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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
14 books312 followersA Washington Post columnist since 1999. Worked for The Economist from 1986 - 1999.
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Displaying 1 - 30 of 105 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.
May 9, 2026
Every book Sebastian Mallaby writes is phenomenal and this one is no exception (full disclosure: Sebastian is a friend, a friendship that started more than twenty years ago when I made it clear to him that I did not think every one of his Washington Post columns was phenomenal).
The subject of the biography portion (Demis Hassabis) is fascinating, and the chronicling of recent developments in AI could not be more important and timely. Hassabis is almost unique in the landscape: neuroscientist, chess prodigy, game designer, AI pioneer, and one of the most significant scientists of recent times — AlphaFold alone would secure that verdict. One character says he is the type who would have won a Nobel no matter what, not by luck but inevitably. He seems driven by knowledge and importance rather than money, yet as AI becomes big business he ends up selling to and working for the largest of large companies, Google.
The cradle-to-present biography makes for riveting reading — rarely is the childhood portion of a biography as gripping as this one, which recounts stories of his chess prowess at age five with the same intensity as the later chapters. (Mozart comes to mind.)
As the book gets closer to the present it becomes more of a recounting of events we all lived through, paid attention to at the time, and that somehow feel like the distant past even though they are only a few years ago — from the launch of ChatGPT 3.5 to each subsequent iteration and the responses and one-ups by Gemini. All told with considerable weight on Hassabis' perspective and very little sympathy for some of the other players in the drama, most notably Sam Altman, with Mustafa Suleyman occupying a more ambiguous middle ground.
The book is also an interesting exploration of people who are building a technology they are both excited and ambivalent about — doing it within corporations they are likewise both excited and ambivalent about — while trying, in Hassabis' case, to balance a deep enthusiasm for scientific discovery against commercial imperatives. It uses personalities to provide perspective and insight on some of the biggest and most profound issues in the space.
Ultimately the portrait is of everyone stuck in a race no one (or at least not Hassabis) wants to be in, because there are so many competitors in the United States and increasingly in China that coordination becomes impossible. Normally in the economy this is a good thing — competition drives innovation, lowers consumer prices, and makes collusion both difficult and illegal. But it does make me wonder whether that logic holds in this particular case.
This book is only the first chapter in the story of AI. Whether it is also the first chapter, or the first volume, or the complete book for Hassabis personally remains to be seen. The epilogue teases tantalizing possibilities for future fundamental research in physics and other areas, which I found genuinely exciting. But it is also possible that AI is becoming more about organizations than individuals, or that the central figures going forward will be different from Hassabis — as to some degree they already have been for the past few years. We'll see.
The subject of the biography portion (Demis Hassabis) is fascinating, and the chronicling of recent developments in AI could not be more important and timely. Hassabis is almost unique in the landscape: neuroscientist, chess prodigy, game designer, AI pioneer, and one of the most significant scientists of recent times — AlphaFold alone would secure that verdict. One character says he is the type who would have won a Nobel no matter what, not by luck but inevitably. He seems driven by knowledge and importance rather than money, yet as AI becomes big business he ends up selling to and working for the largest of large companies, Google.
The cradle-to-present biography makes for riveting reading — rarely is the childhood portion of a biography as gripping as this one, which recounts stories of his chess prowess at age five with the same intensity as the later chapters. (Mozart comes to mind.)
As the book gets closer to the present it becomes more of a recounting of events we all lived through, paid attention to at the time, and that somehow feel like the distant past even though they are only a few years ago — from the launch of ChatGPT 3.5 to each subsequent iteration and the responses and one-ups by Gemini. All told with considerable weight on Hassabis' perspective and very little sympathy for some of the other players in the drama, most notably Sam Altman, with Mustafa Suleyman occupying a more ambiguous middle ground.
The book is also an interesting exploration of people who are building a technology they are both excited and ambivalent about — doing it within corporations they are likewise both excited and ambivalent about — while trying, in Hassabis' case, to balance a deep enthusiasm for scientific discovery against commercial imperatives. It uses personalities to provide perspective and insight on some of the biggest and most profound issues in the space.
Ultimately the portrait is of everyone stuck in a race no one (or at least not Hassabis) wants to be in, because there are so many competitors in the United States and increasingly in China that coordination becomes impossible. Normally in the economy this is a good thing — competition drives innovation, lowers consumer prices, and makes collusion both difficult and illegal. But it does make me wonder whether that logic holds in this particular case.
This book is only the first chapter in the story of AI. Whether it is also the first chapter, or the first volume, or the complete book for Hassabis personally remains to be seen. The epilogue teases tantalizing possibilities for future fundamental research in physics and other areas, which I found genuinely exciting. But it is also possible that AI is becoming more about organizations than individuals, or that the central figures going forward will be different from Hassabis — as to some degree they already have been for the past few years. We'll see.
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.
May 1, 2026
先说说比较喜欢这本书的部分：
- 并不局限于某一家公司的产品，而是用比较全面的视角呈现了近20年来人工智能的发展，一直到现在变成 AI arm race 的状态。
- 早期 DeepMind 的崛起部分其实比后来 OpenAI 那些八卦更好看。Alpha Go 打败李世石的情节看得人热血沸腾。
- OpenAI 和微软现在的状态简直就是当年 DeepMind 和 Google 关系的重现
- AI 竞争激烈且残酷，而且现在就「谁是赢家」下定论仍然为时尚早。
觉得有点惋惜的地方：
- 有些情节有点流水账
- 作者八卦心跟看客一样重。这没什么，但有些章节如果只讲八卦就会让内容失去内核
最后，作者当然没这么讲，但看完这本书我更坚信，Altman 就是 AI 时代的 Musk. And, it’s not a compliment.
- 并不局限于某一家公司的产品，而是用比较全面的视角呈现了近20年来人工智能的发展，一直到现在变成 AI arm race 的状态。
- 早期 DeepMind 的崛起部分其实比后来 OpenAI 那些八卦更好看。Alpha Go 打败李世石的情节看得人热血沸腾。
- OpenAI 和微软现在的状态简直就是当年 DeepMind 和 Google 关系的重现
- AI 竞争激烈且残酷，而且现在就「谁是赢家」下定论仍然为时尚早。
觉得有点惋惜的地方：
- 有些情节有点流水账
- 作者八卦心跟看客一样重。这没什么，但有些章节如果只讲八卦就会让内容失去内核
最后，作者当然没这么讲，但看完这本书我更坚信，Altman 就是 AI 时代的 Musk. And, it’s not a compliment.
May 11, 2026
Definitely worth a read, this is right up to date and includes plenty of material 'never seen before in public', Mallaby has spent years putting it together and has spent lots of time with all the players in the story, especially Demis Hassabis. AI is the big story of our time and Demis is one of the key players. The chapter here on the Nobel Prize winning chemistry research AlphaFold (protein folding), is fascinating and SM does an excellent job in making it accessible . DH ultimately wins the Chemistry Nobel prize for this and is at the forefront of AI research, when along comes ChatGPT. How Google Deepmind fights back to catch up with OpenAI is a great story well told, Sergei Brin gets back into Google and appoints DH as the boss of the whole of Google AI, though he stays in London.
