Consider the electric taxi. In 1897, a fleet of battery-powered “Hummingbirds” — named for their distinctive hum and yellow-and-black design — roamed the streets of London. These horseless carriages had swappable batteries, which could be replaced in minutes via hydraulic lifts at central stations. Their inventor, Walter Bersey, sounded a little like Elon Musk when he declared: “There is no apparent limit to the hopes and expectations of the electric artisan.” Yet within two years, the Hummingbirds were scrapped due to their high costs, frequent breakdowns and accidents, and the fact that they were slower than horse-drawn alternatives. Electric cabs didn’t return to London until 2019 — after a 122-year-long pause.

Many other vaunted technologies have met similar fates. Consider DNA cloning. In 1996, the birth of Dolly the sheep proved cloning possible, and subsequent experiments edged toward human replication. Yet, in 2005, after illnesses within cloned animals had been exposed, and fears were raised around the unethical use of human embryos, the United Nations adopted a declaration against all forms of human cloning. While non-binding, 84 member states voted in favor of prohibiting reproductive cloning, embryo research for non-therapeutic purposes, and the exploitative trade of human reproductive materials.

Eugenics, too, promised to shape the future. From the 1880s to the Second World War, eugenics societies flourished across industrialized nations, championed by elite progressives including the English writer H.G. Wells and the Irish playwright George Bernard Shaw. Wells envisioned a world where “fine and efficient” humans thrived while “base and servile types” were eradicated via “mercy killings”. The Nazi atrocities of the Second World War eventually discredited this pseudoscience, putting a stop to it.

Other banished technologies include dangerous drugs such as thalidomide, marketed as a “wonder drug” for morning sickness until it caused severe birth defects. Asbestos, once a “magic mineral”, was banned after links to lung cancer emerged. Leaded gasoline, despite boosting engine performance, was phased out globally by 2021 due to neurotoxicity. Even GM crops, touted as farming’s future a decade ago, now face restrictions over biodiversity and health and safety concerns. Currently 26 countries, including France, Germany, Russia, China, and India have partially or fully banned genetically modified organisms, while another 60 countries have placed significant restrictions on them.

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AI’s 1930s moment

By Tom Chivers

Faster-than-sound passenger travel also faltered. The Concorde, a marvel of Sixties engineering, halved flight times from Europe to the USA, but was retired in 2003 after a fatal crash. Similarly, Apollo-era dreams of lunar bases dissolved when funding dried up; today, even the National Space Foundation admits that NASA no longer has the specific technologies, tooling and manufacturing capabilities that created the Sixties Apollo program, making the reconstruction of such powerful technologies as the Apollo Saturn V’s F-1 rockets impossible.

Although we are encouraged to think of technology as developing in a smooth, upwards curve over time, the reality is that the graph of tech adoption shows many collapses and failed promises, as technologies have proved to be either too expensive or too dangerous to continue producing.

When it comes to AI, companies have embraced the accelerationist myth of unstoppable, exponential growth that can’t possibly be contained. The story goes like this: large language models (LLMs) will lead to human-level artificial general intelligence (AGI), creating a direct pathway through exponential self-learning to “the singularity” and AI superintelligence — the all-powerful digital deity. This epic story has inspired hundreds of billions in investment since the early 2000s.

But as recent tests and studies of LLMs have shown, this technology is not the pathway to AGI that we were promised. The hopes that “scaling”, “emergent properties” and “reasoning” would lead to AGI have all failed. The path to AGI lies elsewhere, if anywhere. As Yann LeCun, chief AI scientist at Meta, has said: “There’s absolutely no way that autoregressive LLMs… will reach human intelligence. It’s just not going to happen.”

He has been joined by an increasing number of technologists and public figures including Gary Marcus, Sandeep Reddy, and Thomas Dietterich, as well as François Chollet, who claimed that “LLMs are a dead end to AGI”. Last year’s calls of “AGI is near” — a line championed by Anthropic’s Dario Amodei, Google’s Eric Schmidt, and Elon Musk — now echo as an embarrassing cliché, trotted out to lessening effect each time the goalposts for “near” are moved into the future. The recent excitement over Google’s Deep Mind winning a gold medal in the International Mathematical Olympiad (IMO), and Open AI claiming its model had done the same, changes nothing: these were specialised systems trained for this specific task. Their success does not imply broad reasoning ability, let alone stand as examples of “general intelligence”.

