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The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence
From one of our leading chroniclers of the intersection of innovation and capitalism, a landmark reckoning—based on unprecedented access—with one of the world’s most brilliant and driven tech visionaries, and his game-changing company
Even by the standard of a tech industry stacked with so-called geniuses, Demis Hassabis is a special case. Born poor in North London to immigrant parents, a chess prodigy by age five and wizard coder in his teens, he turned down a seven figure offer before turning 18 to feed his insatiable scientific curiosity at Cambridge. Later, he added a neuroscience PhD to his computer science skills to pursue the dream of artificial general intelligence, the ultimate goal being to unravel the mysteries of biology and theoretical physics and to usher in super-abundance. Alongside a small group of fellow travelers, that is the path he is still on, leading the AI research at Google, winning a Nobel Prize along the way, and imagining machines that will compound, or possibly supplant, the human understanding of the universe.
Hassabis has given Sebastian Mallaby a great deal of his time, sitting for over thirty hours of conversation. But Mallaby has also drawn from Hassabis's detractors, such as his estranged cofounder Mustafa Suleyman; from his rivals, such as OpenAI's leading scientist Ilya Sutskever; and from academic pioneers who now fear for human survival, such as Nobel laureate Geoffrey Hinton. The result is a revelatory account of a singular figure and his company and a profound reckoning with this protean field as it leaps from the periphery to the center of our consciousness.
No one questions Hassabis’s brilliance. There are those who, like Elon Musk, have at times regarded him as an "evil genius." He is in a game where the stakes are matched only by the exorbitant costs — for talent, and for compute. Celebrated scientists pursue the technology because they cannot resist the sweetness of discovery. Others pursue it for money or power. The inventors believe they control their technology, but often, the technology controls them.
Despite Hassabis’s pivotal role inside Google’s engine room, this is not a Silicon Valley story. Hassabis deals with the Valley and takes its money, but remains outside and furiously critical of it, lambasting its leaders in conversation with Mallaby. The end of this race cannot be known, but as this great book shows us, Hassabis's quest to will a new form of cognition into the world is a defining story for our era.
Even by the standard of a tech industry stacked with so-called geniuses, Demis Hassabis is a special case. Born poor in North London to immigrant parents, a chess prodigy by age five and wizard coder in his teens, he turned down a seven figure offer before turning 18 to feed his insatiable scientific curiosity at Cambridge. Later, he added a neuroscience PhD to his computer science skills to pursue the dream of artificial general intelligence, the ultimate goal being to unravel the mysteries of biology and theoretical physics and to usher in super-abundance. Alongside a small group of fellow travelers, that is the path he is still on, leading the AI research at Google, winning a Nobel Prize along the way, and imagining machines that will compound, or possibly supplant, the human understanding of the universe.
Hassabis has given Sebastian Mallaby a great deal of his time, sitting for over thirty hours of conversation. But Mallaby has also drawn from Hassabis's detractors, such as his estranged cofounder Mustafa Suleyman; from his rivals, such as OpenAI's leading scientist Ilya Sutskever; and from academic pioneers who now fear for human survival, such as Nobel laureate Geoffrey Hinton. The result is a revelatory account of a singular figure and his company and a profound reckoning with this protean field as it leaps from the periphery to the center of our consciousness.
No one questions Hassabis’s brilliance. There are those who, like Elon Musk, have at times regarded him as an "evil genius." He is in a game where the stakes are matched only by the exorbitant costs — for talent, and for compute. Celebrated scientists pursue the technology because they cannot resist the sweetness of discovery. Others pursue it for money or power. The inventors believe they control their technology, but often, the technology controls them.
Despite Hassabis’s pivotal role inside Google’s engine room, this is not a Silicon Valley story. Hassabis deals with the Valley and takes its money, but remains outside and furiously critical of it, lambasting its leaders in conversation with Mallaby. The end of this race cannot be known, but as this great book shows us, Hassabis's quest to will a new form of cognition into the world is a defining story for our era.
480 pages, Kindle Edition
Published March 31, 2026
About the author
Sebastian Mallaby
13 books297 followersA Washington Post columnist since 1999. Worked for The Economist from 1986 - 1999.
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Displaying 1 - 25 of 25 reviews
April 1, 2026
It's very quickly clear that Sebastian Mallaby is a huge Demis Hassabis fan - writing about the only child prodigy and teen genius ever who was also a nice, rounded personality. After a few chapters, though, things settle down (I'm reminded of the description of the Hitchhiker's Guide to the Galaxy) and we get a good, solid trip through the journey that gave us DeepMind, their AlphaGo and AlphaFold programs, the sudden explosion of competition on the AI front and thoughts on artificial general intelligence.
Although Mallaby does occasionally still go into fan mode - reading this you would think that AlphaFold had successfully perfectly predicted the structure of every protein, where it is usually not sufficiently accurate for its results to have direct practical application - we get a real feel for the way this relatively unusual company was swiftly and successfully developed away from Silicon Valley. It's readable and gives an important understanding of where some our key AI expertise came from.
Perhaps not surprisingly, where the book really takes off is in the later chapters, after the successes of AlphaGo and AlphaFold. when the DeepMind people were left behind by OpenAI's generative AI work and had to rapidly change gear under outside pressure. Let's face it, a story of responding to threat is much more interesting than one of straight success. Not only was this phase of development one where the team was caught on the back foot, a huge rift emerged over whether developers should be heading towards artificial general intelligence (AGI) very carefully for safety reasons (the initial DeepMind approach) or just going for it full throttle like OpenAI.
It's arguable some of the safety concerns were fruitless in that large language models seem unlikely ever to be the starting point for a true AGI, but there was (and is) still a very real threat from this software in areas such as privacy, copyright theft and environmental impact. This part of the book is unputdownable stuff.
Overall, The Infinity Machine is a little too long, giving too much detail of the people outside the core group and of every step along the way of the company's development. However, this doesn't really matter as it provides excellent documentation of a key player in the rise and rise of AI.
Although Mallaby does occasionally still go into fan mode - reading this you would think that AlphaFold had successfully perfectly predicted the structure of every protein, where it is usually not sufficiently accurate for its results to have direct practical application - we get a real feel for the way this relatively unusual company was swiftly and successfully developed away from Silicon Valley. It's readable and gives an important understanding of where some our key AI expertise came from.
Perhaps not surprisingly, where the book really takes off is in the later chapters, after the successes of AlphaGo and AlphaFold. when the DeepMind people were left behind by OpenAI's generative AI work and had to rapidly change gear under outside pressure. Let's face it, a story of responding to threat is much more interesting than one of straight success. Not only was this phase of development one where the team was caught on the back foot, a huge rift emerged over whether developers should be heading towards artificial general intelligence (AGI) very carefully for safety reasons (the initial DeepMind approach) or just going for it full throttle like OpenAI.
It's arguable some of the safety concerns were fruitless in that large language models seem unlikely ever to be the starting point for a true AGI, but there was (and is) still a very real threat from this software in areas such as privacy, copyright theft and environmental impact. This part of the book is unputdownable stuff.
Overall, The Infinity Machine is a little too long, giving too much detail of the people outside the core group and of every step along the way of the company's development. However, this doesn't really matter as it provides excellent documentation of a key player in the rise and rise of AI.
April 17, 2026
Sebastian Mallaby's biography of Demis Hassabis is the do-gooder story of a biracial North Londoner, a Cypriot-Singaporean second-generation immigrant, a scholarship boy and chess master, a child prodigy and a polymath, with advanced degrees in computer science and neuroscience, who, in his rapid career, would make a lucrative profit as a video-game designer, build an AI program that could defeat the top Go player in the world, and solve the mystery of protein folding—ultimately winning the Nobel Prize in Chemistry. It's an incredible life-story for a man still in his prime. Mallaby's biography paints a portrait of a straight-talking, unpretentious scientist driven purely by the desire to understand the cosmos, and determined to build a superintelligence that can tackle the most intractable problems. Showing all his typical swaggering confidence and scientific ambition, the epilogue finishes with a teaser—will Demis Hassabis use AI to rewrite quantum mechanics next?