DH and Deepmind's relationship with Google makes for some odd episodes, which were unknown before this book - for some reason Demis and his cofounders try to renegotiate the company structure, to create a controlling board independent of Google, and a not-for-profit structure, but they do this years after they've sold the entire business to Google. I'm not sure we've quite got to the bottom of this because the plan also seemed to feature shares for staff, and investment of $5 billion by new investors, I don't see how that makes much sense without any future profits.
But the end result is that the Google AI division is largely run out of London, and Google Gemini is one of the best large language models, which reflects very well on Demis Hassabis.
Strongly recommend.
DH and Deepmind's relationship with Google makes for some odd episodes, which were unknown before this book - for some reason Demis and his cofounders try to renegotiate the company structure, to create a controlling board independent of Google, and a not-for-profit structure, but they do this years after they've sold the entire business to Google. I'm not sure we've quite got to the bottom of this because the plan also seemed to feature shares for staff, and investment of $5 billion by new investors, I don't see how that makes much sense without any future profits.
But the end result is that the Google AI division is largely run out of London, and Google Gemini is one of the best large language models, which reflects very well on Demis Hassabis.
Strongly recommend.
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 24, 2026
Excellent new perspective on the AI Arms Race
I have read multiple background books on the different companies involved in the race for AI dominance over the past year. Empire of AI does a good job showing the interworkings of OpenAI and Careless People gives you an inside look into Meta. I guess I could include Isaacson's biography of Elon Musk in the mix too, but it didn't take the xAI elements too into consideration in that book.
This book gives another valuable perspective in the AI arms race. The history of Hassabis and Deepmind is a critical piece of history in the development of AI. The stories related to his early life at Bullfrog Games, Elixir and many many Deepmind / Google projects such as Atari gaming, AlphaGo, AlphaFold, and the AI models up to current Gemini models are all highly interesting and insightful. In particular I enjoyed the book talking about the actual technical assertions and building blocks of the Deepmind research. It taught me quite a bit about why they pursued the directions they did while others such as OpenAI pursued others.
The two most revealing elements of the book are seeing the contrast of Deepmind with it's primary focus of scientific research in AI (researcher led) vs OpenAI's primary focus on shipping products (engineer led). This contrast would play out quite a bit in the history of the past decade and I am surprised I wasn't more aware of it. The second thing that is really interesting is Hassabis's deep interest in Reinforced Learning (RL) which was the primary basis for the AlphaGo triumphs. Deepmind had very little interest in LLMs until OpenAI thrashed them so thoroghly that they were forced to accept this other approach was a better way than they were purusing. Interestingly, later versions of GPT actually started to explore back in the realm of RL, and Hassabis still believes that long term the research will come back around to RL as opposed to LLMs. We will see.
If you enjoy biographies or want to have better context on the current AI debates or progress, this is a great book to pick up.
I have read multiple background books on the different companies involved in the race for AI dominance over the past year. Empire of AI does a good job showing the interworkings of OpenAI and Careless People gives you an inside look into Meta. I guess I could include Isaacson's biography of Elon Musk in the mix too, but it didn't take the xAI elements too into consideration in that book.
This book gives another valuable perspective in the AI arms race. The history of Hassabis and Deepmind is a critical piece of history in the development of AI. The stories related to his early life at Bullfrog Games, Elixir and many many Deepmind / Google projects such as Atari gaming, AlphaGo, AlphaFold, and the AI models up to current Gemini models are all highly interesting and insightful. In particular I enjoyed the book talking about the actual technical assertions and building blocks of the Deepmind research. It taught me quite a bit about why they pursued the directions they did while others such as OpenAI pursued others.
The two most revealing elements of the book are seeing the contrast of Deepmind with it's primary focus of scientific research in AI (researcher led) vs OpenAI's primary focus on shipping products (engineer led). This contrast would play out quite a bit in the history of the past decade and I am surprised I wasn't more aware of it. The second thing that is really interesting is Hassabis's deep interest in Reinforced Learning (RL) which was the primary basis for the AlphaGo triumphs. Deepmind had very little interest in LLMs until OpenAI thrashed them so thoroghly that they were forced to accept this other approach was a better way than they were purusing. Interestingly, later versions of GPT actually started to explore back in the realm of RL, and Hassabis still believes that long term the research will come back around to RL as opposed to LLMs. We will see.
If you enjoy biographies or want to have better context on the current AI debates or progress, this is a great book to pick up.
April 29, 2026
"Intelligence is fundamental. It is the root of all else. It is the mechanism through which humans perceive reality."
Fascinating to hear the backstory behind the current AI wars unfolding right in front of us. Quite interesting that Mallaby was able to write and publish this so quickly, while we are still in the thick of frontier lab warfare and a clear winner has yet to emerge.
Docking a star just because unfortunately, the biography seemed a bit too biased towards Hassabis (as one might expect though). I do think it's more valuable to read biographies like those by Walter Isaacson—where the author is able to pry deeply into an individual's life and interview people involved from both the good and bad sides, painting a portrait of them as a whole person rather than a pure hero.
We might need a Part 2 soon, once Superintelligence enters the picture...
-------------------------------------------
"The mind interprets the world."
"What I cannot build, I cannot understand."
"The most successful founders do not create companies. They are on a mission to create something closer to a religion."
"People make a mistake in thinking of themselves as small. They don't think of how they personally affect history."
"Technology happens because it's possible."
Fascinating to hear the backstory behind the current AI wars unfolding right in front of us. Quite interesting that Mallaby was able to write and publish this so quickly, while we are still in the thick of frontier lab warfare and a clear winner has yet to emerge.
Docking a star just because unfortunately, the biography seemed a bit too biased towards Hassabis (as one might expect though). I do think it's more valuable to read biographies like those by Walter Isaacson—where the author is able to pry deeply into an individual's life and interview people involved from both the good and bad sides, painting a portrait of them as a whole person rather than a pure hero.
We might need a Part 2 soon, once Superintelligence enters the picture...
-------------------------------------------
"The mind interprets the world."
"What I cannot build, I cannot understand."
"The most successful founders do not create companies. They are on a mission to create something closer to a religion."
"People make a mistake in thinking of themselves as small. They don't think of how they personally affect history."
"Technology happens because it's possible."
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.
May 15, 2026
Demis is one of the most important people in AI. Regardless of how you feel about this technology, its impact on our world is unmistakable—whether good or bad. It is, and will only continue to be, a co-author of our future. Read this book to better understand why and how.
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 29, 2026
This book is a very good history of DeepMind.
Ender versus the pure scientist
An important theme of the book is that Hassabis identifies as pure scientist, valuing knowledge for its own sake. He wants to surpass Newton and Einstein at understanding reality.
He also identifies powerfully with Ender Wiggin, wanting to play a pivotal role in a critical interaction between two groups of intelligences.
I see some important tension between these two goals. Are they sufficiently similar that he can accomplish them both? Or is he dividing his effort between them in a way that sabotages his chances of success? Mallaby doesn't answer.
Mallaby documents some downsides to Hassabis's choice to emphasize science over engineering.