Large language model technology has also failed to generate a return on investment for many companies. While Open AI CEO Sam Altman may boast of 20 million paying subscribers of ChatGPT, their contributions are minuscule compared to the vast amount of venture capital that has been sunk into AI companies: an estimated $600 billion. A recent study from Boston Consulting Group shows that 75% of companies which have invested in AI haven’t seen any return yet. Like other technologies that fail in the open marketplace, AI companies are now turning to the military for financing.

So much for AI’s unstoppable march forward. If we dig into the history of AI development, we also find that the technology has not been on an unbroken exponential growth curve over time. Since the Seventies, there have in fact been two “AI winters”: periods during which AI research funding and research froze.

The first winter lasted roughly from 1974 to 1980, when overhyped expectations about narrow AI led to profound disillusionment when promised breakthroughs didn’t materialize. The US Defense Advanced Research Projects Agency (DARPA) pulled its funding for five years, after discovering that “many researchers were caught up in a web of increasing exaggeration” while “what they delivered stopped considerably short”. Meanwhile, in Britain, the Lighthill Report claimed that the government-funded experiments in AI and robotics “fail to reach their more grandiose aims” and called for a halt to all government subsidy. By 1974, investment in AI projects in the UK and US had dwindled.

The second AI winter lasted roughly from 1998 to 2005, after early neural networks, “expert systems” and machine learning models underperformed as a result of insufficient data and computing power. Once again there was a failure to deliver on the big promises. Once again DARPA, the US government and private investors pulled their funding in a rush of capital flight. In all, 300 AI companies shut down.

Oddly enough, you don’t hear much about the two devastating AI winters from tech companies these days. This is probably because the tech companies are fearful that any whisperings about the true history of AI failure could burst the large language model bubble and usher in the third AI winter. But all the same elements are visible today: grandiose claims, hype and market frenzy followed by a failure to deliver.

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Why I’m boycotting AI

By Sam Kahn

In 1970, AI pioneer Marvin Minsky told Life Magazine: “In three to eight years we will have a machine with the general intelligence of an average human being.” Fifty-five years later, his promise has yet to come true — though the likes of Altman and Musk are hawking the same dream. Who is to say this third attempt at AI isn’t another dead end?

This leads us to Silicon Valley’s fatal flaw. It fuses “fake it till you make it” tech optimism with the Californian belief in manifestation: the idea that enough belief and investment can will anything into existence. This characteristic reveals a naïve faith in historical fatedness, as if progress were preordained and capital could rewrite reality.

Now that the “LLMs are the pathway to AGI” narrative is collapsing, the only factors stopping the AI bubble from bursting are the Silicon Valley mechanisms of hype and denial, the sunk-cost fallacy of investors, and the fact that chatbots and generative AI have become widely diffused across the Internet. Today, there may be 115 million daily users of AI.

Far from accelerating us into a perfect future, this flawed technology is instead slowing us down. We are inundated with the sludge and slop of AI generated material — failings which seem incurable within LLMs. Google and Bing now force AI-generated answers to the top of search results, with hallucination rates that only increase as AI gets more powerful; social media is flooded with AI sludge and bot-farm boosted ads; app stores and websites are swamped with fake reviews, while image banks are choked with generative AI slop. AI is even contaminating the news. As a result, early-adopter companies and employees are now turning against AI, realizing it is bad for productivity and that using it to replace human labor has been counterproductive. A study from MIT also claims that LLM use may be eroding cognitive skills.

It’s clear the AI genie isn’t going to become the digital God we were promised, but can we put it back into the bottle? The clean-up will be enormous, given AI is now lurking within millions of locations, but it has already begun, with companies that replaced humans with AI now re-hiring humans again. Companies are also employing “slop cleaners”’ to tidy up the mess AI has made.

Yet given that governments have naïvely bought into the AI ethos, the clean-up is going to fall to us citizens. We may not have political power, but we have history on our side. Against the myth of fated progress, history shows even the most hyped technologies can be stopped when they fail humanity and society demands they be contained, corked and put back on the shelf. If this were not true, then we would all currently be inhabiting the Metaverse while wearing 3D headsets.

