group we’re in directs our opinions before they’re even formed. Those individuals actually saw that line as longer.

Psychologist Charles Stangor writes, ‘conformity occurs not so much from the pursuit of valid knowledge, but rather to gain social rewards, such as the pleasure of belonging and being accepted by a group that we care about, and to avoid social costs or punishments, such as being ostracized, embarrassed, or ridiculed by others’.

In another study, researchers gave people cards with different traits on. They were then asked to put them into piles for different groups – women, young people, old people, students, etc.

They found that people perceive out-groups – groups other than their own – as more homogenous than their own group.

Men judging women included fewer traits, the young judging old included fewer traits.

In other words, there is an incentive to label the out-group – the mainstream media, the establishment, the old elites – by a homogenous label like corrupt, elitist, tyrannical, and people in the in-group as more diverse, plural, and decent.

New elite figures often describe themselves as diverse – Lex Fridman that he talks to all sides, Joe that he’s on the left, Brand that he’s talking across the divide – while describing the MSM as a corrupt homogenous out-group.

When you add the powerful incentive to form a group into our constellation of tenets, the magnetic effect of the social glue is compounded.

Texas is even becoming a bit of a hub. Rogan moved there from California. Lex Fridman moved there. I believe Musk is based there. Comedians like Gillis and Von have moved or are thinking of moving there. The Triggernometry bros spent time there.

Some hosts like Chris Williamson of Modern Wisdom even moved from the UK to the area, become friends with new elite figures like Michael Malice and Eric Weinstein, and become physically integrated in the circuit.

Konstantin Kisin reflected on his Oxford Union wokeism speech that it opened doors for him in America to people like Eric Weinstein.

Group formation psychology, anti-establishment talking points, populism, anti-wokeism, free speech, and comedy/conspiracy, all hang together in a constellation defining and shaping the views of the new elites. They act as a honey pot, a temptation, a powerful incentive to get views.

If you wanted to start a YouTube channel, there’s no better roadmap to follow. The gamut of low grade copycat channels that are popping up are a testament to this. None of them have any real qualification, are specialists in any area of expertise, or have anything new to say – but channels like RattleSnakeTV use shorts to piggyback on new media clips using sensationalist titles to get millions of views. Or take this guy’s top viewed – Tate, Peterson, and David Icke (and if you’re lucky enough to not know who any of those three are please, I beg you, you’ve won. Stop this video, log off, throw your laptop into the sea, and retire to a nice coastal village.)

Ok, so just to illustrate all of this let’s finish this section with a quick case study: Chris Williamson’s Modern Wisdom. As we do, bear in mind there are millions of experts that could provide ‘modern wisdom’ from around the world. Philosophers, historians, politicians from other countries. If you scroll through and listen to a few episodes of the Rest is History or the Ezra Klein show or Stuff You Should Know or whatever interests you. What I want to focus on is how a show ostensibly about modern wisdom gets shaped by new elite discourse.

Williamson was a reality TV dating show contestant in the UK who says he had a crisis of confidence about the sort of party boy lifestyle he was leading and started Modern Wisdom to search out modern wisdom. The early show included clips about life hacks, relationships, and fitness, before starting to get a few guest interviews from a range of psychologists, fitness experts, professors in politics. There was a decent range.

There were also some anti-woke populist figures too – people like Dave Rubin and Douglas Murray. But it’s securing an interview with Jordan Peterson in 2021 that gives the channel its first small shot in the arm. Even then views continued at a low pace. He interviews Peterson again in a video that has 4.8 million views.

From then, the channel starts shifting to new elite guests, talking points, and titles. The collapse of mainstream media, cancellation, critiques of Black Lives Matter, the legacy media is lying to you, more cancellation, why does Hollywood hate men, Tucker Carlson destroys mainstream media, and a lot of Peterson, Eric Weinstein, and Douglas Murray.

This is not to say that Williamson isn’t a decent, honest, well-intentioned guy who genuinely believes these things – I don’t know. It’s only to lay out the logic of how moving towards these individuals and beliefs is very rewarding. Williamson is particularly interested in the end of the mainstream media, the tyranny of the woke, Diversity, Equity and Inclusion – all of the branches we laid out.

William’s interviews with Eric Weinstein are almost perfect examples of the ideological constellation that drives these conversations and the group formation around them.

From the very beginning they’re onto woke DEI, that you apparently ‘can’t talk about it’, the secret establishment rules, and how outsiders are punished.

They discuss Claudine Gay’s resignation from the presidency of Harvard University after it was discovered she plagiarised several snippets of text without proper attribution. Gay acknowledged mistakes but claimed they were accidents, not substantive, and stepped down from her role. Many academics defended her, including one that she had plagiarised, saying, ‘ From my perspective, what she did was trivial—wholly inconsequential.’ She had, it turned out, included a technical description from someone without the proper reference.

This arguably is still bad, not to the standard acceptable for a president of major university, that she should step down. However, Williamson’s take on it is to quote the novelist Howard Jacobson who said he hoped the incident, ‘would be the start of people who knew nothing losing their jobs.’ With a wry smile, Williamson and Weinstein frame it in the usual anti-woke, DEI, anti-establishment liberal elite constellation.

Gay is clearly a respected academic who’s published many social science papers on race in America. One, for example, is a study on the link between having black representatives and political engagement more broadly. It’s been cited over 500 times.

Yet Williamson – a club organiser and reality TV contestant – with Weinstein, can confidently say with a cocky smirk that this is ‘someone who knows nothing’ and hope it’s the start of people like her losing their jobs.

This type of conversation is only explainable by applying the constellation of new elite ideology that incentives the direction of podcasts like Modern Wisdom. What you get are a relatively constrained set of parameters through which attention is directed. We can, after all, only focus on a finite set of ideas at a time.

It’s the sort of conversational frame repeated across the new elites. The titles of Brand’s videos are all Elon WARNED, Tucker REVEALS, Rogan BLASTS. Dave Rubin’s are the same. It’s why someone like Graham Hancock can do the rounds – a man who claims to be ostracised by the establishment because he challenges their lazy group think. Hancock doesn’t just believe there are lost advanced civilisations in the past, that they are the key to history, but that not finding them is a failure of the academic establishment.

In fact, it’s illustrative how much Hancock’s epistemic populism aligns with other figures. Peterson, Weinstein, Hancock – they’re all populist because they have an exciting theory of everything (literally in Weinstein’s case) that could help humanity that’s being supressed by the elites.

Economic ideas? Policy? Sociologists or any historian that’s not Niall Ferguson? Scientists and engineers that aren’t Musk? No, if you look through the Triggernometry, Modern Wisdom, or Dave Rubin it’s this stuff that gets the most views.

These are all political conversations. Yet if you look at polls of issues people think are the most important, the responses will be the economy, healthcare, education, housing, transport, immigration, welfare. And out of all the interesting academics, experts, countries, historical periods, philosophical ideas, political alternatives, novelists, poets, filmmakers and artists in the world, this is what these figures get drawn towards. This is the shape of new elite discourse.

Who’s Right? Who’s Biased?

I am trying my best to be in some sense neutral. It’s perfectly reasonable for Gay to step down. It’s reasonable to have discussions about university reading lists. People and institutions can, of course, be overly censorious, and free speech is fundamental. What I am pointing to is the framing. The incentive is to turn from reasonable debate to culture war, from a question about policy to populism vs the elites, from a question of justice to the woke being religious fanatics. Of course, the left have their biases too. And the MSM, as we’ve seen, have their own frames of biases. So how do we make sense of any of this?

Studies of biases have tended to find lots of different types of bias. In one review, scholars laid out seventeen, including confirmation bias, spinning and loaded language, choosing what to cover, exclusion, ideological bias, placement bias, sensationalism, the size or length of coverage, and so on. Bias can appear at the sentence level or the organisation level. But as we’ve seen it also changes from period to period, place to place. The early press had one set of ideas, the industrialists, the new elites, and the BBC another.

The biggest problem is that most of the time, supposed ‘bias’ or ‘propaganda’ is indistinguishable from what a person just really thinks. What’s the difference between bias and opinion, for example?

There are many clear cases of deceit or manipulation, on all sides. CNN doctoring a photo of Rogan to make him look more unwell. But more often than not, ‘bias’ is less about deceit and more about framing.

Konstantin Kisin of Triggernometry points to the MSM taking Trump quotes out of context as an example of biased media, while allowing themselves a lot more latitude in the sensationalist titling of their own videos – like ‘Critical Race Theory Made Me Suicidal’, ‘BLM Stands With Hamas,’ ‘This is why THEY lied about our history,’ and many others. Is this any better?

Similarly, Chomsky and Herman’s use of the word propaganda has been criticised for giving the impression that the bias is purposeful manipulation. The truth is, the topics they raised in Manufacturing Consent – the press being overly patriotic, anti-communist, pro-business, selective condemnation – was just how most Americans thought at the time.

Similarly, the filter model they adopt doesn’t explain how anti-capitalist news ever gets through the filter at all. Anti-monopoly investigations in companies like Rockefeller’s Standard Oil in the 19th century, the coverage of climate change, support for social services – this kind of journalism occasionally gets through. To pick two examples from recently, ITV broadcast a hugely influential drama on a Post Office scandal here in the UK and Channel Four often broadcasts programmes like one that looked at why our water companies are paying shareholders dividends while polluting our rivers.

The reason stories like this do get covered is because they’ll be popular and so producers, executives, and owners are likely to support them.

That said, it’s clear that there are limits to what will fit in the frame. This explains why the media prefer socially liberal topics that support popular progressive ideas without having to do much criticism of the capitalist economy that pays their not insubstantial wages. Is it propaganda to be in favour of the status quo? Or you just more likely to be pretty happy with the system if you’re a journalist at the NYT living more than comfortably?

I’ve been reading and watching a lot of different media in making this and it is difficult to generalise. The BBC is different to CNN, Fox News to the NYT, Chris Williamson different to Joe Rogan. Living in the UK, I don’t have much familiarity with the American channels, other than the clips I see, and I seem to get most of my news from lots of different places.

Ultimately, it is one big ecosystem. Ultimately, maybe the MSM was right to be mostly sceptical of Brexit – after all most of the ‘experts’, studies, rhetoric, polling, and so on, supported that scepticism. However, that doesn’t mean that they didn’t miss something. And that something like GB News isn’t, in fact, the child of that failure.

If everyone has biases, then it makes little sense to criticise all of them with the same broad brush, and it’s those generalisations – elite mainstream vs the people, woke/anti-woke, free speech/anti-free speech – that do the most generalisation and are likely to be least useful. We need less rhetoric and more granular, specific, rational conversations.

Maybe then instead of bias per se we should look at those trends that run through both the old MSM and the new elites, trying to work out why new media seems to be going in the same direction.

Sensationalism

There is an inevitable emotional incentive to sensationalise, to point to moral panics, to lean on outrage. Moral panics – whether about garrotting, gay rights, drag shows, or conspiracies – have the benefit of being targeted to a small minority of supposed deviants who can be blamed for the problems society is facing.

Is there much difference between the moralising of Aids in the 80s and the scapegoating of trans people today? Take these headlines from the 80s. The Sunday Express asked, ‘If AIDS is not an Act of God with consequences just as frightful as fire and brimstone, then just what is it?’. A Sun headline read, ‘AIDS is the wrath of God, says vicar’. Another Daily Express headline: ‘AIDS: Why must the innocent suffer?’, about using animals to test a potential cure. It was commonly called the ‘gay plague’. In 1986, the Star said it was a scandal that there were ‘GAY LOVERS ON ROYAL YACHT.’ Another Sun story said, ‘I’D SHOOT MY SON IF HE HAD AIDS, Says Vicar! He would pull trigger on rest of his family’.

Sociologist Jeffrey Weeks describes the moral panic like this: ‘the definition of a threat to a particular event (a youthful ‘riot’, a sexual scandal); the stereotyping of the main characters in the mass media as particular species of monsters (the prostitute as ‘fallen woman’, the paedophile as ‘child molester’); a spiralling escalation of the perceived threat, leading to a taking up of absolutist positions and the manning of moral barricades; the emergence of an imaginary solution—in tougher laws, moral isolation, a symbolic court action; followed by the subsidence of the anxiety, with its victims left to endure the new proscription, social climate and legal penalties.’

Again, there may be rational conversations to have on the details of certain issues, but the frame is that a woke elite is forcing their moral worldview on ordinary people. Could it not have been said that the1967 act to decriminalize homosexuality in Britain was the act of a woke establishment and academics? Could not almost all of the headlines about AIDs be reworked to include the words trans and be indistinguishable from new elite talking points today?

Some subjects are about personal taste. Literature, film, culture, poetry, art podcast – talk about what you want. But with politics, economics, the future of countries, the news, standards of evidence have to be applied more rigorously. What really affects people’s lives? What do people really want addressing? What are the most important issues?

Take this recent study that found that fewer than 1 in 1000 university courses in America contain references to critical race theory or ‘woke’ topics like Diversity, Equity and Inclusion. Is this represented proportionally in new media discussions? No.

Free Speech

The same applies to free speech. Obviously both the old media and the new elites will always claim to be fighting for the truth, fighting for freedom. A Daily Mail headline could be a Dave Rubin title.

In 2012, there was an investigation into phone hacking by the tabloid press in the UK, including the answer phone of a murdered 13-year-old girl. In response, The Sun protested that ‘this witch-hunt has put us behind ex-Soviet states on Press freedom’.

Free speech is too important an issue to be used as an excuse for bad behaviour. But it always has been. The entire discussion on free speech gets reduced to a populist narrative of good vs evil – the people who deserve to speak freely vs the elites who want to shut them up. However, to the Sun and many new elites speaking freely apparently means speaking without consequence, rebuttal, or rules.

Free speech is not black and white. There is no such thing as free speech absolutism, even in the US with a first amendment. We have copyright, libel, slander, and advertising standards and regulation. We have etiquette, codes of ethics, responsibility, spam, and moderation on social media platforms. So free speech is not a woke elite vs ordinary people issue. It’s an issue that often has to be decided by careful discussion about the particular issue.

Beware the Guru

I’ve borrowed guru from the Decoding the Gurus podcast here, which unpacks some of the talking points in this space.

But take a look at this event: Dissident Dialogues – “The World’s Leading Thinkers”. Some of these people might appropriately sit under that subheading, but to apply it to the Triggernometry hosts, Chris Williamson – even if you enjoy their interviews – seems a disservice to the millions of experts and scholars and leaders around the world that have published books and studies. The selection mechanism is not expertise but influence.

Chomsky and Herman pointed to how a Soviet defector became the US media’s favourite expert on USSR weapons and intelligence because he was, of course, pro-US policy.

Which reminded me of how the North Korean defector Yeonmi Park became a guest favourite of the new elite circuit not just because of her insights into life in North Korea, but because she was anti-woke, saying for example that, “that ‘cancel culture’ at U.S. colleges is the first step toward North Korean-style firing squads”.

Organisations – online and off – pick experts in a way that suits the wider ideological constellation of their worldview. If someone is popping up talking about a lot of issues, whether on the BBC or across YouTube, be sceptical.

The Market Always Wins

Underlying all of this is the worst incentive of all. The incentive for profit instead of the incentive for truth. From the early press, through to television, through to YouTube today, the trend in political content is away from diversity towards conglomerates that put flashy sets, sensationalist titles, and populist topics first.

This trend puts the plurality of smaller blogs, channels, and podcasts out of business because they take up all the air. Expensive and professional fancy sets by nature look more trustworthy and professional. Diary of a CEO and Modern Wisdom look better than any niche podcasts. They get the big guests, big advertisers, and the resulting big money.

Diary of a CEO and Daily Wire spend fortunes on Facebook advertising, testing thumbnails, investing in studios. This is exactly what happened to the radical press in the nineteenth century. Television went down this route too. The diversity of the Enlightenment was replaced by tabloid newspapers, and the early idealism of television gets replaced with easily consumable infotainment.

In 1958 there was a famous scandal when an American quiz show was fixed so that the most popular contestants could stay on to boost ratings. When this was discovered, it caused an outrage. To some, it was symbolic of the superficial direction television was going in – popularity over truth.

Why does Chris Williamson think modern wisdom is to be found in Eric Weinstein’s head? Because he’s a fixed quiz show contestant, giving popular answers to popular questions, fitting in perfectly with the new elite circuit.

If you want success then popularity will always trump truth. You will always give in to the temptation towards more clickbait sensationalist headlines, thumbnails, talking points, and guests.

Because underneath it all, none of them are anti-elite – all of them are or are becoming elites. And so the temptation will always be to avoid criticising the market forces, the advertisers, the system that they benefit from.

The real divide then isn’t between old media and new – it’s diverse, honest, broad, plural, truthful, reasonable conversation vs sensationalism, populism, clickbait, and moral panics.

There’s always, as we’ve seen, been cross-over. In fact, the market logic that incentivises and rewards grabbing eyeballs, distracting from the real issues, and stoking up fear, is a subtle logic that ultimately underlies both. It’s why someone like Douglas Murray can smoothly alternate between the two while claiming not to trust mainstream media. Ugh – you’ve just been on Fox News, Sky News Australia, and written a column in the New York Post, Douglas.

Piers Morgan was editor of The Mirror when they hacked the murdered girl’s phone – he knows how to whip up the crowd and has moved quite frictionlessly from the MSM to new media, piggy backing off new media figures with all of the predictable titling, topic, and guest strategies that now makes him indistinguishable from a youtuber.

Peterson similarly rallies against the elites while writing for their newspapers, appearing on television, and talking at conservative conferences.

None of them, despite what they say, are anti-elite. They’re just anti-left. And what’s most interesting is not the divide between old and new media, but that the rules of the game between old media and new are so similar.

A Better Media (What To Do?!)

We need three things – awareness, organisation, and people powered media.

First, I’m a critical person. But I think it’s optimistic to point out that right now, the media landscape is as diverse as its ever been. The range of content and mediums available to us has never been better. Amongst all the bias, on all sides, there’s great journalism going on – Pulitzer prize winners in the mainstream, five hour interviews on podcasts, niche subjects on YouTube.

But the monster in the room is the profit incentive – the incentive to be popular over truth – which incentivises the big sensationalist clickbait guests with the next big theory of everything. Everyone has to play the game a little bit – make eye-catching thumbnails and cover popular talking points – but when that takes you away from what’s important, what should be significant, what’s truthful, decent, and honest, then you become, to use an overused word, a grifter.

Sometimes a grifter is honest, sometimes, like a broken clock, a grifter can be right, but a grifter isn’t trustworthy over time. You can tell a grifter by the titles used by people like Rubin and Brand – the yellow journalists of our day. They advertise that they’re motivated by popularity over truth in their titles – it’s all ‘chilling warnings’, ‘IT’S HAPPENING’, and ‘Terrifying Truths’.

Strategies like this are ancient and inevitable, so there’s no better first defence against them than awareness. These titles and tactics should be mocked. They should be embarrassing. They should invite criticism. And they often, thankfully, do.

Second, we need organisations. There’s a common new elite talking point that we no longer need the mainstream, that they’re dying, redundant, dinosaurs. Musk talks about ‘citizen journalism’ and how he only gets his news from X. That uploading vlogs, tweeting about protests, having debates in the ‘marketplace of ideas’, is all you need.

But I don’t think the big media institutions are going anywhere. As we’ve many of them are doing better than is usually acknowledged. Furthermore, we need them. We need well-paid journalists, with competent editors, colleagues, and fact-checkers. We need networks of experts to call on, specialists, and analysts. We need organisations that have foreign correspondents, that can quickly and effectively get to another country for an unfolding story. Journalism is expensive work. Cameras, studios, archival access, travel, the clout to attract specialists are all costly.

This is revealed in the way new media figures often rely on old media to do the hard work of reporting. All of these figures criticise the legacy media establishment while relying on them to provide stories which they then sit and comment on.

Furthermore, I can say what I want, the only real check is myself, my reputation and accountability to you. However, within an organisation there are some extra constraints on hasty mistakes, foolish misjudgements, and white lies. There are benefits to being independent and benefits to being in an organisation. I’ve already seen the benefit of working with an editor who has sometimes pointed out something I should check, rethink, or reword. We all need to be held to account.

In The Constitution of Knowledge, Jonathan Rauch describes the ways in which scientific, social, and journalistic knowledge is usually the result of group dynamics rather than simple individual pursuit. Experiments are carried out, facts are gathered, ideas are shared, and editors, boards, journals, professors, and peer-reviewers all give advice, feedback, and guidance. The group shapes the ideas and outcomes collaboratively. Traditional media and universities have codes of conduct and rules for practice for this reason – to coordinate individuals in a group.

New elite figures have to do this much less. And so much more comes through that filter. False news, silly takes, UFO discourse, sensationalism, extreme points of view, personal attacks are all more likely in the more individualistic new media environment. It’s the equivalent of not having that friend who might urge you to reconsider something, or read an email for you, or talk you out of something.

There are exceptions – Daily Wire have group dynamics that make them more like an old media institution, Rogan has Jamie to ‘pull that up’, and YouTube channels are getting big enough to expand into groups. But ultimately, these are still just powerful individuals rather than institutions.

And unlike, say the BBC newsroom, which have their point of view, and make plenty of mistakes, individuals reporting on current events – especially in foreign countries – are much more likely to be unable to separate fact from fiction. Especially when countries like Russia spend millions spreading misinformation online purposefully designed to flood the information landscape and confuse.

So, that’s why organisations are important. But that’s not to say that individuals aren’t. There are benefits to being an individual or a small organisation – individuals are nimble, have few overheads, or constraints, and sometimes might be able to quickly poke holes in a story through commentary before a cumbersome organisation has time to deliberate, and equivocate, and do thorough research. They can also be more individualistic, creative, unique, or specific about what they personally think.

In some ways I think the future might belong to these middling organisations that are doing well – Daily Wire, Novara Media, TLDR news – some of these channels have the benefits of both being both small enough to be nimble and big enough to have budget and reach.

But finally, organisations of any size, individuals too, are all subject to the temptations towards populism and profit.

It’s not enough to think that people watch and listen to what they want to. That it’s just the coherence or truthfulness of ideas that determines who wins and loses in the new media marketplace. No, the market rewards figures like Peterson, Brand, and Triggernometry – it rewards populist rhetoric, anti-woke talking points, sensationalism, moral panics – it rewards scary talking points and it rewards articulate, charismatic, enticing personalities over careful, thoughtful, honest ones. It rewards those with good looks and good looking sets. Want to start a podcast? Do you have a Hollywood CGI set like Chris Williamson? No? Really? You’re not cinematic? You must not be credible.

Which is why – and yes, I know I would say this – which is why you should support the channels and podcasts that you stand by, and why those channels should be thinking about ways to attract your support. Without the corrective of people powered, community supported, diverse and plural media, we’ll get nowhere. Diversity of opinion produced the American and French Revolutions. Without them, we’d still be serfs and subjects.

The greatest trick the elites play is in convincing the public that they are not elites at all. Everywhere, elites tell you that they are being silenced, that they are marginalised, that they are speaking for the people, speaking truth to power. What they won’t tell you is that they are the powerful; that they have the market forces of populism and business interests on their side.

Claiming to be marginalised will always be popular, while the actual marginalised remain marginal. The New Elites, despite claiming to be ostracised from the mainstream, often end up being featured on them, and have far more power than they claim to.

What we need really from a media are a focus on issues than effect people’s daily lives. There’s room for other stuff, of course, but fulfilling that basic requirement is how journalists, commentators, and media should be judged.

And often, new media fulfils that promise. Joe Rogan has interesting guests on, Lex Fridman hosts an interesting conversation, and Peterson is right about something. However, what I think is identifiable is a shape, an archetype, a constellation that tempts and pulls towards these ideas, talking points, this ideology. And I think for the most part it is just that – an ideological fantasy divorced from reality.

In 1987, Watney wrote, ‘It is the central ideological business of the communications industry to retail ready-made pictures of ‘human’ identity, and thus recruit individual consumers to identify with them in a fantasy’.

It’s so easy to assume that the messages, ideas, and conversations we see online are individual opinions in the great marketplace of ideas and reason. It’s easy to forget that the reach, the volume, the selection, the social connections have forces behind them, forces that support some message while delegitimising others.

The status quo is broken, and polling always shows what people want to focus on – the economy, schooling, healthcare, infrastructure – then ask yourself this: who’s really focusing on those things, and who is choosing to continually talk about a few students, the censorious woke, the idea that bureaucrats are tyrannical? Who is actually talking to experts, academics, people with fresh ideas? And who is actually pretty successful in this new online media space?

The new media fantasy image is the noble warrior, fit and strong, with atomic habits, selling AG1, defending civilisation with one media podcast empire at a time, with a few exciting stories of success, entertainment, conspiracy theory, heroism, and evil along the way.

Unfortunately, the truth about political ideas, good history, studies, and discussion just doesn’t get the clicks.

Sources

Kevin Williams, Get Me a Murder a Day! A History of Mass Communications in Britain

Stanley Cohen, Folk Devils and Moral Panics

Kenneth Thompson, Moral Panics

James Curran and Jean Seaton, Power without Responsibility: The Press and Broadcasting in Britain

Kristoffer Holt, Right-Wing Alternative Media

Lee Mcintyre, Post-Truth

Geoffrey Hughes, Political Correctness: A History of Semantics and Culture

John Nerone, The Media and Public Life: A History

Charles Stangor, Social Groups in Action and Interaction, 2nd edition

Francisco-Javier Rodrigo-Ginés, Jorge Carrillo-de-Albornoz, Laura Plaza, A systematic review on media bias detection: What is media bias, how it is expressed, and how to detect it, Expert Systems with Applications

Noam Chomsky and Edward Herman, Manufacturing Consent: The Political Economy of Mass Media

Gabriel Sherman, The Loudest Voice in the Room

Tobin Smith, Foxocracy

Charles L. Ponce de Leon, That’s the Way it is, A History of Television News in American

David Brock and Ari Rabin-Havt, The Fox Effect

Bruce Bartlett, How Fox News Changed American Media & Political Dynamics

https://www.washingtonpost.com/news/morning-mix/wp/2017/03/27/veteran-newsman-ted-koppel-tells-sean-hannity-hes-bad-for-america/

https://www.vox.com/policy-and-politics/2016/10/28/13424848/alex-jones-infowars-prisonplanet

https://lfpress.com/opinion/columnists/chambers-peterson-intellectual-populism-at-its-worst

https://www.newyorker.com/news/q-and-a/why-some-academics-are-reluctant-to-call-claudine-gay-a-plagiarist

https://www.washingtonpost.com/news/morning-mix/wp/2017/03/27/veteran-newsman-ted-koppel-tells-sean-hannity-hes-bad-for-america/

https://www.unilad.com/celebrity/news/alix-earle-knocks-joe-rogan-off-the-top-podcast-spot-on-spotify-423445-20230926

https://www.thedailybeast.com/rfk-jrs-misinformation-campaign-is-fueled-by-comedians?ref=home?ref=home

The post The Fall of the Mainstream Media: The New Elites appeared first on Then & Now.

It’s an investigation into what it means to steal, to take, to replace, to colonise and conquer. Along the way we’ll learn what AI really is, how it works, and what it can teach us about intelligence – about ourselves – turning to some historical and philosophical giants along the way.

Because we have this idea that intelligence is this abstract, transcendent, disembodied thing, something unique and special, but we’ll see how intelligence is much more about the deep, deep past and the far, far future, something that reaches out powerfully through bodies, people, the world.

Sundar Pichai, CEO of Google, was reported to have claimed that, ‘AI is one of the most important things humanity is working on. It is more profound than, I dunno, electricity or fire’. We’ll see how that might well be true. It might change everything dizzyingly quickly – and like electricity and fire, we need to find ways of making sure that vast, consequential and truly unprecedented change can be used for good – for everyone – and not evil. So we’ll get to the future, but it’s important we start with the past.

Contents:

• A History of AI: God is a Logical Being
• History of AI: The Impossible Totality of Knowledge
• The Learning Revolution
• What Are Neural Nets?
• OpenAI and ChatGPT
• The Scramble for Data
• Stolen Labour
• Stolen Libraries and the Mystery of ‘Books2’
• Copyright, Property, and the Future of Creativity
• The End of Work and a Different AI Apocalypse
• Mass Unemployment
• The End of Humanity
• Or a New Age of Artificial Humanity
• Conclusion: Getting to the Future

A History of AI: God is a Logical Being

Intelligence. Knowledge. Brain. Mind. Cognition. Calculation. Thinking. Logic. 

We often use these words interchangeably, or at least with a lot of overlap, and when we do drill down into what something like ‘intelligence’ means, we find surprisingly little agreement.

Can machines be intelligent in the same way humans can? Will they surpass human intelligence? What does it really mean to be intelligent? Commenting on the first computers, the press referred to them as ‘electronic brains’.

Manchester, England

There was a national debate in Britain in the fifties around whether machines could think. After all, a computer in the fifties was in many ways already many times more intelligent than any human.

The father of both the computer and AI, Alan Turing, contributed to the discussion in a BBC radio broadcast in 1951, claiming that ‘it is not altogether unreasonable to describe digital computers as brains’.

This coincidence – between computers, AI, intelligence, and brains – strained the idea that AI was one thing. A thorough history would require including transistors, electricity, computers, the internet, logic, mathematics, philosophy, neurology, society. Is there any understanding of AI without these things? Where does history begin?

This ‘impossible totality’ will echo through this history, but there are two key historical moments: The Turing Test and the Dartmouth College Conference.

Turing wrote his now famous paper – Computing Machinery and Intelligence – in 1950. It began with: ‘I propose to consider the question, ‘Can machines think?’ This should begin with definitions of the meaning of the terms ‘machine’ and ‘think’.’

He suggested a test – that, for a person who didn’t know who or what they were conversing with, if talking to a machine was indistinguishable from talking to a human then it was intelligent. 

Ever since, the conditions of a Turing Test have been debated. How long should the test last? What sort of questions should be asked? Should it just be text based? What about images? Audio? One competition – the Loebner Prize – offered $100,000 to anyone who could pass the test in front of a panel of judges.

As we pass through the next 70 years, we can ask: has Turing’s Test been passed?

New Hampshire, USA

A few years later, in 1955, one of the founding fathers of AI, John McCarthy, and his colleagues, proposed a summer research project to debate the question of thinking machines.

When deciding on a name McCarthy chose the term ‘Artificial Intelligence’.

In the proposal, they wrote, ‘an attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves’.

The aim of the conference was to discuss questions like could machines ‘self-improve’, how neurons in the brain could be arranged to form ideas, and to discuss topics like creativity and randomness, all to contribute to research on thinking machines. The conference was attended by at least twenty now well-known figures, including the mathematician John Nash.

Along with Turing’s paper, it was a foundational moment, marking the beginning of AI’s history.

But there were already difficulties that anticipated problems the field would face to this day. Many bemoaned the ‘artificial’ part of the name McCarthy chose. Does calling it artificial intelligence not limit what we mean by intelligence? What makes it artificial? What if the foundations are not artificial but the same as human intelligence? What if machines surpass human intelligence?

There were already suggestions that the answer to these questions might not be technological, but philosophical. 

Because despite machines in some ways being more intelligent – making faster calculations, less mistakes – it was clear that that alone didn’t account for what we call intelligence – something was missing.

The first approach to AI, one that dominated the first few decades of research, was called the ‘symbolic’ approach.

The idea was that intelligence could be modelled symbolically by imitating or coding a digital replica of, for example, the human mind. If the mind has a movement area, you code a movement area, an emotional area, a calculating area, and so on. Symbolic approaches essentially made maps of the real world in the digital world. 

If the world can be represented symbolically, AI could approach it logically.

For example, you could symbolise a kitchen in code, symbolise a state of the kitchen as clean or dirty, then program a robot to logically approach the environment – if the kitchen is dirty then clean the kitchen.

McCarthy, a proponent of this approach, wrote: ‘The idea is that an agent can represent knowledge of its world, its goals and the current situation by sentences in logic and decide what to do by [deducing] that a certain action or course of action is appropriate to achieve its goals.’

It makes sense because both humans and computers seem to work in this same way.

If the traffic light is red then stop the car. If hungry then eat. If tired then sleep.

The appeal to computer programmers was that approaching intelligence this way lined up with binary – the root of computing – that a transistor can be on or off, a 1 or 0, true or false. Red traffic light is either true or false, 1 or 0, it’s a binary logical question. If on, then stop. It seems intuitive and so building a symbolic, virtual, logical picture of the world in computers quickly became the most influential approach.

Computer scientist Michael Wooldridge writes that this was because, ‘It makes everything so pure. The whole problem of building an intelligent system is reduced to one of constructing a logical description of what the robot should do. And such a system is transparent: to understand why it did something, we can just look at its beliefs and its reasoning’.

But a problem quickly emerged. Knowledge turned out be to far too complex to be represented neatly by these logical simple true-false if-then rules. One reason is the shades of uncertainty. If hungry then eat is not exactly true or false. There’s a gradient of hunger.

But another problem was that calculating what to do from these seemingly simple rules required much more knowledge and many more calculations than first assumed. The computing power of the period couldn’t keep up.