OpenAI would have failed if it chose a "no intelligence" approach, but underestimating brute force has been a more widespread mistake in AI than underestimating intelligence. See the Bitter Lesson.
DeepMind underestimated scaling laws circa 2019, and fell behind for a while as a result.
Early Funding
DeepMind got adequate funding at its founding in 2010, but gradually developed uncomfortable funding constraints that led to selling out to Google in 2014.
Peter Thiel was comfortable investing in DeepMind in 2010, partly because it was a clearly contrarian stance. But by late 2012, he abandoned further investment because it was too expensive and mainstream. I understand the too expensive part, but too mainstream in 2012 sounds bizarre.
Hassabis was unenthusiastic about joining Google. But he disliked needing to spend time and effort on fundraising. His attitude in resigning himself to cede significant control seems to reflect a decision to prioritize being a scientist over being Ender.
Mallaby documents some later interactions with Google CEO Sundar Pichai which indicate significant tension between Hassabis and Pichai around 2017, with Pichai sounding a bit myopic about the potential of AI. Google's founders had a better grasp on the future of AI, but they weren't active enough in the company to offset Pichai's nearer-term focus.
Facebook offered more money than Google to buy DeepMind. Hassabis talked with Zuckerberg, and rejected him on the grounds that he couldn't see that AI was more important than virtual reality or 3D printing.
Elon Musk
Musk plays important roles in this story, laden with disturbing contradictions.
He played a possibly important role in prompting Google to buy DeepMind, by bragging to Larry Page about Musk's investment in DeepMind.
Musk recognized Hassabis's competence early on. But he switched to disliking Hassabis in 2014 after trying to buy DeepMind and being rejected in favor of Google.
Why did DeepMind reject Musk's bid? Mallaby doesn't say much here. Other sources provide conflicting impressions as to which offer promised more autonomy for DeepMind. Hassabis mainly decided on the basis that Google credibly promised to provide enough compute. Without Thiel's support, Musk's ability to raise enough money looked questionable. But in hindsight, the examples of OpenAI and Anthropic indicate that it was possible, but hard, to raise enough money as an independent company.
Musk has often expressed a fear of AI being controlled by a big corporation. That's strange to read now that Musk has merged his AI company into a trillion dollar conglomerate. Was some of Musk's concern specific to Google? Musk had a strong negative reaction to Larry Page's position that it was speciesist to worry about AI replacing humanity.
Hassabis hoped to manage the risks of powerful AI by getting the best AI developers on a single team, sometimes likened to a Manhattan Project.
That didn't survive contact with the egos that AI attracted. From a chapter aptly titled Out of Eden:
An ominous note:
OpenAI
But Musk's fracturing of the AI industry wasn't the only obstacle to cooperative AI development. Maybe the biggest setback came when OpenAI released ChatGPT. AI companies at the time seemed to have a policy of being pretty cautious about releasing new models. OpenAI changed their policy to release ChatGPT rather hastily in response to a false rumor that Anthropic was about to release the similar product that it had created. Mallaby concludes:
Five months later, Hassabis told Mallaby:
Mustafa Suleyman
The book significantly raised my opinion of Suleyman. My review of his book was unenthusiastic.
Apparently he sounds a bit more forceful when talking to CEOs, and consistently focused on some medium to large risks associated with AI.
He was the most eager of DeepMind leaders to pressure Google into agreeing on an ethics and safety board.
Alas, he doesn't have the political skills to be effective at his desired role.
In my review I dismissed his concern that "a jobs recession will crater tax receipts". I recently examined this more carefully, and concluded that Suleyman's concern's are reasonable, and that there's likely to be a few months or years of COVID-level stress before government revenues become ample.
My Complaints
Most of the book seems carefully researched. Mallaby understands AI well enough that I don't have any criticisms there.
Here are some flaws that annoyed me, but which don't detract much from the book.
That's misleading at best. There's significant evidence of earlier abstract thought. It seems likely that abstraction emerged gradually, over a long period.
Mallaby wrote "Oxford's Future of Life Institute", when he meant to refer to the Future of Humanity Institute.
I like attempts to explain AI progress by estimating the equivalent human ages, but my experience indicates that AI is advancing at more like two years of age per calendar year. Mallaby's estimates seem like they're the result of cherry-picking the most impressive responses. I focus somewhat on their planning abilities, whereas I doubt that Mallaby puts any weight on those abilities. I say AI is just now reaching the nine-year-old level.
Mallaby quotes Hinton as saying "There aren't any examples of more intelligent things being controlled by less intelligent things".
While I approve of Mallaby's concerns about this kind of risk, he ought to have pointed out that Hinton exaggerated. Some counter-examples:
- Toxoplasma gondii controlling mice
- Zombie Ant Fungus
- The "pointy-haired boss" trope
- Cats training humans to feed and pet them
- The hunger-signaling part of the human mind overruling the part that wants to lose weight
Concluding Thoughts
The book provides good insights into why AI became a race rather than a Manhattan Project.
Mallaby leans toward the conclusion that a race was inevitable. I'm not convinced.
This seems wrong:
(I'm unsure whether Mallaby endorsed that view, or was merely reporting Hassabis's view.) We know from nuclear non-proliferation treaties that cooperation is possible between nations that are more hostile than the current US-China conflict. This quote comes after Mallaby has devoted plenty of analysis that points toward Altman and Musk triggering a race for reasons that seem completely unrelated to China. I remain confused as to why people treat China as anything more than a rationalization for pursuing a competition that they want for less noble reasons.
The book convinced me that Hassabis is making a mistake by trying to be both Einstein and Ender.
Hassabis has achieved partial success at Einstein-level science, while, like Einstein, working at another job.
But being Ender is really a full-time job. It currently looks like Hassabis is not narrowly enough focused on being Ender to be the pivotal person in AI's destiny to remake the world.
Mallaby hints at Oppenheimer as a role model that might be appropriate for Hassabis. Maybe the world would be better off if Hassabis had aimed more for that role. Mallaby seems to say it's much too late to try that.
More Quotes
According to David Silver:
I'm puzzled. I don't see signs of Jedi-level charisma.
Ender versus the pure scientist
An important theme of the book is that Hassabis identifies as pure scientist, valuing knowledge for its own sake. He wants to surpass Newton and Einstein at understanding reality.
He also identifies powerfully with Ender Wiggin, wanting to play a pivotal role in a critical interaction between two groups of intelligences.
I see some important tension between these two goals. Are they sufficiently similar that he can accomplish them both? Or is he dividing his effort between them in a way that sabotages his chances of success? Mallaby doesn't answer.
Mallaby documents some downsides to Hassabis's choice to emphasize science over engineering.
One of the things that Sutskever loved about OpenAI was that it revered engineers ... AI has academic roots, and academics tend to look down on the dirty work of engineering. ... Hassabis and his colleagues disparaged OpenAI's work as engineering-led: all brute force and no intelligence.
OpenAI would have failed if it chose a "no intelligence" approach, but underestimating brute force has been a more widespread mistake in AI than underestimating intelligence. See the Bitter Lesson.
