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Demis Hassabis explains Isomorphic Labs' 10x drug discovery engine, and why solving disease could also create a multi-hundred-billion-dollar business "If you ask me, the number one thing AI can do for humanity, it would be to solve hundreds of terrible diseases. I can't imagine a better use case for...

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46,918 views • 1 month ago •via X (Twitter)

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📁 Demis Hassabis, CEO of DeepMind, says AI could be the most beneficial technology humanity has ever created. He explains that its potential includes curing diseases, accelerating drug discovery, and solving core biological problems, as seen with AlphaFold and Isomorphic. But he warns the outcome is not guaranteed. He compares it to an Industrial Revolution ten times bigger and ten times faster, bringing massive transformation and unavoidable disruption.
0:53

📁 Demis Hassabis, CEO of DeepMind, says AI could be the most beneficial technology humanity has ever created. He explains that its potential includes curing diseases, accelerating drug discovery, and solving core biological problems, as seen with AlphaFold and Isomorphic. But he warns the outcome is not guaranteed. He compares it to an Industrial Revolution ten times bigger and ten times faster, bringing massive transformation and unavoidable disruption.

Jon Hernandez

21,888 views • 7 months ago

The Nobel Prize winner just said something big. The biggest AI opportunity isn't at Google. It's with you. Demis Hassabis says a kid today could start a multi-billion dollar business by applying AI to something new. The frontier labs can't explore it all. That gap is your opportunity.
1:14

The Nobel Prize winner just said something big. The biggest AI opportunity isn't at Google. It's with you. Demis Hassabis says a kid today could start a multi-billion dollar business by applying AI to something new. The frontier labs can't explore it all. That gap is your opportunity.

Alvin Foo

70,682 views • 4 months ago

Demis Hassabis, Nobel laureate and CEO of Google DeepMind: "I think one day, maybe we can cure all disease with the help of AI. Within the next decade. I don't see why not." This is not a hype account. It is the man whose AI mapped 200 million protein structures in a single year, work that used to take a lifetime. A new drug normally takes ten years and billions of dollars. He thinks AI cuts that to months, maybe weeks. Musk said your future doctor will be AI. Hassabis is saying the disease itself might not be waiting for you by then. We treated illness as the fixed price of being alive. Two Nobel-level labs are now quietly saying that price was a choice.
4:57

Demis Hassabis, Nobel laureate and CEO of Google DeepMind: "I think one day, maybe we can cure all disease with the help of AI. Within the next decade. I don't see why not." This is not a hype account. It is the man whose AI mapped 200 million protein structures in a single year, work that used to take a lifetime. A new drug normally takes ten years and billions of dollars. He thinks AI cuts that to months, maybe weeks. Musk said your future doctor will be AI. Hassabis is saying the disease itself might not be waiting for you by then. We treated illness as the fixed price of being alive. Two Nobel-level labs are now quietly saying that price was a choice.

Casper

26,072 views • 1 month ago

Demis Hassabis, the Nobel Prize winner who runs Google DeepMind just described the most consequential project on earth, and most people have no idea it exists. The project is called Isomorphic Labs and the goal is to end the way drugs have been developed for the last century. Here is the problem it is trying to solve. Developing a single drug today takes an average of 10 years, costs billions of dollars, and fails 90 percent of the time before it ever reaches a patient. Of every 10 drugs that enter clinical trials, only one makes it through. The other nine years of work, the other billions of dollars, the other scientific careers, gone. Hassabis believes AI can collapse that entire process from identifying a disease target to designing a compound that binds to it, predicts how it behaves in the body, and minimizes side effects , end to end, on a computer, before a single experiment is run. The foundation is AlphaFold, the AI system that solved one of biology's hardest problems predicting the 3D structure of every protein in the human body and won him the Nobel Prize in Chemistry in 2024. But knowing a protein's shape is only one part of designing a drug. Isomorphic is building what Hassabis describes as adjacent systems , AlphaFold 3, AlphaFold 4, and now a unified model called IsoDDE , that take the next steps. From designing the actual chemical compound that binds to the protein, predicting its binding strength, identifying new pockets to target that no one has ever found before. IsoDDE more than doubles the accuracy of AlphaFold 3 on the hardest protein-ligand prediction benchmarks that exist. Isomorphic is already running 18 to 19 live drug programs, cardiovascular disease, cancer, immunology in partnership with Eli Lilly, Novartis, and Johnson and Johnson. The first human clinical trial of a fully AI-designed drug is expected by the end of 2026. If that trial succeeds, it will be the first time in history that a drug put into a human body was designed not by a team of chemists working for a decade but by an AI working for months. Hassabis's long-term vision is even more direct, one day you describe a disease, click a button, and a drug blueprint comes out the other side. AI will solve almost all diseases within 10 years.
1:20