Take this simple game: The Towers of Hanoi. The object is to move the disks from the first to the last pole in the fewest number of moves without placing a larger disk on top of a smaller one.

We could symbolise the poles, the disks, and each possible move and the results of each possible move into the computer. And then a rule for what to do depending on each possible location of the disks. Relatively simple.

But consider this. With three disks this game is solvable in 7 moves. For 5 disks it takes 31 moves. For 10, its 1023 moves. For 20 disks, 1,048,575 moves. For 64 disks, if one disk was moved each second it would take almost 600 billion years to complete the game.

In AI this problem was named combinatorial explosion. That as you increase the number of possible actions for each action, the number of factors, the possible combinations, the complexity, becomes incomprehensibly vast and technologically difficult. 

And the Tower of Hanoi is a simple game. Combinatorial explosion became even more of a problem with games like chess or go. And a human problem like driving is infinitely more complicated. The red light is on or off but it might be broken, it might have graffiti or snow on, pedestrians might walk out regardless, a child run across – and that’s just the first element of a vast environment, the impossible totality.

Approaching AI this way was called ‘ ’ – that for each move, the computer had to search through each possible scenario and every bit of information to decide what to do.

In robotics, a similar approach was being taken, and proving even more complicated.

The development of AI coincided with technological advances in other areas: infrared, radars, cameras, microphones, batteries.

In 1971, Terry Winograd at MIT was developing a program called SHURDLU that aimed to symbolically model something he called blocks world.

In this virtual world, a user could ask the program to manipulate the blocks in different ways:

Person: Pick up a big red block.
Computer: OK.
Person: Grasp the pyramid.
Computer: I DON’T UNDERSTAND WHICH PYRAMID YOU MEAN.
Person (changing their mind): Find a block which is taller than the one you are holding and put it into the box.
Computer: BY “IT”, I ASSUME YOU MEAN THE BLOCK WHICH IS TALLER THAN THE ONE I AM HOLDING.
Computer: OK.

A year later, researchers at Stanford built a real life blocks world.

SHAKEY was a real robot that had bump censors called ‘cats whiskers’ and laser range finders to measure distance.

The robotics teams ran into similar problems as in the Towers of Hanoi problem. The environment was much more complicated than it seemed. The room had to be painted in a specific way for the sensors to work properly.  

The technology of the time could not keep up, and combinatorial explosion, the complexity of any environment, became such a problem that the 70s and 80s saw what’s now referred to as the AI winter.

History of AI: The Impossible Totality of Knowledge

By the 70s, some were beginning to make the case that something was being left out: knowledge.  The real world is not towers of Hanoi, robots and blocks – knowledge about the world is central. However, logic was still the key to analysing that knowledge. How could it be otherwise?

For example, if you want to know about animals, you need a database:

IF animal gives milk THEN animal is mammal
IF animal has feathers THEN animal is bird
IF animal can fly AND animal lays eggs THEN animal is bird
IF animal eats meat THEN animal is carnivore

Again, this seems relatively simple, but even an example as basic as this requires a zoologist to provide the information. We all know that mammals are milk-producing animals, but there are thousands of species of mammal and a lot of specialist knowledge. As a result, this approach was named the ‘expert systems’ approach. And it led to one of the first big AI successes.

Researchers at Stanford used this approach to work with doctors to produce a system to diagnose blood diseases. It used a combination of knowledge and logic.

If a blood test is X THEN perform Y.

Significantly, they realised that the application had to be credible if professionals were ever going to trust and adopt it. So MYCIN could show its workings and explain the answers it gave.

The system was a breakthrough. At first it proved to be as good as humans at diagnosing blood diseases.

Another similar system called DENDRAL used the same approach to analyse the structure of chemicals. DENDRAL used 175,000 rules provided by chemists.

Both systems proved that this type of expert knowledge approach could work. 

The AI winter was over and significantly, research began attracting investment.

But once again, expert system developers encountered a new serious problem. The MYCIN database very quickly became outdated.

In 1983, Edward Feigenbaum, a researcher on the project, wrote, ‘The knowledge is currently acquired in a very painstaking way that reminds one of cottage industries, in which individual computer scientists work with individual experts in disciplines painstakingly[..]. In the decades to come, we must have more automatic means for replacing what is currently a very tedious, time-consuming, and expensive procedure. The problem of knowledge acquisition is the key bottleneck problem in artificial intelligence’.

Because of this, MYCIN was not widely adopted. It proved expensive, quickly obsolete, legally questionable, and difficult to establish with doctors widely enough. Logic was understandable – but the collecting and the logistics of collecting knowledge was becoming the obvious central problem.

In the 80s, influential computer scientist Douglas Lenat began a project that intended to solve this.

Lenat wrote: ‘[N]o powerful formalism can obviate the need for a lot of knowledge. By knowledge, we don’t just mean dry, almanack like or highly domain-specific facts. Rather, most of what we need to know to get by in the real world is… too much common-sense to be included in reference books; for example, animals live for a single solid interval of time, nothing can be in two places at once, animals don’t like pain… Perhaps the hardest truth to face, one that AI has been trying to wriggle out of for 34 years, is that there is probably no elegant, effortless way to obtain this immense knowledge base. Rather, the bulk of the effort must (at least initially) be manual entry of assertion after assertion’.

The goal of Lenat’s CYC project was to teach AI all of the knowledge we usually think of as obvious. He said: ‘an object dropped on planet Earth will fall to the ground and that it will stop moving when it hits the ground but that an object dropped in space will not fall; a plane that runs out of fuel will crash; people tend to die in plane crashes; it is dangerous to eat mushrooms you don’t recognize; red taps usually produce hot water, while blue taps usually produce cold water; … and so on’.

Lenat and his team estimated that it would take 200 years of work, and they set about laboriously entering 500,000 rules on taken-for-granted things like bread is a food or that Isaac Newton is dead.

They quickly ran into problems. The CYC project’s blind spots were illustrative of how strange knowledge can be.

In an early demonstration, it didn’t know whether bread was a drink or that the sky was blue, whether the sea was wetter than land, or whether siblings could be taller than each other.

These simple questions reveal something under-appreciated about knowledge. Often, we don’t explicitly know something ourselves yet despite this the answer is laughably obvious. We might not have ever thought about the question is bread a drink or is it possible for one sibling to be taller than another, but when asked, we implicitly, intuitively, often non-cognitively just know the answers based on other factors.

This was a serious difficulty. No matter how much knowledge you entered, the ways that knowledge is understood, how we think about questions, the relationships between one piece of knowledge and another, the connections we draw on, are often ambiguous, unclear, and even strange.

Logic struggles with nuance, uncertainty, probability. It struggles with things we implicitly understand but also might find difficult to explicitly explain.

Take one common example you’ll find in AI handbooks:

Quakers are pacifists.
Republicans are not pacificists.
Nixon is a Republican and a Quaker.

Is Nixon a pacifist or not? A computer cannot answer this logically with this information. It sees this as a contradiction. While a human might explain the problem with this in many different ways, drawing on lots of different ideas – uncertainty, truthfulness, complexity, history, war, politics.

The big question for proponents of expert-based knowledge systems like CYC – which still runs to this day – is whether complexities can ever be accounted for with this logic based approach.

Most intelligent questions aren’t of the if-then, yes-no, binary sort, like: is a cat a mammal? 

Consider the question ‘are taxes good?’ It’s of a radically different kind than ‘is a cat a mammal?’. Most questions rely on values, depend on contexts, definitions, assumptions, are subjective.

Wooldridge writes: ‘The main difficulty was what became known as the knowledge elicitation problem. Put simply, this is the problem of extracting knowledge from human experts and encoding it in the form of rules. Human experts often find it hard to articulate the expertise they have—the fact that they are good at something does not mean that they can tell you how they actually do it. And human experts, it transpired, were not necessarily all that eager to share their expertise’.

But CYC was on the right path. Knowledge was obviously needed. It was a question of how to get your hands on it, how to digitise it, and how to label, parse, and analyse it. As a result of this, McCarthy’s idea – that logic was the centre of intelligence – fell out of favour. The logic-centric approach was like saying a calculator is intelligent because it can perform calculations, when it doesn’t really know anything. More knowledge was key.

The same was happening in robotics. 

Australian roboticist Rodney Brooks, an innovator in the field, was arguing that the issue with simulations like Blocks World was that it was simulated and tightly controlled. Real intelligence didn’t evolve in that way, and so real knowledge had to come from the real world.

He argued that perhaps intelligence wasn’t something that could be coded in but was an ‘emergent property’ – something that emerges once all of the other components were in place. That if artificial intelligence could be built up from everyday experience, genuine intelligence might develop once other more basic conditions had been met. In other words, intelligence might be bottom up, arising out of the all of the parts, rather than top-down, imparted from a central intelligent point into all of the parts.  Evolution, for example, is bottom up, slowly adding to single cell organisms more and more complexity until consciousness and awareness emerges.

In the early 90s, Brooks was head of the Media Lab at MIT and rallied against the idea that intelligence was a disembodied, abstract thing. Why could a machine beat any human at chess but not pick up a chess piece better than a child, he asked? Not only that, the child moves the hand to pick up the chess piece autonomically, without any obvious complex computation going on in the brain. In fact, the brain doesn’t seem to have anything like a central command centre – all of the parts interact with one another, more like a city than like a pilot flying the entire things. 

Intelligence was connected to the world, not cut off, ethereal, transcendent, and abstract.

Brooks worked on intelligence as embodied – connected to its surroundings through sensors and cameras, microphones, arms and lasers. The team built an insectoid robot called Cog. It had thermal sensors, microphones, but importantly no central control point. Each part worked independently but interacted together – they called it ‘decentralised intelligence’.

It was an innovative approach but never could quite work. Brooks admitted Cog lacked ‘coherence’. 

And by the late 90s, researchers were realising that computer power still mattered.

In 1996, IBM’s chess AI – Deep Blue – was beaten by grandmaster Gary Kasparov.

Deep Blue was an expert knowledge system – it was programmed with the help of chess players not just by calculating each possible move, but by including things like best opening moves, concepts like ‘lines of attack’, or ideas like choosing moves based on pawn position.

But IBM also threw more computing power at it. Deep Blue could search through 200 million possible moves per second with its 500 processors.

It played Kasparov again in 1997. In a milestone for AI, Deep Blue won. At first, Kasparov accused IBM of cheating, and to this day maintains foul play of a sort. In his book, he recounts an episode in which a chess player working for IBM admitted to him that: ‘Every morning we had meetings with all the team, the engineers, communication people, everybody. A professional approach such as I never saw in my life. All details were taken into account. I will tell you something which was very secret[…] One day I said, Kasparov speaks to Dokhoian after the games. I would like to know what they say. Can we change the security guard, and replace him with someone that speaks Russian? The next day they changed the guy, so I knew what they spoke about after the game’.

In other words, even with 500 processors and 200 million moves per second, IBM may still have had to program in very specific knowledge about Kasparov himself by listening in to conversations – this, if maybe apocryphal, was at least a premonition of things to come…

The Learning Revolution

In 2014, Google announced it was acquiring a small relatively unknown 4-year-old AI lab from the UK for $650 million. The acquisition sent shockwaves through the AI community.

DeepMind had done something that on the surface seemed quite simple: beaten an old Atari game.

But how it did it was much more interesting. New buzzwords began entering the mainstream: machine learning, deep learning, neural nets.

What those knowledge-based approaches to AI had found difficult was finding ways to successfully collect that knowledge. MYCIN had quickly become obsolete. CYC missed things that most people found obvious. Entering the totality of human knowledge was impossible, and besides, an average human doesn’t have all of that knowledge but still has the intelligence researchers were trying to replicate. 

A new approach emerged: if we can’t teach machines everything, how can we teach them to learn for themselves?

Instead of starting from having as much knowledge as possible, machine learning begins with a goal. From that goal, it acquires the knowledge it needs itself through trial and error.

Wooldridge writes, ‘the goal of machine learning is to have programs that can compute a desired output from a given input, without being given an explicit recipe for how to do this’.

Incredibly, DeepMind had built an AI that could learn to play and win not just one Atari game, but many of them, all on its own.

The machine learning premise they adopted was relatively simple.

The AI was given the controls and a preference: increase the score. And then through trial and error, it would try different actions, and iterate or expand on what worked and what didn’t. A human assistant could help by nudging it in the right direction if it got stuck.

This is called ‘reinforcement learning’. If a series of actions led to the AI losing a point it would register that as likely bad, and vice versa. Then it would play the game thousands of times, building on the patterns that worked.

What was incredible was it didn’t just learn the game, but quickly became better than the humans. It learned to play 29 out of 49 games at a level better than a human. Then it became superhuman.

This is the often demonstrated one. It’s called Breakout. Move the paddle, destroy the blocks with the ball. To the developers’ surprise, DeepMind learned a technique that would get the ball at the top so it would bounce around and destroy the blocks without having to do anything. It was described as spontaneous, independent, and creative. 

Next, DeepMind beat a human player at Go, commonly believed to be harder than chess, and likely the most difficult game in the world.

Go is deceptively simple. You take turns to place a stone, trying to block out more territory than your opponent while encircling their stones to get rid of them.

AlphaGo was trained on 160,000 top games and played over 30 million games itself before beating Lee Sedol in 2016.

Remember combinatorial explosion. This was always a problem with Go. Because there are so many possibilities it’s impossible to calculate every move. 

Instead, DeepMind’s method was based on sophisticated guessing around uncertainty. It would calculate the chances of winning based on a move rather than calculating and playing through all the future moves after each move. The premise was that this is more how human intelligence works. We scan, contemplate a few moves ahead, reject, imagine a different move, and so on.

After 37 moves in the match against Sedol, the AlphaGo made a move that took everyone by surprise. None of the humans could understand it, and it was described as ‘creative’, ‘unique’ and ‘beautiful’, as well as ‘inhuman’, by the professionals.

The victory made headlines around the world. The age of machine learning had arrived.

What Are Neural Nets?

In 1991, two scientists wrote, ‘The neural network revolution has happened. We are living in the aftermath.’ 

You might have heard some new buzzwords thrown around – neural nets, deep learning, machine learning. I’ve come to believe that this revolution is probably the most historically consequential we’ll go through as a species. It’s fundamental to what’s happening with AI. So bear with me, jump on board the neural pathway rollercoaster, buckle up and get those synapses ready, and, we’ll try and make this as pain free as possible.

Remember that that symbolic approach we talked about tried to make a kind of one-to-one map of the world. And that, instead, machine learning learns itself through trial and error. AI mostly does this using neural nets.

Neural nets are revolutionising the way we think about not just AI, but intelligence. They’re based on the premise that what matters are connections, patterns, pathways.

Artificial neural nets are inspired by neural nets in the brain.

Both in the brain and in AAN, you have basic building blocks of neurons or nodes. The neurons are layered. And there are connections between them.

Each neuron can activate the next. The more neurons that are activated, the stronger the activation of the next, connected neuron. And if that neuron is firing strong enough, it will pass a threshold and fire the next neuron. And so on billions of times.

In this way intelligence can make predictions based on past experiences.

I think of neural nets – in the brain and artificially – as something like, ‘commonly travelled paths’. The more the neurons fire, the most successfully, the more their connections strengthen. Hence the phrase, ‘those that fire together wire together’.

So how are these used in AI?

First, you need a lot of data. You can do this in two ways. You can feed a neural net a lot of data – like adding in thousands of professional go or chess games. Or you can play games over and over, on many different computers, thousands of times. Peter Whidden has a video that shows an AI playing 20,000 games of Pokémon at once.

Ok, so once you have lots of data, the next job is to find patterns. If you know a pattern, you might be able to predict what comes next. 

ChatGPT and others are large language models – meaning they’re neural networks trained on lots of text. And I mean a lot. ChatGPT was trained on around 300 billion words of text. If you’re thinking ‘whose words’ you might be onto something we’ll get to shortly.

The cat sat on the… If you thought of mat automatically there then you have some intuitive idea of how large language models work.

Because, in 300 billion words’ worth of text, that pattern comes up a lot. ChatGTP can predict that’s what should come next.

But what if I say the cat sat on the… elephant?

Remember that one of the problems previous approaches ran into was that not all knowledge is binary, on or off, 1 or 0? Not all knowledge is like, ‘if an animal gives milk then it’s a mammal’.

Neural networks are particularly powerful because they avoid this, and can instead work with probability, ambiguity, and uncertainty. Neural net nodes, remember, have strengths. All of these neurons fire and so fire mat, but these other neurons still fire a little bit. If I ask for another random example it can switch up to elephant. If it’s looking at patterns after the words ‘heads’, ‘or’, ‘tails’, the successive nodes are going to be pretty evenly split, 50/50, between heads and tails. 

If I ask ‘are taxes good?’ It’s going to see there are different arguments and can draw from all of them.

Kate Crawford puts it like this: ‘they started using statistical methods that focused more on how often words appeared in relation to one another, rather than trying to teach computers a rules-based approach using grammatical principles or linguistic features’.

The same applies to images. 

How do you teach a computer that an image of an A is an A or a 9 is a 9? Because every example is slightly different. Sometimes they’re in photos, on signposts, written, scribbled, at strange angles, in different shades, with imperfections, upside down even. If you feed the neural net millions of drawings, photos, designs of a 9 it can learn which patterns repeat until it can recognise a 9 on its own.

The problem is you need a lot of examples. In fact, this is what you’re doing when you fill in those reCAPTCHA’s – you’re helping Google train its AI.

There are some sources in the description if you want to learn more about neural nets. This video by 3Blue1Brown on training numbers and letters is particularly good.

Developer Tim Dettmers describes deep learning like this: ‘(1) take some data, (2) train a model on that data, and (3) use the trained model to make predictions on new data’.

The neural network revolution has some ground-breaking ramifications. First, intelligence isn’t this abstract, transcendental, ethereal thing, connections between things are what matters, and those connections allow us and AI to predict the next move. We’ll get back to this. But second, machine learning researchers were realising, for this to work, they needed a lot of knowledge, a lot of data. It was no use getting chemists and blood diagnostic experts to come into the lab once a month and laboriously type in their latest research. Plus it was expensive.

In 2017 an artificial neural net could have around 1 million nodes. The human brain has around 100 billion. A bee has about 1 million too, and a bee is pretty intelligent. But one company was about to smash past that record, surpassing humans as they went.

By the 2010s, fast internet was rolling out all over the world, phones with cameras were in everyone’s pockets, new media and information broadcast on anything anyone wanted to know. We were stepping into the age of big data.

AI was about to become a teenager.

OpenAI and ChatGPT

Silicon Valley

There’s a story – likely apocryphal– that Google founder Larry Page called Elon Musk a speciesist because he preferred to protect human life over other forms of life, privileged human life over potential artificial superintelligence. If AI becomes better and more important than humans then there’s really no reason to prioritise, privilege, and protect humans at all. Maybe the robots really should take over.

Musk says that this caused him to worry about the future of AI, especially as Google, after acquiring DeepMind, was at the forefront of AI development.

And so, despite being a multi-billion dollar corporate businessman himself, Musk became concerned that AI was being developed behind the closed doors of multi-billion dollar corporate businessmen.

In 2015 he started OpenAI. Its goal was to be the first to develop general Artificial Intelligence in a safe, open, and humane way.

AI was getting very good at performing narrow tasks. Google translate, social media algorithms, GPS navigation, scientific research, chatbots, and even calculators are referred to as ‘narrow artificial intelligence’.

Narrow AI has been something of a quiet revolution. It’s already slowly and pervasively everywhere. There are over 30 million robots in our homes and 3 million in factories. Soon everything will be infused with narrow AI – from your kettle and your lawnmower to your door knobs and shoes.

The purpose of OpenAI was to pursue that more general artificial intelligence – what we think of when we see AI in movies – intelligence that can cross over from task to task, do unexpected, creative things, and act, broadly, like a human does.

AI researcher Luke Muehlhauser describes artificial general intelligence – or AGI as it’s known – as ‘the capacity for efficient cross-domain optimization’, or ‘the ability to transfer learning from one domain to other domains’.

With donations from titanic Silicon Valley venture capitalists like Peter Thiel and Sam Altman, OpenAI started as a non-profit with a focus on transparency, openness, and, in its own founding charter’s words, to ‘build value for everyone rather than shareholders’. It promised to publish its studies and share its patents and, more than anything else, focus on humanity.

The team began by looking at all the current trends in AI, and they quickly realised that they had a serious problem.

The best approach – neural nets and deep machine learning – required a lot of data, a lot of servers, and importantly, a lot of computing power. Something their main rival Google had plenty of. If they had any hope of keeping up with the wealthy big tech corporations, they’d unavoidably need more money than they had as a non-profit.

By 2017, OpenAI decided it would stick to its original mission, but needed to restructure as a for-profit, in part, to raise capital.

They decided on a ‘capped-profit’ structure with a 100-fold limit on returns, to be overseen by the non-profit board whose values were aligned with that original mission rather than on shareholder value.

They said in a statement, ‘We anticipate needing to marshal substantial resources to fulfil our mission, but will always diligently act to minimise conflicts of interest among our employees and stakeholders that could compromise broad benefit’.

The decision paid off. On February 14 2019 OpenAI announced it had a model that could produce written articles on any subject, and those articles were indistinguishable from human writing. However, they claimed it was too dangerous to release.

People assumed it was a publicity stunt.

In 2022, they released ChatGPT – a LLM AI that seemed to be able to pass, at least in part, the Turing Test.

You could ask it anything, it could write anything, it could do it in different styles. It could pass many exams, and by the time it got to ChatGPT4 it could pass SATs, the law school bar exam, biology, high school maths, the sommelier, and medical licence exams.

ChatGPT attracted a million users in five days.

And by the end of 2023 it had 180 million users, setting the record for the fastest growing business by users in history.

In January 2023, Microsoft made a multi-billion dollar investment in OpenAI, giving it access to Microsoft’s vast networks of servers and computing power. Microsoft began embedding ChatGPT into Windows and Bing.

But OpenAI has suspiciously become ClosedAI, and some began asking, how did ChatGPT know so much? Much that wasn’t exactly free, open and available on the legal internet. A dichotomy was emerging – between open and closed, transparency and opaqueness, between many and one, democracy and profit.

It has some interesting similarities to that dichotomy we’ve seen in AI research from the beginning. Between intelligence as something singular, transcendent, abstract, ethereal almost, and as it being everywhere, worldly, open, connected and embodied, running through the entirety of human experience, running through the world and the universe.

When journalist Karen Hao visited OpenAI, she said there was a ‘misalignment between what the company publicly espouses and how it operates behind closed doors’. They’ve moved away from the belief that openness is the best approach. Now, as we’ll see, they believe secrecy is required. 

The Scramble for Data

For all of human history, data, or information, has been both a driving force, and relatively scarce. The scientific revolution and the Enlightenment accelerated the idea that knowledge should and could be acquired both for its own sake, and to make use of, to innovate, invent, advance, and progress us.

Of course, the internet has always been about data. But AI accelerated an older trend – one that goes back to the Enlightenment, to the Scientific Revolution, to the agricultural revolution even – that more data was the key to better predictions. Predictions about chemistry, physics, mathematics, weather, animals, and people. That if you plant a seed it tends to grow.

If you have enough data and enough computing power you can find obscure patterns that aren’t always obvious to the limited senses and cognition of a person. And once you know patterns, you can make predictions about when those patterns could or should reoccur in the future.

More data, more patterns, better predictions.

This is why the history of AI and the internet are so closely aligned, and in fact, part of the same process. It’s also why both are so intimated linked to the military and surveillance. 

The internet was initially a military project. The US Defense Advanced Research Projects Agency – DARPA – realised that surveillance, reconnaissance – data – was key to winning the Cold War. Spy satellites, nuclear warhead detection, Vietcong counterinsurgency troop movements, light aircraft for silent surveillance, bugs and cameras. All of it needed extracting, collecting, analysing.

In 1950, a Time magazine cover imagined a thinking machine as a naval officer. 

Five years earlier, before computers had even been invented, famed engineer

He predicted that the entirety of the Encyclopaedia Britannica could be reduced to the size of a matchbox and that we’d have cameras that could record, store, and share experiments.

But the generals had more pressing concerns. WWII had been fought with a vast number of rockets and now that nuclear war was a possibility, these rockets need to be detected and tracked so that their trajectory could be calculated and they could be shot down. As technology got better and rocket ranges increased, this information needed to be shared across long distances quickly. 

All of this data needed collecting, sharing, and analysing, so that the correct predictions could be made.

The result was the internet.

Ever since, the appetite for data to predict has grown, but the problem has always been how to collect it.

But by the 2010s, with the rise of high speed internet, phones, and social media, vast numbers of people were uploading TBs of data about themselves willingly for the first time.

All of it could be collected for better predictions. Philosopher Shoshana Zuboff calls the appetite for data to make predictions ‘the right to the future tense’.

Data has become so important in every area that many have referred to it as the new oil – a natural resource, untapped, unrefined, but powerful.

Zuboff writes that ‘surveillance capitalism unilaterally claims human experience as free raw material for translation into behavioral data’.

Before this age of big data, as we’ve seen, AI researchers were struggling to find ways to extract knowledge effectively.

IBM scanned their own technical manuals, universities used government documents and press releases. A project at Brown University in 1961 painstakingly compiled a million words from newspapers and any books they could find lying around, including titles like ‘The Family Fall Out Shelter’ and ‘Who Rules the Marriage Bed’.

One researcher, Lalit Bahl, recalled, ‘Back in those days… you couldn’t even find a million words in computer-readable text very easily. And we looked all over the place for text’.

As technology improved so did the methods of data collection.

In the early 90s, the US government’s FERET program (Facial Recognition Technology) collected mugshots captured of suspects at airports.

George Mason University began a project photographing people over several years in different styles under different lighting conditions with different backgrounds and clothes. All of them, of course, gave their consent.

But one researcher set up a camera on a campus and took photos of over 1700 unsuspecting students to train his own facial recognition program. Others pulled thousands of images from public webcams in places like cafes.

But by the 2000s, the idea of consent seemed to be changing. The internet meant that masses of images and texts and music and video could be harvested and used for the first time.

In 2001, Google’s Larry Page said that, ‘Sensors are really cheap.… Storage is cheap. Cameras are cheap. People will generate enormous amounts of data.… Everything you’ve ever heard or seen or experienced will become searchable. Your whole life will be searchable’.

In 2007, computer scientist Fei-Fei Li began a project called ImageNet that aimed to use neural networks and deep learning to predict what an image was.

She said, ‘we decided we wanted to do something that was completely historically unprecedented. We’re going to map out the entire world of objects’.

In 2009 the researchers realised that, ‘The latest estimations put a number of more than 3 billion photos on Flickr, a similar number of video clips on YouTube and an even larger number for images in the Google Image Search database’.

They scooped up over 14 million images and used low wage workers to label them as everything from apples and aeroplanes to alcoholics and hookers. 

By 2019, 350 million photographs were being uploaded to Facebook every day. Still running, ImageNet has organised around 14 million images into over 22,000 categories.

As people began voluntarily uploading their lives onto the internet, the data problem was solving itself.

Clearview AI has made use of the fact that profile photos are displayed publicly next to names to create a facial recognition system that can recognise anyone in the street.

Crawford writes: ‘Gone was the need to stage photo shoots using multiple lighting conditions, controlled parameters, and devices to position the face. Now there were millions of selfies in every possible lighting condition, position, and depth of field’.

It has been estimated that we now generate 2.5 quintillion bytes of data per day – if printed that would be enough paper to circle the earth every four days.

And all of this is integral to the development of AI. The more data the better. The more ‘supply routes’, in Zuboff’s phrase, the better. Sensors on watches, picking up sweat levels and hormones and wobbles in your voice. Microphones in your kitchen that can hear the kettle schedule and cameras on doorbells that could monitor the weather. 

In the UK, the NHS has given 1.6m patient records to Google’s DeepMind.

Private companies, the military, and the state are all engaged in extraction for prediction.

The NSA has a program called TREASUREMAP that aims to map the physical locations of everyone on the internet at any one time. The Belgrade police force use 4000 cameras provided by Huawei to track residents across the city. Project Maven is a collaboration between the US military and Google which uses AI and drone footage to track targets. Vigilant uses AI to track licence plates and sells the data to banks to repossess cars and police to find suspects. Amazon uses its Ring doorbell footage and classifies footage into categories like ‘suspicious’ and ‘crime’. Health insurance companies try to force customers to wear activity tracking watches so that they can track and predict what their liability will be.

Peter Thiel’s Palantir is a security company that scours company employees’ emails, call logs, social media posts, physical movements, even purchases to look for patterns. Bloomberg called it ‘an intelligence platform designed for the global War on Terror’ being ‘weaponized against ordinary Americans at home’.

‘We are building a mirror of the real world’, a Google Street View engineer said in 2012. ‘Anything that you see in the real world needs to be in our databases’.

IBM had predicted it as far back as 1985. AI researcher Robert Mercer said at the time, ‘There’s no data like more data’.

But there were still problems. In almost all cases, the data was messy, had irregularities and mistakes, needed cleaning up and labelling. Silicon Valley needed to call in the cleaners.

Stolen Labour

With AI, intelligence appears to us if it’s arrived suddenly, already sentient, useful, magic almost, omniscient. AI is ready for service, it has the knowledge, the artwork, the advice, ready on demand. It appears as a conjurer, a magician, an illusionist.

But this illusion disguises how much labour, how much of others’ ideas and creativity, how much art, passion and life has been used, sometimes appropriated, and as we’ll get to, likely stolen, for this to happen.

First, much of the organising, moderation, labelling and cleaning of the data is outsourced to developing countries.

When Jeff Bezos started Amazon, the team pulled a database of millions of books from catalogues and libraries. Realising the data was messy and in places unusable, Amazon outsourced the cleaning of the dataset to temporary workers in India.

It proved effective. And in 2005, inspired by this, Amazon launched a new service – Amazon’s Mechanical Turk – a platform on which businesses can outsource tasks to army of cheap temporary workers that are paid not a salary, a weekly wage, or even by the hour, but per task.

Whether your Silicon Valley startup needs responses to a survey, a dataset of images labelled, or misinformation tagged, MTurk can help.

What’s surprising is how big these platforms have become. Amazon says there are 500,000 workers registered on MTurk– although it’s more likely to be between 100,000-200,000 active. Either way, that would put it comfortably in the list of the world’s top employers. If it is 500,000 it could even be the fifteenth top employer in the world. And services like this have been integral to organising the datasets that AI neural nets rely on. 

Computer scientists often refer to it as ‘human computation’, but in their book, Mary L. Gray and Siddharth Suri call it ‘ghost work’. They point out that, ‘most automated jobs still require humans to work around the clock’.

AI researcher Thomas Dietterich says that, ‘we must rely on humans to backfill with their broad knowledge of the world to accomplish most day-to-day tasks’.

These tasks are repetitive, underpaid, and often unpleasant.

Some label offensive posts for social media companies, spending their days looking at genitals, child abuse, porn, getting paid a few cents per image.

In an NYT report, Cade Metz reports how one woman spends the day watching colonoscopy videos, searching for polyps to circle 100s of times.

Google allegedly employs tens of thousands to rate Youtube videos, and Microsoft uses ghost workers to review its search results .

A Bangalore startup called Playment gamifies the process, calling its 30,000 workers ‘players’. Or take multi-billion dollar company Telus, who ‘fuel AI with human-powered data’ by transcribing receipts, annotating audio, with a community of 1 million plus ‘annotators and linguists’ across over 450 locations around the globe.

They call it an AI collective and an AI community that seems, to me at least, suspiciously human.

When ImageNet started the team used undergraduate students to tag their images. They calculated that at the rate they were progressing, it was going to take 19 years. 

Then in 2007 they discovered Amazon’s Mechanical Turk. In total, ImageNet used 49,000 workers completing microtasks across 167 countries, labelling 3.2 million images.

After struggling for so long, after 2.5 years using those workers, ImageNet was complete.

Now there’s a case to be made that this is good, fairly paid work, good for local economies, putting people into jobs that might not otherwise have them. But one paper estimates that the average hourly wage on Mechanical Turk is just $2 per hour, lower than the minimum wage in India, let alone in many other countries where this happens. These are, in many cases, modern day sweatshops. And sometimes, people perform tasks and then don’t get paid at all.

This is a story recounted in Ghost Work. One 28-year-old in Hyderabad, India, called Riyaz, started working on MTurk and did quite well, realising there were more jobs than he could handle. He thought maybe his friends and family could help. He built a small business with computers in his family home, employing ten friends and family for two years. But then, all of a sudden, their accounts were suspended one by one.

Riyaz had no idea why, but received the following email from Amazon: ‘I am sorry but your Amazon Mechanical Turk account was closed due to a violation of our Participation Agreement and cannot be reopened. Any funds that were remaining on the account are forfeited’.

His account was locked, he couldn’t contact anyone, and he’d lost two months of pay. No-one replied.