DeepMind underestimated scaling laws circa 2019, and fell behind for a while as a result.
Early Funding
DeepMind got adequate funding at its founding in 2010, but gradually developed uncomfortable funding constraints that led to selling out to Google in 2014.
Peter Thiel was comfortable investing in DeepMind in 2010, partly because it was a clearly contrarian stance. But by late 2012, he abandoned further investment because it was too expensive and mainstream. I understand the too expensive part, but too mainstream in 2012 sounds bizarre.
Hassabis was unenthusiastic about joining Google. But he disliked needing to spend time and effort on fundraising. His attitude in resigning himself to cede significant control seems to reflect a decision to prioritize being a scientist over being Ender.
Mallaby documents some later interactions with Google CEO Sundar Pichai which indicate significant tension between Hassabis and Pichai around 2017, with Pichai sounding a bit myopic about the potential of AI. Google's founders had a better grasp on the future of AI, but they weren't active enough in the company to offset Pichai's nearer-term focus.
Facebook offered more money than Google to buy DeepMind. Hassabis talked with Zuckerberg, and rejected him on the grounds that he couldn't see that AI was more important than virtual reality or 3D printing.
Elon Musk
Musk plays important roles in this story, laden with disturbing contradictions.
He played a possibly important role in prompting Google to buy DeepMind, by bragging to Larry Page about Musk's investment in DeepMind.
Musk recognized Hassabis's competence early on. But he switched to disliking Hassabis in 2014 after trying to buy DeepMind and being rejected in favor of Google.
Why did DeepMind reject Musk's bid? Mallaby doesn't say much here. Other sources provide conflicting impressions as to which offer promised more autonomy for DeepMind. Hassabis mainly decided on the basis that Google credibly promised to provide enough compute. Without Thiel's support, Musk's ability to raise enough money looked questionable. But in hindsight, the examples of OpenAI and Anthropic indicate that it was possible, but hard, to raise enough money as an independent company.
Musk has often expressed a fear of AI being controlled by a big corporation. That's strange to read now that Musk has merged his AI company into a trillion dollar conglomerate. Was some of Musk's concern specific to Google? Musk had a strong negative reaction to Larry Page's position that it was speciesist to worry about AI replacing humanity.
Hassabis hoped to manage the risks of powerful AI by getting the best AI developers on a single team, sometimes likened to a Manhattan Project.
That didn't survive contact with the egos that AI attracted. From a chapter aptly titled Out of Eden:
But to believers in the singleton vision, OpenAI's founding represented the Fall: the moment when the serpent brought evil into the garden ... Hassabis, ever practical, was also angry in a simpler way. Musk and Hoffman had been invited to the SpaceX gathering [a DeepMind safety board meeting] in good faith. They had sat through the meeting, listened to DeepMind's plans, and then used what they had heard to double-cross him.
... in early 2014, when Elon Musk tried to buy DeepMind, allegedly to safeguard it for humanity. A year later, Musk remained bitter that his bid had been spurned; if he couldn't be the one to build AI, he wanted nobody to do so.
Musk ... continued to fulminate against DeepMind, denouncing Hassabis as an evil genius, the evidence being that Hassabis had once worked on a computer game called Evil Genius.
An ominous note:
in 2013, Elon Musk's wife, Talulah Riley - an actress known for playing a seductive TV robot who takes to massacring humans
OpenAI
But Musk's fracturing of the AI industry wasn't the only obstacle to cooperative AI development. Maybe the biggest setback came when OpenAI released ChatGPT. AI companies at the time seemed to have a policy of being pretty cautious about releasing new models. OpenAI changed their policy to release ChatGPT rather hastily in response to a false rumor that Anthropic was about to release the similar product that it had created. Mallaby concludes:
Once ChatGPT had been embraced by consumers, the incentives for gradualism crumbled.
Five months later, Hassabis told Mallaby:
This is wartime, OpenAI and Microsoft have literally parked the tanks on the lawn.
Mustafa Suleyman
The book significantly raised my opinion of Suleyman. My review of his book was unenthusiastic.
Apparently he sounds a bit more forceful when talking to CEOs, and consistently focused on some medium to large risks associated with AI.
He was the most eager of DeepMind leaders to pressure Google into agreeing on an ethics and safety board.
Alas, he doesn't have the political skills to be effective at his desired role.
In my review I dismissed his concern that "a jobs recession will crater tax receipts". I recently examined this more carefully, and concluded that Suleyman's concern's are reasonable, and that there's likely to be a few months or years of COVID-level stress before government revenues become ample.
My Complaints
Most of the book seems carefully researched. Mallaby understands AI well enough that I don't have any criticisms there.
Here are some flaws that annoyed me, but which don't detract much from the book.
... Homo sapiens acquired the capacity for abstract thought, some seventy thousand years ago
That's misleading at best. There's significant evidence of earlier abstract thought. It seems likely that abstraction emerged gradually, over a long period.
Mallaby wrote "Oxford's Future of Life Institute", when he meant to refer to the Future of Humanity Institute.
In 2019, GPT-2 had barely been able to count up to five; it was impressive in the same way that a four-year-old might be. In 2020, GPT-3 was like a nine-year-old
I like attempts to explain AI progress by estimating the equivalent human ages, but my experience indicates that AI is advancing at more like two years of age per calendar year. Mallaby's estimates seem like they're the result of cherry-picking the most impressive responses. I focus somewhat on their planning abilities, whereas I doubt that Mallaby puts any weight on those abilities. I say AI is just now reaching the nine-year-old level.
Mallaby quotes Hinton as saying "There aren't any examples of more intelligent things being controlled by less intelligent things".
While I approve of Mallaby's concerns about this kind of risk, he ought to have pointed out that Hinton exaggerated. Some counter-examples:
- Toxoplasma gondii controlling mice
- Zombie Ant Fungus
- The "pointy-haired boss" trope
- Cats training humans to feed and pet them
- The hunger-signaling part of the human mind overruling the part that wants to lose weight
Concluding Thoughts
The book provides good insights into why AI became a race rather than a Manhattan Project.
Mallaby leans toward the conclusion that a race was inevitable. I'm not convinced.
This seems wrong:
The US-China race dynamic made it almost impossible to stanch the intra-US race dynamic.
(I'm unsure whether Mallaby endorsed that view, or was merely reporting Hassabis's view.) We know from nuclear non-proliferation treaties that cooperation is possible between nations that are more hostile than the current US-China conflict. This quote comes after Mallaby has devoted plenty of analysis that points toward Altman and Musk triggering a race for reasons that seem completely unrelated to China. I remain confused as to why people treat China as anything more than a rationalization for pursuing a competition that they want for less noble reasons.
The book convinced me that Hassabis is making a mistake by trying to be both Einstein and Ender.
Hassabis has achieved partial success at Einstein-level science, while, like Einstein, working at another job.
But being Ender is really a full-time job. It currently looks like Hassabis is not narrowly enough focused on being Ender to be the pivotal person in AI's destiny to remake the world.
Mallaby hints at Oppenheimer as a role model that might be appropriate for Hassabis. Maybe the world would be better off if Hassabis had aimed more for that role. Mallaby seems to say it's much too late to try that.