Demis Hassabis, the Nobel Prize winner who runs Google DeepMind just described the most consequential project on earth, and most people have no idea it exists. The project is called Isomorphic Labs and the goal is to end the way drugs have been developed for the last century. Here is the problem it is trying to solve. Developing a single drug today takes an average of 10 years, costs billions of dollars, and fails 90 percent of the time before it ever reaches a patient. Of every 10 drugs that enter clinical trials, only one makes it through. The other nine years of work, the other billions of dollars, the other scientific careers, gone. Hassabis believes AI can collapse that entire process from identifying a disease target to designing a compound that binds to it, predicts how it behaves in the body, and minimizes side effects , end to end, on a computer, before a single experiment is run. The foundation is AlphaFold, the AI system that solved one of biology's hardest problems predicting the 3D structure of every protein in the human body and won him the Nobel Prize in Chemistry in 2024. But knowing a protein's shape is only one part of designing a drug. Isomorphic is building what Hassabis describes as adjacent systems , AlphaFold 3, AlphaFold 4, and now a unified model called IsoDDE , that take the next steps. From designing the actual chemical compound that binds to the protein, predicting its binding strength, identifying new pockets to target that no one has ever found before. IsoDDE more than doubles the accuracy of AlphaFold 3 on the hardest protein-ligand prediction benchmarks that exist. Isomorphic is already running 18 to 19 live drug programs, cardiovascular disease, cancer, immunology in partnership with Eli Lilly, Novartis, and Johnson and Johnson. The first human clinical trial of a fully AI-designed drug is expected by the end of 2026. If that trial succeeds, it will be the first time in history that a drug put into a human body was designed not by a team of chemists working for a decade but by an AI working for months. Hassabis's long-term vision is even more direct, one day you describe a disease, click a button, and a drug blueprint comes out the other side. AI will solve almost all diseases within 10 years.

Milk Road AI

36,062 views • 4 months ago

Demis Hassabis: If you know the structure of a protein, the real question becomes—where will your drug bind, and what will it actually do? That’s where the next wave of AI comes in. Not just predicting structures, but modeling interactions, outcomes, and real biological impact. At Isomorphic Labs, this is already happening—with 17 active drug programs and partnerships with giants like Eli Lilly and Novartis. The goal? Scale that to 100. This is a fundamental shift in how medicine gets built. Instead of slow, expensive trial-and-error in wet labs, AI allows researchers to run thousands of hypotheses in silico—hundreds to thousands of times more efficiently. The wet lab becomes validation, not exploration. Drug discovery is turning into a computational problem. Faster cycles, smarter predictions, and potentially massive breakthroughs in human health.
0:59

Demis Hassabis: If you know the structure of a protein, the real question becomes—where will your drug bind, and what will it actually do? That’s where the next wave of AI comes in. Not just predicting structures, but modeling interactions, outcomes, and real biological impact. At Isomorphic Labs, this is already happening—with 17 active drug programs and partnerships with giants like Eli Lilly and Novartis. The goal? Scale that to 100. This is a fundamental shift in how medicine gets built. Instead of slow, expensive trial-and-error in wet labs, AI allows researchers to run thousands of hypotheses in silico—hundreds to thousands of times more efficiently. The wet lab becomes validation, not exploration. Drug discovery is turning into a computational problem. Faster cycles, smarter predictions, and potentially massive breakthroughs in human health.