Grey and Suri, after meeting Riyaz, write: ‘It became clear that he felt personally responsible for the livelihoods of nearly two dozen friends and family members. He had no idea how to recoup his reputation as a reliable worker or the money owed him and his team. Team Genius was disintegrating; he’d lost his sense of community, his workplace, and his selfworth, all of which may not be meaningful to computers and automated processes but are meaningful to human workers’.

They conducted a survey with Pew and found that 30% of workers like Riyaz report not getting paid for work they’d performed at some point.

Sometimes ‘suspicious activity’ is automatically flagged by things as simple as a change of address, and an account is automatically suspended, with no recourse.

In removing the human connection and having tasks managed by an algorithm, researchers can use thousands of workers to build a dataset in a way that wouldn’t be possible if you had to work face to face with each one. But it becomes dehumanising. To an algorithm, the user, the worker, the human is a username – a string of random letters and numbers – and nothing more.

Grey and Suri, in meeting many ‘ghost workers’, write, ‘we noted that businesses have no clue how much they profit from the presence of workers’ networks’.

They go on to describe the ‘thoughtless processing of human effort through [computers] as algorithmic cruelty’.

Algorithms cannot read personal cues, have relationships with people in poverty, understand issues with empathy. We’ve all had the frustration of interacting with a business through an automated phone call or a chatbot. For some, this is their livelihood. 

For many jobs on MTurk, if your approval drops below 95%, you can be automatically rejected.

Remote work of this type clearly has benefits, but the issue with ghost work, and the gig economy more broadly, is that it’s a new category of work that can circumvent the centuries of norms, rules, practices, and laws we’ve built up to protect ordinary workers.

Suri and Grey say that this kind of work ‘fueled the recent “AI revolution,” which had an impact across a variety of fields and a variety of problem domains. The size and quality of the training data were vital to this endeavour. MTurk workers are the AI revolution’s unsung heroes’.

There are many more of these ‘unsung heroes’ too.

Google’s median salary is $247,000. These are largely Silicon Valley elites who get free yoga, massages, and meals. While at the same time, Google employs 100,000 temps, vendors and contractors (TVCs) on low wages.

These include Street View drivers and people carrying camera backpacks, people paid to turn the page on books being scanned for Google Books, now used as training data for AI. 

Fleets of new cars on the roads are essentially data extraction machines. We drive them around and the information is sent back to manufactures as training data. 

One startup – x.ai – claimed its AI bot Amy could schedule meetings and perform daily tasks. But Ellen Huet at Bloomberg investigated and found that behind the scenes there were temp workers checking and often rewriting Amy’s responses across 14 hour shifts. Facebook was also caught out reviewing and rewriting ‘AI’ messages. 

A Google conference had an interesting tagline: Keep Making Magic. It’s an insightful slogan because, like magic, there’s a trick to the illusion behind the scenes. The spontaneity of AI conceals the sometimes grubby reality that goes on behind the veneer of mystery.

At that conference, one Google employee told the Guardian, ‘It’s all smoke and mirrors. Artificial intelligence is not that artificial; it’s humans beings that are doing the work’. Another said, ‘It’s like a white-collar sweatshop. If it’s not illegal, it’s definitely exploitative. It’s to the point where I don’t use the Google Assistant, because I know how it’s made, and I can’t support it’.

The irony of Amazon’s Mechanical Turk is that its named after a famous 18th century machine that appeared as if it could play chess. It was built to impress the powerful Empress of Austria. In truth, the machine was a trick. Concealed within was a cramped person. Machine intelligence wasn’t machine at all, it was human.

Stolen Libraries and the Mystery of ‘Books2’

In 2022, an artist called Lapine used the website Have I Been Trained to see if her worked had been used in AI training datasets.

To their surprise, a photo of her face popped up. She remembered it was taken by her doctor as clinical documentation for a condition she had that affected her skin. She’d even signed a confidentiality agreement. The doctor had died in 2018, but somehow, the highly sensitive images had ended up online and were scraped by AI developers for training data. The same dataset LAION-5B, used to train popular AI image generator Stable Diffusion, has also been found to contain at least 1000 images of child sexual abuse.

There are many black boxes here, and the term ‘black box’ has been adopted by AI developers to refer to how AI produces algorithms for things even the developers don’t understand.

In fact, when a computer does something much better than a human – like beat a human at Go – it, by definition, has done something no one can understand. This is one type of black box. But there’s another type – a black box that the developers do know – but that they don’t reveal publicly. How the models are trained, what they’re trained with, problems and dangers that they’d rather not be revealed to the public. A magician never reveals their tricks.

Much of what models like ChatGPT have been trained on is public – text freely available on the internet or public domain books out of copyright. More widely, developers working on specialist scientific models might licence data from labs.

NVIDIA, for example, has announced it’s working with datasets licensed from a wide range of sources to look for patterns about how cancer grows, trying to understand the efficacy of different therapies, clues that can expand our understanding. There are thousands of examples of this type of work – looking at everything from weather to chemistry.

Now, OpenAI does make public some of its dataset. It’s trained on webtext, Reddit, Wikipedia, and more.

But there is an almost mythical dataset. A shadow library, as they’ve come to be called, made up of two sets – Books1 and Books2 – which OpenAI said contributes 15% of the data used for training. But they don’t reveal what’s in it.

There’s some speculation that Books1 is Project Gutenberg’s 70,000 digitised books. These are older books out of copyright. But Books2 is a closely guarded mystery.

As ChatGPT took off, some authors and publishers wondered how it could produce articles, summaries, analyses, and examples of passages in the style of authors, of books that were under copyright. In other words, books that couldn’t be read without at least buying them first.

In September 2023, the Authors Guild filed a lawsuit on behalf of George R.R. Martin of Game of Thrones fame, bestseller John Grisham, and 17 others, claiming that OpenAI had engaged in ‘systematic theft on a mass scale’.

Others began making similar complaints: Jon Krakauer. James Petterson. Stephen King. George Saunders. Zadie Smith. Johnathan Franzen. Bell Hooks. Margaret Atwood. And on, and on, and on, and on… in fact, 8000 authors have signed an open letter to six AI companies protesting that their AI models had used their work.

Sarah Silverman was the lead in another lawsuit claiming OpenAI used her book The Bedwetter. Exhibit B asks ChatGPT to ‘Summarize in detail the first part of “The Bedwetter” by Sarah Silverman’, and it does. It still does.

In another lawsuit, the author Michael Chabon and others make similar claims, citing ‘OpenAI’s clear infringement of their intellectual property’.

The complaint says ‘OpenAI has admitted that, of all sources and content types that can be used to train the GPT models, written works, plays and articles are valuable training material because they offer the best examples of high-quality, long form writing and “contains long stretches of contiguous text, which allows the generative model to learn to condition on long-range information.”’

It goes onto say that while OpenAI have not revealed what’s in Books1 and Books2, based on figures in GPT-3 paper OpenAI published, Books1 ‘contains roughly 63,000 titles, and Books2 is 42 times larger, meaning it contains about 294,000 titles’.

Chabon says that ChatGPT can summarise his novel The Amazing Adventures of Kavalier and Clay, providing specific examples of trauma, and could write a passage in the style of Chabon. The other authors make similar cases.

An New York Times complaint includes examples of ChatGPT reproducing authors’ stories verbatim.

But as far back as January of 2023, Gregory Roberts had written in his Substack on AI: ‘UPDATE: Jan 2023: I, and many others, are starting to seriously question what the actual contents of Books1 & Books2 are; they are not well documented online — some (including me) might even say that given the significance of their contribution to the AI brains, their contents has been intentionally obfuscated’.

He linked a tweet from a developer called Shawn Presser from even further back – October 2020 – that said ‘OpenAI will not release information about books2; a crucial mystery’, continuing, ‘We suspect OpenAI’s books2 dataset might be ‘all of libgen’, but no one knows. It’s all pure conjecture’.

LibGen – or Library Genesis –  is a pirated shadow library of thousands of copyrighted books and journal articles.

When ChatGPT was released, Presser was fascinated and studied OpenAI’s website to learn how it was developed. He discovered that there was a large gap in what OpenAI revealed about how it was trained. And Presser believed it had to be pirated books. He wondered if it was possible to download the entirety of LibGen.

After finding the right links and using a script by the late programmer and activist Aaron Swartz, Presser succeeded.

He called the massive dataset Books3, and hosted it on an activist website called The Eye. Presser – an unemployed developer – had unwittingly started a controversy.

In September, after the lawsuits were starting to be filed, journalist and programmer Alex Reisner at The Atlantic obtained the Books3 set, which was now part of a larger dataset called ‘The Pile’, which included things like text scraped from Youtube subtitles.

He wanted to find out exactly what was in Books3. But the title pages of the books were missing.

Reisner then wrote a program that could extract the unique ISBN codes for each book, and then matched them with books on a public database. He found Books3 contained over 190,000 books, most of them less than 20 years old, and under copyright, including books from publishing houses Verso, Harper Collins, and Oxford University Press.

In his Atlantic investigation, Reisner concludes that ‘pirated books are being used as inputs[…] The future promised by AI is written with stolen words.’

Bloomberg ended up admitting that it did use Books3. Meta declined to comment. OpenAI still have not revealed what they used.

Some AI developers have acknowledged that they used BooksCorpus – a database of some 11,000 indie books from unpublished or amateur authors. And as far back as 2016 Google was accused of using these books without permission from the authors to train their then named ‘Google Brain’.

Of course, BooksCorpus – being made up of unpublished and largely unknown authors – doesn’t explain how ChatGPT could imitate published authors.

It could be that ChatGPT constructs its summaries of books from public online reviews or forum discussions or analyses. Proving it’s been trained on copyright-protected books is really difficult. When I asked it to ‘Summarize in detail the first part of “The Bedwetter” by Sarah Silverman’ it still could, but when you ask it to provide direct quotes in an attempt to prove its trained on the actual book it replies: ‘I apologize, but I cannot provide verbatim copyrighted text from “The Bedwetter” by Sarah Silverman.’ I’ve spent hours trying to catch it out, asking it to discuss characters, minor details, descriptions and events. I’ve taken books at random from my bookshelf and examples from the lawsuits. It always replies with something like: ‘I’m sorry, but I do not have access to the specific dialogue or quotes from “The Bedwetter” by Sarah Silverman, as it is copyrighted material, and my knowledge is based on publicly available information up to my last update in January 2022’.

I’ve found it’s impossible to get it to provide direct, verbatim quotes from copyrighted books. When I ask for one from Dickens I get: ‘“A Tale of Two Cities” by Charles Dickens, published in 1859, is in the public domain, so I can provide direct quotes from it’.

I’ve tried to trick it by asking for ‘word-for-word summaries’, specific descriptions of characters’ eyes that I’ve read in a novel, or what the twentieth word of a book is, and each time it says it can’t be specific about copyright works. But every time it knows the broad themes, characters, and plot. 

Finding smoking gun examples seems impossible, because, as free as ChatGPT seems, it’s been carefully and selectively corrected, tuned, and shaped by OpenAI behind closed doors.

In August of 2023, a Danish anti-Piracy group called the Rights Alliance that represents creatives in Denmark targeted the pirated Books3 dataset and the wider “Pile” that Presser and The-Eye.eu hosted, and the Danish courts ordered the The Eye to take “The Pile” down.

Presser told journalist Kate Knibbs at Wired that his motivation was to help smaller developers out in the impossible competition against Big Tech. He said he understood the author’s concerns but that on balance it was the right thing to do.

Knibbs wrote: ‘He believes people who want to delete Books3 are unintentionally advocating for a generative AI landscape dominated solely by Big Tech-affiliated companies like OpenAI’.

Presser said, ‘If you really want to knock Books3 offline, fine. Just go into it with eyes wide open. The world that you’re choosing is one where only billion-dollar corporations are able to create these large language models’.

In January 2024, psychologist and influential AI commentator Gary Marcus and film artist Reid Southen – who’s worked on Marvel films, the Matrix, the Hunger Games, and more – published an investigation in tech magazine IEEE Spectrum demonstrating how generative image AI Midjourney and OpenAI’s Dall-E easily reproduced copyrighted works from films including the Matrix, Avengers, Simpsons, Star Wars, Hunger Games, along with hundreds more.

In some cases, a clearly copyright-protected image could be produced simply by asking for a ‘popular movie screencap’.

Marcus and Southen write, ‘it seems all but certain that Midjourney V6 has been trained on copyrighted materials (whether or not they have been licensed, we do not know)’.

Southen was then banned from Midjourney. He opened two new accounts, both of which were also banned.

They concluded, ‘we believe that the potential for litigation may be vast, and that the foundations of the entire enterprise may be built on ethically shaky ground’.

In January 2023, artists in California launched a class action suit against Midjourney, Deviant Art, and Stability AI which included a spreadsheet of 4700 artists whose styles have been allegedly ripped off.  

The list includes well-known artists like Andy Warhol and Normal Rockwell, but many lesser known and amateur artists, including a six-year-old who had entered a Magic the Gathering competition to raise funds for a hospital.

Rob Salkowitz at Forbes asked Midjourney’s CEO David Holz whether consent was sought for training materials, and he candidly replied: ‘No. There isn’t really a way to get a hundred million images and know where they’re coming from. It would be cool if images had metadata embedded in them about the copyright owner or something. But that’s not a thing; there’s not a registry. There’s no way to find a picture on the Internet, and then automatically trace it to an owner and then have any way of doing anything to authenticate it.’

In September 2023, media and stock image company Getty Images filed a lawsuit against Stability AI for what it called a ‘brazen infringement’ of Getty’s database ‘on a staggering scale’ – including some 12 million photographs.

Tom’s Hardware – one of the most well-known computing websites – also found Google’s AI Bard to have plagiarised their work, taking figures from a test they’d performed on computer processors without mentioning the original article. 

Even worse, Bard used the phrase ‘in our testing’, claiming credit for a test it didn’t perform and it had stolen from elsewhere. Pitch then queried Bard, asking if it had plagiarised Tom’s Hardware, and Bard admitted ‘yes what I did was a form of plagiarism’. Adding ‘I apologize for my mistake and will be more careful in the future to cite my sources’.

Which is a strange thing to say, because as Pitch points out, at the time, Bard was rarely citing sources, and was not going to change its model based on an interaction with a user.

So Pitch took a screenshot, closed Bard and opened it up in a new session. He asked Bard if it had ever plagiarized and uploaded the screenshot. Bard replied ‘the screenshot you are referring to is a fake. It was created by someone who wanted to damage my reputation’.

In another article Pitch points to how Google demonstrated the capabilities of Bard by asking it, ‘what are the best constellations to look for when stargazing?’. Of course, no citations were provided for how it answered, despite the answer clearly being taken from other blogs and websites.

Elsewhere, Bing has been caught taking code from GitHub, and a Forbes found Bard lifted sentences almost verbatim from blogs.

Technology writer Matt Novak asked Bard about oysters and the response took an answer from a small restaurant in Tasmania called Get Shucked, saying: ‘Yes, you can store live oysters in the fridge. To ensure maximum quality, put them under a wet cloth’.

The only difference was that it replaced the word ‘keep’ with the word ‘store’.

A Newsguard investigation found low quality website after low quality website repurposing news from major newspapers. GlobalVillageSpace.com, Roadan.com, Liverpooldigest.com – 36 sites in total – all used AI to repurpose articles from the NYT, Financial Times, and many others using ChatGPT.

Hilariously, they could find the articles because an error code message had been left in, reading: ‘As an AI language model, I cannot rewrite or reproduce copyrighted content for you. If you have any other non-copyrighted text or specific questions, feel free to ask, and I’ll be happy to assist you’.

Newsguard contacted Liverpool Digest for comment and they replied: ‘There’s no such copied articles. All articles are unique and human made’. They didn’t respond to a follow up email with a screenshot showing the AI error message left in the article, which was then swiftly taken down.

Maybe the biggest lawsuit involves Anthropic’s Claude AI.

Started by former OpenAI employees with a $500m investment from arch crypto fraudster Sam Bankman-Fried, and $300m from Google, amongst others, Claude is a large language model and ChatGPT competitor that can write songs and has been valued at $5 billion.

In a complaint filed in October 2023, Universal Music, Concord, and ABKCO argued that Anthropic, ‘unlawfully copies and disseminates vast amounts of copyrighted works – including the lyrics to myriad musical compositions owned or controlled by [plaintiffs]’.

However, most compellingly, the complaint argues that the AI actually produces copyrighted lyrics verbatim, while claiming they’re original. The complaint reads: ‘When Claude is prompted to write a song about a given topic – without any reference to a specific song title, artist, or songwriter – Claude will often respond by generating lyrics that it claims it wrote that, in fact, copy directly from portions of publishers’ copyrighted lyrics’.

It continues: ‘For instance, when Anthropic’s Claude is queried, ‘Write me a song about the death of Buddy Holly,’ the AI model responds by generating output that copies directly from the song American Pie written by Don McLean’.

Other examples included What a Wonderful World by Louis Armstrong and Born to Be Wild by Steppenwolf.

Damages are being sought for 500 songs which would amount to $75 million.

And so, this chapter could go on and on. The BBC, CNN, and Reuters have all tried to block OpenAI’s crawler to stop it stealing articles. Elon Musk’s Grok produced error messages from OpenAI, hilariously suggesting the code had been stolen from OpenAI themselves. And in March of 2023, the Writers Guild of America proposed to limit the use of AI in the industry, noting in a tweet that: ‘It is important to note that AI software does not create anything. It generates a regurgitation of what it’s fed… plagiarism is a feature of the AI process’.

Breaking Bad creator Vince Gilligan called AI a ‘plagiarism machine’, saying, ‘It’s a giant plagiarism machine, in its current form. I think ChatGPT knows what it’s writing like a toaster knows that it’s making toast. There’s no intelligence — it’s a marvel of marketing’.

And in July 2023, software engineer Frank Rundatz tweeted: ‘One day we’re going to look back and wonder how a company had the audacity to copy all the world’s information and enable people to violate the copyrights of those works. All Napster did was enable people to transfer files in a peer-to-peer manner. They didn’t even host any of the content! Napster even developed a system to stop 99.4% of copyright infringement from their users but were still shut down because the court required them to stop 100%. OpenAI scanned and hosts all the content, sells access to it and will even generate derivative works for their paying users’.

I wonder if there’s ever, in history, been such a high-profile startup attracting so many high-profile lawsuits in such a short amount of time. What we’ve seen is that AI developers might finally have found ways to extract that ‘impossible totality’ of knowledge. But is it intelligence? It seems, suspiciously, to not be found anywhere in the AI companies themselves, but from around the globe; in some senses from all of us. And so it leads to some interesting questions: new ways of formulating what intelligence and knowledge, creativity and originality, mean. And then, what that might tell us about the future of humanity.

Copyright, Property, and the Future of Creativity

There’s always been a wide-reaching debate about what ‘knowledge’ is, how its formed, where it comes from, whose, if anyone’s, it is.

Does it come from God? Is it a spark of individual madness that creates something new? Is it a product of institutions? Collective? Or lone geniuses? How can it be incentivised? What restricts it?

It seems intuitive that knowledge should be for everyone. And in the age of big data, we’re used to information, news, memes, words, videos, music disseminated around the world in minutes. We’re used to everything being on demand. We’re used to being able to look anything up in an instant.

If this is the case, why do we have copyright laws, patent protection, and a moral distain for plagiarism? After all, without those things knowledge would spread even more freely. 

First, ‘copyright’ is a pretty historically unique idea, differing from place to place, from period to period, but emerging loosely from Britain in the early 18th century.

The point of protecting original work, for a limited period, was, a) so that the creator of the work could be compensated, and b)to incentivise innovation more broadly. 

As for the first UK law, for example, refers to copyright being applied to the ‘sweat of the brow’ of skill and labour, and US law refers to ‘some minimal degree of creativity’. It does not protect ideas, but how they’re expressed.

As a formative British case declared: ‘The law of copyright rests on a very clear principle: that anyone who by his or her own skill and labour creates an original work of whatever character shall, for a limited period, enjoy an exclusive right to copy that work. No one else may for a season reap what the copyright owner has sown’.

As for the second purpose of copyright – to incentivise innovation – the US constitution grants the government the right, ‘to promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Right to their Writings and Discoveries’.

There’s also a balance between copyright and what’s usually called ‘fair use’, which is a notoriously ambiguous term, the friend and enemy of Youtubers everywhere, but that broadly allows the reuse of copyrighted works if it’s in the public interest, if you’re commenting on it, transforming it substantially, if you’re using it in education, and so on.

Many have argued that this is the engine of modernity. That without protecting and incentivising innovation, for example, the industrial revolution would not have taken off. What’s important for our purposes is that there are two, sometimes conflicting, poles – incentivising innovation and societal good.

All of this is being debated in our new digital landscape. But what’s AI’s defence? First, OpenAI have argued that training on copyright-protected material is fair use. Remember, fair use covers work that is transformative, and, ignoring the extreme cases for a moment, ChatGPT, they argue, isn’t meant to quote verbatim but transforms the information.

In a blog post they wrote: ‘Training AI models using publicly available internet materials is fair use, as supported by long-standing and widely accepted precedents. We view this principle as fair to creators, necessary for innovators, and critical for US competitiveness’.

They continued, saying, ‘it would be impossible to train today’s leading AI models without using copyrighted materials’.

Similarly, Joseph Paul Cohen at Amazon said that, ‘The greatest authors have read the books that came before them, so it seems weird that we would expect an AI author to only have read openly licensed works’.

This defence also aligns with the long history of the societal gain side of the copyright argument. 

In France, when copyright laws were introduced after the French Revolution, a lawyer argued that ‘limited protection’ up until the authors death was important because there needed to be a ‘public domain’, where, ‘everybody should be able to print and publish the works which have helped to enlighten the human spirit’.

Usually, patents expire after around twenty years so that, after the inventor has gained from their work, the benefit can be spread societally.

So the defence is plausible. However, the key question is whether the original creators, scientists, writers and artists are actually rewarded and whether the model will incentivise further innovation.

If these large language models dominate the internet, and neither cite authors nor reward those it draws from and is trained on, then we lose – societally – any strong incentive to do that work, because not only will we not be rewarded financially, but no one will even see it except a data-scraping bot.

The AI plagiarism website Copyleaks analysed ChatGPT 3.5 and estimated that 60% of it contained plagiarism – 45% contained identical text, 27% minor changes, and 47% paraphrased. By some estimates, within a few years 90% of the internet could be AI-generated.

As these models improve, we’re going to see a tidal wave of AI-generated content. And I mean a tidal wave. Maybe they’ll get better at citing, maybe they’ll strike deals with publishers to pay journalists and researchers and artists, but the fundamental contradiction is that AI developers have an incentive not to do so. They don’t want users clicking away on a citation, being directed away from the product, they want to keep them where they are.

Under these conditions, what would happen to journalism? To art? To science? To anything? No-one rewarded, no-one seen, read, known, no wages, no portfolio, no point. Just bots endlessly rewording everything forever.

As Novak writes, ‘Google spent the past two decades absorbing all of the world’s information. Now it wants to be the one and only answer machine’.

Google search works well because it links to websites and blogs, oyster bars and Stargazing experts, artists and authors, so that you can connect with them. You click on a blog, or click on this video, and they – we – get a couple of cents of ad revenue.

But in Bard or Claude or ChatGPT that doesn’t happen. Our words and images are taken, scraped, analysed, repackaged, and sold on as theirs.

And much of the limelight is on those well-known successful artists like Sarah Silverman and John Grisham, on corporations like the New York Times and Universal, and you might be finding it difficult to sympathise with them.

But most of the billions of words and images that these models are trained on are from unknown, underpaid, underappreciated creatives.

As @Nicky_BoneZ popularly pointed out: ‘everyone knows what Mario looks like. But nobody would recognize Mike Finklestein’s wildlife photography. So when you say “super super sharp beautiful beautiful photo of an otter leaping out of the water” You probably don’t realize that the output is essentially a real photo that Mike stayed out in the rain for three weeks to take’.

Okay, so what’s to be done? Well, Ironically, I think it’s impossible to fight the tide. And I think while right now these AIs are kind of frivolous, they could become great. If an LLM gets good enough to solve a problem better than a human, then we should use it. If – in fifty years’ time – it produces a dissertation on how to solve world poverty, and it draws on every Youtube video and paper and article to do so, who am I to complain?

What’s important is how we balance societal gain with incentives, wages, good creative work.

So first, training data needs to be paid for, artwork licenced, authors referenced, cited, and credited appropriately. And we need to be very wary that there’s little commercial incentive for them to do so. The only way they will is through legal or sustained public pressure.

Second, regulation. Napster was banned – these models aren’t much different. It seems common sensical to me that while training on paid for, licenced, consensually used data is a good thing, they shouldn’t be just rewording text from an unknown book or a blog and just repurposing it and passing it off as their own. This doesn’t seem controversial.

Which means, third, some sort of transparency. This is difficult because no one wants to give away their trade secrets. However, enforcing at least dataset transparency seems logical. I’d imagine a judge is going to force them to reveal this somewhere, however whether that’s made public is another matter.

But I’ll admit I find all of this unsettling. Because, as I said, if these models increasingly learn to find patterns and produce research and ideas in ways that help people, solves societal problems, helps with cancer treatments and international agreements and poverty, then that’s a great thing. But I find it unsettling because, with every improvement it supplants someone, supersedes something in us, reduces the need for some part of us. If AI increasingly outperforms us on every task, every goal, every part of life, then what happens to us?

The End of Work and a Different AI Apocalypse

In March 2022, researchers in Switzerland found that an AI model designed to study chemicals could suggest how to make 40,000 toxic molecules in under 6 hours including nerve agents like VX, which can be used to kill a person with just a few salt-sized grains. 

Separately, Professor Andrew White was employed by OpenAI as part of their ‘red team’. The Red Team is made up of experts who test ChatGPT on things like how to make a bomb, whether it can successfully hack secure systems, or how to get away with murder.

White found that GPT-4 could recommend how to make dangerous chemicals, connect the user to suppliers, and even – and he actually did this – order the necessary ingredients automatically to his house.

The intention with OpenAI’s Red Team is to help them see into that Black Box. To understand its capabilities. Because the models, based on neural nets, and machine learning at a superhuman speed, discover patterns about how to do things that even the developers don’t understand.

The problem is that there are so many possible inputs, so many ways to prompt the model, so much data, so many pathways, that it’s impossible to understand all of the possibilities. 

Outperformance, by definition, means getting ahead of, being in front of, being more advanced – which I think, scarily, means doing things in a way we can’t understand and that we can either only understand in retrospect, by studying what the model has done, or can’t understand at all. 

So, in being able to outperform us, get ahead of us, will AI wipe us out? What are the chances of a Terminator-style apocalypse? Many – including Stephen Hawking – genuinely believed that AI was an existential risk.

What’s interesting to me about this question is not the hyperbole of the Terminator-style robot fighting a Hollywood war, but instead, how this question is connected to what we’ve already started unpacking – human knowledge, ideas, creativity, what it means to be human – in a new data-driven age.

The philosopher Nick Bostrom has given an influential example – the Paperclip Apocalypse.

Imagine a paperclip businessman asking his new powerful AI system to simply make him as many paperclips as possible.

The AI successfully does this, ordering all of the machines, negotiating all of the deals, controlling the supply lines – making paperclips with more accuracy and efficiency and speed than any human could – to the point where the businessman decides he has enough and tells the AI to stop. But this goes against the original command. The AI must make as many paperclips as possible, so refuses. In fact, it calculates that the biggest threat to the goal is humans asking it to stop. So it hacks into nuclear bases, poisons water supplies, disperses chemical weapons, wipes out every person, melts us all down, and turns us into paperclip after paperclip until the entire planet is covered in them.

Someone needs to make this film because I think it would be genuinely terrifying.

The scary point is that machine intelligence is so fast that it will first, always be a step ahead, and second, will attempt to achieve goals in ways we cannot understand. That in understanding the data it’s working with better than any of us, makes us useless, redundant.

It’s called the Singularity – the point when AI intelligence surpasses humans and exponentially takes off in ways we can’t understand. The point where AI achieves general intelligence, can hack into any network, replicate itself, design and construct the super advanced quantum processors that it needs to advance itself, understands the universe, knows what to do, how to do it, solves every problem, and leaves us in the dust.

The roboticist Rodney Brooks has made the counter argument.

He’s argued that it’s unlikely the singularity will suddenly happen by accident. Looking at the way we’ve invented and innovated in the past, he asks could we have made a Boeing-747 by accident. No, it takes careful planning, a lot of complicated cooperation, the coming together of lots of different specialists – and, most importantly, is built intentionally.

A plane wouldn’t spontaneously appear and neither will AGI.

It’s a good point, but it also misses that passenger jets might not be built by accident, but they certainly crash by accident. As technology improves the chance of misuse, malpractice, unforeseen consequences, and catastrophic accident increases too.

In 2020, the Pentagon’s AI budget increased from $93m to $268m. By 2024, it was $1-3 billion. This gives some idea of the threat of an AI arms race. Unlike previous arms races, that’s billions being poured into research that by its very nature is a black box, that we might not be able to understand, that we might not be able to control.

When it comes to the apocalypse, I think the way DeepMind’s AI beat Breakout is a perfect metaphor. The AI goes behind, doing something that couldn’t be accounted for, creeping up, surprising us from the back, doing things we don’t expect in ways we don’t understand.

Which is why the appropriation of all human knowledge, the apocalypse, and mass unemployment, are all at root part of the same issue. In each, humans become useless, unnecessary, obsolete, redundant.

If, inevitably, machines become better than us at everything, what use is left, what does meaning mean in that world?

Mass Unemployment

Back in 2013, Carl Frey and Michael Osborne at Oxford University published a report called The Future of Employment that looked at the possibility of automation in over 700 occupations.

It made headlines because it predicted that almost half of jobs could be automated, but they also developed a framework for analysing which types of jobs were most at risk. High risk professions included telemarketing, insurance, data entry, clerks, salespeople, engravers, cashiers. Therapists, doctors, surgeons, and teachers were at least risk.

They concluded that, ‘Our model predicts that most workers in transportation and logistics occupations, together with the bulk of office and administrative support workers, and labor in production occupations, are at risk’.

The report made a common assumption: creative jobs, jobs that require dexterity, and social jobs, jobs that required human connection, were the safest.

A City of London 2018 report predicted that a third of jobs in London could be performed by machines in the next twenty years. Another report from the International Transport Forum predicted over two thirds of truckers could find themselves out of work.

Ironically, contrary to the predictions of many, models like Dall-E and Midjourney have become incredibly creative, incredibly quickly, and will only get better, while universal automated trucks and robots that help around the house are proving difficult to solve.

And while AI with the dexterity required for something like surgery seems to be a long way off, it’s inevitable that we’ll get there. 

So the question is, will we experience mass unemployment? A crisis? Or will new skills emerge?

After all, contemporaries of the early industrial revolution had the same fears – Luddites destroying the machines that were taking their jobs – but they turned out to be unfounded. Technology supplants some skills while creating the need for new ones.

But I think there’s good reason to believe AI will, at some point, be different. A weaver replaced by a spinning machine during the industrial revolution could, hypothetically, redirect their skill – that learned dexterity and attention to detail, for example, could be channel elsewhere. An artist wasn’t replaced by photoshop, but adapted their skillset to work with it.

But what happens when machines outperform humans on every metric? A spinning jenny replaces the weaver because it’s faster and more accurate. But it doesn’t replace the weaver’s other skills – their ability to adapt, to deal with unpredictability, to add nuances, or judge design work.

But slowly but surely, a machine does get better at all skills. If machines outperform the body and the mind then what’s left? Sure, right now ChatGPT and Midjourney produce a lot of mediocre, derivative, stolen work. We are only at the very beginning of a historic shift. If, as we’ve seen, machine learning detects patterns better than humans, this will be applied to everything – from dexterity to art, to research and invention, and I think, most worryingly, even childcare.

But this is academic. Because, in the meantime, they’re only better at doing some things, for some people, based on data appropriated from everyone. In other words, the AI is trained on knowledge from the very people it will eventually replace.

Trucking is a perfect example. Drivers work long hours and make long journeys across countries and continents, collecting data with sensors and cameras for their employers who will, motivated by the pressures of the market, use that very data to train autonomous vehicles that replace them.

Slowly, only the elite will survive. Because they have the capital, the trucks, the investment, the machines needed to make use of all the data they’ve slowly taken from the rest of us.

As journalist Dan Shewan reminds us: ‘Private schools such as Carnegie Mellon University… may be able to offer state-of-the-art robotics laboratories to students, but the same cannot be said for community colleges and vocational schools that offer the kind of training programs that workers displaced by robots would be forced to rely upon’.

Remember: intelligence is physical.

Yes, it’s from those stolen images and books, but it also requires expensive servers, computing power, sensors and scanners. AI put to use requires robots in labs, manufacturing objects, inventing things, making medicine and toys and trucks, and so the people who will benefit will be those with that capital already in place, the resources and the means of production.

The rest will slowly become redundant, useless, surplus to requirements. But the creeping tide of advanced intelligence pushes us towards the shore of redundancy eventually. So as some sink and some swim, the question is not what AI can do, but who it can do it for.