By the Nineties, the project slumped into embarrassing failure, from the end of modernist town planning to the collapse of the Berlin Wall. The results of the 20th-century experiments were stagnation, famine, gulags, censorship, and eugenics.

Unfortunately, the Californian counter-culturists who fled the collapse of the Sixties dream and found refuge in Silicon Valley didn’t get the memo. Locked in their rooms, with their rudimentary computers, they set about creating a non-earthly utopia. They dreamed about other worlds and other intelligences, fusing anarchism with New Ageism and libertarianism with science fiction.

So began the tragi-comic rebirth of utopianism. Since then, the ideology has evolved with the growth of AI and cybernetics. It is now mainstream right across the tech world, with adherents including tech guru Ray Kurzweil, and billionaires Elon Musk, Sam Altman, Peter Thiel, and Marc Andreessen. Today’s pioneers of repeating-the-past predict a future of unbounded technological and economic growth.

The “Tech Utopians” have two main projects. The first is abundance: an end to poverty, hunger, sickness, and back-breaking labor. This will supposedly be achieved through vast social engineering projects, masterminded by an elite class, and with the aid of genius machines.

The second is the improvement of humans through scientific progress. A perfect “blank slate” human will be created for utopia 2.0, and they will be morally, intellectually, and physically enhanced. Humans won’t just be cured of illness. They’ll be cured of mortality itself.

These ideas, however, are hardly new. They appeared in nascent form as far back as Plato. Then, during the Enlightenment, they grew stronger, with the promise of a blank-slate society built upon rationalism and science, in which a better type of human would emerge. Today’s Tech Utopians, though mostly claiming to be libertarians, are attempting the same projects — only this time they plan to use AI and biotech to achieve their goals. Elon Musk, for one, envisages a “future of abundance” once AI reaches human-level intelligence (so-called AGI).

At the moment, there is a split between so-called libertarian accelerationists (like Musk and Peter Thiel), the technocrats (like the late Klaus Schwab) and the Left-accelerationists (like Aaron Bastani) over the question of how exactly abundance will be brought about. At one extreme, you have Bastani’s Left-accelerationist “Fully Automated Luxury Communism”, with its vision of a robotics-enhanced society, with Universal Basic Income and free housing, food, education, electricity and healthcare for all. On the other you have Thiel and the Effective Accelerationism plan to destroy all bureaucracies and to replace democracy with a “cognitive elite” of tech specialists who will rule autocratically over newly formed city states.

Uniting the two sides is the idea that the abundance problem will be solved by complex-systems analysis, powered by exponentially growing AI superintelligence.

Is this progress? Alas, no. Even in 1810, French socialist Charles Fourier envisioned a vast “calculating machine” that could solve all human problems. This machine would organize labor, reward undesirable work with tokens, and even assign optimal sexual partners for better offspring and enhanced pleasure. In this way, the “machine calculante” would ensure universal happiness. Perhaps not incidentally, Fourier also believed that his perfect society could turn the sea into lemonade by using gigantic parabolic mirrors to focus moonlight on the waves.

Fourier’s ideas may have been insane, but they were part of a growing faith in the ability of “systems” to deliver harmony and abundance. These ideas were tested in the many agrarian socialist collectives of the 1880s, and then later in the planned economies of communism. Within the USSR, for instance, there were repeated attempts to link military computers with civilian ones, to make a totalising “Automated System for State Planning”.

The National Socialists also hoped to realize their dream of an abundant New Germania through calculation. Hitler himself envisaged a Crimea rich with fruit and cotton, and a Black Sea inexhaustibly full of fish. Yet Hitler’s fantasies had darker echoes too: according to one historian, the Nazis worked with IBM to devise a computer system that organized the transport of millions of people to the gas chambers.

When Tech Utopians tell us that AI will guarantee abundance, then, they are unwittingly pushing us back into the dangerous utopian delusions that emerged from the 19th century — and that failed so catastrophically in the 20th century.