Yet, ironically, as the book progresses, Demis Hassabis recedes into the background. The success of his AI company, DeepMind, always depended on a team of like-minded AGI believers and businessmen, fellow scientists and Silicon-Valley bankrollers eager to make partnerships, take risks and blow off the plodding caution and corseting constraints of academia. Each chapter, and each project that Hassabis spearheaded, ultimately redounds on a rotating cast of characters: Shane Legg, Daan Wiestra, Vhlad Mnih, David Silver, Koray Kavukcuoglu, among many others. It's a success story with several protagonists. Demis Hassabis becomes less a figure of towering genius and more of a managerial overlord, coordinating projects, securing financial backers, defusing media crises, wrangling legal contracts, and working all hours of the night across multiple timezones.
It's a biography that, naturally, has to be diffuse—this is not about one person. There are too many people, too many projects, too many groundbreaking scientific papers. Mallaby's biography has the difficult task of balancing the stories of business chicanery and technological revolutions, from Peter Thiel skeptically funding projects he was betting against to Ilya Sutskever using Reinforcement Learning and Transformer architecture to build the first convincing Large Language Model. Personally, I preferred it when Mallaby gave a behind-the-scenes insight into the eccentric personalities—like the time Hassabis went to the birthday party of Elon Musk's wife in a rented-out castle in Tarrytown New York. All the men had to dress as samurai warriors and Musk himself took on the wold champion sumo wrestler. Mallaby generally refrains from editorial comment—except to defend Hassabis and AI from criticism—but it's moments like this that betray the adolescent antics driving and financing the frontier of artificial intelligence.
At the end of it all, I can't help but feel that the book barely dug beneath the surface. Musk lifting a world champion sumo wrestler, Mustafa Suleyman being kicked out of DeepMind for bullying, Larry Page having to be reminded that hummingbirds and whales are real, Sam Altman double-crossing everyone, and even Demis Hassabis holding progress meetings at 3am—you get the sense that people in the tech world are not well, fiercely intelligent yes, but sometimes lacking the "general" intelligence that they idolize. I wish Mallaby's biography had more vignettes, more interviews, and sharper character profiles. Something seems to be missing from this fascinating story.
Yet, ironically, as the book progresses, Demis Hassabis recedes into the background. The success of his AI company, DeepMind, always depended on a team of like-minded AGI believers and businessmen, fellow scientists and Silicon-Valley bankrollers eager to make partnerships, take risks and blow off the plodding caution and corseting constraints of academia. Each chapter, and each project that Hassabis spearheaded, ultimately redounds on a rotating cast of characters: Shane Legg, Daan Wiestra, Vhlad Mnih, David Silver, Koray Kavukcuoglu, among many others. It's a success story with several protagonists. Demis Hassabis becomes less a figure of towering genius and more of a managerial overlord, coordinating projects, securing financial backers, defusing media crises, wrangling legal contracts, and working all hours of the night across multiple timezones.
It's a biography that, naturally, has to be diffuse—this is not about one person. There are too many people, too many projects, too many groundbreaking scientific papers. Mallaby's biography has the difficult task of balancing the stories of business chicanery and technological revolutions, from Peter Thiel skeptically funding projects he was betting against to Ilya Sutskever using Reinforcement Learning and Transformer architecture to build the first convincing Large Language Model. Personally, I preferred it when Mallaby gave a behind-the-scenes insight into the eccentric personalities—like the time Hassabis went to the birthday party of Elon Musk's wife in a rented-out castle in Tarrytown New York. All the men had to dress as samurai warriors and Musk himself took on the wold champion sumo wrestler. Mallaby generally refrains from editorial comment—except to defend Hassabis and AI from criticism—but it's moments like this that betray the adolescent antics driving and financing the frontier of artificial intelligence.
At the end of it all, I can't help but feel that the book barely dug beneath the surface. Musk lifting a world champion sumo wrestler, Mustafa Suleyman being kicked out of DeepMind for bullying, Larry Page having to be reminded that hummingbirds and whales are real, Sam Altman double-crossing everyone, and even Demis Hassabis holding progress meetings at 3am—you get the sense that people in the tech world are not well, fiercely intelligent yes, but sometimes lacking the "general" intelligence that they idolize. I wish Mallaby's biography had more vignettes, more interviews, and sharper character profiles. Something seems to be missing from this fascinating story.
April 14, 2026
The Infinity Machine left me deeply conflicted, much like many contemporary tech history books chasing the current AI gold rush. At its heart, it promises an insider’s look at the rise of DeepMind and the leadership of Demis Hassabis, but it ultimately struggles to find its footing between serious journalism and a carefully curated corporate memoir.
The biggest hurdle is the book’s unclear target audience. The material simply doesn’t cover the technical or historical landscape in enough depth for anyone actually working in the field, making it redundant for experts who already know the players and the problems. Yet, for the general reader, it rambles through industry tangents and insider anecdotes that feel more like scattered notes than a cohesive narrative. It’s caught in that frustrating middle ground: too superficial for specialists, yet too dense with unexplained context for newcomers.
My primary criticism, however, lies in how the book handles Demis Hassabis and the broader AI leadership class. The narrative leans heavily into the now-familiar trope of these tech founders as modern-day saints, deeply concerned with AI alignment and the preservation of humanity. But where was that same ethical vigilance when foundational IP was systematically scraped from millions of creators without consent or compensation? The text conveniently sidesteps this two-faced reality. Furthermore, despite Hassabis’s public insistence on a pure, almost academic pursuit of science, the book barely acknowledges the grueling, burnout-inducing work culture he’s known to cultivate, nor does it address the discrepancies between his public statements and internal practices. The disconnect between the polished ethos and the operational reality is stark, and the author does little to interrogate it.
All this being said, the level of access is undeniable. The book is clearly built on extensive interviews, and there are fleeting moments where the behind-the-scenes glimpses into the AI race feel genuinely illuminating. But the relentless whitewashing makes you question whether the author truly wrote this book or merely transcribed a legacy project. If you’re looking for a critical, unvarnished examination of the AI boom and its architects, you’ll need to look elsewhere. For industry enthusiasts who don’t mind reading between the lines, it offers a passable, if deeply flawed, window into the machine.
The biggest hurdle is the book’s unclear target audience. The material simply doesn’t cover the technical or historical landscape in enough depth for anyone actually working in the field, making it redundant for experts who already know the players and the problems. Yet, for the general reader, it rambles through industry tangents and insider anecdotes that feel more like scattered notes than a cohesive narrative. It’s caught in that frustrating middle ground: too superficial for specialists, yet too dense with unexplained context for newcomers.
My primary criticism, however, lies in how the book handles Demis Hassabis and the broader AI leadership class. The narrative leans heavily into the now-familiar trope of these tech founders as modern-day saints, deeply concerned with AI alignment and the preservation of humanity. But where was that same ethical vigilance when foundational IP was systematically scraped from millions of creators without consent or compensation? The text conveniently sidesteps this two-faced reality. Furthermore, despite Hassabis’s public insistence on a pure, almost academic pursuit of science, the book barely acknowledges the grueling, burnout-inducing work culture he’s known to cultivate, nor does it address the discrepancies between his public statements and internal practices. The disconnect between the polished ethos and the operational reality is stark, and the author does little to interrogate it.
All this being said, the level of access is undeniable. The book is clearly built on extensive interviews, and there are fleeting moments where the behind-the-scenes glimpses into the AI race feel genuinely illuminating. But the relentless whitewashing makes you question whether the author truly wrote this book or merely transcribed a legacy project. If you’re looking for a critical, unvarnished examination of the AI boom and its architects, you’ll need to look elsewhere. For industry enthusiasts who don’t mind reading between the lines, it offers a passable, if deeply flawed, window into the machine.
April 14, 2026
The Infinity Machine : Demis Hassabis, DeepMind, and the Quest for Superintelligence (2025) by Sebastian Mallaby is a biography of the remarkable Demis Hassabis and of the current race to build machine intelligence. Mallaby is a writer who has been a Washington Post columnist and who has written various books on Alan Greenspan and Hedge funds among other topics.