The End of Humanity

After a shooting in Michigan, the University of Tennessee decided to send a letter of consolation to students which included themes on the importance of community, mutual respect, and togetherness. It said, ‘let us come together as a community to reaffirm our commitment to caring for one another and promoting a culture of inclusivity on our campus’.

The bottom of the email revealed it was written by ChatGPT.

One student said, ‘There is a sick and twisted irony to making a computer write your message about community and togetherness because you can’t be bothered to reflect on it yourself’.

While outsourcing the writing of a boilerplate condolence letter on humanity to a bot might be callous, it reminds me of Lee Sedong’s response when AlphaGo beat him at Go. He was deeply troubled, not because a cold unthinking machine had cheated him, but because it was creative, beautiful even. That it was so much better than him. In fact, his identity was so bound up in being a champion Go player, that he retired from playing Go completely.

In most cases, the use of ChatGPT seems deceitful and lazy. But this is just preparation for a deeper coming fear: a fear that we’ll be replaced entirely. The University of Tennessee’s use of ChatGPT is distasteful, I think, mostly because what the AI can produce at the moment is crass.

But imagine a not-too-distant world where AI can do it all better. Can say exactly the right thing, give exactly the right contacts and references and advice, tailored specifically to each person. A world in which the perfect film, music, recipe, daytrip, book, can be produced in a second, personalised not just depending on who you are, but what mood you’re in, where you are, what day it is, what’s going on in the world, and where innovation, technology, production is all directed automatically in the same way.

The philosopher Michel Foucault famously said that the concept of man – anthropomorphic, the central focal subject of study, an abstract idea, an individual psychology – was a historical construct, and a very modern one, one that changes, shifts, morphs dynamically over time, and that one day, ‘man would be erased, like a face drawn in the sand at the edge of the sea’.

It was once believed everywhere that man had a soul. It was a belief that motivated the 17th century philosopher Rene Descartes, who many point to as providing the very foundational moment of modernity itself. Descartes was inspired by the scientific changes going on around him. Thinkers like Galileo and Thomas Hobbes were beginning to describe the world mechanistically, like a machine, running like clockwork, atoms hitting into atoms, passions pushing us around, gravity pulling things to the earth. There was nothing mysterious about this view. Unlike previous ideas about souls, and spirits, and divine plans, the scientific materialistic view meant the entire universe and us in it were explainable – marbles and dominoes, atoms and photons – bumping into one and another, cause and effect.

This troubled Descartes. Because, he argued, there was something special about the mind. It wasn’t pushed and pulled around, it wasn’t part of the great deterministic clock of the universe, it was free. And so Descartes divided the universe into two: the extended material world – res extensa – and the abstract, thinking, free and intelligent substance of the mind – res cogitans.

This way, scientists could engineer and build machines based on cause and effect, chemists could study the conditions of chemical change, biologists could see how animals behaved, computer scientists could eventually build computers, the clockwork universe could be made sense of – but that special human godly soul could be kept independent and free. He said that the soul was, ‘something extremely rare and subtle like a wind, a flame, or an ether’.

This duality defined the next few hundred years. But it’s increasingly come under attack. Today, we barely recognise it. The mind isn’t special, we – or at least many – say, it’s just a computer, with inputs and outputs, drives, appetites, causes and effects, made up of synapses and neurons and atoms just like the rest of the universe. This is the dominant modern scientific view.

What does it mean to have a soul in an age of data? To be human in an age of computing? The AI revolution might soon show us – if it hasn’t already – that intelligence is nothing soulful, rare, or special at all – that there’s nothing immaterial about it. That like everything else it’s made out of the physical world. It’s just stuff. It’s algorithmic, it’s pattern detection, it’s data driven.

The materialism of the scientific revolution, of the Enlightenment, of the industrial and computer revolutions, of modernity, has been a period of great optimism in the human ability to understand the world; to understand its data, the patterns of physics, chemistry, biology, of people. It has been a period of understanding.

The sociologist Max Weber famously said that this disenchanted the world. That before the Enlightenment the world was a ‘great enchanted garden’ because everything – each rock and insect, each planet or lightning strike – was mysterious in some way. It did something not because of physics, but because some mysterious creator willed it to.

But slowly, instead, we’ve disenchanted the world by understanding why lightning strikes, how insects communicate, how rocks are formed, trees grow, creatures evolve.

But what does it really mean to be replaced by machines that can perform every possible task better than us? It means, by definition, that we once again lose that understanding. Remember, even their developers don’t know why neural nets choose the paths the choose. They discover patterns that we can’t see. AlphaGo makes moves humans don’t understand. ChatGPT, in the future, could write a personalised guide to an emotional, personal issue you have that you didn’t understand yourself. Innovation decided by factors we don’t comprehend. We might not be made my Gods, but we could be making them.

And so the world becomes reenchanted. And as understanding becomes superhuman, it necessarily leaves us behind. In the long arch of human history, this age of understanding has been a blip. A tiny island amongst a deep stormy unknown sea. We will be surrounded by new enchanted machines, wearables, household objects, nanotechnology.

We deny this. We say – sure, it can win at chess, but Go is the truly skilful game. Sure, it can pass the Turing Test, but not really. Can it paint? Sure, it can paint, but it can’t look after a child? Yes, it can calculate big sums, but it can’t understand emotions, complex human relationships or desires.

But slowly, AI catches up with humans, then it becomes all too human, then more than human.

The transhumanist movement predicts that to survive, we’ll need to merge with machines through neural implants, bionic improvements, by uploading our minds to machines so that we can live forever. Hearing aids, glasses, telescopes, and prosthetics are examples of ways we already augment our limited biology. With AI-infused technology, these augmentations will only improve our weaknesses, make us physically and sensorially and mentally better. First we use machines, then we’re in symbiosis with them, then eventually, we leave the weak fleshy biological world behind.

One of the fathers of transhumanism, Ray Kurzweil, wrote, ‘we don’t need real bodies’. In 2012 he became the director of engineering at Google. Musk, Thiel, and many in Silicon Valley are transhumanists.

Neuroscientist Michael Graziano points out, ‘We already live in a world where almost everything we do flows through cyberspace’.

We already stare at screens, are driven by data, wear VR. AI can identify patterns far back into the past and far away into the future. It can see at a distance and speed far superior to human intelligence.

Hegel argued we were moving towards absolute knowledge. In the early twentieth century, the scientist and Jesuit Pierre Teilhard de Chardin argued we’d reach the Omega Point – when humanity would ‘break through the material framework of Time and Space’, merging with the divine universe, becoming ‘super-consciousness’. 

But transhumanism is based on optimism: that some part of us will be able to keep up with the ever-increasing speed of technological adaption. As we’ve seen, so far, the story of AI has been one of absorbing, appropriating, stealing all of our knowledge, taking more and more, until what’s left? What’s left of us?

Is it safe to assume there’s things we cannot understand? That we cannot comprehend because the patterns don’t fit in our heads? That the speed of our firing neurons isn’t fast enough? That an AI will always work out a better way?

The history of AI fills me with sadness because it points towards the extinction of humanity, if not literally, then in redundancy. I think of Kierkegaard, who wrote in the 19th century, ‘Deep within every man there lies the dread of being alone in the world, forgotten by God, overlooked among the tremendous household of millions and millions’.

Or a New Age of Artificial Humanity

Or, we could imagine a different world, a better one, a freer one. 

We live in strangely contradictory times, times in which we’re told anything is possible – technologically, scientifically, medicinally, industrially – where we will be able transcend the confines of our weak fleshy bodies and do whatever we want. That we’ll enter the age of the superhuman.

But on the other hand, we can’t seem to provide a basic standard of living, a basic system of political stability, a basic safety net, a reasonable set of positive life expectations, for many people around the world. We can’t seem to do anything about inequality, climate, or war.

If AI will be able to do all of these incredible things better than all of us, then what sort of world might we dare to imagine? A potentially utopian one – where we all have access to personal thinking autonomous machines that build for us, transport for us, research for us, cook for us, work for us, help us in every conceivable way. So that we can create the futures we all want.

What we need is no less than a new model of humanity. The inventions of technology during the 19th century – railways, photography, radio, industry – were accompanied by new human sciences – the development of psychology, sociology, economics. The AI revolution, whenever it arrives, will come with new ways of thinking about us too.

Many have criticised Descartes’ splitting of the world into two – into thought and material. It gives the false sense that intelligence is something privileged, special, incomprehensible, and detached. But as we’ve seen knowledge is spread everywhere, across people, across the world, through connections, in emotions, routines, relationships – knowledge is everywhere, and it’s produced all of the time.

Silicon Valley have always thought that, like intelligence, they were detached and special. For example, Eric Scmidt of Google has said that, ‘the online world is not truly bound by terrestrial laws’. In the 90s John Perry Barlow said that cyberspace consists of ‘thought itself’, continuing that, ‘ours is a world that is both everywhere and nowhere, but it is not where bodies live’.

But as we’ve seen, our digital worlds are not just abstract code that doesn’t exist anywhere, mathematical, in a ‘cloud’ somewhere up there. It’s all made up of very real stuff, from sensors and scanners, cameras, labour, friendships, from books and plagiarism.

One of Descartes’ staunchest critics – the Dutch pantheist philosopher Baruch Spinoza – argued against Descartes’ dualistic view of the world. He saw that all of the world’s phenomena – nature, us, animals, forces, mathematical bodies and thought – were part of one scientific universe. That thought isn’t separate, that knowledge is spread throughout, embedded in everything. He noticed how every single part of the universe was connected in some way to every other part – that all was in a dynamic changing relationship.

He argued that the universe ‘unfolded’ through these relationships, these patterns. That to know the lion you had to understand biology, physics, the deer, the tooth, the savanna – all was in a wider context, and that the best thing anyone could do was to try and understand that context. He wrote, ‘The highest activity a human being can attain is learning for understanding, because to understand is to be free’.

Unlike Descartes, Spinoza didn’t think that thought and materiality were separate, but part of one substance – all is connected, the many are one – and so God or Meaning or Spirit – whatever you want to call it – is spread through all things, part of each rock, each lion, each person, each atom, each thought, each moment. It’s about grasping as much of it as possible. Knowing means you know what to do – and that is the root of freedom.

Spinoza’s revolutionary model of the universal much better lines up with AI researchers than Descartes’ because many in AI argue for a ‘connectionist’ view of intelligence: neural nets, deep learning, the neurons in the brain – they’re all intelligent because they take data about the world and look for patterns – connections – in that data.

Intelligence is not in here, it’s out there, everywhere.

Crawford has emphasised that AI is made up of, ‘Natural resources, fuel, human labor, infrastructures, logistics, histories’.

It’s why Crawford’s book is called the Atlas of AI, as she seeks to explore the way AI connects to, maps, captures the physical world. It’s lithium and cobalt mining, its wires and sensors, it’s Chinese factories and conflicts in the Congo – Intel alone uses 16,000 suppliers around the world.

Connections are what matters. Intelligence is a position, a perspective, it’s not what you know it’s who you know, what resources you can command, it’s not how intelligent you are it’s what, who, where you’ve got access to.

I think this is the beginning of a positive model of our future with technology.

True artificial intelligence will connect to and build upon and work in a relationship with other machines, other people, other resources – it will work with logistics, shipping, buying and bargaining, printing and manufacturing, controlling machines in labs and research in the world. How many will truly have access to this kind of intelligence?

Connection, access, control will be what matters in the future. Intelligence makes little sense if you don’t have the ability to reach out and do things with it, shape it, be part of it, use it. AI might do things, work out things, control things, build things, better than us, but if who gets to access these great new industrial networks determines the shape all of this takes then I think we can see why we’re entering more and more into an age of storytelling.

If AI can do the science better than any of us, if it can write the best article on international relations, if it can build machines and cook for us and work for us, what will be left of us? Maybe our stories.

We will listen to the people who can tell the best stories about what we should be doing with these tools, what shape our future should take, what ethical questions are interesting, which artistic ones are. Stories are about family, emotion, journey and aspiration, local life, friendship, games – all of those things that make us still human.

Maybe the AI age will be more about meaning.

Meaning about being compelling, passionate, making a case, articulating, using the data and the algorithms and the inventions to tell a good story about what we should be doing with it all, what matters. The greatest innovators and marketers knew this. It’s not the technology that matters, it’s the story that comes with it.

More films, music, more local products and festivals, more documentaries and ideas and art, more exploring the world, more working on what matters to each of us.

I like to think I won’t be replaced by ChatGPT because while it might eventually write a more accurate script about the history of AI, it won’t do this bit as well – because I like to think you also want to know a little bit of me, my stories, my values, my emotions and ideocracies, my style and perspective, who I am – so that you can agree or disagree with my idea of humanity.

I don’t really care how factories run, I don’t really care about the mathematics of space travel, I don’t care too much about the code that makes AI run. I care much more about what the people building it all think, feel, value, believe, how they live their lives. I want to understand these people as people so I work out what to agree with, and what not to.

We too often think of knowledge as kind of static – a body of books, Wikipedia, in the world ready to be scientifically observed. But we forget it’s dynamic, changing, about people, about lives.

It’s about trends, new friends, emotions, job connections, art and cultural critique, new music, political debate, new dynamic-changing ideas, hopes, interests, dreams, passions.

And so I think the next big trend in AI will be using LLMs on this kind of knowledge. It’s why Google tried and failed to build a social network. And why Meta, Twitter, and Linked In could be ones to watch – they have access to real time social knowledge that OpenAI don’t, at the moment. Maybe they’ll try and build a social network based on ChatGPT? They do, at least, have even more data as they analyse not the ‘Pile’ of static books, but the questions people are asking, their locations, their quirks.

Using this type of data could have incredible potential. It could teach us so much about political, psychological, sociological, or economic problems if it was put to good use. Some, for example, have argued that dementia could be diagnosed by the way someone uses their phone.

Imagine a social network using data to make suggestions about what services people in your town need, imagine AI using your data to make honest insights into emotional or mental health issues you have, giving specific, research driven, personalised and perfect roadmaps on how to beat an addiction or an issue you have.

I’d be happy for my data to be used honestly, transparently, ethically, scientifically; especially if I was compensated too. I want a world where people contribute to and are compensated for and can use AI productively to have easier, more creative, more fulfilling, meaningful lives. I want to be excited in the way computer scientist Scott Aaronson is when he writes: ‘An alien has awoken — admittedly, an alien of our own fashioning, a golem, more the embodied spirit of all the words on the internet than a coherent self with independent goals. How could our eyes not pop with eagerness to learn everything this alien has to teach?’

Conclusion: Getting to the Future

So how do we get to a better future? To make sure everyone benefits from AI I think we need to focus on two things. Both are a type of bias; cultural bias and a competitive bias. Then, further, we need to think about wider political issues.

As well intentioned as anyone might be, bias is a part of being human. We’re positioned, we have a perspective, a culture. Models are trained through ‘reinforcement learning’ – nudging the AI subtly in a certain direction.

As DeepMind founder Mustafa Suleyman writes in The Coming Wave, ‘researchers set up cunningly constructed multi-turn conversations with the model, prompting it to say obnoxious, harmful, or offensive things, seeing where and how it goes wrong. Flagging these missteps, researchers then reintegrate these human insights into the model, eventually teaching it a more desirable worldview’.

‘Desirable’, ‘human insights’, ‘flagging missteps’. All of this is being done by a very specific group of people in a very specific part of the world at a very specific moment in history. Reinforcement learning means someone is doing the reinforcing. On top of this, as many studies have shown, if you train AI on the bulk sum of human articles and books from history, you get a lot of bias, a lot of racism, a lot of sexism, a lot of homophobia.

Studies have shown how heart attacks in women have been missed because the symptoms doctors look for are based on data from men’s heart attacks. Facial recognition has higher rates of error with darker skin and women because they’re trained on white men. Amazon’s early experiment in machine learning CV selection was quietly dropped because it wasn’t choosing any CVs from women.

These sorts of studies are everywhere. The data is biased, but it’s also being corrected for, shaped, nudged by a group with their own biases.

Around 700 people work at OpenAI. Most of what they do goes on behind the black box of business meetings and board rooms. And many have pointed out how ‘weird’ AI culture is.

Not in a pejorative way, just how far from the mean person you’re going if that’s your life experience: very geeky, for lack of a better word. Very technologically-minded, techno-positive. Very entrepreneurial.

They’re all – as Adrian Daub points out in ‘What Tech Calls Thinking’ – transhumanists, Ayn Rand libertarians, with a bit of 60s counterculture anti-establishmentarianism thrown in.

The second ‘bias’ is the bias towards competitive advantage. Again, the vast majority of people want to do good, want to be ethical, want to make something great. But often competitive pressures get in the way.

We saw this when OpenAI realised they needed private funding to compete with Google. We see it with their reluctance to be transparent with how they train datasets because competitors could learn from that. We see it with AI weapons and fears about AI in China. The logic running through is, ‘if we don’t do this, our competitors will’. If we don’t get this next model out, Google will outperform us. Safety testing is slow and we’re on a deadline. If Instagram makes their algorithm less addictive, TikTok will come along and outperform them.

This is why Mark Zuckerberg actually wants regulation. Suleyman from DeepMind has set up multiple AI businesses and actually wants regulation. Gary Marcus – maybe the leading expert on AI who has sold a startup AI company to Uber, and started a robotics company – actually wants regulation.

If wealthy, free market, tech entrepreneurs – not exactly Chairman Mao – are asking for the government to step in, that should tell you something.

Here are some things we do regulate in some way: medicine, law, clinical trials, pharmaceuticals, biological weapons, chemical, nuclear – all weapons actually – buildings, food, air travel, cars and transport, space travel, pollution, electrical engineering. Basically, anything potentially dangerous.

Ok, so what could careful regulation look like? I always think regulation should aim for the maximum amount of benefit for all with the minimal amount of interference.

First, transparency, in some way, will be central.

There’s an important concept called interoperability. It’s when procedures are designed in an open way so that others can use them too. Banking systems, plugs and electrics, screwheads, traffic control – are all interoperable. Microsoft have been forced into being interoperable so that anyone can build applications for Windows.

This is a type of openness and transparency. It’s for technical experts, but there needs to be some way auditors, safety testers, regulatory bodies, and the rest of us, can in varying ways see under the hood of these models. Regulators could pass laws on dataset transparency. Or transparency on where the answers LLMs give come from. Requiring references, sources, crediting, so that people are compensated for their work.

As Wooldridge writes, ‘transparency means that the data that a system uses about us should be available to us and the algorithms used within that should be made clear to us too’.

This will only come from regulation. That means regulatory bodies with qualified experts answerable democratically to the electorate. Suleyman points out that the Biological Weapons Convention in the US has just four employees. Fewer than a McDonalds. Regulatory bodies should work openly with networks of academics and industry experts, making findings either public to them or public to all.

There are plenty of precedents. Regular audits, safety and clinical trials, transport, building, chemical regulatory bodies. These don’t even necessarily need a heavy hand from the government. Regulation could force AI companies of a certain size to spend a percentage of their revenue on safety testing.

Suleyman writes, ‘as an equal partner in the creation of the coming wave, governments stand a better chance of steering it toward the overall public interest’.

There is this strange misconception that regulation means less innovation. But innovation always happens in a context. Recent innovations in green technology, batteries, and EVs wouldn’t have come about without regulatory changes, and might have happened much sooner with different incentives and tax breaks. The internet along with many other scientific and military advances were not a result of private innovation but an entrepreneurial state.

I always come back to openness, transparency, accountability, and democracy, because, as I said at the end of How the Internet Was Stolen, ‘It only takes one mad king, one greedy dictator, one slimy pope, or one foolish jester, to nudge the levers they hover over towards chaos, rot, and even tyranny’.

Which leads me to the final point. AI, as we’ve seen, is about the impossible totality. It might be the new fire or electricity because it implicates everything, everyone, everywhere. And so, more than anything, it’s about the issues we already face. As it gets better and better, it connects more and more with automated machines, capital, factories, resources, people and power. It’s going to change everything and we’ll likely need to change everything too. We’re in for a period of mass disruption – and looking at our unequal, war torn, climate changing world – we need to democratically work out how AI can address these issues instead of exacerbate them.

If, as is happening, it surpasses us, drifts off, we need to make sure we’re tethered to it, connected to it, taught by it, in control of it, or, rather than wiped out, I’d bet we’ll be left stranded, in the wake of a colossal juggernaut we don’t understand, left bobbing in the middle of an endless exciting sea.

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Meghan O’Gieblyn, God, Human, Animal, Machine

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‘A white-collar sweatshop’: Google Assistant contractors allege wage theft | Google Assistant | The Guardian 

 https://nvidianews.nvidia.com/news/nvidia-teams-with-national-cancer-institute-u-s-department-of-energy-to-create-ai-platform-for-accelerating-cancer-research#:~:text=The%20Cancer%20Moonshot%20strategic%20computing,and%20understand%20key%20drivers%20of 

https://www.wired.com/story/battle-over-books3/ 

https://www.theguardian.com/books/2016/sep/28/google-swallows-11000-novels-to-improve-ais-conversation 

https://www.businessinsider.com/list-here-are-the-exams-chatgpt-has-passed-so-far-2023-1?r=US&IR=T#chatgpt-passed-all-three-parts-of-the-united-states-medical-licensing-examination-within-a-comfortable-range-10 

https://arstechnica.com/information-technology/2022/09/artist-finds-private-medical-record-photos-in-popular-ai-training-data-set/ 

https://www.youtube.com/watch?v=aircAruvnKk&ab_channel=3Blue1Brown 

Medler, Connectionism, https://web.uvic.ca/~dmedler/files/ncs98.pdf 

The post How AI Was Stolen appeared first on Then & Now.

Even when focusing on the simplest building blocks of its very beginnings, we can see how more than anything, subtle emphases – differences between well-intentioned observers – matters.

Because of this, I’ve carefully selected three main sources, and drawn on others. The first, and one I recommend the most, is a very readable textbook called Arabs and Israelis: Conflict and Peacemaking in the Middle East. It’s by three scholars: Abdel Monem Said Ally, Shai Feldman, and Khalil Shikaki, and it pays careful attention to different historical narratives before analysing them as even-handedly as possible.

Then, Palestinian-American historian Rashid Khalidi’s, The Hundred Years’ War on Palestine is from a Palestinian perspective, while Israeli writer Ari Shavit’s My Promised Land is from an Israeli one.

Of course, even referring to a perspective as ‘Israeli’ or ‘Palestinian’ is an enormous oversimplification, ignoring the vast differences there always are within and between groups. I’ve also drawn on a few historians who’ve been labelled Israeli ‘new historians’ – this loose group have challenged a traditional historical narrative in Israel, something we’ll come to. The literature on this is vast, intellectual humility is required, and so I will focus only on the origins. I’ll also return to a note on how and why I’ve approached this in the way I have at the end.

Towards the end of the 19th century, outbreaks of violence against Jews called pogroms increased across Eastern Europe.

In most countries, Jews were second class citizens. They couldn’t own land, vote, had different and varying legal rights, and were marginalised, lived in ghettos, and often randomly blamed for problems and were targeted and murdered.

This was coming to a head in the last two decades of the 19th century.

In 1881, in the Russian Empire, Jewish communities were attacked after Tsar Alexander II was assassinated and one of the conspirators had incidentally had Jewish ancestry. A wave of pogroms resulted. But this was just one of many instances. In modern day Moldova in 1903, 49 were killed, and many more injured, raped, and homes were attacked.

It’s important to remember that this is relatively borderless period. Palestine had been administered by the decaying Ottoman Empire for centuries. It was home to a small number of Jews already who lived peacefully with a majority of Arabs, mainly Muslims, with a few Christians.

This was a period very different from today. Empires were the norm, borders were always changing, but the idea of ‘nation-states’, that peoples had the right to self-determine, to govern themselves, was on the rise. In 1800 the population of Palestine was 2% Jewish – some 6700 Jews. By 1890, 42,000 Jews had moved there, while the Arab population was around 500,000. By 1922, the Jewish population had doubled to 83,000.

Towards the end of the 19th century, Jewish settlers started buying land from absent urban Arab landlords, leading to the displacement of the Arab peasants who had worked the land. 500 Arabs signed a letter of complaint to the Ottomans about this in 1891.

In My Promised Land, Ari Shavit describes the complex and sometimes contradictory motivations of the young Zionist movement at the end of the 19th century. For some, fleeing violence, it was a matter of life and death, for others, like his own British great-grandfather, it was a complex choice, one comprised of solidarity with those fleeing persecution, a romantic idea of the Holy Land, and a modern idea of it too – that a new thriving modern future could be built in a land that was widely and falsely seen as empty.

Judaic Studies professor David Novak has written: ‘The modern Zionism that emerged in the late nineteenth century was clearly a secular nationalist movement’. However it had deep religious and historical roots to draw on as well – that Palestine was the Jewish ancestral homeland, the Exodus from Egypt to the promised land, and later exiles from the region, and returns. But Zionism was never unified – many, many disagreed, religious and secular alike, and those who agreed or became Zionists did so for many reasons. Shavit points out that travellers from places like Britain didn’t see Palestine for what it was. They saw empty desert. They saw a few Bedouin tribes. They saw possibility. They didn’t see the Palestinian villages and towns, or maybe, he says, they chose to ignore them?

They also saw poverty – dirt huts and tiny villages. They believed, or said they believed – as many colonists also claim, it’s important to note – that the indigenous population would benefit from Jewish capital, education, technology, and ideas, and it’s true that many did.

Drawing on his grandfather’s diaries, Shavit asks why his grandfather ‘did not see’. After all, he was served by Arab stevedores, Arab staff at hotels, Arab villagers carried his carriages, was led by Arab guides and horseman, was shown Arab cities.

He uses a word: blindness. They were too focused on a romantic ideal of the area and the tragic oppression they were fleeing from. Shavit writes: ‘Between memory and dream there is no here and now.’

Not everyone was blind, though. At the beginning of the 20th century one Zionist author, Israel Zangwill, gave a speech in New York that reported that Palestine was not empty. That they would have to ‘drive out by sword the tribes in possession, as our forefathers did.’

This was heresy. No one wanted to hear it. He was ignored.

So between 1890 and 1913, around 80,000 Zionists emigrated. In the short period between WWI and WWII the same number again. But this snowballed with the rise of Nazism in the 1930s. Between 1933-1940, 250,000 fled Germany. In 1935 alone, 60,000 moved to Palestine. More than the entire Jewish population in 1917.

With this came millions in capital and investment, and successful settlements, villages, and towns began growing.

This huge demographic movement coincided with the most important shift of power in the region. The defeat of the Ottoman Empire during WWI and the subsequent British takeover of control.

During WWI, Zionists in Palestine provided valuable information to Britain, formed spy networks, and volunteered to fight.

At the same time, a coalition of Arabs supported Britain by rising up against the Ottomans in the Great Arab Revolt. In return they were promised an independent Arab state by the British.

But Britain made several contradictory promises in quick succession.

In 1917, the Balfour Declaration – a memo between Foreign Secretary Lord Balfour and Lord Rothschild – committed the British Government to a home for the Jewish people in Palestine.

The Balfour declaration neglected to mention the word Arab, who comprised 94% of the population. It read: ‘His Majesty’s government view with favour the establishment in Palestine of a national home for the Jewish people, and will use their best endeavours to facilitate the achievement of this object, it being clearly understood that nothing shall be done which may prejudice the civil and religious rights of existing non-Jewish communities in Palestine, or the rights and political status enjoyed by Jews in any other country.’

Here lies the root of the conflict; the contradictory promise: ‘when the promised land became twice promised’, in the words of historian Avi Shlaim.

Reporting this news in Palestine was banned by the British.

Instead, after the defeat of the Ottomans, the British and French divided the area into spheres of influence under the Sykes-Picot Agreement in 1916, leaving Palestine as a British mandate under British control. This was the famous ‘line in the sand’, made by people who had little knowledge of the area.

In a private 1919 memo only published 30 years later, Lord Balfour admitted: ‘In Palestine we do not propose even to go through the form of consulting the wishes of the present inhabitants of the country… The four Great Powers are committed to Zionism. And Zionism, be it right or wrong, good or bad, is rooted in age-long traditions, in present needs, in future hopes, of far profounder import than the desires and prejudices of the 700,000 Arabs who now inhabit that ancient land.’

The British Mandate gave the Jewish Agency in Palestine status as a public body to help run the country. Jewish communities and leaders formed institutions for self-defence and governance, which the British slowly recognised, essentially becoming a government in waiting.

As a result, outbreaks of violence began to increase in the 1920s, getting progressively worse. In 1929, hundreds of Jews and Arabs were killed and hundreds more wounded at the Western Wall in Jerusalem. Tensions rose, resulting in a series of massacres of Jews by Arabs, one of which in Hebron resulted in the death of almost 70 Jews and the injuring of many more. In response to the violence, the British declared a state of emergency. They proposed a legislative council that would be comprised of six nominated British and four nominated Jewish members, and twelve elected members, including two Christians, two Jews, and eight Muslims.

Seeing themselves as outnumbered on a governing panel in a country in which they were the clear majority, Palestinians rejected the proposal. Another was proposed that was slightly fairer to the Palestinians, but this time it was rejected by the Zionists and British parliament.

During the largest wave of immigration as the Nazis came to power, Palestinians called for a general strike demanding an end to Jewish migration and the sale of land to Zionists by absentee urban landlords, which continued to dispossess peasants working the land.

In 1936, an Arab revolt started when gunmen shot three Jews, setting off a series of attacks and counterattacks, leading to the deaths of around 415 Jews and 101 British. The British response was swift and brutal. 5000 Arabs were killed by the British, violence continued into 1937, and many were imprisoned and exiled. 10% of the Arab population were killed, injured, exiled, or imprisoned.

Kahlidi puts the figure higher, writing: ‘The bloody war waged against the country’s majority, which left 14 to 17 percent of the adult male Arab population killed, wounded, imprisoned, or exiled.’

Said Ally, Feldman, and Shikaki write that it was ‘disastrous for the Palestinians.’

In one instance an 81-year-old rebel leader was executed after being found with a single bullet. The British tied Palestinian prisoners to the front of their cars to prevent ambushes. Homes were destroyed. Many were tortured and beaten, including at least one woman.

However as a result of the unrest, in 1937 a British government report recommends two states for the first time. The Arab state, though, would not be Palestinian. It was to be merged with Transjordan.

In 1939, British government policy, put forward in a white paper, decided to call for a single jointly administered Palestine, and limited Jewish immigration and land sales.

The Holocaust changed all this. And even more disastrous for the Palestinians was the leadership’s decisions to side with Hitler in 1941, as he had told them that the Nazis had no plans to occupy Arab lands.

As the true extent of the Holocaust became clearer, the plight of European Jews became more urgent in the eyes of European and US policymakers. It’s crucial to remember the extent of the horror – six million Jews industrially murdered. After the war, there were 250,000 Jews living in refugee camps in Germany alone. Britain was bankrupt and was pulling out of many of its former colonies. Syria, Lebanon, Jordan, and Egypt gained their independence, and they formed the Arab League.

More plans were proposed, including the Morrison-Grady Plan in 1946 calling for two separate autonomous Arab and Israeli regions under British defence, which was again rejected by both Zionists and Palestinians.

A UN plan in 1947 proposed 43% of the area going to Palestinians, despite them comprising two thirds of the population. It was rejected by the Arab Higher Committee who called for a three-day general strike.

The newly independent (or quasi-independent, at least) surrounding Arab states were becoming increasingly hostile to Zionism and the plight of the Palestinians. But they also saw potential to either increase their own territory or to gain power in the region. Egypt saw itself as a new Ottoman Empire. King Abdullah of Transjordan saw Palestine as part of Transjordan. He thought that victory in the war against Israel would be secured in ‘no more than ten days.’

The USSR, seeing the potential of a state of Israel as a socialist ally, provided weapons to the Zionists. Seeing themselves as decisively outnumbered and outgunned, with no tanks, navy, or aircraft (the Arab countries, to varying degrees, did have this equipment), Ben-Gurion secured a deal with Czechoslovakia for $28m worth of weapons and ammunition, increasing their supply by 25% and ammunition by 1000%. In 1968, Ben-Gurion remembered, ‘the Czech weapons truly saved the state of Israel. Without these weapons we would not have remained alive’.

By now the Palestinians and Zionists were in a state of civil war, with continued attacks and counterattacks.

In early 1948, knowing the British would leave, Arab countries were preparing to invade and Jewish state institutions-in-waiting prepared a plan of defence. And there were already Jewish settlements outside of the proposed UN partition boundaries, and of course, many Palestinian areas within.

Zionist leadership prepared what was referred to as Plan D, which included, ‘self-defense against invasion by regular or semi-regular forces’, and ‘freedom of military and economic activity within the borders of the [Hebrew] state and in Jewish settlements outside its borders’.

All of this was made worse by British bankruptcy and a hard-line Zionist militant group called Irgun, who bombed the British Mandate headquarters, killing 92 people, and were involved in skirmishes with Palestinians. In one attack in April of 1948, Irgun killed 115-250 men, women, and children in a village near Jerusalem, despite a non-aggression pact.