More Quotes
"I think political systems will use it to terrorize people," Hinton answered.
"Then why are you doing the research?" Bostrom asked.
"I could give you the usual arguments," Hinton replied. "But the truth is that the prospect of discovery is too sweet."
According to David Silver:
When Demis talked about the influence of his mother and his horror of manipulating others, he meant it. But it was one thing to abhor the idea of controlling colleagues. Given his Jedi-level charisma, it was quite another to avoid it.
I'm puzzled. I don't see signs of Jedi-level charisma.
Because of the black-box nature of these networks, the scientists who built them often sounded like surprised parents. Look, my child can say so many more words than just a week ago!
Hassabis was never part of the Singularity crowd. But he shared the assumption that a "singleton" scenario provided the best shot at safe AI ... He imagined convening a band of elite scientists in a secluded research center, there to focus single-mindedly on the birthing of safe superintelligence. This mash-up of Ender's clandestine space station and the Manhattan Project's secret encampment in New Mexico bubbled up in conversation periodically
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.
May 3, 2026
All I could think about was how much better this would’ve been if it was written by Walter Isaacson
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.
May 3, 2026
This accessible book has good narrative flow and explains in clear language how Generative AI works through such techniques as Reinforcement Learning. The book looks at AI through the lens of Demis Hassabis, the founder of DeepMind, which was acquired by Google. He seems genuinely motivated by the scientific potential for AI and within Google appears to occupy the same place wrangling researchers as Oppenheimer did in the Manhattan project. Speaking as a Brit, I am very pleased that he has almost single handedly transformed London and King’s Cross into the world’s third major AI powerhouse after Silicon Valley and China, helping rebalance our economy away from finance and professional services.
While the book portrays Demis in a good light, it does point out some of his flaws. He was blindsided by transformer architecture and the ability to discover truth though Large Language Models, because of his neuroscience training and his focus on Reinforcement learning. This failure allowed OpenAI to steal a march on Google. I also find his slightly sniffy attitude towards using AI to create consumer products rather than for grand scientific endeavours is unhelpful. Consumer products that gain wide spread use are valuable otherwise people wouldn’t use them, and I suspect it will be innovation aimed at consumers, that will enable bigger breakthroughs, not vice versa. Evidence of this is that AI and Large Language Models are only possible because of the consumer internet. The author also takes on face value the importance of the AlphaFold protein folding discovery. Impressive as this was, the reality is that a few years on this has not led to new drug discoveries of note or sped up the rate of drug discovery. The main barrier here is not identifying molecules to research but the cost and duration of human trials.
The book also talks about Demis’s fellow founder of DeepMind Mustafa Suleyman. It’s remarkable that these two children of working class immigrants from North London were schoolhood friends and founded one of the most important tech companies in the world. Their economic contribution to the UK is a major rebuttal to those who wish the UK closed the door on immigration. However, I was puzzled by what Mustafa Suleyman, who has no background in computing, brought to the table. He struck me as someone who fitted snugly into Corbonite wing of the Labour Part, so how he has ended up as head of AI at MSFT is a mystery. The book doesn’t really explain.
The book also dwells on the safety issues of AI which I find extremely dull and a lot of hand-wringing over nothing. Others may disagree. The problem is that 3 years ago labs were fretting about releasing models to the world, that were far less capable than today, and yet their release has not caused any mishaps. It strikes me more as the hubris of the researchers and a marketing ploy than a genuine issue to worry about. Maybe AI will run amok one day, but the technology, beyond perhaps giving dodgy advice, seems harmless.
While the book portrays Demis in a good light, it does point out some of his flaws. He was blindsided by transformer architecture and the ability to discover truth though Large Language Models, because of his neuroscience training and his focus on Reinforcement learning. This failure allowed OpenAI to steal a march on Google. I also find his slightly sniffy attitude towards using AI to create consumer products rather than for grand scientific endeavours is unhelpful. Consumer products that gain wide spread use are valuable otherwise people wouldn’t use them, and I suspect it will be innovation aimed at consumers, that will enable bigger breakthroughs, not vice versa. Evidence of this is that AI and Large Language Models are only possible because of the consumer internet. The author also takes on face value the importance of the AlphaFold protein folding discovery. Impressive as this was, the reality is that a few years on this has not led to new drug discoveries of note or sped up the rate of drug discovery. The main barrier here is not identifying molecules to research but the cost and duration of human trials.
The book also talks about Demis’s fellow founder of DeepMind Mustafa Suleyman. It’s remarkable that these two children of working class immigrants from North London were schoolhood friends and founded one of the most important tech companies in the world. Their economic contribution to the UK is a major rebuttal to those who wish the UK closed the door on immigration. However, I was puzzled by what Mustafa Suleyman, who has no background in computing, brought to the table. He struck me as someone who fitted snugly into Corbonite wing of the Labour Part, so how he has ended up as head of AI at MSFT is a mystery. The book doesn’t really explain.
The book also dwells on the safety issues of AI which I find extremely dull and a lot of hand-wringing over nothing. Others may disagree. The problem is that 3 years ago labs were fretting about releasing models to the world, that were far less capable than today, and yet their release has not caused any mishaps. It strikes me more as the hubris of the researchers and a marketing ploy than a genuine issue to worry about. Maybe AI will run amok one day, but the technology, beyond perhaps giving dodgy advice, seems harmless.
May 4, 2026
Effectively a detailed, insider-access biography of Demis Hassabis and his firm Deep Mind from a journalist and non-fiction author (who has previously written on the history of both the hedge fund and venture capital industries and a biography of Alan Greenspan).
The account I thought was particularly strong on Alpha-Go – giving a really clear and enlightening account of the different methods used to perfect different stages of Deep Mind’s Go-playing AI – probably the clearest account I have read of AI developments. I found the account of Protein folding and the resulting Nobel Prize a little harder (perhaps due to a lack of even basic knowledge of molecular biology) but again the way in which this developed from the idea of treating thing as a game (which not just fitted Alpha-Go but went back further to Habassis’s pre-University days in gaming) was really interesting.
I also found the politics with Google interesting – albeit perhaps more in overview than the blow-by-blow accounts given.
There was an tantalising end to the book when Hassabis presents himself as a champion of Turing’s approach and as a clear opponent of Roger Penrose’s ideas which have un turn inspired quantum computing.
The author – perhaps given his English pedigree as a form of national self-effacement does rather make in my view a little too much of the apparent obscurity of the UK/England/London.
The “unprecedented access” (as per the blurb) that the author gained – particularly to Hassabis – does at times make this perhaps stary a little to far into hagiography, at least when it comes to Hassabis’s ultimate motivations.