Hassabis was born in London to a Greek Cypriot father and a Singaporean mother. They had little money. Demis learnt chess at four and was a chess champion by six. At six he qualified for the British Under 14 Championship. By the age of 12 he had decided that becoming very good at chess wasn’t wise. He thought intelligence should be used for more. Hassabis used money won in chess to buy a ZX Spectrum. At 12 he bought an Amiga and started to learn to program. He started by trying to write chess programs. He soon wrote an Othello program that worked.
Hassabis did incredibly well at school and skipped grades and got admission to Cambridge early. They told him to wait and so he worked for the Bullfrog games company. There he coded and helped develop their hit game Theme Park. He then went to Cambridge and did extremely well there. After Cambridge he founded the company Elixir and sold that before doing a PhD in neuroscience at University College London.
After that he founded DeepMind with Shane Legg and Mustafa Suleyman. Deepmind became one of the world’s premier AI labs and was taken over by Google. DeepMind created AlphaGo that beat the world’s best Go player. They then went on to create AlphaFold that solved the problem of what protein came from what amino acid sequence. For this work Hassabis was awarded the Nobel Prize in Chemistry.
Following AlphaFold the next big breakthrough in Machine Learning was Transformers and the rise of the LLM. Here the book gets into how Google invented the Transformer but failed to exploit it. OpenAI, led eventually by Sam Altman took the lead and released ChatGPT. The book goes into detail about how DeepMind and Google reacted to this. There is little mention of Anthropic, one of the other major AI labs. The incredible leaps in ability of LLMs and how they forced Google to change their organisation and operation are interesting to read about. Hassabis and others were impressed by these models but somewhat skeptical of their practical applications. However this has changed as the models have improved.
The Infinity Machine is a well written, interesting read about the remarkable figure of Hassabis and the recent history of AI development. Hassabis comes across as he does everywhere as an incredibly driven, incredibly intelligent person who is also a decent human being. The book does veer a bit into hagiography, but it does appear to concur with almost everything written about Hassabis. This is in sharp contrast to writing about Sam Altman. For anyone interested in the personalities behind AI and its recent history the book is well worth a read.
Hassabis was born in London to a Greek Cypriot father and a Singaporean mother. They had little money. Demis learnt chess at four and was a chess champion by six. At six he qualified for the British Under 14 Championship. By the age of 12 he had decided that becoming very good at chess wasn’t wise. He thought intelligence should be used for more. Hassabis used money won in chess to buy a ZX Spectrum. At 12 he bought an Amiga and started to learn to program. He started by trying to write chess programs. He soon wrote an Othello program that worked.
Hassabis did incredibly well at school and skipped grades and got admission to Cambridge early. They told him to wait and so he worked for the Bullfrog games company. There he coded and helped develop their hit game Theme Park. He then went to Cambridge and did extremely well there. After Cambridge he founded the company Elixir and sold that before doing a PhD in neuroscience at University College London.
After that he founded DeepMind with Shane Legg and Mustafa Suleyman. Deepmind became one of the world’s premier AI labs and was taken over by Google. DeepMind created AlphaGo that beat the world’s best Go player. They then went on to create AlphaFold that solved the problem of what protein came from what amino acid sequence. For this work Hassabis was awarded the Nobel Prize in Chemistry.
Following AlphaFold the next big breakthrough in Machine Learning was Transformers and the rise of the LLM. Here the book gets into how Google invented the Transformer but failed to exploit it. OpenAI, led eventually by Sam Altman took the lead and released ChatGPT. The book goes into detail about how DeepMind and Google reacted to this. There is little mention of Anthropic, one of the other major AI labs. The incredible leaps in ability of LLMs and how they forced Google to change their organisation and operation are interesting to read about. Hassabis and others were impressed by these models but somewhat skeptical of their practical applications. However this has changed as the models have improved.
The Infinity Machine is a well written, interesting read about the remarkable figure of Hassabis and the recent history of AI development. Hassabis comes across as he does everywhere as an incredibly driven, incredibly intelligent person who is also a decent human being. The book does veer a bit into hagiography, but it does appear to concur with almost everything written about Hassabis. This is in sharp contrast to writing about Sam Altman. For anyone interested in the personalities behind AI and its recent history the book is well worth a read.
April 9, 2026
Pretty well-written, very interesting subject matter, but not enough of a critical lens.
At the very beginning, it mentions that Hassabis decided to sit down and give interviews for the book because he believes it's good for the public to know what makes AI company leaders tick, and I think the book did a pretty good job of that for Hassabis, but not for DeepMind/Google as a whole.
The author is not very investigative/critical, and glosses lightly over DeepMind's failures (missing the transformer, poor performance of early models, etc), and very rarely challenges interviewees.
The book was a semi-chronological narrative that is heavily weighted towards more recent events. Hassabis's early life and DeepMind's first decade or so were totally unfamiliar to me and thus very very interesting, but the post-ChatGPT era (a solid 1/3rd or so of the book, due to the recency-heaviness) was a pain to get through because I've been so over-exposed to the topic.
Overall captivating read, but the optimal approach would be to just read whichever parts you're curious about.
At the very beginning, it mentions that Hassabis decided to sit down and give interviews for the book because he believes it's good for the public to know what makes AI company leaders tick, and I think the book did a pretty good job of that for Hassabis, but not for DeepMind/Google as a whole.
The author is not very investigative/critical, and glosses lightly over DeepMind's failures (missing the transformer, poor performance of early models, etc), and very rarely challenges interviewees.
The book was a semi-chronological narrative that is heavily weighted towards more recent events. Hassabis's early life and DeepMind's first decade or so were totally unfamiliar to me and thus very very interesting, but the post-ChatGPT era (a solid 1/3rd or so of the book, due to the recency-heaviness) was a pain to get through because I've been so over-exposed to the topic.
Overall captivating read, but the optimal approach would be to just read whichever parts you're curious about.
April 12, 2026
Excellent
A couple high points outside of a very well done and detailed profile of Hassabis:
-very clear high level descriptions of AlphaGo, AlphaFold, and others (better than what I've read before)
-interesting internal background of Google's efforts in AI/LLMs over past few years
A couple high points outside of a very well done and detailed profile of Hassabis:
-very clear high level descriptions of AlphaGo, AlphaFold, and others (better than what I've read before)
-interesting internal background of Google's efforts in AI/LLMs over past few years
March 30, 2026
A book that deeply believes in the goodness of both the guy (Hassabis) and the pursuit of a techno-utopia. The corporate intrigue stuff with Elon, Google, etc is very good.
Hassabis has said that his work is like “reading the mind of god.” But he also believes that computers will reach the same level of intelligence precisely because intelligence is mechanic.
Hassabis has said that his work is like “reading the mind of god.” But he also believes that computers will reach the same level of intelligence precisely because intelligence is mechanic.
Review of advance copy received from Netgalley
Dreaming of Godlike Code: “The Infinity Machine” and the Strange, Human Drama Behind Artificial Intelligence
By Demetris Papadimitropoulos | March 6th, 2026
Watercolor Piece by Demetris Papadimitropoulos
For a technology that is supposed to be about the future, “The Infinity Machine” is, at heart, a book about temperament. Not just the temperament of Demis Hassabis, its central figure – the chess prodigy, game designer, neuroscientist and DeepMind founder whose life has the compressed velocity of several ambitious men’s biographies stacked one atop another – but the temperament of an age that has persuaded itself that intelligence can be built, scaled, monetized, regulated, feared, worshiped and perhaps, if luck holds, steered. The book is about artificial intelligence, certainly. It is also about mission as a form of power, about the seductions of abstraction, about the old human habit of insisting that history can still be managed even after it has begun to stampede.