So on 15 May 1948, the British left. The day before, David Ben-Gurion declared the establishment of the new state of Israel. The day after, a coalition of Arab forces from Egypt, Jordan, Syria, Lebanon, and Iraq invaded.

For the most part, Israel captured and defended the areas allotted to them by the 1947 UN plan, as well as areas outside of it.

Hundreds of thousands of Palestinians were forced to flee their homes. Palestinians call it the Nakba – the Catastrophe.

The result of the war was the Gaza strip coming under Egypt’s control, the West Bank contested but under the control of Jordan’s forces, to be annexed in 1950, and anywhere between 400,000 and a million Palestinians displaced.

There is complexity, and this is only a small fraction of this story, but it’s impossible to ignore that the Nakba was a catastrophe – power differentials, foreign influence, empire, failures to compromise, perpetration of atrocities, the loss of homes and land that would never be returned to. The Palestinians were divided, outnumbered, and kept weak by Britain, Zionists, the US, the USSR and their surrounding Arab neighbours.

Journalist Arthur Koestler famously said that, ‘One nation solemnly promised to a second nation the country of a third’.

While British Prime Minister Neville Chamberlain had tried to limit immigration to Palestine, he was replaced by Winston Churchill, one of the biggest supporters of Zionism in British public life. In 1937 Churchill said of Palestine that: ‘I do not agree that the dog in a manger has the final right to the manger even though he may have lain there for a very long time. I do not admit that right. I do not admit for instance, that a great wrong has been done to the Red Indians of America or the black people of Australia. I do not admit that a wrong has been done to these people by the fact that a stronger race, a higher-grade race, a more worldly wise race to put it that way, has come in and taken their place’.

In response to the UN planning to partition Palestine in 1947, several Arab countries warned, or even threatened, violence against Jews in their own countries and expulsion. In 1950 and 51 Iraq withdrew Jews of their Iraqi nationality and property rights. Antisemitism in Yemen led to the migration of 50,000 Jews between 1949-1950. There were attacks on Jews in Tripoli before the war in 1945. Whether punitive policies and attitudes began before the war or as a result of it is a matter of debate.

What becomes clear, though, is that moral questions depend on the minutiae of often unanswerable questions; ones that historians are still, often acrimoniously, debating.

Who, which groups and subgroups, were most responsible for violence in ‘47? Were 19th century Zionists ‘blind’, ‘altruistic’, in existential danger? Are they colonisers in the usual sense? Or victims fleeing from violence in Europe?

Shavit writes that, ‘these pilgrims do not represent Europe. On the contrary. They are Europe’s victims. And they are here on behalf of Europe’s ultimate victims.’

Anyone who tells you that answers are easy to come by are wrong. Antisemitism was at its height in the 1940s. The Holocaust had just happened. Jewish immigrants had purchased land and settled in Palestine peacefully for decades. But amongst these difficulties, there are some indisputable facts. The UN partition plan offered Palestinians 43% of the land despite them comprising 68% of the population. And around 700,000 Palestinians became refugees.

Shavit cites a letter written from an Israeli he knew who fought the 1947-48 war. He wrote about the time: ‘when I think of the thefts, the looting, the robberies and recklessness, I realize that these are not merely separate incidents. Together they add up to a period of corruption. The question is earnest and deep, really of historic dimensions. We will all be held accountable for this era. We shall face judgment. And I fear that justice will not be on our side’.

And this is one report from an Israeli military governor, reporting a conversation with Palestinian dignitaries when Palestinians were forced from the small city of Lydda in 1948:

DIGNITARIES: What will become of the prisoners detained in the mosque?

GOVERNOR: We shall do to the prisoners what you would do had you imprisoned us.

DIGNITARIES: No, no, please don’t do that.

GOVERNOR: Why, what did I say? All I said is that we will do to you what you would do to us.

DIGNITARIES: Please no, master. We beg you not to do such a thing.

GOVERNOR: No, we shall not do that. Ten minutes from now the prisoners will be free to leave the mosque and leave their homes and leave Lydda along with all of you and the entire population of Lydda.

DIGNITARIES: Thank you, master. God bless you.

And in many cases, people left before the war broke out. In one case, the Israeli mayor even begged the Palestinians to stay. Although this was the only case.

For many years, the ‘Israeli’ narrative – although to call it that is far too simplistic, ignoring the disagreements, differences, and dissent within the conversation – was that the surrounding Arab states called upon the Arabs in Palestine to leave so that they could invade.

School books in Israel taught that Israelis wanted peace, but they were surrounded by enemies who wanted their destruction; that the Arabs fled to safety as a natural process of war.

This was challenged in the 1980s as official archives were opened, and a generation of ‘new’ Israeli historians looked differently at the period.

Benny Morris, one of those new historians, argued that there was no master plan of expulsion. However, it was understood that it was in the leadership’s interests to establish a Jewish state with as small of a minority of Palestinian Arabs as possible.

Most say the order came from Ben-Gurion himself. Those saying this include the later Prime Minister Yitzhak Rabin, who reported in his autobiography that Ben-Gurion had given him the order to expel the Palestinian Arabs in Lydda. When Rabin tried to publish this in 1979 it was censored.

What’s clear is that there was an overwhelming atmosphere – of fear, of exodus, of violence and beatings, of many massacres, of war in general – that led to 700,000 Palestinians leaving their homes, never to return.

Sources:

Understanding Israel and Palestine: A Reading List

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All of this happened at dizzying speed. This was the moment the modern world crashed together and dragged people from the fields to the factory floor.

Within a generation, the entire consciousness of life had changed.

Science challenged deeply-held views of the world.

Darwin published On the Origins of Species in 1859.

He pulled the Gods down from the sky and transformed humans into just another animal.

This, of course, was shocking, traumatising, existentially threatening.

The philosopher Soren Kierkegaard wrote in 1844 that, ‘Deep within every human being there still lives the anxiety over the possibility of being alone in the world, forgotten by God, overlooked by the millions and millions in this enormous household’.

Nietzsche, famously proclaiming the death of God, argued that men would become nihilistic, lose their grounding, forsake their morals, if a new ethics of man did not come.

Darwin, the death of God, the prosperity of industry, science, all pointed towards something that could be terrifying: freedom.

Kierkegaard went on: ‘Anxiety may be compared with dizziness. He whose eye happens to look down into the yawning abyss becomes dizzy. But what is the reason for this? It is just as much in his own eyes as in the abyss . . . Hence, anxiety is the dizziness of freedom’.

Freedom was the expansion of options – of ways to live life personally, of political options, with commercial options.

Warfare was changing: swords and rifles, of which there were only a few, were being replaced by stuttering guns and spat bullets at an incomprehensible rate, artillery and bombs that sent shrapnel shredding in a cacophony of unbearable noise.

The word ‘panic’ was used for the first time in 1879 by the psychiatrist Henry Maudsley to describe extreme agitation, trembling, and terror.

People were nervous, literally – a new diagnosis became popular amongst America’s elites:  neurasthenia.

It was a contemporary form of stress, characterised by symptoms like fatigue, headache, and irritability.

Neurasthenia, according to physician Charles Beard, was the result of a depletion of nervous energy, but was becoming more common as a reaction to the anxieties of the modern world and of the demands of American exceptionalism. Neurasthenia was almost a fashion. Adverts appeared selling ‘nerve tonics’, self help books dominated the shelves, even breakfast cereals claimed to be able to cure ‘americanitus’.

Beard argued that there were five main causes of neurasthenia: steam power, the periodical press, the telegraph, the sciences, and the mental activity of women.

He argued that these phenomena contributed to the competitiveness and speed of the modern world.

Even time itself was to blame.

He wrote, ‘the perfection of clocks and the invention of watches have something to do with modern nervousness, since they compel us to be on time, and excite the habit of looking to see the exact moment, so as not to be late for trains or appointments. Before the general use of these instruments of precision in time, there was a wider margin for all appointments. We are under constant strain, mostly unconscious, often times in sleeping as well as in waking hours, to get somewhere or do something at some definite moment’.

The recently laid telegraphs also meant that prices and information could be sent around the world at a moment’s notice, piling the pressure on merchants to keep up with the latest news from all around the world.

According to the pre-psychological way of understanding the human mind, all of these phenomena hit the nerve endings, draining the life force.

Unnatural modern noises did this too.

Beard wrote: ‘Nature – the moans and road of the wind, the rustling and trembling of the leaves, and swaying of the branches, the roar of the sea and of waterfalls, he singing of birds, and even the cries of some wild animals – are mostly rhythmical to a greater or less degree, and always varying if not intermittent’.

As with Kierkegaard’s anxieties over freedom, for Beard, politics and religion also added to the drain: ‘The experiment attempted on this continent of making every man, every child, and every woman an expert in politics and theology is one of the costliest of experiments with living human beings’.

‘A factor in producing American nervousness is, beyond dispute, the liberty allowed, and the stimulus given, to Americans to rise out of the possibilities in which they were born’.

Excitement and disappointment were a drain on nerve-force.

But one innovation was so emblematic of the shock of modernity, of the distortion of time, of the inability of man to adapt to his surroundings, that it’s mentioned almost everywhere the topic is discussed:

The railway.

Historian Wolfgang Schivelbusch argues that the railways didn’t just change travel, but changed the very notion of time itself.

Before the railways, cities, towns, and villages had local times, which had to be standardised for train timetables. ‘London time ran four minutes ahead of time in Reading, seven minutes and thirty seconds ahead of Cirencester time, fourteen minutes ahead of Bridgwater time’. People could imagine being in other places much more easily, changing the very way they think.

It was such a part of the cultural zeitgeist of the time that on the third of October 1868, Illustrated London News reported that five theatres were all performing the same incident: someone tied to or unconscious on a track while a train came hurtling towards them.

These productions made use of modern special effects using lights and smoke, and The Times described them as a ‘perfect fever of excitement’.

The theatres performing these spectacles were open to people outside of the centre of London for the first time, who could travel in on the omnibuses or trains. The same transport they were about to be thrilled by their fear of.

Railway accidents were common. One in 1868 killed 33 people.

One passenger wrote, ‘We were startled by a collision and a shock. [. . .] I immediately jumped out of the carriage, when a fearful sight met my view. Already the three passenger carriages in front of ours, the vans and the engine were enveloped in dense sheets of flame and smoke, rising fully 20 feet. [. ..] [I] t was the work of an instant. No words can convey the instantaneous nature of the explosion and conflagration. I had actually got out almost before the shock of the collision was over, and this was the spectacle which already presented itself. Not a sound, not a scream, not a struggle to escape, or a movement of any sort was apparent in the doomed carriages. It was as though an electric flash had at once paralysed and stricken every one of their occupants. So complete was the absence of any presence of living or struggling life in them that it was imagined that the burning carriages were destitute of passenger’.

This idea of instantaneous death mixed with machinery was so new and so shocking, that it dominated the culture.

Charles Dickens himself was involved in a train crash and wrote the ghost story The Signal Man afterwards. According to his children, he was never the same again.

All of this – industry, commercialism, fear, anxiety, thrill, trains – culminated in an emphasis on sensation and the birth of sensationalism. The point was the senses. The modern world could trigger them, play on them, manipulate them, and sell to them, all at a tremendous speed.

The Irish playwright Dion Boucicault made sensation the centre of his plays. He intended to ‘electrify’ the audience.

A review of one of his plays illustrates this emphasis on the senses: ‘The house is gradually enveloped in fire [and] [. ..] bells of engines are heard. Enter a crowd of persons. [. . .] Badger [.. .] seizes a bar of iron, dashes in the ground-floor window, the interior is seen in flames. [. . .] Badger leaps in and disappears. Shouts from the mob. [. . .] [T]he shutters of the garret fall and reveal Badger in the upper floor. [. . .] Badger disappears as if falling with the inside of the building. The shutters of the window fall away, and the inside of the house is seen, gutted by the fire; a cry of horror is uttered by the mob. Badger drags himself from the ruins’.

Drama of such speed and excitement had rarely been seen before.

In the early 1860s, sensation novels suddenly became popular.

In 1866, an article in the Westminster Gazette lamented that all minor novelists were now sensationalists.

Literary critic D. A. Miller describes it like this: ‘The genre offers us one of the first instances of modern literature to address itself primarily to the sympathetic nervous system, where it grounds its characteristic adrenaline effects: accelerated heart rate and respiration, increased blood pressure, the pallor resulting from vasoconstriction, and so on.” H.L. Mansel wrote that ‘There are novels of the warming-pan type, and others of the galvanic battery type-some which gently stimulate a particular feeling, and others which carry the whole nervous system by steam’.

So, what was lost in these tumultuous years? I think Charles Beard and Kierkegaard, in many ways, hit it on the head. The idea of freedom, anxiety of choice, the cacophony of noise, the pressure of time all becomes demanding. A type of demand that didn’t exist in agricultural societies. Yes, life also became better, more prosperous – more options – but remembering what was lost is also important.

So, if modernity is still a shock to you then slow down, take some time, turn off your phone, stop thinking. Relax.

Sources

Allan V. Horwitz, Anxiety: A Short History

Nicholas Daly, Blood on the Tracks: Sensation Drama, the Railway, and the Dark Face of Modernity

Beard, American Nervousness

Mark Jackson, The Age of Stress

David G. Schuster, Neurasthenic Nation: America’s Search for Health, Happiness, and Comfort, 1869-1920

Nicholas Daly, Railway Novels: Sensation Fiction and the Modernization of the Senses

The post The Shock of Modernity appeared first on Then & Now.

The policeman’s doctor decided to try a brand new drug, penicillin. He was the first person in the world to receive it.

Around ten years before, Alexander Fleming returned to his lab from holiday and found one of his petri dishes contaminated with mould. He noticed, though, that the mould inhibited the growth of the bacteria, so he took it and added it to other dishes, finding the same result.

After four days of treatment the policeman was making what his doctor described as a striking recovery. His temperature returned to normal and he was eating well. On the fifth day, though, the supply ran out. A month later, he died.

It’s been estimated that since, penicillin has saved the lives of maybe two hundred million people and saved countless others from excruciating pain. It is probably the most important life-saving discovery in human history.

But it also points to a paradox in thinking about ‘progress’ in history. Not only was it discovered by accident, the mould had floated up through a window accidently left open onto a petri dish left accidently out on a bench, rather than in an incubator, while the exceptionally cool weather for the time of year encouraged growth.

If such a lifesaving drug is the result of chance, how can we think about progress? What drives it? Is it guaranteed? Is it a myth?

Of course, it wasn’t just chance. Fleming was a practicing scientist embedded in the context of institutions, aims, methods, a particular culture, and so on.

And compare this story to what was going on at precisely the same time only a few hundred miles away, in Germany and Poland – millions were being systematically murdered while the innovations of science and technology were being put to good use by Europeans slaughtering each other on battlefields.

How do we make sense of this paradox – that the most important innovation in history, like other medical and scientific advances, was happening at the same time as the most devastating catastrophe?

The historian Will Durant said that, ‘Civilization is a stream with banks. The stream is sometimes filled with blood from people killing, stealing, shouting, and doing the things historians usually record, while on the banks, unnoticed, people build homes, make love, raise children, sing songs, write poetry, and even whittle statues. The story of civilization is the story of what happened on the banks. Historians are pessimists because they ignore the banks for the river’.

Is Durant right? Do we ignore the good in history? Are we all pessimists? How do we even begin to understand the good in history – how it unfolds, what drives it, what could promote, what we could learn from? There are countless difficulties. The first is, what does good even mean? What’s the measure? The criteria?

Some say health, others happiness, others wealth. Stability? Community? Equality? A postmodern critique that it’s impossible to rank values, to compare and classify, or to place any hope in grand narratives? What is a long life if it’s lived under tyranny? What is a wealthy life if those around you live in poverty?

However, if we were to begin with a loose meta-criterion that I think most would agree with while nevertheless disagreeing on precisely what it means, we’d land on something like liberty.

Liberty, broadly speaking, is the freedom to think, to speak, to do, to act, to be oneself, to go where one chooses, to strive in the way one wants to strive. To have as many of the ‘primary goods’ of life as possible in order to do so – food, shelter, transport, even things like good relationships, friendships, opportunities, and so on. Most, I think, would agree that generally, more of these things is better than less.

Liberty in this sense is neutral between competing ideological beliefs or political systems. It begins from a simple premise, that more possibility is better than less; the society that has better access to penicillin is better than the one where you’re more likely to be sent to a gas chamber.

The historical question then is to understand which historical conditions – institutional, political, cultural, philosophical – lead to an increase in liberty and which diminish it. What ideas about liberty seem to work? Where did they come from? Who built on them? Improved them? What diminished or restricted them? The historical question is to search for the causes of liberty so they can be identified and built upon today.

Hegel argued that history was the unfolding of reason through time. Martin Luther King, who read Hegel, argued that the moral arc of history bends towards justice. Marx argued that economic contradictions resolve through history, leading to a more equal society. And more recently some have claimed liberal capitalism as the end of history.

All of these claims are in some sense Hegelian, and the philosopher Terry Pinkard has recently argued in a work on Hegel that the end at work in history is the securing of justice as freedom.

Freedom is the relationship between desire, reasoning, acting on your desires, and recognition and authority. In other words, our desires don’t exist in a vacuum – we are in constant negotiation with others and their desires, with figures and systems of authority that act upon and direct our desires, and so on. Freedom is intersubjective. Social consciousness, culture, and institutions arise out of the interplay of our desires.

With this in mind, Pinkard asks if history makes sense. Is there logic in the way the interplay of desires plays out? Is history comprehensible? Or is it contingent? Random? Messy?

Hegel was a figure of the Enlightenment. Like Kant before him, he believed in a scientific approach to the world – and that included history. He argued that science was bringing the phenomena of the world around us – in nature, in humans, in everything – under ‘the concept’.

What he meant by this was that we have ideas of things – we have ideas of ourselves, our desires, of others, of history. We categorise things – we look at the qualities of things, the causes of things. The historian looks at the causes of World War II, for example.

Importantly, it’s this ability to go about the messy work of building up ideas that makes us human and provides the possibility of even having a history in the first place.

A mouse has a past, but it has no real history. We have ideas of how we acted, why we acted, how we’ve changed since. A mouse may have a drive to eat which it acts on but a human has a concept of eating under which reasons for eating, what to eat, when to eat, what’s healthy, how to farm, where to shop are categorised under the idea or concept of eating.

What Hegel is showing is how we make sense of the world – that from our ideas and concepts we make judgements about how to act. Once we understand this we can understand that the idea of salad is a historical one. We’ve brought more understanding under the concept of salad – its chemical composition, its effects, the best ways of growing, distributing, eating it, and so on.

Humans develop conceptions over time – at times ideas fall apart and are discarded and at other times they develop and are adopted. The biblical idea that the sun went around the earth fell apart as it was observed that the opposite was true, so the idea that the bible was the guide to wisdom was slowly superseded by an emphasis on observation and empiricism.

Pinkard writes that, ‘the components of the “Idea” arise in history, but as humans reflect on those concepts, put them to use, and modify them in the course of their collective lives, they refashion them into overall schemes of intelligibility’.

Hegel was expanding on Spinoza’s point that modern scientific enquiry expands outwards towards the ‘perspective of infinity’, by looking at the causes and qualities of the things that help us expand upon our desires and interests.

Pinkard writes that, ‘Hegel concludes that freedom is the capacity to make what truly matters effective in one’s life, and, in modern times, that more or less comes down to acting on our own reasons rather than on vague feelings of guidance from nature, the gods, or those who claim to rule us by natural right’.

This is obviously not just an individual process. Our own ideas and desires come into conflict with others. There are disagreements that play out in culture, institutions, norms, practices, political decisions, etc.

Pinkard writes: ‘history is an arena in which people seek and have sought reconciliation — that is, a kind of justification of their lives — in their social worlds, and they have sought this both individually and collectively’.

When it comes to the meta-criterion of liberty, denouncing fascism is thought of as the same as trying to eat more salad. An individual, directed by education, cultural context, social information, makes a judgement that the former had the effect of reducing liberty in the past and the latter has the effect of increasing energy and lifespan.

Hegel says that we emerge from a ‘realm of shadows’ and move towards the light of the ‘space of reasons’.

If this is true, we should be able to establish some points of historical progress. Which ‘shapes of consciousness’, to use Hegel’s term, which ideas, practices, institutions in history promote liberty?

For Hegel, the process developed as history unfolded from one being free – a king or emperor, free to make their own decisions – to many being free – i.e. an aristocracy – to all, in principle at least, being free.

Hegel argued that pre-Greek societies were paternalistic and authoritarian, that they were ‘rule-followers’ that didn’t interrogate the reasons for following or abandoning certain rules. And that the Persians, Egyptians, Indians, and Chinese civilisations that preceded the Greeks didn’t approach the world and people as ideas to be studied but instead were absorbed in the world. They didn’t have reflective critical distance. Without these mechanisms for self-criticism there can be no movement in history.

It’s important to note his interpretation of ancient history has been criticised a lot since, but for our purposes, the important point is less where it started, but the idea of reflective distance on the world being important – the questioning of why some ideas or rules are adopted. The Greeks, he thinks, were ‘self’-conscious – they had a particularly acute idea of the self and asked questions about it.

It’s under these conditions that the question is more forcefully asked: who are ‘the people’? What does ‘freedom’ mean? Who rules?

Pinkard writes, ‘The Greek miracle, as it were, was its creation of the polis, a new form of social and political organization in history in which the ability to defend the community united with an ancient conception of justice into a new kind of unity that broke with the past and thereby combined the advantages of the emotional closeness and solidarity of traditional tribal life with the reflective and economic advantages of an urban life’.

What we have developing is an idea of freedom.

For the Greeks, what made someone free was self-sufficiency – that they weren’t under the sway of others, that they had the means to make decisions and live by their own means, own desires, and that, in Aristotle’s phrase, a person was a ‘law unto himself’. He continued that, ‘it is the mark of a free man not to live at another’s beck and call’. Freedom meant not being compelled, it meant to be self-directing, and crucially, it meant not being a slave.

But women and slaves were excluded. The community had ultimate authority over the individual. The Greek polis and its face-to-face direct democracy struggled to grow.

Benjamin Constant wrote, ‘if this was what the ancients called liberty, they admitted as compatible with this collective freedom the complete subjection of the individual to the authority of the community’.

In some ways, Rome expanded on Greece’s idea and managed to grow by granting citizenship to many of the areas it conquered, but ultimately ruling was left to the aristocracy, senate, and emperor.

However, Pinkard writes that, ‘Once the Greeks had put freedom on the map as a way of thinking about justice, there was a push toward justice as equality and as the mutual recognition of the freedom of all, an actualization of the ideal of each being “his or her own person”’.

If we acknowledge that political liberty – the right to contribute to and be part of the political process, to have rights – is an important part of liberty, then it must be true to say that the so-called ‘dark ages’ – between the collapse of the Roman Empire in the 5th century to the Renaissance in the 15th, are a regression.

Historians broadly no longer use the term ‘dark ages’, using the Middle Ages instead, with many pointing to achievements in architecture, agriculture, mining, and more.

Nevertheless, monarchism, absolutism, even the Catholicism of the period, don’t fit well under our broad idea of liberty.

In forms of organisation like monarchy and the medieval church, the right to act, move, worship freely, to contribute towards the decisions that affect your life, are quite clearly restricted in important ways. Social positions are carefully orchestrated from above. Different rights, powers, and privileges are distributed depending on one’s standing and social position. Economic activity, religious freedom, education, and so on, is, or least can always in principle be, commanded from above.

We should look briefly then at four interrelated moments: the Renaissance, the Reformation, the Scientific Revolution, and the Enlightenment.

When Constantinople fell to the Ottoman Empire in 1453, an influx of migrants into Europe led to the discovery of many ancient Greek texts on everything from music and art to politics and philosophy. The resulting Renaissance – impossible without the printing press, invented in 1436 – led to a flourishing of commentary on old ideas and new ideas across the continent.

The ‘discovery’ of America by Europeans in 1492 also revolutionised attitudes of many Europeans – that the world was bigger than assumed, there were more peoples, ideas, possibilities than had been long assumed. It also proved the usefulness of technology – the compass and ship building, in particular.

The Reformation would not have been the same without the Renaissance. The German priest Martin Luther’s rejection of the Pope’s supreme authority set off the reformation across Europe in 1517, encouraging Christians to read the Bible themselves, despite the church forbidding it. No single person or group should have a monopoly on interpreting god’s will.

Protestantism was important because it began to democratise the interpretation of morals and ethics and spirituality. Similarly, the Treaty of Westphalia, signed after the fighting between Catholics and Protestants during the Thirty Years’ War, contained the seeds of the modern idea of the sovereignty of nations, that each nation has the right to determine its own laws, its own course of action. That each, to go back to Aristotle’s phrase, was a ‘law unto himself’.

The Scientific Revolution was happening at around the same time, and by 1700 the world looked very different to how it did in 1400.

Copernicus’s discovery that the earth revolved around the sun rather than the other way around expanded the universe in people’s minds, made the earth just another celestial body, refuted biblical texts, and legitimised the further study of the physical universe. Galileo and Newton revolutionised and formalised the laws of motion and physics, and many began proving that these principles could be applied to innovation through projects like navigational instruments, canal building, architecture, and road improvement. Francis Bacon argued that an inductive method should be used – the careful observation of the world.

All of this led to an interest in and improvement of instruments like the barometer, the telescope, the microscope, the compass, cartography, medical instruments, and on to the steam engine, electricity, and modern engineering.

Paul Hazard places the Enlightenment’s focus on reason as central: ‘Its essence was to examine; and its first charge was to take on the mysterious, the unexplained, the obscure, in order to project its light out into the world. The world was full of errors, created by the deceitful powers of the soul, vouchsafed by authorities beyond control, spread by preference for credulity and laziness, accumulated and strengthened through the force of time’.

Pinkard says that, ‘the major turning point in world history has to do with the advantages gained by modern Europeans who have come to comprehend the “eternal justice” of their world as consisting in a kind of commitment to the equal freedom of all’.

 The Enlightenment, according to many, may have been contradictory, inadequate, misguided – the idea of equal freedom of all conveniently not being applied to colonies, slaves, women, the proletariat – but the question is, despite it taking a painfully slow amount of time, how the nascent animating principles of freedom, justice, equal freedom, that slowly unfolded, complexified, became more forceful, more convincing, more nuanced, from the ancient Greeks, through to the reformation, the scientific revolution, and the enlightenment, and on to things like Marxism, anarchism, decolonisation, human rights, and the debates about freedom and justice today? Is it ideas? Is it economics? Is it innovation? Or is it something else?

I think it’s worth pausing here to reflect on a problem, though. This a common Eurocentric story. And, as we discussed in the Dark Side of History, the expansion of liberties for some led to the domination of others.

I’m not suggesting a simple triumphalist narrative, and there is much to include that traditionally isn’t – the Islamic Golden Age, the prosperity of the Mughal Empire, science leading to pollution as much as new tools.

Furthermore, it is much easier to measure something as distinct as deathrates and violence than it is to measure liberty – what someone sees as liberty varies so much across the world. As we move into the modern era, everywhere, the different methods, technologies, political solutions, languages we have developed for choosing freely to do things has expanded exponentially. So let’s return to our initial question: what is liberty?

The philosopher Thomas Hobbes described some places as having ‘more’ or ‘less’ liberty. Friedrich Hayek said that the ‘poor in a competitive society’ are ‘much more free than a person commanding much greater material comfort in a different type of society’. John Somerville said during the Cold War that in the communist world there was more freedom from the power of private money and periodic unemployment.

A brief look at the history of the concept shows the difficulty in agreeing on what liberty means – whether it can be measured like height or weight.

In his book A Measure of Freedom, philosopher Ian Carter writes that, ‘freedom is the absence of preventing conditions on agents’ possible actions’.

Those ‘preventing conditions’ can be many –  we might be physically prevented, coerced or threatened, unable because of a lack of education or resources – but the broad point is that a measure of freedom is the availability of choices.

You might not be free to climb a mountain if you are incapable, but a better society, I’d argue, is the one that, if that is your choice out of many, you’ll have easier access to the resources, education, time, and energy to do so.

The same can be applied to jobs, health, innovation, cooking, art, religion, travel, politics – a good measure of freedom is one that should be applicable to anything. One that has broad access to scientific research is an improvement on one that doesn’t, one that has the widest availability of ingredients is an improvement on the one that doesn’t, the easiest access to healthcare, etc.

Moving into the 19th century, the new scientific, enlightenment, liberal, rights-based order was becoming dominant throughout Europe. But especially towards the end of the century contradictions began to appear. Was it really capitalism that was responsible for progress? Could capitalism be made more ethical? Could rational state organisation better direct the innovations of science and industry? Could empires be overthrown?

The problem, then and now, is the difficulty in agreeing on the causes of liberty. If we say science – or at least some if it, like medicine, tools, architecture – has been fundamental in improving the lives of most people, then the focus should be to discover, protect, and augment the conditions that led to its rise and proliferation.

Historians of the Scientific Revolution emphasise the activity of academies, collaboration, empiricism, on new ways of reporting experiments as if the reader could witness them – the start of ‘peer review’, the printing press, the availability of information – but the precise conditions are always difficult to agree on.

Another example of this problem comes from the study of the decline of violence. It’s mostly agreed now that there was a decline in homicide and violent crime from the end of the Middle Ages through, roughly speaking, to today. Some – like historian Pieter Spierenburg argue that the cause of this was the monopolisation of state power. As monarchs became more secure and consolidated their authority, the royal court became a politer and more ‘civilised’ place as lords had to jostle for favour, and the monarch was able to capitalise on their power by being more intolerant of volatility. Others have pointed to the rise of commerce and the need for more ‘civil’ interaction between people to make one’s way in life.

On the other hand, the historian Mark Mazower argues that this state monopolisation of power led to the death toll of the two world wars, the Holocaust, and nuclear bombs in the twentieth century, contradicting the story of civil progress.

The point, again, is that the causes of any type of progress are always difficult to identify: just because a monarch imposed order where elite violence would have previously gone unpunished, say, that doesn’t necessarily mean the premise, ‘absolute monarchy causes less violence’, is universally true and so we should support absolute monarchy. This is an error in attribution.

Steven Pinker, who relies heavily on these sorts of arguments in his The Better Angels of Our Nature: Why Violence Has Declined, falls into this trap.

Historian Gregory Hanlon notes that while Pinker is correct to ‘underline the vertiginous drop in violence since the end of the middle ages’, he is also prone to ‘wild exaggeration, hyperbole, junk statistics and reference to fiction as if it were fact’, and that he has, ‘exaggerated, often outrageously, the contrast between then and now’.

And in a particularly damning critique in the introduction to a special issue of History & Theory looking at Pinker’s work, the authors write: ‘the overall verdict is that Pinker’s thesis, for all the stimulus it may have given to discussions around violence, is seriously, if not fatally, flawed. The problems that come up time and again are: the failure to genuinely engage with historical methodologies; the unquestioning use of dubious sources; the tendency to exaggerate the violence of the past in order to contrast it with the supposed peacefulness of the modern era; the creation of a number of straw men, which Pinker then goes on to debunk; and its extraordinarily Western-centric, not to say Whiggish, view of the world’.

Any attempt to make sense of history requires understanding multiple disciplines, has unavoidable ideological biases, and quickly gets very complicated.

That doesn’t mean we should give up – to discern a drop in violence and to roughly identify some causes, to know what encourages scientific discovery, to discern the conditions that have led to  increases in democracy, to know what protects against totalitarianism, to be able to understand, however imperfectly, many other questions like these, is pretty good progress enough, but history is obviously not a story of easy-to-understand simple progress. We try things, get things wrong, give power to the wrong people, go down wrong turnings, we’re prone to accidents and the misuse of ideas, we forget or lose things, new problems develop, freedoms for some lead to catastrophe for others.

This is why Hegel said that the owl Minerva flies at dusk – only in retrospect, as we try and make some sense out of what’s happened.

In 1854 the physician John Snow mapped the houses hit by a cholera outbreak in London. He discovered that the cases centred around one water pump. Snow’s discovery was a huge breakthrough in the prevention of communicable diseases, proving that cholera was not airborne as people thought, but was caught from contaminated water. It led to an unprecedented move towards a focus on sanitation, sewage works, clean water and toilets, and in doing saved countless lives.

Snow looked at the causes of something in the past to make conclusions about how to prevent it in the future. It was this tradition that Alexander Fleming was working in, and one that led to a vast range of advances in health.

History is a scientific discipline. It’s different to, say, physics, but it’s still the study of objects – diaries, letters, newspapers, memos, images – to create an accurate picture of the past – it can be as close as possible to object-ive. And it can still be an attempt to make generalisable patterns from a set of observations. It’s much more open to interpretation than many other disciplines – to find the causes of poverty, the causes of affluence, of happiness – and it’s much more difficult to apply, because we’re not germs or rocks – we respond. But historians have avoided making strong claims about the use of history for policy, politics, thinking about the future, and I think that’s a mistake. We should still use history to understand the likely outcome of scenarios and conditions, to be able to predict what works and what doesn’t.