Although the book does reluctantly concede a number of his mistakes – including a slowness to go-to-market (with Open AI stealing a march with Chat-GPT) and too deep a commitment to reinforcement learning (including what was for their early stages the huge breakthrough of the model effectively providing its own reinforcement learning – such as AlphaGo training itself on games it simulated) and (although I felt that it was perhaps clearer from Parmy Olson’s more even handed “Supremacy” – which deliberately sets out to present competing visions) obsession with duplicating the workings of the human brain – which lead to a underplaying of the possibilities of vast scaling when applied to large language models. Related to this Hassabis and his team are obsessed with the concept of “grounding” (in real world issues) and that it necessarily meant detailed human feedback to train models without perhaps realising that the canon of information ingested by the scaled transformer-powered LLMs (the transformers ironically perfected by Deep Mind’s parent Google) effectively would produce such grounding as a by-product. There is also a related issue that it seems Reinforcement Learning does not respond so well to scaling as it does not seem to exhibit the “transfer learning” that has really powered LLM models.
Overall a very worthwhile read.
May 3, 2026
Dear Sebastian,
May I call you Sebastian —
I finished *The Infinity Machine* with the rare recognition of alignment, not with your conclusions, but with the way you chose to see.
What struck me first was not the subject, but your restraint. You allow the system to emerge. You resist the urge to declare meaning before the reader has earned it. In a moment when AI is being flattened into inevitability or fear, you chose patience, and in doing so, you preserved truth.
You wrote a story about extraordinary individuals, Demis, Shane, and the cast orbiting DeepMind, but you did not let them become mythology. You held them inside constraints: capital, competition, institutions, and geopolitics. The result is something rarer than a "great tech book." It is a credible map of a system under pressure.
I read this from inside the system you are describing, which made the structural honesty land harder. The race condition is no longer metaphorical. It is structural. Open versus proprietary is no longer an ideological question; it is entangled with nation-state incentives. Even the moments that might invite caricature, Zuckerberg, for instance, you render with enough fidelity to show why his moment found him unprepared, without reducing him to something easier.
And then there is Demis at the end.
That admission, that the realization of a fifteen-year dream does not feel the way it was supposed to, may be the most honest line in the book. Not because it is surprising, but because it reveals something fundamental: the conditions that allow a dream to be pursued are not the same conditions under which it must be lived.
Success, in this case, dissolves the boundary that once made it meaningful.
If I were to offer one tension back to you, it is this: the book is already, inevitably, dated. Not in its insight, but in its timing. The frontier is now moving faster than narrative can stabilize it. The traditional arc, research, writing, and publication, cannot keep pace with a domain where the ground shifts every quarter.
I do not experience that as a flaw. I experience it as a signal.
It suggests that what you have done here, patient, multi-perspective, structurally honest interpretation, is not just valuable. It is necessary. And increasingly rare.
The question it leaves me with is whether this kind of work can evolve into a living form, something that maintains your discipline, your neutrality, your earned authority, but moves at the speed of the systems it is describing.
Because the world you have mapped is not settling. It is accelerating. And the people making decisions inside it, builders, operators, institutions, need exactly this kind of clarity, not once, but continuously.
What will survive is not the snapshot but the discipline. The frontier you mapped will be unrecognizable in five years. The way you mapped it should not be.
Thank you for the work.
Doug
May I call you Sebastian —
I finished *The Infinity Machine* with the rare recognition of alignment, not with your conclusions, but with the way you chose to see.
What struck me first was not the subject, but your restraint. You allow the system to emerge. You resist the urge to declare meaning before the reader has earned it. In a moment when AI is being flattened into inevitability or fear, you chose patience, and in doing so, you preserved truth.
You wrote a story about extraordinary individuals, Demis, Shane, and the cast orbiting DeepMind, but you did not let them become mythology. You held them inside constraints: capital, competition, institutions, and geopolitics. The result is something rarer than a "great tech book." It is a credible map of a system under pressure.
I read this from inside the system you are describing, which made the structural honesty land harder. The race condition is no longer metaphorical. It is structural. Open versus proprietary is no longer an ideological question; it is entangled with nation-state incentives. Even the moments that might invite caricature, Zuckerberg, for instance, you render with enough fidelity to show why his moment found him unprepared, without reducing him to something easier.
And then there is Demis at the end.
That admission, that the realization of a fifteen-year dream does not feel the way it was supposed to, may be the most honest line in the book. Not because it is surprising, but because it reveals something fundamental: the conditions that allow a dream to be pursued are not the same conditions under which it must be lived.
Success, in this case, dissolves the boundary that once made it meaningful.
If I were to offer one tension back to you, it is this: the book is already, inevitably, dated. Not in its insight, but in its timing. The frontier is now moving faster than narrative can stabilize it. The traditional arc, research, writing, and publication, cannot keep pace with a domain where the ground shifts every quarter.
I do not experience that as a flaw. I experience it as a signal.
It suggests that what you have done here, patient, multi-perspective, structurally honest interpretation, is not just valuable. It is necessary. And increasingly rare.
The question it leaves me with is whether this kind of work can evolve into a living form, something that maintains your discipline, your neutrality, your earned authority, but moves at the speed of the systems it is describing.
Because the world you have mapped is not settling. It is accelerating. And the people making decisions inside it, builders, operators, institutions, need exactly this kind of clarity, not once, but continuously.
What will survive is not the snapshot but the discipline. The frontier you mapped will be unrecognizable in five years. The way you mapped it should not be.
Thank you for the work.
Doug
April 23, 2026
So, in putting together my review, I put my key notes and takeaways for discussions with others into four different LLM's. Fittingly, I liked Gemini's best! It is what I've pasted below with only a few minor edits that downplay superlatives.
Review: The Infinity Machine by Sebastian Mallaby
Overview
For readers navigating the rapidly expanding library of literature on artificial intelligence, Sebastian Mallaby’s The Infinity Machine stands out as an essential read. It successfully fills a gap in the current landscape, specifically detailing Google's foundational role and ongoing influence in the development of Large Language Models (LLMs) and the broader AI sector.
The Intellectual Foundations of AI
One of the most fascinating aspects of the book is its exploration of the intellectual seeds that sparked today's AI revolution. Mallaby highlights how Douglas Hofstadter’s seminal work, Gödel, Escher, Bach, profoundly influenced founder Demis Hassabis during his formative years. It is a compelling revelation that underscores a common thread among the "movers and shakers" of the tech world: the most disruptive technological advancements often stem from deep, interdisciplinary philosophical and mathematical curiosity.
A Dual Biography: The Man and the Machine
Crucially, Mallaby avoids the trap of writing a narrow, personality-driven narrative. While it serves as an excellent biography of Demis Hassabis, it is equally a "biography" of Large Language Models themselves. The book does an exceptional job of tracking the evolutionary timeline of the technology and, importantly, draws a clear and necessary distinction between the specific mechanics of LLMs and the broader, overarching concept of Artificial Intelligence.
The Moral Crucible: Ethics vs. The Rush to Market
Perhaps the most gripping element of The Infinity Machine is its candid look at the moral dilemmas facing these innovators. The narrative expertly captures the profound tension between ethical caution and the intense pressure to rush products to market. The book draws stark, sobering analogies between the development of AGI and the creation of the nuclear bomb, summarizing the prevailing industry anxiety: "If we don't do it, some bad guy will." The burden placed on these leaders is immense. They are caught in a modern-day Oppenheimer dilemma, forced to simultaneously protect their companies' intellectual capital, safeguard their national interests, and ultimately, protect humanity itself.