What makes the book so unnervingly readable is that it understands a hard truth many books about technology do not: code is never just code. Behind every model sits a philosophy. Behind every funding round, a cosmology. Behind every boast about scale or safety or alignment, a theory – often half-spoken, sometimes barely conscious – of what a human being is and what a machine might become. The author does not merely explain DeepMind’s breakthroughs. He dramatizes the worldviews embedded inside them. Go is never only Go. AlphaFold is never only protein folding. Chatbots are never only chatbots. Each becomes an argument about thought, about reality, about whether consciousness is pattern or miracle, whether scientific discovery is a public good or a private race, whether the people building our next operating system for civilization are visionaries, opportunists, gamblers, priests or some unstable mixture of all four.
Hassabis emerges as the book’s organizing intelligence and its animating contradiction. He is presented as at once chilly and earnest, grandiose and disciplined, future-obsessed yet stubbornly tethered to a scientist’s self-image. The portrait is persuasive because it never quite settles into hagiography. Hassabis is plainly remarkable, but he is also blinkered in the way that all driven founders are blinkered: his gifts are inseparable from his incapacities. He believes in depth, in pattern, in elite concentration, in the possibility that enough thought and computation can prise open nature’s secrets. He distrusts noise, vulgarity, ideological fashion, sloppy thinking. He also underestimates, repeatedly, the messier social dimensions of the world he is helping to transform. What the book sees so clearly is that this is not incidental to his story. It is the story. The same mind that can perceive AI as a route to scientific revelation can also misjudge language models, governance structures, public sentiment, office politics and the speed with which a technology escapes the moral categories of its inventors.
The early sections are superb at showing how DeepMind’s original mythology formed: chess, games, “Gödel, Escher, Bach,” the conviction that intelligence is ultimately about discovering patterns in vast fields of noise. The author is especially good on the way young intellectual lives are shaped less by isolated influences than by convergences – novels, lectures, scientific puzzles, entrepreneurial opportunities, emotional injuries, old humiliations transmuted into grand design. Hassabis’s childhood, Cambridge years and Bullfrog apprenticeship are rendered not as charming backstory but as the forging of a style of mind. By the time DeepMind exists, the reader feels that the company is not merely a startup but the institutionalization of a private metaphysic.
And then comes the first of the book’s great feats: it turns the history of machine intelligence into narrative nonfiction with stakes, rhythm and atmosphere. The chapter on AlphaGo has the inevitability of good tragedy and the suspense of a thriller. The account of Move 37, and of Lee Sedol’s shock before a system that seems not merely competent but alienly creative, is one of the best things here because it captures the existential drama beneath a technical milestone. The scene is no longer man versus machine in the old “Deep Blue” sense, where brute force humiliates a champion. It is something stranger and more disquieting. A machine is not simply searching faster; it is discovering forms of beauty and strategy that human masters did not foresee. The effect is to make the familiar argument about AI feel newly eerie. The question is no longer whether machines can outperform us. It is whether they can begin to generate insight that appears, from our vantage point, almost indistinguishable from intuition.
If the Go material gives the book its theatrical center, AlphaFold gives it its moral one. Here the book becomes more than a corporate or intellectual history and starts to make a persuasive case for why AI, even in the hands of institutions that are compromised, competitive and occasionally messianic, might still deserve genuine wonder. The AlphaFold chapters are extraordinary because they show scientific progress not as a neat march but as a chain of pivots, wrong bets, stubbornness, intuition, ego and sudden conceptual breakthroughs. Reinforcement learning recedes. Biology intrudes. Search gives way to direct folding, then to transformer architectures. People argue, get replaced, double down, rethink. It is a beautiful demonstration of how real discovery often looks: untidy in the lab, crystalline in retrospect. And because the payoff is not an ad-targeting engine or a prettier consumer convenience but a revolution in protein structure prediction, the book can finally make good on AI’s loftiest rhetoric. It can show a machine doing unequivocal good, or something close to it.
That matters because “The Infinity Machine” is, among other things, a sustained brief against the flattening cynicism that now shadows all writing about AI. The easiest modern story is that every model is a hustle, every founder a courtier of capital, every breakthrough another route to surveillance, labor arbitrage or platform dominance. This book does not deny any of that. In fact, some of its sharpest pages concern the corruption of safety talk by competition, the impossibility of “trustless” governance under shareholder capitalism, the way every advisory board or ethical mechanism bends under incentives, ego and geopolitics. Yet the author resists the lazy comfort of treating all motives as interchangeable. Hassabis is not Sam Altman. Mustafa Suleyman is not Larry Page. Geoffrey Hinton is not Yann LeCun. One may object to their ambitions, but the book insists – rightly – that different ambitions produce different institutions, different blind spots and different dangers. That insistence gives the narrative its moral texture.
The sections on governance are especially strong because they refuse the consolation of a clean solution. The SpaceX safety meeting, the failed DeepMind oversight schemes, the “Project Mario” negotiations with Google, the collapse of post-capitalist governance dreams, the later recognition that board structures and charters may be weaker than ordinary trust and weaker, too, than market panic – all of this is handled with a novelist’s sense of human frailty and a reporter’s eye for institutional farce. The lesson is bleak and convincing. Everyone says they want safe AI. Almost nobody can agree what safety means once it begins to interfere with profit, state competition, reputational warfare or the prestige economy of Silicon Valley. The idealists become pragmatists, the pragmatists become rivals, the rivals become executives, and the executives discover that the machine they hoped to govern has become inseparable from the organizations, nations and markets racing to own it.
One of the book’s quieter achievements is its portrait of Suleyman, who might in a lesser work have been reduced to a foil or cautionary subplot. Instead he becomes one of the book’s most interesting embodiments of AI’s moral instability. His NHS project is presented as both visionary and bungled, humane and hubristic, ethically ambitious and organizationally chaotic. He wants AI to touch the real world, to improve systems now rather than in some post-AGI future, and the book is alive to the nobility of that impulse. But it is equally alive to the dysfunctions that accompany messianic urgency. The portrait is painful because it makes visible a problem that extends beyond one executive: technology is full of people who are sincere about social transformation and destructive in the rooms where they try to achieve it.
The middle and late sections, charting the rise of transformers, the OpenAI challenge, “ChatGPT,” the product turn, DeepMind’s forced merger into Google, the reasoning race and the emergence of agents, are so rich in incident and implication that they occasionally threaten to outrun the frame. Yet the author’s prose, which favors elegant acceleration over static explanation, usually keeps the book aloft. He is very good at carrying a reader from scene to system and back again – from a dinner table in Palo Alto to the economics of compute, from a leaked boardroom fight to the metaphysics of induction, from a benchmark score to the rearrangement of an entire industry’s incentives. This is the book’s signature method, and it works because the author knows that exposition without drama feels inert, while drama without exposition reduces complex history to gossip.
The price of this velocity is that the book can, at times, seem almost as intoxicated by its subject as the people inside it. Not quite, but almost. One feels, now and then, the pull of grandeur – the temptation to let mission-language retain some of its glamour rather than wholly puncture it. A colder book might have lingered longer over extractive supply chains, the environmental brutality of data-center expansion, the colonial geometry of compute and energy, the labor hidden under the smooth surfaces of “automation,” the ordinary workers and knowledge professionals who appear here mostly as downstream consequences. A harsher critic might say that the book, while keenly aware of the dangers of mythmaking, remains susceptible to the charisma of high-IQ men narrating destiny. There is truth in that. But there is also a countertruth: books that refuse charisma altogether often fail to explain why these figures keep winning.
And this book does explain it. It explains why “ChatGPT” felt different from AlphaGo, why “DeepSeek” was interpreted as geopolitical rupture, why reasoning models matter, why the move from chatbots to agents marks a more dangerous threshold, why OpenAI’s internal coups mattered, why Google’s innovator’s dilemma became untenable, why a benchmark war can conceal a metaphysical one. It also understands something many more programmatically “critical” books miss: the AI story is not compelling because it is merely commercial. It is compelling because it touches ancient human longings – to build minds, to outsource toil, to know more than we know, to find patterns behind appearances, to climb higher than our species has any right to climb. The book keeps those longings in view even as it shows how tawdrily they are pursued.