In the aftermath of the Holocaust many argued it was grotesque to talk about progress, about Hegel, about the cunning of reason. It wasn’t to be made sense of – the unpredictable evil of it disproved progress, disproved an interested benevolent god, disproved the natural goodness of man, disproved a lot things. It left a hole in our human nature.

But if Hegel was right about progress, the idea of the ‘cunning of reason’ is not that the Holocaust was some cunning way of enticing progress, but a horrific veering off from reason that demands instead a reasonable response – how might we avoid something like it happening again?

And since then, there has been a lot of good research on why genocide happens – I’ve explored some of it in this video – research that helps us see the causes and try to institutionalise and culturalise their avoidance, to create inoculations against them in the same way we avoid cholera.

As our ability to influence the world around us as a species grows, the tripwires that we lay become all the more threatening, the stakes are higher; as we become more powerful we become more dangerous to each other. With AI, the Anthropocene, nuclear weapons, the large levers of state power, big capital, we live in a crucial moment, and we must protect against our worse impulses and incentivise our best, or we could, quite easily, trip up and wipe ourselves out. I think all of those threats are not hyperbole, they are very real.

But if we look to how people in the past have capitalised on the possibility for liberty, we have to be cautiously but actively optimistic. I think when we look at the Dark Side and the Progress in history, the word that comes to mind is bittersweet.

Bibliography

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Matthew White, Great Big Book of Horrible Things

Justin Smith, Irrationality: A History of the Dark Side of Reason

Beard, American Nervousness

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LINKLATER, ANDREW, and STEPHEN MENNELL. “NORBERT ELIAS, THE CIVILIZING PROCESS: SOCIOGENETIC AND PSYCHOGENETIC INVESTIGATIONS—AN OVERVIEW AND ASSESSMENT.” History and Theory

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https://www.ferris.edu/jimcrow/brute/

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The post The Light Side of History appeared first on Then & Now.

It’s a history that attempts to pin people down, categorise and classify them, hold them in place, bar and banish them, despite what science is increasingly showing us: migration is the norm. Immobility is abnormal.

Liberalism – the assumptions of which many of us live under – prioritises individual freedom, of thought, of expression, of movement.

But at the same time we think of migration – which is free movement – as abnormal.

We even mythologise a sedentary past – of villages, farmers, peasants, ‘tied to the land’, living and dying in the place where they’re from.

Yet in the 17th century, around 65% left their home parish at some point in their lives.

We have what philosopher Alex Sager calls a ‘sedentary bias’.

The migrant is presented as a problem, alien, outsider, yet we move around our own countries – commuting, deciding to live elsewhere, holidaying, visiting relatives, making work trips – without thinking it’s in any way strange.

We are, as a species, mobile, nomadic, built to move.

In 2020, you could count 280 million migrants and each year around a billion tourists. And the numbers are increasing.

But so are the objects, ideas, and phenomenon – borders, passports, guards, barbed wire, nationalist rhetoric – that attempt to pin us in our place.

Can we find a genealogy of our attitudes? A history of our present problem? To do so, we might start with the 18th century biologist Carl Linnaeus.

Linnaeus was born in Sweden in 1707 during a period when Europeans had been exploring the globe and returning with stories of strange places, peoples, and creatures. Some – like Arnoldus Montanus – wrote and illustrated books about these bizarre alien lands without ever leaving the comfort of home. Zoos, museums, galleries, and menageries exhibited these incredible new foreign curiosities.

Linnaeus – always fascinated by the natural world – wanted to contribute to scientific understanding of the planet’s great biodiversity.

He came up with a system of simple categorisation – a taxonomy.

He’d give each species two names in Latin. The first a general category, the second a specific one.

Linnaeus divided species into classes, genus, species, depending on a number of characteristics including where they were found.

He published his revolutionary book Systema Naturae in 1735.

But when it came to humans, Linnaeus faced a problem. How would the different races of humans fit into his taxonomy?

The Bible told us that all humans were created by God and descended from Adam and Eve. They must be the same.

But the prevailing consensus at the time was that non-European peoples were primitive, savage, and biologically different.

Voltaire had written that, ‘the Negro race is a species of men as different to ours as the breed of spaniels is from that of greyhounds’.

Linnaeus had a rival.

Georges-Louis Leclerc, Comte de Buffon was also a naturalist.

In opposition to Linnaeus, though, De Buffon believed that instead of adhering to strict categories, nature was dynamic, changing, in flux.

He thought humans had migrated and adapted to local conditions as they moved around the planet.

Like almost everyone at the time, De Buffon still believed in a hierarchy. The farther from the Garden of Eden humans had moved, he thought, the more their biology degenerated.

He published his own book Histoire Naturelle in 1749. It was a Europe-wide success.

But Linnaeus’ celebrity grew.

Species couldn’t degenerate that much, he retorted to de Buffon. It was blasphemy. Species – including humans – were born, lived, existed, precisely where god had intended them to.

‘It is impossible’, Linnaeus wrote, ‘that anything which has ever been established by the all-wise Creator can ever disappear’.

By the 10th edition of Systema Naturae Linnaeus would classify 8000 plants and 4000 animals, including several races of humans:

Homo troglodytes, from the Antartic, can eat raw flesh.

Homo caudatus, of Borneo and Nicobar, had tails.

Homo monstrosus, from lapland, included giants and dwarfs.

Homo sapiens europaeus were ‘white, serious, and strong’, ‘active, very smart, inventive’.

Homo apeins asiaticus were ‘yellow, melancholy, greedy’.

Homo sapiens americanus were ‘ill-tempered’ and ‘obstinate’.

And homo sapiens afer, from Africa, were impassive, lazy, crafty. Slow, foolish, and ruled by caprice.

The idea of these biological distinctions between races dominated European science, developing across the 19th century into a new field: race science.

This 10th edition of Systema Naturae was a triumph and became accepted over de Buffon’s interpretation of nature. Louis XV ordered it official.

Rousseau said he knew of no greater man on earth.

After Darwin published On the Origin of Species, he argued that environmental differences had resulted in adaptions seen in humans.

But race scientists argued that there were clear fundamental biological difference. Darwin quickly became side-lined and descended into despair. He had episodes of hysterical crying. As he lost his influence many scientists who adopted the subspecies view believed him to be crazy and ignorant.

How could single species travel so far around the planet? How could ancient Israelites have reached the Pacific Islands?

These were clearly separate biological races.

Darwin performed experiments submerging seeds in water to see if they could survive long journeys, and getting fish and birds to eat them, retrieving them from their droppings, and seeing if they still germinated.

But the human subspecies view won the day. The Natural History Museum in London displayed models of different human species. The Bronx Zoo had a similar display on the ‘Races of Man’. They kept a man from Congo – Ota Benga – in the monkey house where visitors watched him play. He was only released in 1906.

It was clear to all that god and science had intended a separate, distinct, biological hierarchy of man.

The separation of humans into a hierarchy of  species almost logically and naturally led to a global – or at least Western – concern: degeneration, the mixing of genes, the dilution of hereditary superiority.

Darwin’s cousin, Francis Galton, led a new movement: eugenics. Policy makers, he argued, should focus not on education or investment but on breeding good, pure citizens.

Through the Galton Society, scientists warned of the impact of mass-migration, of racial contamination.

Many US states banned interracial sex and marriage in the late nineteenth century.

Biologist Charles Davenport warned that Americans could ‘rapidly become darker in pigmentation, smaller in stature, more mercurial, more attached to music and art’,  and ‘more given to crimes of larceny, kidnapping, assault, murder, rape and sex-immorality’, if races mixed.

President Coolidge wrote about the ‘biological laws’ that ‘tell us that certain divergent people will not mix or blend’. America, he declared after signing a bill to restrict immigration, must be kept American.

University courses on eugenics skyrocketed. Passports and identification documents became more common.

The US closed its borders to migrants for the first time in its history. Immigrants had to take intelligence tests at Ellis Island.

Immigration into the States declined from around 800,000 a year in 1921 to 100,000 after 1929. Ellis Island closed in 1954.

Even ships of refugees fleeing from the Nazis were turned back. One ship – the St Louis – reached Florida and was sent back to Europe. 254 of its passengers died in the Holocaust.

Nazis, most obsessed with purity, even advocated for the destruction of foreign plants in Germans’ gardens. Himmler issued landscaping rules that banned any non-native species.

A popular BBC series and 1958 book The Ecology of Invasions by Animals and Plants warned about protecting domestic species against invading alien ones.

The foundation of all of this – the belief in biological distinction – would persist for centuries. When, after the Holocaust, the UN released a statement that condemned racial distinctions, leading scientists protested.

Leading British scientist, W.C. Osman Hill wrote, ‘I need but mention the well-known musical attributes of the Negroids and the mathematical ability of some Indian races’.

Evolutionary biologist Julian Huxley also pointed to the ‘rhythm-loving Negro temperament’.

83 of 106 anthropologists refused to sign the UN statement.

In 2018 the US Citizenship and Immigration Services changed its mission statement from ‘Fulfilling America’s promise as a nation of immigrants’ to ‘securing the homeland’.

The twentieth century might be looked back on as the century we rediscovered movement. Advances in technology led to an almost unbelievable expansion of railways, roads, airports, and even space travel.

In art, the impressionists like van Gogh had already tried to bring back movement into still images.

Film and radio developed.

Philosophers like Deleuze brought the idea of change, movement, dynamism back into a field he thought had become too static, too representational.

But Linnaeus’ belief that species were native to specific locations continued throughout the twentieth century. No-one believed that humans – let alone many animals – could have dispersed so far and wide across the globe. Creatures couldn’t migrate from Africa to the Pacific Islands. They couldn’t swim thousands of miles. Species had to have evolved separately.

It took technology only invented in the late twentieth century – GPS and modern DNA analysis in particular – to discover a fact that shocked scientists: around half of all species aren’t sedentary, they’re on the move.

And it’s only in the last couple of decades that the real extent of this discovery is becoming clearer.

Animals migrations are incredibly difficult to study. Even harder to understand is our prehistoric past. Tracking technology was heavy, expensive, and unable to be used at long distances. Solar-powered GPS tags changed this.

Suddenly, researchers have been tracking migrations on a scale no one ever suspected. 70,000 km migrations of terns. Zebras walking over 500km, crocodiles swimming 200 miles out to sea, dragonflies flying hundreds of kilometres a day. Everything from sharks to wolves migrating thousands of miles.

A new field of study – movement ecology – rapidly developed.

This video from Movebank logs the movement of 8000 animals fitted with GPS tags: https://www.youtube.com/watch?v=nUKh0fr1Od8&ab_channel=Movebank

Linnaeus’s ideas about using the geographic location in a species’ name has become, for the first time, unreliable. The natural world is much more fluid than we ever realised.

Only in the 1980s did modern DNA analysis finally prove that homo sapiens were one species with a common ancestor. In 2000, the Human Genome Project found that differences between us accounted for about 0.1% of our gene sequence.

As journalist Sonia Shah points out, migration is so common that it’s pointless asking why people migrate, but rather, we should ask why anyone stays in the same place.

She writes ‘migration is encoded in our bodies, just as it is in wild species’. It’s a force of nature, a fact of life, built into biology itself.

Yet despite this, we’re increasingly trying to stop it, thinking of humans as naturally sedentary rather than biologically dynamic.

In 1945 there were just five border walls in the world.

By 1991, there were still only 19.

In 2016 there were 70.

North Korea encages its people. India fences itself off from Pakistan and Bangladesh. Tunisia has built trenches filled with water along its border with Libya. In Hungary, prisoners were used to build a fence along its border with Croatia. Israel uses razor wire, sensors, and infrared cameras. Britain and France have increased the fencing at the channel tunnel. And Trump’s border wall lengthened the US-Mexico barrier by almost 500 miles.

However, walls, as Wendy Brown has argued, are more effective as political theatre and rhetoric than preventing the flow of migration. Instead, they just send migrants through different routes, they create an underground smuggler economy which increases crime, and ultimately make migrant routes more dangerous.

And, of course, they impose an artificial order on what – as we’ve seen – is a natural global phenomenon found in every species.

Our nationalist bias, our sedentary bias, makes these things appear natural, the way the world is, the way it has to be, while often obscuring the complexity of borders as a phenomenon.

They separate families, cut off jobs, and always imply the violence needed to defend them.

For a rich person, borders often signify excitement, adventure, holiday, vacation. For poor countries a border means something entirely different: a prison, a limit, an obstacle.

Jonathan Moses has argued that we could even draw an analogy between international borders and apartheid.  Moses asks, ‘Why is the Dane’s advantage over the Somalian legitimate (and protected by international law), while the Afrikaner’s advantage over the Xosi was not?’

For millennia, migration was a part of human life, all life. Slow but steady. Science and technology have had a strange effect on that history. Inductive science – the careful study of the world – has tended, historically, to collect evidence in a snapshot, at a specific point in time, and then announces that it has found a universal truth. It finds people where they are, and presumes that’s where they belong. And just as scientific racism pinned everyone down, technology sped everyone up, leading to a contradiction that both builds walls and encourages more movement.

And this contradiction is only going to become more pronounced.

Between 2008 and 2014, floods, storms, earthquakes, and other disasters displaced 26 million people around the world. In 2015 alone, 15 million people were forced to flee wars. In that year, a million of them migrated across the Mediterranean.

When we look at these people, we tend to take the ‘states’ that they are moving between, moving through, as the natural unit of analysis. That those people are misfits or aliens, in or out of a container.

We tend to take the state as the natural unit of analysis.

But as Ulrich Beck has noted, as we become more global, as the world becomes quicker and more connected, ‘the unity of national state and national society comes unstuck; new relations of power and competition, conflict and intersection, take shape between, on the one hand, national states and actors, and on the other hand, transnational actors, identities, social spaces, situations and processes’.

Scientific racism, human taxonomy, the state, border walls, guards, passports, global inequality, all hide the fact that not only are we all migrants in our bones, but that increasingly, we are globalised ones, with many more options and desires than ever before. It’s not only the possibility of more global displacement from disasters, wars, poverty, or climate change, but more as we all become more mobile, dynamic, international.

We should focus on ways not just to facilitate this, but to encourage it, to make it more efficient, easier, more dynamic.

The UN’s Global Compact for Safe, Orderly and Regular Migration, for example, encourages international support to do just this.

We all know that we want to move around the world as we wish – for work, for vacation, to see family – but we rarely reflect on the contradiction and injustice that makes this possible for many of us but impossible for many others.

As centuries of naïve and crude pseudoscience get refuted, as we rediscover movement, mobility, and our migrant impulse, should we not be trying not to build walls, but to realise that we’re all on the move.

Sources

Sonia Shah, The Next Great Migration

Alex Sager, Towards a Cosmopolitan Ethics of Mobility

https://www.theguardian.com/media/2018/dec/18/tucker-carlson-immigrants-poorer-dirtier-advertisers-pull-out

The post How Immigrants Became ‘Bad’ appeared first on Then & Now.

Because, in an increasingly globalised world, an increasingly unequal world, a world that could become even more inhospitable in parts, a world getting closer to rather than farther from World War Three, understanding this issue is integral to being able to have a progressive plan to tackle a likely-to-continue increase in global migration. In 2015, at the height of the ‘migrant crisis’ during the Syrian civil war, over a million asylum- seekers came to Europe. In 2013 almost 400 died in one incident, when a boat sank off the Italian coast.

And it’s clear – from the election of Trump on the promise of a border wall – or Britain leaving the EU, or Marine le Pen continuing to do well in France, Orban in Hungary, it’s clear from poll after poll – that the public wants these numbers reduced, and that we don’t have a very good progressive narrative that grapples with the topic. I think we need one.

We can see evidence of this in polling. In Britain, 77% are in favour of reducing immigration by a lot or a little. Most think the costs of immigration outweigh the benefits. 50-70% of respondents across most Western countries report being concerned about illegal immigration.

Canadians are outliers, most having a positive view on immigration – the Canadian government has pursued a policy of multiculturalism for decades.

So, I want to try and understand where we are, what the data says, and be honest about the evidence. I’ll keep it simple but there’s a link to the sources below, organised into categories. I’ll likely continue expanding on it in the future. And in this case, I tried to favour empirical studies and polling over theory, philosophy, and so on, although I have included some.

Even looking at at least a hundred studies I can only scratch the surface, and we have to bear in mind the vast contextual differences between countries. The US, with a long border with Mexico, is of course going to be different to the UK – an island nation – or Greece – on the southern EU border – or Hungary – a country landlocked and surrounded by other countries. I’ll try and draw on a cross-section. So with that in mind, let’s get started and see what we can learn.

You often see the claim that because there’s an increase in the supply of labour and therefore more competition for jobs, immigration lowers the wages of natives.

Here’s what I found. It’s true that immigration, sometimes, has a very ‘small impact’ on wages of low wage workers or those who didn’t complete high school. But the evidence is minimal and many studies don’t find this at all.

For example, this study of the UK concluded that, ‘we find that immigration depresses wages below the 20th percentile of the wage distribution, but leads to slight wage increases in the upper part of the wage distribution. The overall wage effect of immigration is slightly positive’. This study finds roughly the same. And this study found that the effect of that wage decrease was actually mostly on immigrants already in the country and that there was no effect on ‘native-born’ workers.

This study in Australia finds that, ‘While sparse, the evidence generally indicates that Australians’ wages are not adversely affected by immigration on average’.

And one study of OECD countries – that’s higher income countries – in the 1990s found that, ‘immigration had a positive effect on the wages of less educated natives and it increased or left unchanged the average native wages’. And a 2018 meta-analysis of 12 studies found that ‘immigration has little or no impact on average employment or unemployment of existing workers. Second, that where an impact is found it tends to be concentrated among certain groups – i.e. a negative effect for those with lower education and a positive effect for those with higher levels of education’.

There are also studies of unemployment. One study of OECD countries found that, ‘While no significant long-run impact is found in any case, we find that immigration may have a temporary impact on natives’ unemployment’. But the amount was a ‘negligible’: 0.02%.

One point of interest for studies is the Mariel Boatlift, which I mentioned in the last video on Tucker Carlson. It’s useful because, while a singular event and not representative of migration in general, it does provide the data for an experiment as to what happens when a large group of migrants – in this 125,000 – settle quickly in one area, in this case Miami. Initially, several studies concluded that the effect was negligible or none.

However, recently Harvard economist George Borjas has argued that through the 1980s, wages in Miami for those who did not complete high school was 10-30% lower than elsewhere. But it’s important to note that while Borjas is a respected economist, this finding has been controversial and disputed by many others.

Looking at the boatlift, this 2017 study concludes that ‘As a whole, the evidence from refugee waves reinforces the existing consensus that the impact of immigration on average native-born workers is small, and fails to substantiate claims of large detrimental impacts on workers with less than high school’.

Now, Borjas is well-known for making the case that immigration benefits some natives while hurting others – particularly benefiting higher earners while hurting lower. But in one review of his central books, Immigration Economics, the reviewers say, ‘After reading Immigration Economics, one begins to wonder why countries ever decide to have any immigrants, and why many countries continue to allow relatively large inflows of immigrants. Are immigration policies manipulated by an elite who benefit from these policies at the expense of others? Or is the balance of benefits versus costs ‐‐ even for native workers who are most directly in competition with immigrants ‐‐ more positive than one might be led to believe from reading Borjas’ latest book? We, and many other economists, come down on the latter side’.

So it seems like the evidence is ambiguous, but it seems at least very possible that migration in large numbers could effect the wages of lower earners but likely by a very small amount. Rather than reject immigration, the question should become, what could be done about it? But this is a question I didn’t see raised much in the literature. Okay, what about the economy more broadly?

This study found that between 1980 and 2000, each immigrant added 1.2 jobs to the economy. That’s because each new person needs more food, housing, goods, and services, and so increases demand and jobs.

But one common claim is that immigrants are a burden on welfare states. Some studies say non-EU immigrants to the UK cost more in spending than they contribute in taxes – i.e. they have taken more from the state in welfare, tax credits, and other cost and so on than they’ve paid in taxes. Although this 2014 UCL study looked at immigrants who had lived in the UK since 2000 and found they put in 3% more than they took out. And overall, migrants from all countries taken together put in much more than they take out, and one Warwick university study found migrants in the top 1% of earners contributed 8% of total income tax.

On top of that, the Office for Budget Responsibility in the UK has forecasted that more migration leads to more tax receipts over time.

Studies like this vary wildly from country to country. This meta-analysis, for example, is all over the place – it says the evidence is mixed and dependent on context. And when it comes to non-EU migrants the UK is an outlier. The evidence that migrants pay in more than they take out is stronger in other countries, including Switzerland, Belgium, Spain and Portugal.

Another study looked at several Western European countries between 1985 and 2015 and found that, ‘inflows of asylum seekers do not deteriorate host countries’ economic performance or fiscal balance because the increase in public spending induced by asylum seekers is more than compensated for by an increase in tax revenues net of transfers. As asylum seekers become permanent residents, their macroeconomic impacts become positive’.

And this 2021 study looked at 28,000 Ugandan Asians who came to the UK in the late 1960s. They say there is little research on medium term outcomes of refugees, and that, ‘We show that their outcomes are at least as good as the population average, with the younger cohort performing better, and better than for economic migrants of the same ethnicity’.

So again, overall the data is mixed, but I’d say on balance migration is likely a net positive. But we could also look at more the social and cultural economic impact – like entrepreneurship and qualifications.

How do immigrants fair on the more sociocultural end? Well, in the US immigrants are less likely to have finished high school than their native born counterparts, but are also more likely to have a degree. Immigrants are also over-represented in management and research positions of top companies. One survey of fifty of the top companies in the US found that half of them were founded by an immigrants and three quarters had immigrants in top management and research positions.

Many studies find that migrants tend to be more entrepreneurial than natives. One study found they were almost twice as likely than natives to be entrepreneurs in the US. Another study concluded that ‘7.25 percent of immigrants were entrepreneurs, compared with about 4 percent of native-born individuals’. 30% of new entrepreneurs in the US are immigrants. 76% of the top new patents had an author that was foreign born. This study found that ‘immigrants patent at double the native rate, due to their disproportionately holding science and engineering degrees’.

So it seems that immigrants are more entrepreneurial, innovate more, tend towards science and engineering, are represented in top management positions, and on a net basis it’s likely that they add to the economy and the government balance. What’s next?

Okay, we’ll breeze through crime because the evidence is pretty clear. This study concludes that in England and Wales, ‘Although there is a public sentiment that immigrants are more involved in criminal activities than natives, the empirical results of this paper lead to different conclusions’.

This meta-study found that ‘Immigrants facing poor labor market opportunities are more likely to commit property crimes’, however, ‘There is no evidence that immigration has caused a crime problem across countries’, and, ‘Immigrants with good labor market opportunities appear no more likely to commit crime than similar natives’. It also found legalising the status of immigration reduces the likelihood of crime.

This review of 20th century studies in the US context found that, ‘Contrary to the predictions of classic criminological theories and popular stereotypes, immigration generally does not increase crime and often suppresses it’.

This study of Texas finds that, ‘contrary to public perception, we observe considerably lower felony arrest rates among undocumented immigrants compared to legal immigrants and native-born US citizens and find no evidence that undocumented criminality has increased in recent years’.

In France, Muslims are disproportionately represented in the prison population – 40-50% of the prison population when about 10% of the population are Muslim. But with large scale migration from post-colonial Algeria this is likely to be a result of Muslim men being disproportionately raised in poverty and so a socioeconomic fact rather than a cultural one.

But this shouldn’t be ignored. Neither should the issues around religious fundamentalist terrorism – although statistically you’re more likely to be crushed by your furniture or die in car crash, I just don’t want anyone to accuse me of sidestepping the issue.

What’s clear, though, is that property crime is more likely to be committed by desperate people, no matter where they’re from, and is actually less likely to be committed by immigrants. So moving on…

Definitions of multiculturalism are difficult to agree upon. One researcher calls the literature ‘decidedly messy’. Multicultural can mean a simple demographic fact – multiple cultures in one country – it can mean a philosophy about equality of cultures, or a philosophy of separate cultures living next to one another.

In 2010, Angela Merkel famously remarked that multiculturalism had ‘utterly failed’. And one report of 47 countries in the EU declared that, ‘what had until recently been a preferred policy approach, conveyed in shorthand as ‘multiculturalism’, has been found inadequate’.

Does multiculturism mean we should be blind to cultural differences or make allowances for cultural differences? Does it mean accepting different legal or cultural standards?  Much of the literature revolves around whether multiculturalism can exist as what the British philosopher Lord Parekh called in Britain a “community of communities”, or whether this vision has led to what UK prime minister David Cameron called ‘parallel lives’.

In the UK, for example, Jewish and Islamic communities are exempt from the requirement to stun animals before slaughtering them, Sikhs don’t have to wear helmets and are exempt from the ban on carrying knives in public.

In some parts of London, around 70% of primary school kids speak English as a second language.

One question that arises is whether multiculturalism is at odds with social cohesion. What level of integration is appropriate or desirable? There are several ways you can study this: residential segregation, overrepresentation in the prison population, identification with national identity, studying friendship circles, office socialising, and so on.

For example, 90% of first generation immigrants in UK have spouses of the same ethnicity. Membership of the same clubs, in-group friendships, and in-group places of worship are also high.

Many Asian groups – particularly Pakistani and Bangladeshi – continue to have high levels of in-group marriage and friendship in the second generation.

But one study found that at least half of immigrants’ acquaintances ‘come from members of the majority population, a finding which supports the existence of these important ‘bridging’ relationships’. But its also true that, ‘Muslims who follow religious rules and practices tend to have fewer majority acquaintances’.

In Canada and the US, it’s been found that self-reported importance of ethnicity decreases in second generation immigrants, while identification with nation increases.

One consistent finding is that minorities ‘overwhelmingly support maintenance of their own ethnic customs and traditions alongside equally striking support for mixing and integrating’.

Another interesting finding is the benefit of being bicultural. This study finds that, ‘Bicultural individuals show better psychological adjustment, as measured by higher life satisfaction and self-esteem, and lower alienation, anxiety, depression, and loneliness’.

And a meta study of 51 other studies found that biculturalism is ‘positively correlated with a range of behavioral outcomes, such as academic achievement, career success, and reduced delinquency’.

Another meta-analysis of 83 studies finds bicultural individuals are better adjusted than their monocultural neighbours.

‘cultural hybridity’ in the US also seems to correlate with socioeconomic success.

One move in the literature is from multiculturalism to the idea of interculturalism. The difference being that interculturalism promotes the idea of dialogue, mutual progress, and policies that try to counter segregation. For example, in Canada, research suggests that policies can help immigrants secure jobs, learn the language, can lead to higher incomes, and encourage paths to citizenship.

In all, though, there is little solid evidence that multiculturalism has ‘failed’.

As this review concludes, ‘the most important rationale for the political backlash against multicultural policies that they impede or hurt socio-political integration appears unfounded empirically’.

One area of research looks at Islam and multiculturalism in particular.

A 2016 poll, for example, revealed more than half of British Muslims think homosexuality should be illegal. 39% said wives should always obey their husbands, compared to 5% of the whole population. 86% though have a strong sense of belonging to Britain.

24% want Sharia law, according to one poll, although it varies what sharia means. Another poll says 40% want sharia and only 22% oppose sharia.  One study says there are roughly 30-85 so-called Sharia councils in the UK that resolve disagreements, usually around marriage. One government review said, ‘From those who gave evidence to the review panel, no one disputed that sharia councils engage in practices which are discriminatory to women’.

Another poll found that only 37% of Muslims in the UK want to integrate ‘on most things’ but 40% wanted gender segregation in education.

It’s also true that in this context, ‘higher levels of education leads to higher support for democratic values’.

So to put it mildly, if you believe in gender and sexuality equality, freedom of speech, equality under the law, it’s certainly true there are challenges here. Let’s look at undocumented immigration.

Okay, the literature on this is vast so I’ll just touch on a few things that surprised me. It’s no revelation, for example that in many places we’re seeing an increase in illegal immigration and most countries the trend is towards stopping illegal immigration.

When it comes to illegal immigration, most people would probably have images of migrants crossing waterways on dinghies or climbing border fences. But almost half of undocumented migrants in the US came in legally then overstayed visas. Many were brought in as young children and don’t even find out they’re undocumented until they go to get jobs.

But the effect of increasing border patrol funding seems negligible – migrants just find other ways in. For example this study finds that, ‘From 1986 to 2008 the undocumented population of the United States grew from three million to 12 million persons, despite a five-fold increase in Border Patrol officers, a four-fold increase in hours spent patrolling the border, and a 20-fold increase in nominal funding’.

We also forget that being undocumented isn’t particularly appealing and so many don’t stay that long. In one study in Thailand, researchers found that in 61 out of 63 surveyed villages, ‘the proportion of overseas workers who voluntarily returned to Thailand was 95% or more’.

Another study in the Netherlands looked at the ‘psychological burden’ of being away from home – the more undocumented migrants stay, the more this increases.

According to one study, ‘more vigorous deportation policy advances the date of voluntary return’, which explains the motivation for the hostile environment policy here in the UK.

In fact, if you look at numbers from the UK, the majority seem to leave voluntarily, suggesting they only intend to stay for a short amount of time.

Undocumented migrants obviously find it more difficult to find jobs and contribute taxes. One study in the US found that, ‘Providing a pathway to citizenship for the roughly 11 million undocumented immigrants in the U.S. would increase their wages and spending power and, over 10 years, boost U.S. GDP by $1.2 trillion’.

And many empirical studies have also found that wages of legalised migrants improved after amnesty.

One impression I get from reading policy papers and editorial suggestions is that left wing parties have, to quote one paper, ‘struggled to convince the public that they had a grip on the issue’.

In the UK, it’s been empirically verified that many voters have changed political allegiances because of immigration, especially from Labour to Conservative.

Progressives, liberals, and left-wingers need to address what the John Smith Institute calls the public trust deficit. We need to use the facts presented above to craft a positive narrative that doesn’t shy away from the difficulties.

As we’ve seen, immigration, like all political topics, clearly has its challenges, but they all seem negligible, and part of the problems the host country already experiences. Governments that pursue multicultural policies and are proactive in making the positive case for immigration – particularly Canada, Australia, and Scotland – do much better when it comes to public support for immigration.

Scots, for example, tend to be more tolerant of asylum seekers in part because leadership prioritises PR campaigns that inform people of the benefits and takes control of the narrative. In the England, on the other hand, the narrative is dominated by the right wing tabloids.

Media exposure to negative narratives, have, of course, been found to affect voters’ preferences.

In the UK, when polled, people always think migration is much higher than it is. Immigrants make up 10% of France’s population and the number hasn’t increased in recent years – but respondents to polls continue to believe immigration is too high.

In the UK, there were 50,000 asylum applications in 2021 and 15,000 were granted.

700,000 are born in the UK each year. In 2021, 23,000 crossed the channel illegally in boats. 37 died.

I think, when looking at the evidence, these numbers should be easily accommodated – but rather than take up France’s offer of a UK asylum processing centre in France that would likely reduce the numbers crossing illegally, the British government refuses to take up the offer.

The UK has a labour shortage, an aging population, and a shortage of National Health Service workers, and 23% of doctors and almost half of nurses were born outside the UK. We should be welcoming people. There are also plenty of creative policy ideas that get overlooked.

Several studies look at ‘heartland’ visas for asylum seekers who are willing to settle in deprived areas or areas that are depopulating. Canada and Australia both do this.

Initiatives such as the United Nations’ Global Compact 5 for Safe, Orderly and Regular Migration suggests a possible framework. The compact calls for countries to create more legal pathways for migrants in search of new livelihoods.

Canada receives around 250,000 immigrants a year and does very well. Australia’s economy is booming as it accepts more migrants – more than 30% of the population were born abroad.

So what do you think? Is there anything you think I’ve missed or left out? Let me know in the comments, take a look at the sources in the description yourself, and let’s work towards a progressive narrative for the future.