Conclusion
The Infinity Machine is a thought-provoking read that moves beyond the code to examine the philosophy, history, and heavy ethical burdens of the AI race. I highly recommend it for anyone looking to understand the true stakes of the intelligence era.
Review: The Infinity Machine by Sebastian Mallaby
Overview
For readers navigating the rapidly expanding library of literature on artificial intelligence, Sebastian Mallaby’s The Infinity Machine stands out as an essential read. It successfully fills a gap in the current landscape, specifically detailing Google's foundational role and ongoing influence in the development of Large Language Models (LLMs) and the broader AI sector.
The Intellectual Foundations of AI
One of the most fascinating aspects of the book is its exploration of the intellectual seeds that sparked today's AI revolution. Mallaby highlights how Douglas Hofstadter’s seminal work, Gödel, Escher, Bach, profoundly influenced founder Demis Hassabis during his formative years. It is a compelling revelation that underscores a common thread among the "movers and shakers" of the tech world: the most disruptive technological advancements often stem from deep, interdisciplinary philosophical and mathematical curiosity.
A Dual Biography: The Man and the Machine
Crucially, Mallaby avoids the trap of writing a narrow, personality-driven narrative. While it serves as an excellent biography of Demis Hassabis, it is equally a "biography" of Large Language Models themselves. The book does an exceptional job of tracking the evolutionary timeline of the technology and, importantly, draws a clear and necessary distinction between the specific mechanics of LLMs and the broader, overarching concept of Artificial Intelligence.
The Moral Crucible: Ethics vs. The Rush to Market
Perhaps the most gripping element of The Infinity Machine is its candid look at the moral dilemmas facing these innovators. The narrative expertly captures the profound tension between ethical caution and the intense pressure to rush products to market. The book draws stark, sobering analogies between the development of AGI and the creation of the nuclear bomb, summarizing the prevailing industry anxiety: "If we don't do it, some bad guy will." The burden placed on these leaders is immense. They are caught in a modern-day Oppenheimer dilemma, forced to simultaneously protect their companies' intellectual capital, safeguard their national interests, and ultimately, protect humanity itself.
Conclusion
The Infinity Machine is a thought-provoking read that moves beyond the code to examine the philosophy, history, and heavy ethical burdens of the AI race. I highly recommend it for anyone looking to understand the true stakes of the intelligence era.
May 7, 2026
The book is well written...
First, I should say that I did like it. A history of the AI race through the lens of one of the Cerberus heads propelling things along is fascinating, and though I was loosely familiar with Hassabis through AlphaGo, I was glad to learn more.
But the author used enough glaze to drown a Krispy Kreme. He _loves_ Hassabis, as a close friend, admiring him and his intellect and his apparently magnetic personality. Not necessarily a bad thing, but when paired, then, with a seeming infatuation or at least fascination with AI, and adopting Hassabis' saccharine view that AI will be primarily a force for good (I have my doubts, and by the end of the book, in ~2024, it seems Hassabis himself does too), and that despite all the dangers and threats laid out in the book that we must keep plowing ahead no matter what (is at least my perception of how the author felt)... It was hard to square that circle, when much of the book is spent talking about AI doomerism, with, what I had previously thought to be absurd worries and come away believing that greatest technologists of our age are actively creating a technology that, even if not threatening on its own, will be powerful enough to bring out a new era of war and consolidation of power among those with means and access to the technology.
So it's well written, but I can't help think... what was I supposed to come away with? Compared to many of the people I know, I trend more pro-AI than they do, if still trepidatious. If the book left me feeling this way, I wonder what it's intent was.
Also, I found the regular references to science fiction both charming and highly alarming, when Hassabis compares himself to Ender of Ender's game, and then the author runs with it for the entire book. He references a Dyson Sphere, which while part of the sci-fi zeitgeist, I believe the original scientific paper was written as a joke, and it all just makes me realize that many of the tales that can be read as both or either cautionary and visionary are the media diet that has partly inspired the AI wave, and I can't help but realize that few such stories have favorable endings for humanity.
The book made me _feel_ a whole lot, and I was engaged the whole time, and the writing is clear, compelling, and paints a very comprehensive picture. I just wish the feeling I was left with was less dread and anxiety. Earlier parts were more optimistic, about early AI, and I felt encouraged and excited (although I was familiar with many of those parts of the story already). Unfortunately, though, I knew enough to always know what would be coming around the bend, and the dread of the present managed to poison both the past and the future presented in the book.
First, I should say that I did like it. A history of the AI race through the lens of one of the Cerberus heads propelling things along is fascinating, and though I was loosely familiar with Hassabis through AlphaGo, I was glad to learn more.
But the author used enough glaze to drown a Krispy Kreme. He _loves_ Hassabis, as a close friend, admiring him and his intellect and his apparently magnetic personality. Not necessarily a bad thing, but when paired, then, with a seeming infatuation or at least fascination with AI, and adopting Hassabis' saccharine view that AI will be primarily a force for good (I have my doubts, and by the end of the book, in ~2024, it seems Hassabis himself does too), and that despite all the dangers and threats laid out in the book that we must keep plowing ahead no matter what (is at least my perception of how the author felt)... It was hard to square that circle, when much of the book is spent talking about AI doomerism, with, what I had previously thought to be absurd worries and come away believing that greatest technologists of our age are actively creating a technology that, even if not threatening on its own, will be powerful enough to bring out a new era of war and consolidation of power among those with means and access to the technology.
So it's well written, but I can't help think... what was I supposed to come away with? Compared to many of the people I know, I trend more pro-AI than they do, if still trepidatious. If the book left me feeling this way, I wonder what it's intent was.
Also, I found the regular references to science fiction both charming and highly alarming, when Hassabis compares himself to Ender of Ender's game, and then the author runs with it for the entire book. He references a Dyson Sphere, which while part of the sci-fi zeitgeist, I believe the original scientific paper was written as a joke, and it all just makes me realize that many of the tales that can be read as both or either cautionary and visionary are the media diet that has partly inspired the AI wave, and I can't help but realize that few such stories have favorable endings for humanity.
The book made me _feel_ a whole lot, and I was engaged the whole time, and the writing is clear, compelling, and paints a very comprehensive picture. I just wish the feeling I was left with was less dread and anxiety. Earlier parts were more optimistic, about early AI, and I felt encouraged and excited (although I was familiar with many of those parts of the story already). Unfortunately, though, I knew enough to always know what would be coming around the bend, and the dread of the present managed to poison both the past and the future presented in the book.
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 21, 2026
Sebastian Mallaby’s The Infinity Machine is the rare technology biography that reads like a history of the future. Through the life of Demis Hassabis, Mallaby tells the story of how artificial intelligence moved from academic curiosity to one of the defining forces of the twenty-first century. The result is a gripping narrative about ambition, intellect, and the race to build machines that can think.
The book traces Hassabis’s arc from child chess prodigy to founder of DeepMind, placing landmark moments such as the 2016 AlphaGo match against Lee Sedol in their broader historical context. What makes the story powerful is Mallaby’s extraordinary reporting. He conducted extensive interviews with Hassabis and dozens of collaborators and rivals, producing what critics describe as an “insightful portrait” of the scientist and the high-stakes AI race now reshaping global power. 