Its final movement, in which Hassabis becomes what the book calls “Turing’s champion,” is the strangest and perhaps boldest section. Here the narrative stops being only about labs and models and becomes an argument about the nature of reality itself – about classical computation, induction, Penrose, consciousness, quantum speculation, whether nature’s patterns are ultimately learnable by sufficiently powerful classical systems. Some readers will find this material exhilarating, others exasperating. I found it indispensable, because it reveals the true scale of the book’s ambition. This is not, finally, just a story about a company called DeepMind or a product category called AI. It is a story about the return of a very old faith in reason, now rearmed with planetary-scale computation and venture money, and about what happens when that faith collides with democracy, bureaucracy, nationalism, media panic and the stubborn unpredictability of human beings.
If there is a current-events hum beneath every page, it is because the book understands that we no longer encounter AI as a distant future. We encounter it at work, in search, in school, in medicine, in geopolitics, in the nervous language of earnings calls, in the architecture of state rivalry, in the eerie familiarity of systems that flatter us, code for us, summarize for us, reason for us and sometimes lie to us with unnerving poise. In that sense, this is a book not only for readers of technology but for readers of power. It belongs in conversation with works like “The Code Breaker,” “Chip War,” “The Alignment Problem,” “The Man Who Solved the Market” and even older studies of scientific ambition and institutional overreach. Yet it is also distinctly itself: more literary than most business-tech narratives, more psychologically attentive than most AI explainers, more alive to contingency than most triumphalist accounts.
What lingers after finishing it is not simply admiration, though there is plenty to admire. It is the queasy recognition that the book has captured a hinge moment while refusing to flatter us with certainty. It knows that the people building these systems are not comic-book villains, and not sages either. They are brilliant, partial, driven, compromised, frightened, self-dramatizing, sometimes genuinely public-spirited, often bad at understanding the second- and third-order effects of their own success. In other words, they are human – and that, perhaps, is the most unsettling fact of all. The machines may be changing the world. But for now, the world they are changing still bears the old human signatures: vanity, idealism, rivalry, ingenuity, impatience, fear, longing.
I’d place “The Infinity Machine” at 91 out of 100 – a major, magnetic, intellectually thrilling book that occasionally brushes too close to the aura of its own subjects, but that understands with rare clarity that the race to build artificial intelligence is not merely a technological story. It is a struggle over what kinds of minds will define the future, and what kinds of people we become while trying to build them.
By Demetris Papadimitropoulos | March 6th, 2026
Watercolor Piece by Demetris Papadimitropoulos
For a technology that is supposed to be about the future, “The Infinity Machine” is, at heart, a book about temperament. Not just the temperament of Demis Hassabis, its central figure – the chess prodigy, game designer, neuroscientist and DeepMind founder whose life has the compressed velocity of several ambitious men’s biographies stacked one atop another – but the temperament of an age that has persuaded itself that intelligence can be built, scaled, monetized, regulated, feared, worshiped and perhaps, if luck holds, steered. The book is about artificial intelligence, certainly. It is also about mission as a form of power, about the seductions of abstraction, about the old human habit of insisting that history can still be managed even after it has begun to stampede.
What makes the book so unnervingly readable is that it understands a hard truth many books about technology do not: code is never just code. Behind every model sits a philosophy. Behind every funding round, a cosmology. Behind every boast about scale or safety or alignment, a theory – often half-spoken, sometimes barely conscious – of what a human being is and what a machine might become. The author does not merely explain DeepMind’s breakthroughs. He dramatizes the worldviews embedded inside them. Go is never only Go. AlphaFold is never only protein folding. Chatbots are never only chatbots. Each becomes an argument about thought, about reality, about whether consciousness is pattern or miracle, whether scientific discovery is a public good or a private race, whether the people building our next operating system for civilization are visionaries, opportunists, gamblers, priests or some unstable mixture of all four.
Hassabis emerges as the book’s organizing intelligence and its animating contradiction. He is presented as at once chilly and earnest, grandiose and disciplined, future-obsessed yet stubbornly tethered to a scientist’s self-image. The portrait is persuasive because it never quite settles into hagiography. Hassabis is plainly remarkable, but he is also blinkered in the way that all driven founders are blinkered: his gifts are inseparable from his incapacities. He believes in depth, in pattern, in elite concentration, in the possibility that enough thought and computation can prise open nature’s secrets. He distrusts noise, vulgarity, ideological fashion, sloppy thinking. He also underestimates, repeatedly, the messier social dimensions of the world he is helping to transform. What the book sees so clearly is that this is not incidental to his story. It is the story. The same mind that can perceive AI as a route to scientific revelation can also misjudge language models, governance structures, public sentiment, office politics and the speed with which a technology escapes the moral categories of its inventors.
The early sections are superb at showing how DeepMind’s original mythology formed: chess, games, “Gödel, Escher, Bach,” the conviction that intelligence is ultimately about discovering patterns in vast fields of noise. The author is especially good on the way young intellectual lives are shaped less by isolated influences than by convergences – novels, lectures, scientific puzzles, entrepreneurial opportunities, emotional injuries, old humiliations transmuted into grand design. Hassabis’s childhood, Cambridge years and Bullfrog apprenticeship are rendered not as charming backstory but as the forging of a style of mind. By the time DeepMind exists, the reader feels that the company is not merely a startup but the institutionalization of a private metaphysic.
And then comes the first of the book’s great feats: it turns the history of machine intelligence into narrative nonfiction with stakes, rhythm and atmosphere. The chapter on AlphaGo has the inevitability of good tragedy and the suspense of a thriller. The account of Move 37, and of Lee Sedol’s shock before a system that seems not merely competent but alienly creative, is one of the best things here because it captures the existential drama beneath a technical milestone. The scene is no longer man versus machine in the old “Deep Blue” sense, where brute force humiliates a champion. It is something stranger and more disquieting. A machine is not simply searching faster; it is discovering forms of beauty and strategy that human masters did not foresee. The effect is to make the familiar argument about AI feel newly eerie. The question is no longer whether machines can outperform us. It is whether they can begin to generate insight that appears, from our vantage point, almost indistinguishable from intuition.
If the Go material gives the book its theatrical center, AlphaFold gives it its moral one. Here the book becomes more than a corporate or intellectual history and starts to make a persuasive case for why AI, even in the hands of institutions that are compromised, competitive and occasionally messianic, might still deserve genuine wonder. The AlphaFold chapters are extraordinary because they show scientific progress not as a neat march but as a chain of pivots, wrong bets, stubbornness, intuition, ego and sudden conceptual breakthroughs. Reinforcement learning recedes. Biology intrudes. Search gives way to direct folding, then to transformer architectures. People argue, get replaced, double down, rethink. It is a beautiful demonstration of how real discovery often looks: untidy in the lab, crystalline in retrospect. And because the payoff is not an ad-targeting engine or a prettier consumer convenience but a revolution in protein structure prediction, the book can finally make good on AI’s loftiest rhetoric. It can show a machine doing unequivocal good, or something close to it.
That matters because “The Infinity Machine” is, among other things, a sustained brief against the flattening cynicism that now shadows all writing about AI. The easiest modern story is that every model is a hustle, every founder a courtier of capital, every breakthrough another route to surveillance, labor arbitrage or platform dominance. This book does not deny any of that. In fact, some of its sharpest pages concern the corruption of safety talk by competition, the impossibility of “trustless” governance under shareholder capitalism, the way every advisory board or ethical mechanism bends under incentives, ego and geopolitics. Yet the author resists the lazy comfort of treating all motives as interchangeable. Hassabis is not Sam Altman. Mustafa Suleyman is not Larry Page. Geoffrey Hinton is not Yann LeCun. One may object to their ambitions, but the book insists – rightly – that different ambitions produce different institutions, different blind spots and different dangers. That insistence gives the narrative its moral texture.