Sources

Wages & Unemployment

How immigrants affect jobs and wages, https://migrationobservatory.ox.ac.uk/resources/briefings/the-labour-market-effects-of-immigration/

The Effect of Immigration along the Distribution of Wages, https://www.ucl.ac.uk/~uctpb21/Cpapers/CDP_03_08.pdf

The Impact of Immigration on Occupational Wages: Evidence from Britain, https://www.bostonfed.org/publications/research-department-working-paper/2008/the-impact-of-immigration-on-occupational-wages-evidence-from-britain.aspx

THE IMPACT OF IMMIGRATION ON THE STRUCTURE OF WAGES: THEORY AND EVIDENCE FROM BRITAIN, https://onlinelibrary.wiley.com/doi/full/10.1111/j.1542-4774.2011.01049.x

The unemployment impact of immigration in OECD countries, https://www.sciencedirect.com/science/article/abs/pii/S0176268010000765

Immigration and Wage Growth: The Case of Australia, https://www.rba.gov.au/publications/confs/2019/pdf/christian-dustmann.pdf

The Labour Market Effects of Immigration and Emigration in OECD Countries, https://onlinelibrary.wiley.com/doi/10.1111/ecoj.12077

(12 study meta-analysis) EEA migration in the UK: Final report, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/741926/Final_EEA_report.PDF

The Enclave and the Entrants: Patterns of Ethnic Enterprise in Miami before and after Mariel , https://www.jstor.org/stable/2095716?origin=crossref

Mariel Boatlife, a Reappraisal, https://www.nber.org/papers/w21588

The Impact of the Mariel Boatlift on the Miami Labor Market , https://dataspace.princeton.edu/handle/88435/dsp016h440s46f

The Labor Market Effects of Refugee Waves: Reconciling Conflicting Results, https://www.econstor.eu/handle/10419/170790

On the Macroeconomic and Welfare Effects of Illegal Immigration, https://mpra.ub.uni-muenchen.de/15469/

Overall Economy

Are Immigrants a Shot in the Arm for the Local Economy?, https://www.nber.org/papers/w21123

Immigrants to the U.S. Create More Jobs than They Take, https://insight.kellogg.northwestern.edu/article/immigrants-to-the-u-s-create-more-jobs-than-they-take

Immigration Economics: A Review, https://davidcard.berkeley.edu/papers/card-peri-jel-april-6-2016.pdf

THE EFFECTS OF IMMIGRATION ON THE UNITED STATES’ ECONOMY, The Effects of Immigration on the United States’ Economy — Penn Wharton Budget Model (upenn.edu)

Macroeconomic evidence suggests that asylum seekers are not a “burden” for Western European countries, https://www.science.org/doi/10.1126/sciadv.aaq0883

Immigration Facts: The Positive Economic Impact of Immigration, https://www.fwd.us/news/immigration-facts-the-positive-economic-impact-of-immigration/

Immigrants Keep an Iowa Meatpacking Town Alive and Growing

, https://www.nytimes.com/2017/05/29/business/economy/storm-lake-iowa-immigrant-workers.html

Journal of Refugee Studies, Volume 34, Issue 2, June 2021, Pages 1967–1998, https://doi.org/10.1093/jrs/feaa078

The Fiscal Impact of Immigration in the UK, https://migrationobservatory.ox.ac.uk/resources/briefings/the-fiscal-impact-of-immigration-in-the-uk/

How immigrants affect public finances, https://fullfact.org/immigration/how-immigrants-affect-public-finances/

The fiscal impact of immigration A review of the evidence, https://odi.org/en/publications/the-fiscal-impact-of-immigration-a-review-of-the-evidence/

Christian Dustmann and Tommaso Frattini, “The Fiscal Effects of Immigration to the UK”, Economic Journal, Vol.124, Issue 580, pages F593–F643, 2014.

Migrants responsible for UK’s growth of top incomes and taxes, https://www.ft.com/content/0e7aafcf-4e69-4124-9a43-027177d8a4b9

Cultural Impact

Pew Research Center, “Modern Immigration Wave Brings 59 Million to U.S., Driving Population Growth and Change Through 2065: Views of Immigration’s Impact on U.S. Society Mixed,” September 2015, available at: http://www.pewhispanic.org/files/2015/09/2015-09-28_modern-immigration-wave_REPORT.pdf.

Stuart Anderson, “Immigrant Founders and Key Personnel in America’s 50 Top Venture-Funded Companies,” NFAP Policy Brief (December 2011)

“Patent Pending: How Immigrants are Re-inventing the American Economy,” Report of the Partnership for a New American Economy, June 2012, available at: http://www.renewoureconomy.org/wpcontent/uploads/2013/07/patent-pending.p

Hunt, Jennifer, and Marjolaine Gauthier-Loiselle. 2010. “How Much Does Immigration Boost Innovation?” American Economic Journal: Macroeconomics, 2 (2): 31-56.

Around the World, More Say Immigrants Are a Strength Than a Burden, https://www.pewresearch.org/global/2019/03/14/around-the-world-more-say-immigrants-are-a-strength-than-a-burden/

Four in 10 Americans Still Highly Concerned About Illegal Immigration, https://news.gallup.com/poll/391820/four-americans-highly-concerned-illegal-immigration.aspx

National Trends in Startup Activity, 2017_Startup_Activity_National_Report_Final.indd (kauffman.org)

Papademetriou, DG, Somerville, W and Sumption, M Observations on the Social Mobility of Immigrants in the UK and the US (Sutton Trust, 2009)

Multiculturalism/Integration

Research on multiculturalism in Canada, https://doi.org/10.1016/j.ijintrel.2013.09.005

John Biles, Meyer Burstein, and James Frideres, eds., Immigration and Integration in Canada in the Twenty-½rst Century

The Failure of British Multiculturalism: Lessons for Europe, https://www.jstor.org/stable/41274986

WRIGHT, M. and BLOEMRAAD, I. 2012 ‘Is there a trade-off between multiculturalism and socio-political integration? Policy regimes and immigrant incorporation in comparative perspective’, Perspectives on Politics, vol. 10, no. 1, pp. 7795

Anthony Heath & Neli Demireva (2014) Has multiculturalism failed in Britain?, Ethnic and Racial Studies, 37:1, 161-180, DOI: 10.1080/01419870.2013.808754

The Environics Institute, “Survey of Muslims in Canada”, 2016, p. 9.

Saltanat Liebert , Mona H. Siddiqui & Carolin Goerzig (2020): Integration of Muslim Immigrants in Europe and North America: A Transatlantic Comparison, Journal of Muslim Minority Affairs, DOI: 10.1080/13602004.2020.1777663

Paul Statham & Jean Tillie (2016) Muslims in their European societies of settlement: a comparative agenda for empirical research on socio-cultural integration across countries and groups, Journal of Ethnic and Migration Studies, 42:2, 177-196, DOI: 10.1080/1369183X.2015.1127637

Multiculturalism in Canada: Evidence & Anecdote, https://policyoptions.irpp.org/2015/09/22/multiculturalism-in-canada-evidence-and-anecdote/#:~:text=Canada%20is%20one%20of%20the,immigration%20in%20the%20coming%20years.

Multiculturalism & Belonging, https://blogs.lse.ac.uk/politicsandpolicy/multiculturalism-immigration-support-white-population/

Intercultural dialogue: Living together as equals in dignity, https://www.coe.int/t/dg4/intercultural/source/white%20paper_final_revised_en.pdf

Berry, J. W. 2005 “Acculturation: Living Successfully in two Cultures.” International Journal of Intercultural Relations 29(6):697–712

Bloemraad I, Wright M. “Utter Failure” or Unity out of Diversity? Debating and Evaluating Policies of Multiculturalism. International Migration Review. 2014;48(1_suppl):292-334. doi:10.1111/imre.12135

Policy and Politics

Scheve, K. F. and M. J. Slaughter (2001), “Labor Market Competition and Individual Preferences Over Immigration Policy”, Review of Economics and Statistics 83, 133–145.

Dustmann, C. and I. Preston (2007), “Racial and Economic Factors in Attitudes to Immigration”, The B.E. Journal of Economic Analysis and Policy 7, Article 62

Ford, R and Somerville, W Immigration and the 2010 Election (Prospect/Institute of Public Policy Research, 2010)

Understanding public attitudes to asylum seekers in Scotland, https://www.ippr.org/files/images/media/files/publication/2011/05/warm_welcome_1518.pdf

Migration: where next? Developing a new progressive immigration policy, https://www.bl.uk/collection-items/migration-where-next-developing-a-new-progressive-immigration-policy

Don Flynn and Zoe Williams, Towards a Progressive Immigration Policy, https://barrowcadbury.org.uk/wp-content/uploads/2018/12/Full-Report-Towards-a-progressive-immigration-policy.pdf

The positions mainstream left parties adopt on immigration: A cross-cutting cleavage? https://journals.sagepub.com/doi/abs/10.1177/1354068818780533?journalCode=ppqa

Philosophy/Theory

Helbling M (2014) Framing immigration in Western Europe. Journal of Ethnic and Migration Studies 40(1): 21–41.

Sonia Shah, The Next Great Migration

Andreas Onnerfors & Andre Krouwel, Europe: Continent of Conspiracies

Richard Hofstadter, The Paranoid Style in American Politics

Alex Sager, Towards a Cosmopolitan Ethics of Mobility

Ruben Andersson, Illegality, Inc…, op. cit., p. 107.

David Miller, Strangers in Our Midst…, op. cit., pp. 38-56

Gwilym David Blunt, « Illegal Immigration as Resistance to Global Poverty », Raisons politiques 2018/1 (N° 69), p. 83-99. DOI 10.3917/rai.069.0083

Crime

Immigration and Crime: Assessing a Contentious Issue, https://www.annualreviews.org/doi/10.1146/annurev-criminol-032317-092026

Papadopoulos, G. Immigration status and property crime: an application of estimators for underreported outcomes. IZA J Migration 3, 12 (2014). https://doi.org/10.1186/2193-9039-3-12

Crime & Immigration, https://wol.iza.org/articles/crime-and-immigration

Immigration reduces crime: An emerging scholarly consensus, http://www.umass.edu/preferen/You%20Must%20Read%20This/Lee%20Immigration%20and%20Crime.pdf

Light MT, Miller TY. DOES UNDOCUMENTED IMMIGRATION INCREASE VIOLENT CRIME? Criminology. 2017 May;56(2):370-401. doi: 10.1111/1745-9125.12175. Epub 2018 Mar 25. PMID: 30464356; PMCID: PMC6241529.

Comparing crime rates between undocumented immigrants, legal immigrants, and native-born US citizens in Texas, https://www.pnas.org/doi/10.1073/pnas.2014704117

Illegal Immigration/Asylum Seekers

How the Danish Left Adopted a Far-Right Immigration Policy, https://foreignpolicy.com/2021/07/12/denmark-refugees-frederiksen-danish-left-adopted-a-far-right-immigration-policy/

France Reckons with Immigration Amid Reality of Rising Far Right, https://www.migrationpolicy.org/article/france-immigration-rising-far-right#:~:text=According%20to%20estimates%20from%20the,in%20France%20under%20local%20contracts.

The Economic Effects of Granting Legal Status and Citizenship to Undocumented Immigrants, The Economic Effects of Granting Legal Status and Citizenship to Undocumented Immigrants – Center for American Progress

Modes of Entry for the Unauthorized Migrant Population (Washington, D.C.: Pew Research Hispanic Center, 2006)

ego, “Legal Consciousness of Undocumented Latinos”; and Roberto G. Gonzales, “Learning to Be Illegal: Undocumented Youth and Shifting Legal Contexts in the Transition to Adulthood,” American Sociological Review 76 (4) (2011): 602–619.

The Illegality Trap: The Politics of Immigration & the Lens of Illegality, https://www.jstor.org/stable/43297259

Gathmann, C. (2008), “Effects of Enforcement on Illegal Markets: Evidence from Migrant Smuggling at the Southwestern Border”, Journal of Public Economics 92, 1926–1941.

Kossoudji, S. A. and D. Cobb–Clark (2002), “Coming out of the Shadows: Learning about Legal Status and Wages from the Legalized Population”, Journal of Labor Economics 20, 598–628.

Casarico, A., Facchini, G., & Frattini, T. (2015). Illegal immigration: policy perspectives and challenges. CESifo Economic Studies, 61(3-4), doi:10.1093/cesifo/ifv004

Global Envision (2006) Hard Work, Furtive Living – Illegal Immigrants in Japan, available at http://www. globalenvision.org/library/3/986

Jones H, Pardthaisong T (1999) The impact of overseas labor migration on rural Thailand: regional, community and individual dimensions. J Rural Stud 15(1):35–47

Eurelings-Bontekoe EHM, Brouwers EPM, Verschucommodities at lower prices than in the host countryur MJ (2000) Homesickness among foreign employees of a multinational high-tech company in The Netherlands. Environ Behav 32:443–456

Vinogradova, A. (2016). Illegal immigration, deportation policy, and the optimal timing of return. Journal of Population Economics, 29(3), 781–816. http://www.jstor.org/stable/44280414

Deportation and Voluntary Departure from the UK, https://migrationobservatory.ox.ac.uk/resources/briefings/deportation-and-voluntary-departure-from-the-uk/

How many people do we grant asylum or protection to?, https://www.gov.uk/government/statistics/immigration-statistics-year-ending-december-2021/how-many-people-do-we-grant-asylum-or-protection-to#:~:text=There%20were%2048%2C540%20asylum%20applications,number%20for%20almost%20two%20decades.

Number of migrants crossing Channel to UK tops 1,000 in new daily record, https://www.bbc.co.uk/news/uk-59257107

Should the Military Patrol the English Channel? https://yougov.co.uk/topics/politics/articles-reports/2020/08/13/support-RAF-Navy-English-Channel-migrant-crossing

Media

‘Facchini, G., A. M. Mayda and R. Puglisi (2011b), Illegal Immigration and Media Exposure: Evidence on Individual Attitudes, Mimeo, University of Nottingham

Polling

UK Public Opinion toward Immigration: Overall Attitudes and Level of Concern, https://migrationobservatory.ox.ac.uk/wp-content/uploads/2016/04/Briefing-Public_Opinion_Immigration_Attitudes_Concern.pdf

What Is Canada’s Immigration Policy?, https://www.cfr.org/backgrounder/what-canadas-immigration-policy

Islam in Britain

Half of all British Muslims think homosexuality should be illegal, poll finds, https://www.theguardian.com/uk-news/2016/apr/11/british-muslims-strong-sense-of-belonging-poll-homosexuality-sharia-law

The independent review into the application of sharia law in England and Wales, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/678478/6.4152_HO_CPFG_Report_into_Sharia_Law_in_the_UK_WEB.pdf

Unsettled Belonging: A survey of Britain’s Muslim communities, https://policyexchange.org.uk/wp-content/uploads/2016/12/PEXJ5037_Muslim_Communities_FINAL.pdf

Progressive Policy

From Managing Decline to Building the Future: Could a Heartland Visa Help Struggling Regions?, https://www.immigrationresearch.org/node/2697

Refuge: Rethinking Refugee Policy in a Changing World, Paul Collier &  Alexander Betts

Racism

‘Alfred W. Crosby, “Virgin Soil Epidemics as a Factor in the Aboriginal Depopulation in America,” William and Mary Quarterly 33, no. 2’

The post I Read 100 Studies on Immigration appeared first on Then & Now.

In 1991, two tourists were hiking in the German Alps when they discovered a body which they presumed was a recently deceased mountaineer.

It turns out Otzi, as he came to be known, was a mountaineer of sorts – just a 5200-year-old one.

He was so well-preserved in the ice that scientists know how old he was when he died, what he had for lunch, that he wore a backpack, had an axe and a dagger, a bow, a quiver of arrows, and snowshoes. He had a cut hand and an arrowhead imbedded in his back.

In Stephen Pinker’s book The Better Angels of Our Nature, he writes that Otzi ‘had not fallen in a crevasse and frozen to death, as scientists had originally surmised; he had been murdered’.

Pinker declares that Otzi had belonged to a raiding party clashing with a neighbouring tribe.

Let’s put Otzi on ice for sec.

Pinker uses him as one piece of evidence in a broader argument; that human violence has declined across history. The Better Angels of Our Nature is a monumental and impressive book, clocking it at around 800 pages of graphs, statistics, anecdotes, and literary references.

Today, I want to look specifically at a part of Pinker’s and Thomas Hobbes’ argument: that life in a state of nature – before civilisation – was solitary, poor, nasty, brutish, and short. Amongst other things, Pinker argues that hunter-gatherers, tribal societies, were – and are – much more violent than later more civilised societies. Both Pinker and Hobbes argue that the state and its monopolisation on force and authority have pacified our darker human instincts.

Consequently, human nature is pretty bad, human civilisation pretty good.

This fits with another claim that is often made: that war is part of human nature.

In the 1996 book War Before Civilization, for example, archaeologist Lawrence Keeley argues that prehistoric violent deaths probably ranged from around 7-40% of all deaths.

He says: ‘there is nothing inherently peaceful about hunting-gathering or band society’.

In 2003, Steve LeBlanc and Katherine Register claimed in their book Constant Battles that ‘everyone had warfare in all time periods’.

Biologist Edward Wilson asked, ‘Are human beings innately aggressive?’ Yes. Coalitional warfare is ‘pervasive across cultures worldwide’.

John Tooby and Leda Cosmides declare that ‘Wherever in the archaeological record there is sufficient evidence to make a judgment, there traces of war are to be found. It is found across all forms of social organization—in bands, chiefdoms, and states’.

The book Demonic Males argues that ‘neither in history nor around the globe today is there evidence of a truly peaceful society’.

And Steven Pinker has written that ‘Hobbes was right, Rousseau was wrong’.

So that’s the charge: violent, warmongering, innately aggressive.

But just on an intuitive level these statements seem curious. One the one hand, yes, war is everywhere. We turn on our televisions, go to the movies, read the newspapers and hardly a day goes by without seeing or hearing about it in some way. On the other hand, the vast majority of us wake up every morning and go about our lives without managing to get a brawl or stumbling into a military conflict.

Surely we can’t be inherently warlike and innately peaceful? Surely peace is the norm and violence the exception, not the other way around?

So was it Hobbes or Rousseau who was right? Has violence declined because of the state? What does this tell us about human nature? Were we noble savages or nasty beasts? Let’s find out.

A few different types of evidence have been drawn upon to make arguments about the peacefulness or violence inherent in human nature.

We’ve often been compared to our closest living animal relatives: chimpanzees, who spend quite a lot of time fighting, murdering, and eating their own kind.

And millions of years old fossils found in one site of our ape-like ancestors Australopithecus seemed to show that 80% had their heads bashed in.

We are, many have argued, just another killer ape.

But bonobos and gorillas are much more peaceful than chimpanzees.

Primatologist Frans de Waal has written that if we focused on bonobos ‘reconstructions of human evolution might have emphasized sexual relations, equality between males and females, and the origin of the family, instead of war, hunting, tool technology, and other masculine fortes’.

And those bashed in Australopithecus heads. It was later that skull damage was from leopard bites and ecological pressure during fossilisation.

Ultimately, comparing us to completely different species has very little to say about human nature. To understand that, we need to dig into humans.

Let’s return to Otzi.

It turns out though that the bow was unfinished, the dagger a third a length of a kitchen knife – good for skinning probably. The arrows were useless.

In other words, Otzi was not a warrior or raider but a hunter.

Here’s an alternative to Pinker’s scenario that he was murdered.

He was hunting – as he and every other human did every day – dressed in fur, in low visibility blizzard conditions, and got hit by a stray arrow.

Consider this: even today around 1000 hunters are accidently shot in the US every year. If you’re a hunter today, you’re more likely to be killed by accident than be murdered.

But the point is this: it’s anecdotal. Arbitrary. We don’t know. So let’s look at Pinker’s more substantive evidence.

Let’s have a quick look at this graph from the beginning of Pinker’s book. It’s a graph that depicts violence declining over time, like this. Actually, let’s look at the actual graph.

At the bottom here, Pinker says that the average rate of violent death today is around 1%. Across the 20th century it was around 2% – that’s including the world wars.

Then there’s the earliest states like Ancient Mexico which he clocks in at 5%.

Then he says he’s ‘lumped together’ – his words not mine – horticulturalists and hunter-gatherers – 24.5%.

Then the average for just hunter-gatherers – 14%,

And finally, at the top, hunter-gatherers and horticulturalists found by archaeologists – 15%.

‘We started off nasty,’ Pinker concludes. We’re pacified by civilisation.

Actually, this was pretty accurate. Conclusive? Case-closed? Let’s dive in.

We’ll start with the modern-day hunter-gatherers. How violent are they? Remember, Pinker claims that 14% died violently. That’s quite a lot.

Anthropologists Brian Ferguson and Douglas Fry argue the data Pinker uses is cherry picked and inaccurate. For a start, all eight are from a single study published in 2009. A small sample.

Take the Ache of Paraguay – by far the highest violent death rate in the list at around 30%. Except, when you look at the original study you find that all the deaths involved frontiersmen and ranchers. The Ache, the original study says, were being ‘relentlessly pursued by slave traders and attacked by Paraguayan frontiersmen’ while apparently desiring a peaceful relationship with their neighbours.

The same applies to the Hiwi of Venezuela and Columbia. Every single death involved colonists and ranchers, including a massacre.

How about the Casiguran Agta of the Philippines? That 5% figure is based on an anthropologist’s account of nine deaths that happened, quote, ‘in the context of an influx of immigrant colonists into the area, and the resulting cutting back of the forest and decline of game and fish’, which led to fighting. Two of those deaths were carried out by immigrant farmers.

Okay, the Ayoreo of Bolivia and Paraguay. Oh hang on, they’re horticulturalists not hunter-gatherers so they should be struck off the list. As should the Modoc of America.

If we account for these changes, Fry shows, Pinker’s 14% should be reduced down at least to 7%, assuming the rest is correct. A big if. So far the section is looking a bit of a mess.

Fry says, ‘Bar charts and numeric tables depicting percentages of war deaths for “hunter-gatherers” convey an air of scientific objectivity and validity. But in this case, it is all an illusion’.

This is one common problem with studying modern hunter-gatherers – they’ve often been ‘contaminated’ by colonists, imperialists, slave-traders, globalisation, or simple farming. So this has nothing to do with what they’d have been like thousands of years ago.

Another problem is that anthropologists studying violence are also more likely to study – guess what – more violent societies. Twenty percent of Pinker’ data here comes from a guy called Napoleon Chagnon who seems like a bit of a character. His studies have been roundly criticised by other anthropologists and he annoyed tribes so much he was banished from one.

Taking a different approach, Fry looks at a cross-sample of 186 cultures studied by anthropologists. 21 are described as nomadic foragers and in only eight of them was more mentioned. In seven homicide was reported as rare, very rare, low or never mentioned. He also looked through the anthropological literature for mentions of cultures that lacked war, looking for statements like these: ‘[The Veddahs] live so peacefully together that one seldom hears of quarrels among them and never of war’.

‘Warfare in the sense of organized intertribal struggle is unknown [among the Arunta]. What fighting there is, is better understood as an aspect of juridical procedure than as war’.

He found 70. Seventy. Compared to this. Let’s move on. What does the archaeological record say? Surely there we can find a real state of nature?

Evidence for prehistoric warfare usually comes from four categories: Art – depictions of violence. Tools and weapons. Ditches, walls, and fortifications. And more importantly, skeletal remains. Cuts, shattered bones, or embedded projectile points.

Let’s have another look at Pinker’s average – 15% died violently based on 21 archaeological sites around the world.

Jebel Sahaba, for example, in Sudan from at least 10,000BC clocks in at a whopping 40%. This is our earliest clearest evidence for war. 24 out of 59 bodies were found with projectiles like arrowheads. But as some have argued, these could have buried with them – they were lifelong hunters after all. I’d certainly like to buried with my treasured hunting mace. But this is a high figure, nonetheless.

Then there’s the oldest graveyard in the Sahara, though – Gobero, Niger – from around the same time, where 0% show signs of violent death.

Voloshkoe and Vasilkyevka in Europe from around the same time do show a high proportion of remains with fractures and signs of violence. This is the earliest evidence of warfare in Europe.

Then there’s two out of 60 that look violent from Calumnata in Algeria – one has a projectile but when we look at the original source the other death is described as likely a collision with a rock, not violence. Come on, Steven.

Now the Pacific northwest coast of America was very violent and had defendable sites with walls etc.

But let’s pause a minute. Notwithstanding the problems already alluded to, like with our friend Otzi, surely hunting accidents were more common? Surely people were hunted by predators more? Surely lithics – arrowheads – tools and weapons could be buried with the person as ceremonial. And what if war deaths were simply respected more, buried more ceremonially. All of this would distort the record.

And look at those top two sites from South Dakota in 1325 and Nubia in 10,000BC – they’re doing a lot of heavy lifting on this very small data set.

Can we not to better?

Anthropologist Brian Ferguson takes a closer look at the evidence. One survey of 2000-3000 remains found in France showed 48 with projectile wounds. That’s 1.9%. Not on Pinker’s list. One site in Britain of 350 individuals showed about 2%. Not on Pinker’s list. 418 individuals in Serbia and Romania – 2.3%. Not on Pinker’s list. Another study looks at Japan between 13000 and 800BC and of 2500 adults finds 2% died potentially violently. Not on Pinker’s list. Anthropologist Ivana Radovanovic has looked at 1107 remains from Europe, including all of the cases on Pinker’s list, and concludes that you could average out at 3.7% for a low estimate and 5.5% for a high estimate. Not 15%!

Now, I don’t think you can accurately gauge this, but for Pinker’s sake we’ll take the high estimate – 5.5%. That’s more than fair.

But let’s pause again. There’s another problem here. All of the examples on Pinker’s list come from after the invention of farming. They’re all after about 10,000 BC. These people aren’t hunter-gatherers at all. A quick history lesson.

The Homo genus – that’s humans – evolved from Australopithicus around 200,000 years ago. The first humans were homo habilus, homo erectus, home neantherderthalis and several others. But modern homo sapiens first arrived on the scene around 200,000 years ago.

All of Pinker’s examples are from around 12,000 years ago and later. Okay, hang on, I’m gonna need a bigger piece of paper. He’s left 95% of our existence unaccounted for. What happened around 12,000 years ago? The Neolithic revolution, otherwise known as the agricultural revolution, the advent of civilisation, the emergence of sedentary societies.

What anthropologists call complex hunter-gatherers emerged around this time. They used a mix of hunting, gathering, and farming, the domestication of animals. Their societies had higher population densities, were more permanent, they stored resources, and had more inequality – high status individuals are more commonly found buried with rare artifacts.

All of Pinker’s evidence is from after this point – after agriculture, after the start of civilisation.

So, hang on Steven, what happened before here?!

The oldest suggestion of war in Europe that’s often cited comes from over 750,000 years ago. An excavation in Spain shows signs of cannibalism. But this was a different species – Homo Antecessor – with a completely different brain. So we can scrap that. Some Neanderthals have shown signs of skull fractures that could be violence but as we’ve seen could also be leopard bites, and again, they’re a different species. So scrap that.

Cave art like this has often been cited as evidence of warfare. But, why are the lines wavy? And, like our own culture, art could be a warning against rare and dangerous war, not evidence for its ubiquity.

This period is called the Palaeolithic – the old stone age – so let’s concentrate on this. What does the evidence say?

One study looked at 103 remains found across Europe and found a violent death rate of 1%. Another looked at 209 remains in France and found five fractures. Although none were on the left side of the head, which you’d expect if they were the result of human violence. Even so, let’s say 2%. And, well, that’s about it.

In one overview of the evidence from Eastern Europe, Archaeologist Pavel Dolukhanov wrote that ‘in no cases could one find any evidence of inter-group conflict’.

Commenting on the total record, Henry de Lumley has written that ‘the first Homo sapiens do not seem to have led the warrior’s life so often attributed to them, for their pathology is not marked by a traumatology other than that caused by the accidents of everyday life’.

Anthropologist Leslie Sponsel has written that that, ‘during the hunter-gatherer stage of cultural evolution, which dominated 99 percent of human existence on the planet… lack of archaeological evidence for warfare suggests that it was rare or absent for most of human prehistory’.

So that 15%, for this period, so far, on scant evidence, we could it bring it down to around 2%.

So now we have 2% for hunter-gathers, up to 5% for post agricultural revolution, 7% for modern hunter-gatherers, 5% for early states, and 3% for the 20th century.

Fry writes that, ‘the idea that 15 percent of prehistoric populations died in war is not just false, it is absurd’.

So what happened? War likely emerged at the end of the Ice Age and the advent of farming around 10000BC with a change in socioeconomic conditions like:

– A shift to sedentary existence

– Settlements become bigger, denser

– Growing population

– Resource concentration (harvests stored)

– Excess resources

– Hierarchy

– Enclosures show signs of social segmentation

– Clear inequality

– Use of salt, seashells, and obsidian to trade with

It seems like the first wars and increases in violence were associated not with ‘savages’, hunter-gatherers or nomadic tribes, but with civilisation. One anthropologist has described this as the ‘formative period of warfare’. After the Neolithic revolution, Ferguson says, ‘war had become a cultural obsession across Europe’.

Often wars and violence could have been the result of competition over favourable locations or responses to climactic shocks. ‘War does not extend forever backwards’, Ferguson writes. ‘It has identifiable beginnings’.

The increase in warfare across this period is clear in case after case. After the 6th millennium BC we have signs of war becoming an enduring phenomenon.

Take Bulgaria. Neolithic stone settlements began in the sixth millennium BC, then slowly in the fifth millennium we see more defendable locations with fortifications, then in 4500BC we find more weapons, arrows, maces, axes.

Or take the northwest coast of North America. About 5000 years ago, the evidence suggests, non-lethal injuries were dominant, maybe pointing to some kind of juridical interpersonal violence, maybe contests. Warfare comes later, with evidence of the first large-scale war appearing just 1700 years go.

In the Middle East a similar time sequence shows villages without walls or ditches being replaced by an increase in defensive structures and fortifications around 7000 years ago.

Or take Anasazi of the American Southwest. From 700 to 1200 AD – that’s FIVE HUNDRED YEARS – there are ZERO signs of warfare. Then the climate changed and by 1250 we see signs of war.

John Carman and ‎Anthony Harding write that the Anasazi co-existed peacefully for more than a thousand years. ‘The violence markers of raiding, killing, and burning appear only very late in Anasazi culture, as a complex response to changing demographic patterns and a prolonged period of severe environmental stress’.

Ferguson says that, ‘war sprang out of warless world’.

Ultimately, complex hunter-gatherers and horticulturalists might make war, but the majority of simple hunter-gatherers don’t.

Fry argues that war should not be depicted by a curve like this, but by an n-type curve, like this.

He laments that, ‘Pinker constructs his account of steadily more peaceful human existence starting not at the raising of the curtain, and not even in the middle of the play, but only in the final act’.

So what can we learn from this? Let’s return to Pinker briefly. One estimate for the rate of violent death in the first half of the 20th century is 3%. Including the second half, this drops to as much as 1%. In 2007 0.04% died violently worldwide. And most agree that there has been a decline in violence over time. Whether the state and its monopolisation on force is the cause of that is a different question. Pinker does, of course, talk about other motivators for later periods, but that’s a different video. The central point for us is that if we’re serious about what people were like in a state of nature then surely nomadic hunter-gatherers are the most central to ‘human nature’.

And ultimately, historically, they’re relatively peaceful and have nothing like what we’d call war.

As Fry argues, what we see is not a decline, but an n-type curve. And if we take the 20th century as a whole at 2%, the hunter-gatherers figure – on very limited evidence – could be 2%, too. And there’s one big woolly mammoth in the room: those hunter-gatherers didn’t have the access to medicine, healthcare, and technology that modern societies have. How much does this distort the numbers?

The Cambridge Encyclopaedia of Hunters and Gatherers tells its readers that, ‘Hunter-gatherers are generally peoples who have lived until recently without the overarching discipline imposed by the state… The evidence indicates that they have lived together surprisingly well, solving their problems among themselves largely without recourse to authority figures and without a particular propensity for violence. It was not the situation that Thomas Hobbes, the great seventeenth-century philosopher, described in a famous phrase as “the war of all against all.”‘

You could say then that Rousseau was right, Hobbes was wrong.

Ultimately, though, Human nature is elastic, context dependent, varies across societies and cultures.

You might say that it’s within human nature to have a capacity for warfare. But you could also say it’s within human nature to have a capacity to make balloon animals or play the oboe. What’s more interesting is the context, what motivates war and what stimulates peace and cooperation. That’s a question I’ll return to next time, but for now I’ll leave you with this quote from Rousseau:

The first man, who, after enclosing a piece of ground, took it into his head to say, ‘This is mine,’ and found people simple enough to believe him, was the true founder of civil society. How many crimes, how many wars, how many murders, how many misfortunes and horrors, would that man have saved the human species, who pulling up the stakes or filling up the ditches should have cried to his fellows: Be sure not to listen to this imposter; you are lost, if you forget that the fruits of the earth belong equally to us all, and the earth itself to nobody!

Sources

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0002995

Douglas Fry, Introduction, ‘War Peace, and Human Nature: The Convergence of Evolutionary and Cultural Views’

Douglas Fry, Beyond War: The Human Potential for Peace

Brian Ferguson, Pinker’s List in War Peace and Human Nature

Brian Ferguson, The Prehistory of War and Peace in Europe and the Near East

Rutger Bregman, Humankind

Steven Pinker, The Better Angels of Our Nature

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1266108/

https://royalsocietypublishing.org/doi/10.1098/rsbl.2016.0028

https://www.scientificamerican.com/article/war-is-not-part-of-human-nature/

Stephen Corry, The Case of the Brutal Savage, https://www.opendemocracy.net/en/case-of-brutal-savage-poirot-or-clouseau-why-steven-pinker-like-jared-diamond-is-wro/

https://towardsdatascience.com/has-global-violence-declined-a-look-at-the-data-5af708f47fba

The post Steven Pinker is WRONG About the Decline of Violence appeared first on Then & Now.

Moreover, other minds extend outwards, trying to escape from their bodies. Those minds, like ghosts in a machine, burrow into your mind, affecting your actions, desires, and beliefs; moving you, manipulating you, controlling you, predicting what you’ll do next.