Mallaby writes with the clarity of a historian and the pacing of a thriller. The book captures the tension between scientific discovery and Silicon Valley ambition, showing how DeepMind pursued artificial general intelligence while navigating massive funding pressures, competition for talent, and the ethical questions surrounding powerful new technology. 
What stands out most is the scale of the story. This is not simply a biography. It is a narrative about a turning point in human history. As one description puts it, the book becomes a “revelation-packed portrait of a singular mind” and a reckoning with the AI revolution itself. 
Mallaby succeeds because he understands that the real subject is curiosity. Hassabis’s lifelong obsession with understanding intelligence drives the narrative forward, from childhood games of chess to machines that can solve protein structures and master complex games. The stakes feel immense because they are. The quest to build thinking machines may become one of the most consequential scientific projects ever attempted.
The Infinity Machine is essential reading for anyone trying to understand the present moment in technology. It combines deep reporting, intellectual seriousness, and narrative momentum into one of the most compelling books yet written about the rise of artificial intelligence. A brilliant biography and a sweeping history of the AI age.
The book traces Hassabis’s arc from child chess prodigy to founder of DeepMind, placing landmark moments such as the 2016 AlphaGo match against Lee Sedol in their broader historical context. What makes the story powerful is Mallaby’s extraordinary reporting. He conducted extensive interviews with Hassabis and dozens of collaborators and rivals, producing what critics describe as an “insightful portrait” of the scientist and the high-stakes AI race now reshaping global power. 
Mallaby writes with the clarity of a historian and the pacing of a thriller. The book captures the tension between scientific discovery and Silicon Valley ambition, showing how DeepMind pursued artificial general intelligence while navigating massive funding pressures, competition for talent, and the ethical questions surrounding powerful new technology. 
What stands out most is the scale of the story. This is not simply a biography. It is a narrative about a turning point in human history. As one description puts it, the book becomes a “revelation-packed portrait of a singular mind” and a reckoning with the AI revolution itself. 
Mallaby succeeds because he understands that the real subject is curiosity. Hassabis’s lifelong obsession with understanding intelligence drives the narrative forward, from childhood games of chess to machines that can solve protein structures and master complex games. The stakes feel immense because they are. The quest to build thinking machines may become one of the most consequential scientific projects ever attempted.
The Infinity Machine is essential reading for anyone trying to understand the present moment in technology. It combines deep reporting, intellectual seriousness, and narrative momentum into one of the most compelling books yet written about the rise of artificial intelligence. A brilliant biography and a sweeping history of the AI age.
April 26, 2026
Sebastian Mallaby’s “The Infinity Machine” is not merely a biography of Demis Hassabis; it is a map of the strange country where chess, neuroscience, venture capital, corporate empire, and metaphysics meet. Mallaby presents Hassabis as a North London child prodigy, elite chess player, game designer, neuroscientist, DeepMind cofounder, and later Nobel-winning AI leader, a life arc that would seem overstuffed if fiction tried to invent it. The book’s great strength is that it makes artificial intelligence feel less like a product category than an intellectual obsession. Hassabis’s dream is not just to build clever software, but to create systems that can reveal nature’s hidden order, from protein folding to physics itself. Mallaby is especially good at showing why DeepMind mattered: AlphaGo dramatized machine intuition, AlphaFold turned AI into a scientific instrument, and ChatGPT’s rise forced Google DeepMind to confront a very different path to artificial general intelligence. This is also a study in power. The reader sees how idealism mutates when it enters the arena of compute costs, corporate bureaucracy, talent wars, and geopolitical consequence. Mallaby’s Hassabis is neither cartoon savior nor reckless villain. He is more interesting than either: a disciplined, almost monkish builder who believes intelligence can be engineered, yet who cannot fully control the social and political forces unleashed by that effort. The book sometimes seems too admiring of its central figure, and readers looking for a sharper prosecutorial case against AI ambition may want more skepticism. But its educational value is immense. Mallaby explains the stakes without flattening the science, and he restores a sense of wonder to a debate often trapped between marketing hype and apocalypse talk. “The Infinity Machine” is essential reading for anyone trying to understand why AI’s future is being driven by a handful of brilliant, fallible people.
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 18, 2026
I picked this book to read because I wanted to become more familiar with the development of AI. Since this book was published just a few months ago it seemed like a good bet that it would be current. It did serve as a good introduction to the history of the development of AI.
One thing that became apparent to me early in the book was that the author (Sebastian Mallaby) was extremely sympathetic to Demis Hassabis, and Hassabis was very agreeable to having a hagiographic book written about him. I then found out that Hassabis also stared in a documentary film. Hassabis is definitely a large ego, and as I read about the other key players in this arena it became clear that most of the AI players have hugh enormous egos. These people have enormous power and wealth right now, and I don’t believe they have the character to use that power wisely. Of the largest AI companies and the autocrats that run them, of course Elon Musk is the craziest but Sam Altman is probably the most dishonest. I believe that Mark Zuckerberg is largely amoral. Hassabis and Dario Amodei seem to be the least bad actors. Larry Page and Sergey Brin do not seem worthy of a lot of trust to do the best for humanity. We should not assume that these men will use good judgment in the application of the power these AI agents are capable of.
One thing that became apparent to me early in the book was that the author (Sebastian Mallaby) was extremely sympathetic to Demis Hassabis, and Hassabis was very agreeable to having a hagiographic book written about him. I then found out that Hassabis also stared in a documentary film. Hassabis is definitely a large ego, and as I read about the other key players in this arena it became clear that most of the AI players have hugh enormous egos. These people have enormous power and wealth right now, and I don’t believe they have the character to use that power wisely. Of the largest AI companies and the autocrats that run them, of course Elon Musk is the craziest but Sam Altman is probably the most dishonest. I believe that Mark Zuckerberg is largely amoral. Hassabis and Dario Amodei seem to be the least bad actors. Larry Page and Sergey Brin do not seem worthy of a lot of trust to do the best for humanity. We should not assume that these men will use good judgment in the application of the power these AI agents are capable of.
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.
May 11, 2026
I read The Infinity Machine by Sebastian Mallaby (©2026), enjoyed it, and gave it five stars. Since I already knew a bit about AI, I found it an approachable and rewarding read. The book is a deeply researched yet highly readable account of the race to build artificial general intelligence, centered on Demis Hassabis and the rise of DeepMind. Mallaby skillfully blends biography, corporate history, and science writing to show how a chess prodigy and neuroscientist became one of the leading architects of modern AI. The narrative is especially compelling in its coverage of landmark breakthroughs such as AlphaGo and AlphaFold, while also examining the ethical dilemmas, rivalries, and immense ambitions fueling the AI revolution. Mallaby’s access to Hassabis and many of the field’s major figures gives the story unusual depth and immediacy. Along the way, the book offers clear explanations of reinforcement learning, transformers, protein folding, and other core AI concepts that help readers better understand how these systems actually work. It succeeds both as an engaging human story and as an accessible guide to one of the most consequential technological races of our time.
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