The sections on governance are especially strong because they refuse the consolation of a clean solution. The SpaceX safety meeting, the failed DeepMind oversight schemes, the “Project Mario” negotiations with Google, the collapse of post-capitalist governance dreams, the later recognition that board structures and charters may be weaker than ordinary trust and weaker, too, than market panic – all of this is handled with a novelist’s sense of human frailty and a reporter’s eye for institutional farce. The lesson is bleak and convincing. Everyone says they want safe AI. Almost nobody can agree what safety means once it begins to interfere with profit, state competition, reputational warfare or the prestige economy of Silicon Valley. The idealists become pragmatists, the pragmatists become rivals, the rivals become executives, and the executives discover that the machine they hoped to govern has become inseparable from the organizations, nations and markets racing to own it.
One of the book’s quieter achievements is its portrait of Suleyman, who might in a lesser work have been reduced to a foil or cautionary subplot. Instead he becomes one of the book’s most interesting embodiments of AI’s moral instability. His NHS project is presented as both visionary and bungled, humane and hubristic, ethically ambitious and organizationally chaotic. He wants AI to touch the real world, to improve systems now rather than in some post-AGI future, and the book is alive to the nobility of that impulse. But it is equally alive to the dysfunctions that accompany messianic urgency. The portrait is painful because it makes visible a problem that extends beyond one executive: technology is full of people who are sincere about social transformation and destructive in the rooms where they try to achieve it.
The middle and late sections, charting the rise of transformers, the OpenAI challenge, “ChatGPT,” the product turn, DeepMind’s forced merger into Google, the reasoning race and the emergence of agents, are so rich in incident and implication that they occasionally threaten to outrun the frame. Yet the author’s prose, which favors elegant acceleration over static explanation, usually keeps the book aloft. He is very good at carrying a reader from scene to system and back again – from a dinner table in Palo Alto to the economics of compute, from a leaked boardroom fight to the metaphysics of induction, from a benchmark score to the rearrangement of an entire industry’s incentives. This is the book’s signature method, and it works because the author knows that exposition without drama feels inert, while drama without exposition reduces complex history to gossip.
The price of this velocity is that the book can, at times, seem almost as intoxicated by its subject as the people inside it. Not quite, but almost. One feels, now and then, the pull of grandeur – the temptation to let mission-language retain some of its glamour rather than wholly puncture it. A colder book might have lingered longer over extractive supply chains, the environmental brutality of data-center expansion, the colonial geometry of compute and energy, the labor hidden under the smooth surfaces of “automation,” the ordinary workers and knowledge professionals who appear here mostly as downstream consequences. A harsher critic might say that the book, while keenly aware of the dangers of mythmaking, remains susceptible to the charisma of high-IQ men narrating destiny. There is truth in that. But there is also a countertruth: books that refuse charisma altogether often fail to explain why these figures keep winning.
And this book does explain it. It explains why “ChatGPT” felt different from AlphaGo, why “DeepSeek” was interpreted as geopolitical rupture, why reasoning models matter, why the move from chatbots to agents marks a more dangerous threshold, why OpenAI’s internal coups mattered, why Google’s innovator’s dilemma became untenable, why a benchmark war can conceal a metaphysical one. It also understands something many more programmatically “critical” books miss: the AI story is not compelling because it is merely commercial. It is compelling because it touches ancient human longings – to build minds, to outsource toil, to know more than we know, to find patterns behind appearances, to climb higher than our species has any right to climb. The book keeps those longings in view even as it shows how tawdrily they are pursued.
Its final movement, in which Hassabis becomes what the book calls “Turing’s champion,” is the strangest and perhaps boldest section. Here the narrative stops being only about labs and models and becomes an argument about the nature of reality itself – about classical computation, induction, Penrose, consciousness, quantum speculation, whether nature’s patterns are ultimately learnable by sufficiently powerful classical systems. Some readers will find this material exhilarating, others exasperating. I found it indispensable, because it reveals the true scale of the book’s ambition. This is not, finally, just a story about a company called DeepMind or a product category called AI. It is a story about the return of a very old faith in reason, now rearmed with planetary-scale computation and venture money, and about what happens when that faith collides with democracy, bureaucracy, nationalism, media panic and the stubborn unpredictability of human beings.
If there is a current-events hum beneath every page, it is because the book understands that we no longer encounter AI as a distant future. We encounter it at work, in search, in school, in medicine, in geopolitics, in the nervous language of earnings calls, in the architecture of state rivalry, in the eerie familiarity of systems that flatter us, code for us, summarize for us, reason for us and sometimes lie to us with unnerving poise. In that sense, this is a book not only for readers of technology but for readers of power. It belongs in conversation with works like “The Code Breaker,” “Chip War,” “The Alignment Problem,” “The Man Who Solved the Market” and even older studies of scientific ambition and institutional overreach. Yet it is also distinctly itself: more literary than most business-tech narratives, more psychologically attentive than most AI explainers, more alive to contingency than most triumphalist accounts.
What lingers after finishing it is not simply admiration, though there is plenty to admire. It is the queasy recognition that the book has captured a hinge moment while refusing to flatter us with certainty. It knows that the people building these systems are not comic-book villains, and not sages either. They are brilliant, partial, driven, compromised, frightened, self-dramatizing, sometimes genuinely public-spirited, often bad at understanding the second- and third-order effects of their own success. In other words, they are human – and that, perhaps, is the most unsettling fact of all. The machines may be changing the world. But for now, the world they are changing still bears the old human signatures: vanity, idealism, rivalry, ingenuity, impatience, fear, longing.
I’d place “The Infinity Machine” at 91 out of 100 – a major, magnetic, intellectually thrilling book that occasionally brushes too close to the aura of its own subjects, but that understands with rare clarity that the race to build artificial intelligence is not merely a technological story. It is a struggle over what kinds of minds will define the future, and what kinds of people we become while trying to build them.
April 11, 2026
The future is already here, it's just unevenly distributed. This sentence, borrowed from William Gibson, is a timely reminder for us to take note of how the last few years have changed pretty much everything and poised to do even more. And how we don’t realise it. Reading this book is my attempt to take AI as seriously and as urgently as it merits.
I've spent years trying to keep up with technology, not out of any particular aptitude for it (Math remains - quite stubbornly - an enemy to me), but out of a quiet, persistent insecurity: the fear of being left behind, of being rendered irrelevant. So I've made it a habit to watch for where the ball is going, and to position myself somewhere near that spot, if not always exactly on it. If you understand it, maybe you can play a part in its shaping — and in how it shapes you and the world around you. Law and Justice, of course, remains the lens with which I also see this, as with everything else.
Coming back to the book, The Infinity Machine is definitely excellent primer on the world of AI, on Large Language Models, Machine Learning, Reinforcement Learning, Neural Networks, AGI, and the contested horizon of super-intelligence, told through the life and work of Demis Hassabis. The guy who created Google DeepMind (Responsible for Alpha Go, solving - through AI - a complex protein folding problem which is a big deal in medical/scientific research and - Google’s answer to GPT and Claude, the Google Gemini, amongst others). His life story is genuinely inspiring, especially when set against the broader cast of characters who populate this particular stage of history including but not limited to Sam Altman. Another consequential (and polarising) figure in today’s time who figures repeatedly.
I needed to understand this world. Not merely to satisfy curiosity, but because understanding it seemed like a precondition for participating in it. And participation matters, because if there is any chance of shaping what AI becomes, rather than simply being shaped by it, that chance belongs to those who are paying close attention. We have all seen the Terminator (or, any AI-gone-rogue movie of your choice).
Most of us would prefer a different ending.
But beyond these science-fiction anxieties lies a more pressing concern. Technology and AI present enormous opportunities and equally enormous risks, for entrenching inequality, enabling oppression, and further hollowing-out of individual dignity and privacy. The lesson of social media, delivered daily through daily IG doom-scrolling, is that even our attention and free-will can be tech-engineered away from us. In this background, the stakes for law, ethics, and Justice are not theoretical or merely an option. They are immediate. Pressing. Urgent.
This is why I read books like this one: to understand the game well enough to contribute something to it; to the slow, often relatively less glamorous work of ensuring that as the world is rebuilt, it is rebuilt a little more fairly and equitably. That not every person is left behind, or treated as an afterthought, or simply discarded. Leibniz dreamed of the best of all possible worlds. We would settle, at this point, for a substantially fairer one. Or at any rate, not being obliterated by it.