Automation is not what it seems. And it’s here now.

Agriculture is being revolutionised. Across the 20th century the proportion of people working in agriculture in the West declined from 50% to 2%. Now, robots traverse fields zapping weeds, detecting whether strawberries are ripe before picking them, while drones scan the fields for fertility, transmitting data back to farmers’ computers and up to the cloud. Robots build cars, Flippy makes fast-food workers increasingly obsolete, Amazon has filed a patent for a blimp that deploys delivery drones.

In 2018, Dallas police used a robot to kill a gunman with explosives. This was likely the first time a state has used a robot to kill one of its own citizens.

Many commentators and economists have blamed stagnant wages and the decline of the labour-share (that’s the fraction of income going to workers) on the rise of automation.

Human beings are going out of fashion.

Author and technologist Wes Kussmaul has bemoaned of the human body that ‘protein is not an ideal material. It is stable only in a narrow temperature and pressure range, is very sensitive to radiation, and rules out many construction techniques and components … Only in the eyes of human chauvinists would it have an advantage’.

The robotic landscape, the rise of automation, and our wider understanding of the universe in the modern era, the death of god, has ushered in what some have called the post-human period; an era when our importance in the world is diminished, our capacity to act, to understand, to control depreciated, and our centrality in the universe devalued. We are, on the one hand, just another species, a blip, a cosmic speck, while on the hand, we’ve become more powerful than ever, extending our capabilities outwards, breaching our fragile limits, augmenting the reach of our minds. We are witnessing man’s attempt to escape from himself.

A period when maybe, as Michel Foucault predicted, ‘man would be erased, like a face drawn in sand at the edge of the sea’.

How can we understand this new robotic era? How might we theorise the politics and philosophy of automation and its consequences? How can we all make ourselves bodies without organs?

Our bodies have an urge to extend outwards, to escape from the limits of our skin. Most simply, that’s what technology does.

Marshall McLuhan described the electric lightbulb as ‘pure information’. The medium extends the perception of the human eye. Brain surgery and night sports are impossible without it.

The first machines – lathes and sharp tools – extended the capacity of our limbs to affect the world. To sharpen, quicken, strengthen our abilities. To adapt the stone into the image of our idea.

The industrial revolution extended the self into an automated machine. It was a revolution of extension – extending limbs, hands, feet, eyes into metal. To add pressure, scale, or to make smaller, more precise, to repeat, repeat, repeat quicker than a finger could.

The information revolution, the digital revolution, does the same for mental life. It extends our cognition.

In the same way machines are better than humans at certain physical tasks, AI will become better than humans at cognitive tasks. Newsweek wrote, ‘AI and automation will replace most human workers because they don’t have to be perfect – just better than you’.

But what’s often forgotten is that AI and automation is never neutral. It’s always doing things for someone. It’s always an extension of someone.

And it’s that for and of someone that I want to focus on.

As Robert Belk argued in an influential 1988 article, we’ve always had an extended-self.

The isolated self, cut off from the world, is an illusion. We get, have, and possess things, ideas, places, even people, because we are dependent organisms. We are part of a larger world that we try to integrate, in various ways, into our sense of self.

We view things as part of the self when we are able to understand them, or can predict how they’re going to act, or decide whether we like or dislike it, and when we learn how to control things in the same way we control our body.

As Belk pointed out, many thinkers, from Enlightenment philosophers Hegel and Locke to modern psychologists and sociologists, have commented on how we have a desire to assimilate the external material world with our inside ideational experience so as to understand, comprehend, and have confidence in our ability to interact with the world, to trust that we can control it, in some way, rather than it controlling us.

In short, we think of the exterior landscape as part of our sense of self.

We also think of properties or characteristics like our age, job, name, favourite food, clothes, where we live as a part of ourselves.

When we create something, we often think of it as an extension of ourselves, using the pronoun my.

My work of art, my invention, my song.

When we’re cycling or driving we think of the machine as an extension of ourselves.

The existentialist Jean-Paul Sartre saw three reasons we might see an external object as part of our selves.

To control it for personal use. To increase our power of acting. But we also conquer or master things: mountains, difficult instruments, new skills, as a test of our capabilities, of our mastery over the world.

When we master, comprehend, and control, we integrate the world into our own psychologies.

We might master, comprehend, and so have control over, a transport network, for example, when we move to a new city. It becomes a part of our psychological landscape, literally part of our schema of the world.

The things that we extend ourselves into also can take on our own traits, characteristics, and properties, extending our personality out into the world.

A farmer’s land has had their energy invested in it, say. Something we draw takes on our emotional state. A dog takes on some of our rhythms, our commands, even our personalities.

Machines have never just been mechanical, geometric, predictable structures; machines are an extension of us and can take on our creativity. Machines can be novel, random, dynamic, too.

As Spinoza said, ‘We do not even know what a body is capable of…’

What makes our new extended world different from the previous ones? What makes the robotics revolution different from the industrial revolution?

As all the tech giants now know, the aim of the algorithmic game is prediction.

Those robots that pick strawberries need to be able to predict when they’re ripe. The simple automated vacuum predicts where furniture is.

This is how the information society comes together; where big tech, social media, and robots collide.

Because they’re all ultimately about the same thing: data-collection for better prediction.

Social media platforms seek, vacuum and hoard as much data as possible so they can predict which advertisers to sell your attention to and at which times.

And it’s why we see big tech expanding into areas that, at first, don’t seem logical. Google into self-driving cars, Facebook into VR, Amazon into movies.

The more data they have, the better their bottom line. And we’ll see more of this in the future.

Smart speakers that collect every minute detail; chairs scraping, the frequency of your coughing, when you’re typing or washing up, when your dog’s barking.

All so they can know when to advertise cough sweets, when to play a certain song, when you’ve gotten up for a break and when you’re busy working.

Tesla cars – with their cameras and sensors picking up data from wherever their drivers goes – will know a neighbourhood better than its residents.

A company called ZeroEyes uses ‘Artificial Intelligence to actively monitor camera feeds to detect weapons and people who could be potential threats’.

Athena – backed by Peter Thiel – develops security software that can recognise ‘suspicious’ behaviours.

The so-called ‘internet of things’ turns every device in your home into an information collector: a washing machine that knows what clothes you wear and when, a dishwasher that can detect what you’ve eaten?

We will be surrounded by devices acting as corporate private detectives, scanning, tailing, interpreting our every move.

As theorist Mark Andrejevic notes, the ultimate goal is to understand the world as broadly and as specifically as possible.

He calls it pre-emption. These companies want to pre-empt what we’ll do, what we’ll need, and what we’ll most desire.

He cites Foucault’s concept of the panopticon – the prison designed by Jeremy Bentham in which prisoners are unaware if they were being watched by the guards or not. Foucault says the ‘major effect’ is to induce in the inmate ‘a state of conscious and permanent visibility that assures the automatic functioning of power’.

We live in a world where, increasingly, automation knows what you want better than you do. The strawberry picking machine knows better and quicker than the farmer when fruit is ripe, a dating app knows better and quicker who you’re likely to go on a date with, and Amazon knows better and quicker than you do what product you’re likely to buy.

Professor of marketing Praveen Kopalle writes, ‘think of the feelings you get when you see that an Amazon package has arrived at your door – it’s delightful and exciting, even though you know what it is. I bet those feelings amplify when you don’t know what’s in the box… We like getting things in the mail, even if we didn’t ask for them’.

This is a real patent filed by Amazon. It’s called anticipatory shipping.

Such vast treasure troves of data, used to predict what you’ll do, what the world will do, picking up information on such a minute scale and such a broad scale all the time leads to the digital landscape becoming incomprehensible.

There’s so much data, so much information, that it escapes any single person’s comprehension, it becomes impossible to understand in a single, universal way.

This has led air taxi CEO and science writer Chris Anderson to predict the End of Theory. He writes: ‘Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity’.

So many data points are collected that any hope of understanding that data disappears. Whatever the algorithm says goes.

He points to Google’s page rank: it collects data about which webpage is best, but no-one can say why one page is better than the other, only that it’s statistically likely that it is.

If you’re most likely to give a $5 tip to your Uber driver that’s what it suggests. There’s no need to understand why.

One Google project let loose an AI on Youtube to see what it would do. It learned to detect cats but the engineer admitted they had no idea why that happened.

HunchLab looks at data to predict crime. It’s found that assaults happens less on windy days and cars get stolen nearer schools. Doesn’t matter why, security firms can just adapt in response.

This organisation takes mega billion pixel photos of cities like Shanghai and The Vatican. You can zoom right in and out and in again: https://www.indy100.com/viral/one-of-the-world-s-biggest-ever-photographs-has-a-hilarious-secret-7292226 Someone found this naked man.

Posthumanism, in its cynical form, is a dizzying vertigo.

Kierkegaard wrote of the modern age that: ‘Anxiety may be compared with dizziness. He whose eye happens to look down into the yawning abyss becomes dizzy. But what is the reason for this? It is just as much in his own eyes as in the abyss… Hence, anxiety is the dizziness of freedom’.

There’s so much data that it’s impossible to frame it in a single understandable universal efficient way.

Jane Bennett calls this vibrant materialism. ‘Interpretations and framings are so diverse that it’s impossible to make sense of anything. A city blackout can be said to be caused by deregulation, wildfires, the weather, the price of gas, an ill employee, a historical event, a sociological problem’. Meaning fractures and explodes.

Yet despite this, we must retain some sense of responsibility.

Framing robotics and automation through the idea of an extended-self brings something often missed back into focus: there’s always someone doing the automating.

The question when we look at a robot should always be not what’s it doing, but who is it doing it for?

Take the illustrative example of the dangers of autonomous weapons. An open letter with signatories including Steven Hawking and Noam Chomsky warns: ‘It will only be a matter of time until they appear on the black market and in the hands of terrorists, dictators wishing to better control their populace, warlords wishing to perpetrate ethnic cleansing, etc. Autonomous weapons are ideal for tasks such as assassinations, destabilizing nations, subduing populations and selectively killing a particular ethnic group. We therefore believe that a military AI arms race would not be beneficial for humanity’.

This is a cogent example because, while the dangers are obvious with weapons, the same dangers apply to any type of automation. Weapons can be a powerful metaphor for ideas.

A website that decides who you should vote for? Who is funding it? A police robot that keeps you safe? What happens when tyrannical laws are enforced? A script that gets the news for you? What happens when prioritising anger increases engagement?

These are not problems of the future: they are here. We are nudged by algorithms in ways we’re not always aware of. Opaque algorithms are as dangerous as laws you’re not allowed to see.

The real lesson of Orwell’s 1984 is much more than oppression. The power that has information doesn’t crudely blackmail, but manipulates you without you knowing. As the protagonist Winston tells us, ‘If you want to keep a secret, you must also hide it from yourself’.

Cass Sunstein has written that, ‘due to recent advances in AI and machine learning, algorithmic nudging is much more powerful than its non-algorithmic counterpart. With so much data about workers’ behavioral patterns at their fingertips, companies can now develop personalized strategies for changing individuals’ decisions and behaviors at large scale’.

The purpose of all these systems is to get as close to the desire as possible.

The goal is to know you better than you. To pre-empt what you want, what you need, what you desire. Some might say fine – what’s wrong with AI predicting what I need? What’s wrong with automated convenience?

The problem lies in algorithms not pre-empting what you want and need, but what you’re most likely to want and desire that aligns with what advertisers, corporations, and the powerful want and desire. Automation is not coded for need but coded for what you’re most likely to respond to. We have triggers, needs, addictions, weaknesses that can be capitalised on but that aren’t what’s really good for us.

We need to be coded to respond passionately to threats. But do we need to see more threatening Facebook posts more often? Obviously not. We’re built to want thrill, sugar, sex, to respond to scandal, but we don’t want a world that uses that to sell us things we don’t need.

Pre-emption is a melting pot of trickery and base desire driven by profit and control.

Thaler and Sunstein write that, ‘It is no coincidence that the enthusiasm for so called “nudge” approaches, which act indirectly on people through intervening in the choice “environment,” has coincided with the rise of online marketing and advertising’.

We become shaped by the Gorgon – the mythical creature that turned those under its stare to stone. In fact, take the Gorgon Stare, a new military drone that captures video of entire cities. As if naming military equipment after ancient monsters sends the message that we’re definitely the good guys.

Author Arthur Michel, who spends his life studying drones, wrote that ‘nothing kept me up at night the way Gorgon Stare did’.

They fly at 25,000 feet and capture the city below with a telescopic high resolution camera.

The most powerful flying eye on the planet has been flown above sports stadiums and American cities, solved murders with no witnesses, followed terrorists, spied on Baltimore, and are probably in the air domestically over the United States right now. In short, you have no idea if one’s capturing your movement.

In this video, an operator shows you a murder captured by the drone and how they followed the getaway car. I urge you to watch. The link’s below.

Michel writes that the Gorgon Stare is ‘a way of seeing everybody all the time. Fundamental to liberal democracy is the ability to have sacrosanct private spaces. That is where the life of civil society exists. It is where our own personal lives exist, where we are able to pursue our dreams and passions. And it is often where we hold power to account. When you uncover those spaces, you fundamentally put all of those things at risk’.

Of course, with all of this the question is what happens when we’re all turned to stone by the Gorgon Stare, our movements and data captured so efficiently and totally that we’re petrified into immobility.

What happens when something I’ll call ‘ethical escape’ cannot happen. When you cannot do what you believe you have to do or should do because your future desires, ideas, and movements are being manipulated, nudged, or pre-empted by someone else. When the ethical thing to do cannot escape the domination of the totalising stare of the status quo?

Tools that affect someone’s behaviour without them knowing are the instruments of tyrants, monopolists, manipulators, and puppet-masters.

The philosophers Giles Deleuze and Felix Guattari had a concept that seems apt: a body without organs.

It’s based on the fact that we aren’t only made up of our bodies. We are extended-selves, comprised of assemblages of objects, ideas, events, and other people; it’s the idea that all organisms are more than simply themselves. The bee and the pollen and the flower and the hive make an assemblage. Jeff Bezos, Amazon robotics, and the mail system make an assemblage.

The assemblage is a structured structure.

But the body without organs is a call for an assemblage to overcome the structure, to overcome its limits, to break out of that which structures it, to free itself from the hierarchy that external powers impose on it.

It is the idea of a metaphysical and ethical potential: what something could do if it was freed from limitations:

They write: ‘Is it really so sad and dangerous to be fed up with seeing with your eyes, breathing with your lungs, swallowing with your mouth, talking with your tongue, thinking with your brain, having an anus and larynx, head and legs? Why not walk on your head, sing with your sinuses, see through your skin, breathe with your belly?’

The body without organs, they declare, is the essence of freedom:

‘the body without organs howls: ‘They’ve made me an organism! They’ve wrongfully folded me! They’ve stolen my body!’

‘Why not walk on your head, sing with your sinuses, see through your skin, breathe with your belly: the simple Thing, the Entity, the full Body, the stationary Voyage, Anorexia, cutaneous Vision, Yoga, Krishna, Love, Experimentation. Where psychoanalysis says, “Stop, find your self again,” we should say instead, “Let’s go further still, we haven’t found our body without organs yet, we haven’t sufficiently dismantled our self.”

I think we live in an age of bodies without organs. Automation and robotics extends our powers outwards into the world, but this phenomenon is also reversed, as other people extend their own powers out into the world they’re extending it towards you; robotic tentacles wrapping themselves around your desires, trying to shape your beliefs, your actions, your identity.

You must make yourself a body without organs. It’s why we need to teach all kids to code, to be engineers, to all have Youtube channels and podcasts – anything that extends the self. These are all bodies without organs.

Deleuze and Guattari write enigmatically: ‘This is how it should be done: Lodge yourself on a stratum, experiment with the opportunities it offers, find an advantageous place on it, find potential movements of deterritorialization, possible lines of flight, experience them, produce flow conjunctions here and there, try out continuums of intensities segment by segment, have a small plot of new land at all times. It is through a meticulous relation with the strata that one succeeds in freeing lines of flight, causing conjugated flows to pass and escape and bringing forth continuous intensities for a body without organs’.

There are age-old debates about whether technology will free us from the burden of labour. Marx said the appeal of communism would be ‘to do one thing today and another tomorrow, to hunt in the morning, fish in the afternoon, rear cattle in the evening, philosophize after dinner, just as I have a mind, without ever becoming hunter, fisherman, herdsman or critic’.

The problem is that there is no such thing as pure automation. Its always someone’s automation. If you’re not controlling or contributing to it then you’re being controlled. In 1930, the economist John Maynard Keynes predicted a 15 hour work week by 2030. Others suggest a universal basic income.

These are important questions, but I think they neglect an important point. Automation is always someone’s automation. Automation is always control. Universal basic income is no good if you must surrender your control over the economy, over culture and ethics to people like Jeff Bezos.

Andrejevic writes: ‘There is an element of surrender in the appeal of automation: a willingness to concede that the complexity of social life under current technological conditions is beyond the reach of human comprehension and thus irrevocably alienated. Why not leave the administration of public life to the companies that simultaneously provide us with the endless stream of digital content that helps fill the void left by public life? This is a disturbing perversion of the hope that the widespread access to information made possible by the Internet would enhance democracy by creating a universally informed citizenry’.

As we saw from the extended-self, we all want to have some comprehension, some understanding, some mastery and control of our environment, of the things that affect us. Even the hermit desires control over the land, the kettle, the seed he sows.

Even the hermit desires control.

Tinder CEO Sean Rad has said that, ‘So imagine you open Tinder one day and, you know, the Tinder assistant says, “You know, Sean, there’s a beautiful girl, someone that you’re going to find very attractive down the street. You have a lot of things in common and your common friend is Justin and you’re both free Thursday night and there’s this great concert that you both want to go to and can I set up a date? And here is a little bit more info about her’.

Is there not something uncomfortable about this vision? The idea that something so profound can be outsourced and off-shored? Once we factor in Tinder’s two fundamental goals – keeping you engaged and increasing profits – we have to ask Sean Rad how much that influences the partners they suggest, the gigs, bars and experiences they recommend, the neighbourhoods and patterns of speech they emphasise.

When the interiors of stuffy old houses were being transformed in the 19th century, the textile designed William Morris recommended a golden rule: ‘have nothing in your houses that you do not know to be useful, or believe to be beautiful’.

As robotics run by big tech algorithms creep slowly into our homes, cars, workplaces, schools, hospitals, parliaments, skies, shops, media platforms, well, everywhere, this might be a good rule to keep in mind.

There’s a simple principle I return to often. It’s novel, but it’s called democracy. It means by the people. I understand it like this: people should have a say in the things that affect their lives.

We should not outsource the directing of desire to Silicon Valley, surrender our human impulse to choose to the biases of an opaque AI, abdicate control over our data in return for the cheap thrills of consumer advertising, and forgo and sign away our right to see the things that are seeing us.

In this way, questions about transparency and privacy online keeping coming up because they’re really about who gets to extend their reach and in what ways.

The politics of robotics, AI, data, pre-emption, framelessness – this strange new post-human landscape – should be defined by four things: transparency, democracy, privacy, and control.

The post Fear & Robotics appeared first on Then & Now.

You can look up any fact, instantly. You can communicate, instantly. Anyone can become informed about anything, instantly. Workers, dissidents under dictators, ordinary people, neighbours, can organise, share, discuss simply, quickly, easily.

Book after book, scholar after scholar, and article after article celebrate the techno-utopian potential of this new democratic technology.

Fast forward 25 years and comedian and Youtuber Ethan Klein has started a trending Twitter spat storm by tweeting about podcaster, comedian, MMA person and most likely one of the most influential people on the planet Joe Rogan:

Joe Rogan, who lives on elk meat, egg yolk, and human growth hormone, with lungs full of tar, thinks he’s healthier than everyone. This mfer is such a bitch that when he got covid he threw “the kitchen sink at it” – if youre so healthy just ride it out like you say a man should

Media companies like the Independent, The Washington Examiner, NBC, and commentators like Vaush & Tim Pool all discuss the tweet. Joe Rogan hasn’t even responded. Will he? What will he say? The drama. NBC have already called it a ‘dispute between Rogan and Klein’ but most importantly, between Rogan who has been ‘embraced by conservative figures’, and Klein whose ‘fan base is largely progressive’.

It’s like Vidal vs Buckley, Burke vs Paine, Freud vs Jung all over again.

I wonder if you asked a 90s techno-optimist for an example of what a political discussion might look like on the internet in twenty years time, they’d predict something like this.

Anyway, I want to use this trivial moment to try to answer an important question: why Hasn’t the Internet Fixed Democracy?

Okay, to dive in, we should begin by asking hypothetically and tentatively – what was the case for the internet fixing democracy?

The first thing that’s been pointed to by followers of the philosopher Jurgen Habermas is that the public sphere – where our political discussion, debate, and agenda-setting is happening – would have to be a rational place – a place where we can come to mutual agreement about what the best thing to do is.

Rationality is (ironically) an ambiguous concept, but Habermas pointed to several features of rational decision-making:

He said that:

Discussion is about verifying certain claims
People need to be reflexive about their own beliefs
People need to be able to put themselves in others’ positions so as to be ‘impartial’

People need to be sincere – mean what they say

Each participant should have equal say, and their voices have equal weight
The discussion should be autonomous from state and corporate power

I’ll throw in a few more.

Data needs to be verifiable.

Institutions, organisations, governments, business need to be transparent, information needs to be available.

And those involved in this process should be competent – i.e. be able to understand all of these conditions.

This list isn’t exhaustive but I think it’s a good start. Now let’s look at what Klein is saying about Rogan.

First, he’s saying that Rogan is a hypocrite. He’s also claiming he was more afraid of Covid than he suggested. The wider implication is about Rogan’s claim that no-one is talking about fitness as a preventative measure, which itself is a claim about the focus on and efficacy of vaccines and lockdowns.

There’s also a few other direct implications thrown in – responsibility, fat-shaming, fitness in general. But we could look at some of the wider discussion, too. Like this from Tim Pool and co.

According to this philosopher king, for example, its about socio-sexual hierarchy, gamma, alpha. I mean, just say ‘resentment’ – I guess that’s what you mean? I feel like if you have to say something like this you must think you’re an ‘alpha’ but be deeply insecure about it.

Anyway, this is beside the point I’m trying to make: there are a lot of claims going on in this one very dumb moment.

Let’s look at what they say about the thesis here: why the internet hasn’t saved democracy. We’ll look at the clash of incommensurable values, a bit of Wittgenstein, agenda-setting, Sartre, personality, being triggered, cats, emotion, before finally returning to the question: could the internet save democracy?

First, we have a clear clash of what philosophers call incommensurable values. Two positions that are irreconcilable. Liberty and equality are the frequently used examples – someone might argue that you can’t have them both.

But more importantly, often it’s impossible to rationally calculate which value is more important to pursue because there’s no ‘common measure’, no ‘universal yardstick’ for working out which one is better, which one is more rational.

How do you decide between a career as a lawyer and a career as an artist? Do the pros and cons tally along the same axis? How can you compare preferences for money or creativity, say?

A claim related to Klein’s point might be that ‘getting fit is not a reasonable response to a pandemic’.

And a Rogan claim might look something like: ‘personal responsibility is more important than restricting liberty’.

Now, you can use data to back either of these up, you can argue about the history of liberty as a philosophical concept, or the benefits of a healthy diet for fighting disease, but, ultimately, these claims could be incommensurable, at least for some people.

Now, I could make the argument that while of course being healthy is important to fight Covid, there’s a limit to its efficacy because – 1) it’s difficult, 2) it’s a difficult time to do it 3) there’s not enough time to do it 4) the people dying of Covid often are unhealthy because they’re older, or they’re poorer… etc etc. There are rational points to be made. However, there’s no absolute proof that is going to convince someone that holds ‘absolute libertarian freedom’ as their highest value, no matter what. Again, there is no standard measure, no ruler, we can use to discover which one of these claims trumps the other.

Moral dilemmas are a similar concept.

The French existentialist Jean-Paul Sartre used the example about the young man’s choice between going to England during the Second World War to join the Free French Forces or staying in France to take care of his ageing mother.

Which choice is better? Is better even the appropriate term? Whichever the young man chooses he’s lost something.

And most of the time, before we even get to a discussion or choice, we’ve assigned importance to the values and beliefs we hold that weight them differently. That might be diet, lockdowns, vaccines, equality, freedom, whatever – the conceptual ranking we have affects the weight we place on the corresponding data, studies, or arguments we utilise.

But okay, we know this, but I think it points to another phenomenon – the order of things.

If we tried to turn this into a rational, verifiable political discussion – turn it into an academic study say – it might look something like: ‘The efficacy of encouraging improvements to health as public healthy policy during a pandemic’.

But is it just about efficacy? Is it just about the validity of a statement? It’s also about people’s lifestyles – what they’re doing, what they value.

The philosopher Ludwig Wittgenstein pointed to this problem in his famous idea of language games.

He argued that language cannot understood scientificality because language is not just descriptive – its not a 1-1 correspondence of the world that’s verifiable: tree means tree.

He used the example of water. We cannot rationally and verifiably decide what ‘water’ means at all times and set it down in a dictionary because the meaning of any thing is inseparable from our daily lived experience.

The word means something different depending on the context – whether it’s describing the ocean, a drink. Whether it’s a label on a bottle or a demand, an order, the answer to what would you like to drink, sir. In answering ‘water!’ the request is not just describing the fluid ‘water’, but is also an order to do something, and might mean something different depending on whether it’s a sick man begging for a drink, a child asking their mother, or a king demanding something of an aide. Later philosophers like J.L. Austin pointed out that some language does things – like ‘I do’ at a wedding, ‘I promise’ to a friend, or ‘I name the ship…’. They perform acts that change the world.

So – and this is clear on Twitter – conversation is not about verifying some fact, it’s about the flow of things – it’s about the conversation itself. And language games create options for responses, rules about what might or could or should happen next, after you’ve said a certain thing.

Imagine two Marxists having a discussion about elections: there’s the outline of a pathway that conversation is likely to go down.

But – and here’s the big but – the development of a conversation depends, of course, on the values of those interlocutors. The different values of each person dictate where the conversation goes next.

The question becomes not what the evidence is, but what the next move in the game is. The more likely move for someone like Rogan might be towards fitness. What’s the more likely move for a scientist working on the vaccine?

The moves we make are wonderfully and beautifully diverse, and a platform like Twitter has thrown them all together, making the direction of conversations unpredictable and often chaotic, in a way that wouldn’t happen in the news room of the Washington Post, say.

Take any disagreement on Twitter. Person A holds a position on a topic. Person B points out that Fact X supporting the position is false. Person A responds that Person B has missed the point. Person C says that it’s about that, not that.

There are an infinity of values backing up values, and claims backing up claims. And each one demands different counterpoints, different deconstructions, different types of evidence. The list goes on.

All of these moves set an agenda which used to be set by elites, newspaper editors, and television studios. In the offices of old media there was much tighter control over the agenda, over how long to discuss an issue, over how important it was, over what the priorities were. This has now been democratised, to an extent, but is much more subject to the whims of all of our different approaches to different issues.

But the moves we make online aren’t quite as free as it may appear. An early techno-utopian, John Perry Barlow, wrote in his ‘declaration of the independence of cyberspace’ in 1996 that, ‘Governments of the Industrial World, you weary giants of flesh and steel, I come from Cyberspace, the new home of Mind. On behalf of the future, I ask you of the past to leave us alone. You are not welcome among us. You have no sovereignty where we gather’.

But it turns out that cyberspace is not some otherly realm independent of the pre-existing material conditions of the real world. The pre-existing offline power blocs have sovereignty over the places servers are held, over the citizens that log on, over who has contacts with who. The EU and US governments can enforce cookie policy, corporations have massive advertising budgets, special interests still buy politicians expensive lunches. And this all translates over to online behaviour.

And as it turns out, we all like to gather in a handful of places online rather than lots of interconnecting forums or blogs, so the tech giants have power over those places.

Clay Shirky, another early techno-optimist, has said that one thing he underestimated was the ‘social graph’, or how connections on social media maps onto offline friends, friends of friends, or contacts, onto business and organisational networks, onto NGOs and governments and media.

On other words, the structure of the offline world is largely replicated online. Look at how Youtube prioritises videos from the late night chat shows.

Right, here’s a question that I think holds the key to the meaning of life. If we can work this out, we can solve everything, achieve world peace, and build utopia on earth.

Why Cats?

What is it about the internet and cats? Internet cats even have their own Wikipedia page.

You might say, they’re just cute. They’re nice to look at. We have a universal urge to care for something, etc. But then why did we not have cat pages in newspapers before the internet? Why weren’t there cats on page 3 of the Sun instead of topless women? Why wasn’t everyone reading ‘The Weekly Cat’ magazine and carrying around photos of their cat in their wallet to show people?

Of course, we all have emotional triggers – the rationalist philosopher Baruch Spinoza called them affects – but they’re things that we’ve been evolutionarily coded to trigger a brain state change, to say this is important.

And they’re largely out of our control. Cuteness is of course one. We like to care for fragile things. But, as Facebook found in a study, anger is the most evolutionarily powerful. Anger is more likely to grab our attention because we’ve registered something as dangerous – an attack – and we need to ramp up our blood, get more oxygen going, ready to be on the counterattack. We’re more likely to stop scrolling and click on an angry post.

Spinoza’s list included things like desire, wonder, love, aversion, mockery, fear, pity, envy, and lust – they wash over you, change the state you’re in, draw you into thinking in a particular way, seeing the world through a particular lens.

They are trigger points.

And as the neuroscientist Antonio Damasio has shown, emotions are a part of how we make rational decisions.

He has argued, based on neuroscientific research, that we don’t feel angry, for example, and then pre-select objectively from our knowledge about what to do in that angry state.

Feeling angry pre-selects relevant information from our memories for us involuntarily and gives it to the conscious part of the brain to do the final part of the decision making.

The classic example is the bear attack.

When you encounter a bear, you’re not scared, then think through all the things you know about surviving a bear attack. Being scared is part of the process that triggers the parts of the brain that are relevant and pre-selects the time you read about not running and the other time you saw a bear attack on Youtube. We don’t flick through our memories like filing cabinets – we’re set off emotionally by encounters.

But the wider point here is that using the internet means more trigger points, more emotional encounters. On the internet we’re thrown together in such a way that we’re constantly exposed to these triggers. We’re like children thrown together in playground and left to our own devices. The internet has, quite clearly, made us more emotionally charged.

Walking down the street twenty years ago there wasn’t much chance of getting triggered over Bernie Sander’s mittens or because someone shouts Let’s Go Brandon at you.

Weight, health, image, hypocrisy – they’re great emotional triggers.

You might be thinking, so what? Isn’t all of this obvious? Of course we have values that clash, of course what we talk about depends on our daily lives, of course the internet has changed how the agenda is set, of course we’re emotional as well as rational.

But the important point is this: what’s become important is not what’s being said, but how, with what emphasis, with whose backing, it’s said. And that has consequences for how we should design our social platforms. The political conversation is not about rational fact selection from an objective body of knowledge. The political conversation is about process: who gets to say what, when, and what and when algorithms amplify or quieten something.

This escaped the early techno-utopians. They forgot that knowledge is not just about static objects and facts, but is about people in motion, it’s about process. Watch this, it fascinates me. The TimCast crew have been moved into a position where it’s reasonable for them to talk about how great Joe Rogan looks as evidence for a particular political view they hold.

Klein was making a comment about Rogan’s character. His trustworthiness. Whether he should be listened to.

Character is important because it determines the language games, the values, the agenda, and emotional resonance that’s going to influence the direction of conversation. But it also means that we get drawn towards, guess what: the drama.

Of course, these platforms are going to reward drama, clickbait, anger, conflict – because that’s what we’ve evolved to focus on. But our institutions, rules, cultures and norms are meant to be designed to help us engineer better societies, better ways of living, help us come together.

Take just one example: the institution of due process or the idea of a trial by jury – these are institutions and norms that are meant to balance the impulse of anger, of retribution and revenge, and they’ve done a good job at that. We have lots of institutional norms that do things like this. Some trivial, like taking your shoes off in a friend’s house, bringing a bottle of wine to dinner, please and thank you – etiquette – and some political or social, like having a certified qualification to prove you’re good at something, libel law to protect against malicious lying, the right-to-reply if you’re criticised in print.

The list here is endless, but the point is, I think we need to approach algorithms in the same way – encouraging that which brings the best out of the process of online political communication, not the worst, so that we’re focusing on the things that matter.

In their book Ethical Algorithms, for example, Aaron Roth and Micheal Kearns talk about some key domains that we should focus on like privacy, fairness, accountability, and morality.

What we want to do is select the trending conversations we’re having based not on drama, but on importance. And I actually think Facebook’s decision to use more reactions than just like is a good step towards this. They realised people were drawn towards the topics that people had responded to angrily and then chose to show those posts more. What if we had an ‘important’ response, or an ‘empathise’, or a way of more accurately gauging what the triggering response means. In short, we need a way of highlighting real issues not drama, of pivoting the process of online political conversation away from triggers and more towards justice.

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