Some might argue that the reason utopia failed in the past was that we didn’t have a super-intelligent computer. Now that we’re close to creating one, all our problems will soon be solved. But such an argument is built on the fallacy that humans are no more than rational beings in cages of flesh. Therefore, all interactions can be planned, all diseases cured. This couldn’t be more wrong.

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The age of nihilism is over

By Mary Harrington

Then there’s the question of improved humans. If you’re going to create a perfect society, you need to enhance people, or else they will bring with them all of the flaws of biology and history — suffering, jealousy, hatred, genetic sickness. As American transhumanist Zoltan Istvan said: “Death is the first major challenge for the species to overcome to truly transcend our biology. There’s nothing in the way out there saying we must continue to die.” Meanwhile entrepreneur Martine Rothblatt, who is working on a technology called “Mindcloning”, says: “We’re able to put into computer form people’s mannerisms, personality, recollections, feelings, beliefs attitudes and values and keep them alive in a cyber form, looking forward to the future when their minds might be able to be downloaded back into a regenerated human body.”

In a similar spirit, Tech Utopians are now pushing for CRISPR DNA improvements in babies to prevent genetically inheritable diseases, such as sickle cell disease. The next step, for some, is “designer babies” — which, while legally controversial, is now technically possible.

Meanwhile, the transhumanists intend to use AI, genetic engineering, bio-tech, and cybernetics to create a post-human species which will overcome aging and disease. Transhumanist leaders such as Kurzweil and Nick Bostrom, co-founder of Humanity+, now form a powerful belief-core inside Silicon Valley.

And then there is Musk, whose company, Neuralink, is experimenting with new technologies like Brain Chip Interface (BCI), which enables direct communication between the brain’s electrical activity and computers. Musk hopes to implant surgically installed brain chips into millions of people within a decade. He and his acolytes believe these enhanced human subjects will soon be able to communicate telepathically and download knowledge in an instant. Eventually, they insist, we will see a merger of human consciousness with superintelligent AI, allowing us to achieve digital immortality.

Are these utopian technologies leading us forward? Once again: no. Rather, they are regressing back to dogma found in Plato’s Republic, where selective breeding is proposed to create the “guardian class” of philosopher-kings to run the ideal society.

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ChatGPT will kill off the Romantic genius

By Sam Leith

There are even echoes of Stalin’s scientists, who sought to achieve a breakthrough in human evolution in the form of the “New Soviet Man”. To this end, Soviet scientist Ilya Ivanov tried to create a super-strong human-chimpanzee hybrid, while Alexander Bogdanov sought immortality through blood-transfusion experiments. It’s an experiment that we’ve recently seen repeated by “longevity biohacker” and transhumanist guru Bryan Johnson, who took blood from his teenage son in the attempt to stay young.

This is not the only poignant parallel. Nikolai Koltsov, a Soviet-era biologist, developed eugenic “sperm banks for geniuses”. Nowadays, one of the goals of transhumanist organization Humanity + this decade is to use technology “to greatly enhance human intellectual, physical, and psychological capacities”.

Such connections are arguably not coincidental but causal. According to tech historians and activists Timnit Gebru and Émile P. Torres, leaders of the AGI movement “subscribe to [a] set of ideologies which have emerged from the modern eugenics movement and therefore have inherited similar ideals”. They say that the Tech Utopians are guilty of discriminatory attitudes around racial differences in IQ, and that the Tech Utopian goal is “to create a superior being akin to a machine-god”, a process which is resulting in experimental technological “systems that are unscoped and thus unsafe”.

There is evidence for this in the degree of suffering caused in the Brain Chip Interface experiments. Neuralink is currently under federal investigation for the claim that 1,500 animals died during surgical BCI experiments, with the company accused of “causing needless suffering”, related to “Musk’s need for speed research”.

The transhumanist mission will inevitably require a large number of human test subjects. After all, Tech Utopians, with their belief in “long termism”, tend to view short-term sacrifices as essential to the attainment of their transcendent future goals.

Yet as history shows, utopianism always demands its blood sacrifices in the name of the distant paradise. It excludes those deemed unworthy, or it uses them as its lab rats in the name of “improvement”. In other words, today’s Tech Utopian visions aren’t about progress. They are a regressive repeat of history, born out of a willful blindness to the past. We are accelerating backwards into preventable disasters we thought we had left behind.