The Infinity Machine delivers on the promise of its subject with extraordinary storytelling and solid intellectual weight. It did not disappoint. Strongly recommend this one.
I've spent years trying to keep up with technology, not out of any particular aptitude for it (Math remains - quite stubbornly - an enemy to me), but out of a quiet, persistent insecurity: the fear of being left behind, of being rendered irrelevant. So I've made it a habit to watch for where the ball is going, and to position myself somewhere near that spot, if not always exactly on it. If you understand it, maybe you can play a part in its shaping — and in how it shapes you and the world around you. Law and Justice, of course, remains the lens with which I also see this, as with everything else.
Coming back to the book, The Infinity Machine is definitely excellent primer on the world of AI, on Large Language Models, Machine Learning, Reinforcement Learning, Neural Networks, AGI, and the contested horizon of super-intelligence, told through the life and work of Demis Hassabis. The guy who created Google DeepMind (Responsible for Alpha Go, solving - through AI - a complex protein folding problem which is a big deal in medical/scientific research and - Google’s answer to GPT and Claude, the Google Gemini, amongst others). His life story is genuinely inspiring, especially when set against the broader cast of characters who populate this particular stage of history including but not limited to Sam Altman. Another consequential (and polarising) figure in today’s time who figures repeatedly.
I needed to understand this world. Not merely to satisfy curiosity, but because understanding it seemed like a precondition for participating in it. And participation matters, because if there is any chance of shaping what AI becomes, rather than simply being shaped by it, that chance belongs to those who are paying close attention. We have all seen the Terminator (or, any AI-gone-rogue movie of your choice).
Most of us would prefer a different ending.
But beyond these science-fiction anxieties lies a more pressing concern. Technology and AI present enormous opportunities and equally enormous risks, for entrenching inequality, enabling oppression, and further hollowing-out of individual dignity and privacy. The lesson of social media, delivered daily through daily IG doom-scrolling, is that even our attention and free-will can be tech-engineered away from us. In this background, the stakes for law, ethics, and Justice are not theoretical or merely an option. They are immediate. Pressing. Urgent.
This is why I read books like this one: to understand the game well enough to contribute something to it; to the slow, often relatively less glamorous work of ensuring that as the world is rebuilt, it is rebuilt a little more fairly and equitably. That not every person is left behind, or treated as an afterthought, or simply discarded. Leibniz dreamed of the best of all possible worlds. We would settle, at this point, for a substantially fairer one. Or at any rate, not being obliterated by it.
The Infinity Machine delivers on the promise of its subject with extraordinary storytelling and solid intellectual weight. It did not disappoint. Strongly recommend this one.
April 7, 2026
It will become clear to any reader of "The Infinity Machine" that Sebastian Mallaby is rather enamoured with his subject matter. This does not detract from what is a detailed and page-turning history of Demis Hassabis, DeepMind, and the AI arms race. In fact, the reader should not be surprised, as Mallaby repeatedly notes Hassabis' charisma and "Jedi Mind Tricks" that allow him to charm any unwitting person.
Much of Hassabis' origin story is already well-known thanks to several documentaries on DeepMind and his work, but Mallaby adds depth and emotional backing to these parts. As an example, the documentaries all mention Hassabis' epiphany at a chess tournament that the competitors were wasting their minds. What they omit and Mallaby details, is the extent of the pressure heaped on the young Hassabis by his desperate father. We should not be surprised, then, that Hassabis takes his father's lesson that you must always do your best so painfully literally, at some points in his life taking him close to the edge.
There are two elements of the book that stand out. The first is the way in which Mallaby chronicles Hassabis' transformation from AI-utopian to wearied realist, jaded from the constant debates over safety, both externally and internally. We see Hassabis realise after release of ChatGPT that the cat is out of the bag, and that no amount of calls for restraint will stop inevitable risks from being taken. The second is the extent to which Google's internal politics interfered. The original logic of selling DeepMind to Google was maintaining autonomy whilst accessing massive compute. Mallaby makes it clear that the former was impossible - once AI became central to Google's plans, there was no way Google would allow the status quo to remain. The time consumed by this governance wrangling is particularly shocking and Hassabis himself certainly seems to blame it for some of DeepMind's missteps. It is a strong reminder of the perils of large corporate bureaucracy.
One slight criticism is that the last quarter of the book becomes a bit of a news feed of product updates from the LLM providers. Mallaby's insistence on LLM benchmarks as a measure of model quality allows him to continue his praise for Hassabis, when even after our hero gets involved, the user feedback on Gemini has lagged that of Claude or ChatGPT.
As to where the book leaves the reader, it will be very personal. Some may come away hopeful that the AI race is partially in the hands of what seems a principled man. Others may think of Geoffrey Hinton ruing the 'sweetness of discovery' and despair that Hassabis et al have repeated his error. Time will tell which camp is correct.
Much of Hassabis' origin story is already well-known thanks to several documentaries on DeepMind and his work, but Mallaby adds depth and emotional backing to these parts. As an example, the documentaries all mention Hassabis' epiphany at a chess tournament that the competitors were wasting their minds. What they omit and Mallaby details, is the extent of the pressure heaped on the young Hassabis by his desperate father. We should not be surprised, then, that Hassabis takes his father's lesson that you must always do your best so painfully literally, at some points in his life taking him close to the edge.
There are two elements of the book that stand out. The first is the way in which Mallaby chronicles Hassabis' transformation from AI-utopian to wearied realist, jaded from the constant debates over safety, both externally and internally. We see Hassabis realise after release of ChatGPT that the cat is out of the bag, and that no amount of calls for restraint will stop inevitable risks from being taken. The second is the extent to which Google's internal politics interfered. The original logic of selling DeepMind to Google was maintaining autonomy whilst accessing massive compute. Mallaby makes it clear that the former was impossible - once AI became central to Google's plans, there was no way Google would allow the status quo to remain. The time consumed by this governance wrangling is particularly shocking and Hassabis himself certainly seems to blame it for some of DeepMind's missteps. It is a strong reminder of the perils of large corporate bureaucracy.
One slight criticism is that the last quarter of the book becomes a bit of a news feed of product updates from the LLM providers. Mallaby's insistence on LLM benchmarks as a measure of model quality allows him to continue his praise for Hassabis, when even after our hero gets involved, the user feedback on Gemini has lagged that of Claude or ChatGPT.
As to where the book leaves the reader, it will be very personal. Some may come away hopeful that the AI race is partially in the hands of what seems a principled man. Others may think of Geoffrey Hinton ruing the 'sweetness of discovery' and despair that Hassabis et al have repeated his error. Time will tell which camp is correct.
April 8, 2026
“If the brainstorming is fluid, if the creativity is high, then you go forward with your project.” — Demis Hassabis
The Infinity Machine is an impressively well-written book covering the founding story of DeepMind, arguably one of the most important companies in the AI revolution, and its visionary founder, Demis Hassabis.
At a time when AI leaders such as Elon Musk and Sam Altman are being scrutinised over their motives, this book presents Demis as a rare counterpoint: someone committed to building AGI responsibly, with the goal of creating an abundance of resources that improves the lives of billions of people. Seemingly unmotivated by money or power, he is driven by scientific discovery, the desire to make a real difference, and a determination to leave a lasting legacy.
Having never read a biography before, I didn’t expect to find one so compelling. Following such a pivotal figure in the world of AI, tracing Demis’s life and the journey that led him to where he is today, was far more gripping than I anticipated.
This book has done nothing but inspire new ideas in me, even during a period when I’m struggling to find the time to pursue them.
I have no doubt that Demis is the Oppenheimer of AI. AGI will have its atomic bomb moment, and I have no doubt that Demis will be there when it happens. I would love to see this story turned into a film.
If you’re looking for an accurate, in-depth exploration of the AI world, the origins of the AI arms race, and an engaging way to understand how AI actually works — this is the book to read.
Thank you, Sebastian, for the book and an incredible read.
The Infinity Machine is an impressively well-written book covering the founding story of DeepMind, arguably one of the most important companies in the AI revolution, and its visionary founder, Demis Hassabis.
At a time when AI leaders such as Elon Musk and Sam Altman are being scrutinised over their motives, this book presents Demis as a rare counterpoint: someone committed to building AGI responsibly, with the goal of creating an abundance of resources that improves the lives of billions of people. Seemingly unmotivated by money or power, he is driven by scientific discovery, the desire to make a real difference, and a determination to leave a lasting legacy.
Having never read a biography before, I didn’t expect to find one so compelling. Following such a pivotal figure in the world of AI, tracing Demis’s life and the journey that led him to where he is today, was far more gripping than I anticipated.
This book has done nothing but inspire new ideas in me, even during a period when I’m struggling to find the time to pursue them.
I have no doubt that Demis is the Oppenheimer of AI. AGI will have its atomic bomb moment, and I have no doubt that Demis will be there when it happens. I would love to see this story turned into a film.
If you’re looking for an accurate, in-depth exploration of the AI world, the origins of the AI arms race, and an engaging way to understand how AI actually works — this is the book to read.
Thank you, Sebastian, for the book and an incredible read.
April 7, 2026
The Infinity Machine is a biography of Demis Hassabis and a narrative of DeepMind’s quest to build artificial general intelligence, blending childhood prodigy stories, startup drama, scientific breakthroughs, and the ethical tension of creating world‑changing technology.
DeepMind was founded on a simple but audacious idea: solve intelligence, and all other problems become solvable and it goes thorugh all the various phases including AlphaGo, Google
The book frames the struggle as a philosophical and strategic battle between profitability/corporation and research/safety concerns and independence -- similar to the one OpenAI had. I only started deepmind because i thought it was the best way to get the mission off the ground. If i had stayed in academia i wouldnt have the resources, but anyway AGI should be gifted to the world eventually, i mean, AGI is infinitely bigger than a company or a person or a set of owners. Its bigger than capitalism or national ecomonomies, its humanity sized really... Its humanities invention and its going to affect all humanity - so humanity should run it.
“To understand intelligence was to understand ourselves — and perhaps to transcend what we are.”
Well recommended read to better understand the time we are in.
DeepMind was founded on a simple but audacious idea: solve intelligence, and all other problems become solvable and it goes thorugh all the various phases including AlphaGo, Google
The book frames the struggle as a philosophical and strategic battle between profitability/corporation and research/safety concerns and independence -- similar to the one OpenAI had. I only started deepmind because i thought it was the best way to get the mission off the ground. If i had stayed in academia i wouldnt have the resources, but anyway AGI should be gifted to the world eventually, i mean, AGI is infinitely bigger than a company or a person or a set of owners. Its bigger than capitalism or national ecomonomies, its humanity sized really... Its humanities invention and its going to affect all humanity - so humanity should run it.
“To understand intelligence was to understand ourselves — and perhaps to transcend what we are.”
Well recommended read to better understand the time we are in.
April 12, 2026
The Infinity Machine by Sebastian Mallaby is a fun and engaging ready (4/5 stars). He does a good job of explaining the technical topics (deep learning, reinforcement learning, etc) in a clear way to help with the story.
Read it if you want a solid history of the highlights of AI over the past 10/20 years. Demis Hassabis is an excellent choice to both be an engaging story himself but act as a “Forrest Gump” of AI to tell its overall story while focusing on his journey in the field.
A few interesting highlights that I had not know before reading the book:
-Eliezer Yudkowsky played a key role in helping DeepMind get its initial funding. Counter productive I guess to his concerns on AI safety?
-Sam Altman is a big fan of Robert Caro’s LBJ books as lessons in power. That should tell you a lot about the man if you have read those books…
-Their concerns in 2017/18/19 on AI safety and not letting any AI any access to the internet and tightly managing it is hilariously quaint in the age of OpenClaw!
Read it if you want a solid history of the highlights of AI over the past 10/20 years. Demis Hassabis is an excellent choice to both be an engaging story himself but act as a “Forrest Gump” of AI to tell its overall story while focusing on his journey in the field.
A few interesting highlights that I had not know before reading the book:
-Eliezer Yudkowsky played a key role in helping DeepMind get its initial funding. Counter productive I guess to his concerns on AI safety?
-Sam Altman is a big fan of Robert Caro’s LBJ books as lessons in power. That should tell you a lot about the man if you have read those books…
-Their concerns in 2017/18/19 on AI safety and not letting any AI any access to the internet and tightly managing it is hilariously quaint in the age of OpenClaw!
April 13, 2026
Insanely Great Book
Sebastian Mallaby does a really great job of capturing not only his subject’s history and motivations but the history of AI; from the deep learning breakthroughs of the early 2010s with AlexNet to reinforcement learning (RL)’s ‘move 37’ and to AlphaZero (and of course the Nobel prize winning AlphaFold). Then comes the explosion of the transformer architecture and language models.
Reading this book made me reminiscent of the first time I read Steve Jobs by Walter Isaacson. All in all, the author did a thorough research and created magnum opus page turner.
But the story is not yet done, we’re yet to see the crossing of the Rubicon into AGI (ignoring the definition of AGI), what exactly that would be like. With so many players and interests at play.
Sebastian Mallaby does a really great job of capturing not only his subject’s history and motivations but the history of AI; from the deep learning breakthroughs of the early 2010s with AlexNet to reinforcement learning (RL)’s ‘move 37’ and to AlphaZero (and of course the Nobel prize winning AlphaFold). Then comes the explosion of the transformer architecture and language models.
Reading this book made me reminiscent of the first time I read Steve Jobs by Walter Isaacson. All in all, the author did a thorough research and created magnum opus page turner.
But the story is not yet done, we’re yet to see the crossing of the Rubicon into AGI (ignoring the definition of AGI), what exactly that would be like. With so many players and interests at play.
April 12, 2026
Well written, entertaining, and explains complicated ideas for the general reader. Hassabis is an interesting character in the world of generative AI. Fueled by a deep desire to understand the truth, he comes off as a modern day Captian Ahab, hell bent on finding what he beleives will reveal reality, no matter what the cost.
April 17, 2026
I found this an extremely interesting and compelling read.
It isn't only a biography of Demis Hassabis; it is also an overall history of the development of artificial intelligence in recent time.
I really recommend it if you're interested in this subject. I think we all should be in this time of change.
It isn't only a biography of Demis Hassabis; it is also an overall history of the development of artificial intelligence in recent time.
I really recommend it if you're interested in this subject. I think we all should be in this time of change.
April 6, 2026
A very interesting history of AI work
Really enjoyed seeing the story build from the ground up with Demis. The details and the stories wove in nicely with the progress. I wish there had been a bit more on Anthropic but overall an enjoyable and thought provoking read.
Really enjoyed seeing the story build from the ground up with Demis. The details and the stories wove in nicely with the progress. I wish there had been a bit more on Anthropic but overall an enjoyable and thought provoking read.
April 11, 2026
Two good reasons to read this book: (1) nuggets of power struggle (not derogatory, all change occurs via power struggle) (2) examples of people orienting their life towards pursuing something great and greater than himself
April 6, 2026
It’s a fascinating insight into what goes on behind, what motivates the many players. The title of the book Infinity machine alludes to the Turing machine. So is AGI a computable problem ?
April 8, 2026
Demis is awesome
April 11, 2026
errs a little too much on a messianic portrayal, otherwise an exposition in the mind and working of the forefathers of agi
April 12, 2026
A very good read for anyone who wants to understand the modern development of digital intelligence. Much broader than Demis Hassabis’ journey to leading AI at Google.
April 13, 2026
This was pretty interesting. Got a little bogged down toward the end when it tried to describe product release timelines, but the more biographical/motivational stuff about Hassan’s was worthwhile.
Want to Read
April 15, 2026Economist review 1 Apr 2026
https://www.economist.com/culture/202...
https://www.economist.com/culture/202...
April 15, 2026
So the book was obviously very interesting. Would have preferred more insight into company structure and way less safety talk. Didn’t make me particularly bullish about gdm in the ai race though
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