Ian Miles Cheong

93,975 views • 5 months ago

The Demis Hassabis HUGE* Conversation (in full) 00:00 What is the hardest problem AI has already solved? 12:30 What is the cutting edge of drug discovery with AI? 21:53 Why did Demis say he “would have left AI in the lab longer”? 43:09 How should militaries use AI? 50:13 What can humans do that AI won't? 58:17 What does Demis Hassabis want his legacy to be? (And 1:04:40 Can I beat Demis at Jenga?) Recorded March 5, 2026 in London.
1:05:10

The Demis Hassabis HUGE* Conversation (in full) 00:00 What is the hardest problem AI has already solved? 12:30 What is the cutting edge of drug discovery with AI? 21:53 Why did Demis say he “would have left AI in the lab longer”? 43:09 How should militaries use AI? 50:13 What can humans do that AI won't? 58:17 What does Demis Hassabis want his legacy to be? (And 1:04:40 Can I beat Demis at Jenga?) Recorded March 5, 2026 in London.

Cleo Abram

514,305 views • 3 months ago

Demis Hassabis, CEO of Google DeepMind, explains how young founders can build billion-dollar companies using AI tools the labs themselves haven't fully explored: He argues that the frontier of opportunity in AI is shifting away from building models and toward applying them. His advice to young founders: "You've got to just go with the flow of the direction. I would immerse myself in every tool available and just become almost like superpowered." Even at the frontier labs, the team can only scratch the surface of what's possible with their own technology. Demis Hassabis states: "So for us like Veo and Nano Banana and Gemini, even we can only explore a fraction of what the applied things you could do with it, the applications you could make with it." And the pace of new releases is making this even harder to keep up with: "I think that gap's getting bigger and bigger in terms of the overhang of the capabilities, all the cool stuff from the latest models, and the release schedules are getting faster and faster on that." This creates an enormous opening for outsiders willing to go deep on the tools. His conclusion is striking: "A kid these days could probably start a multi-billion dollar business in some ways using these tools in some new way that no one had thought about."
1:13

Demis Hassabis, CEO of Google DeepMind, explains how young founders can build billion-dollar companies using AI tools the labs themselves haven't fully explored: He argues that the frontier of opportunity in AI is shifting away from building models and toward applying them. His advice to young founders: "You've got to just go with the flow of the direction. I would immerse myself in every tool available and just become almost like superpowered." Even at the frontier labs, the team can only scratch the surface of what's possible with their own technology. Demis Hassabis states: "So for us like Veo and Nano Banana and Gemini, even we can only explore a fraction of what the applied things you could do with it, the applications you could make with it." And the pace of new releases is making this even harder to keep up with: "I think that gap's getting bigger and bigger in terms of the overhang of the capabilities, all the cool stuff from the latest models, and the release schedules are getting faster and faster on that." This creates an enormous opening for outsiders willing to go deep on the tools. His conclusion is striking: "A kid these days could probably start a multi-billion dollar business in some ways using these tools in some new way that no one had thought about."

Big Brain AI

21,899 views • 3 months ago

Demis Hassabis reveals the 100,000-person synthetic country that took years to hand-code, and says AI may now generate it "We tried to simulate a whole country with 100,000 living, breathing people sort of going about their daily lives and all the politics of it" "And you were supposed to create a revolution" "So it was a pretty ambitious game that we had to write by hand" "And what's amazing now, and it took years" "But what's amazing now is that we might be close to this possibility of actually just sort of generating that" The leap is not better graphics. It is the cost curve of simulation: from years of hand-coding one world to asking a model to generate it. That turns synthetic societies from bespoke software projects into a reusable AI primitive. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind, at Semafor Tech
0:26

Demis Hassabis reveals the 100,000-person synthetic country that took years to hand-code, and says AI may now generate it "We tried to simulate a whole country with 100,000 living, breathing people sort of going about their daily lives and all the politics of it" "And you were supposed to create a revolution" "So it was a pretty ambitious game that we had to write by hand" "And what's amazing now, and it took years" "But what's amazing now is that we might be close to this possibility of actually just sort of generating that" The leap is not better graphics. It is the cost curve of simulation: from years of hand-coding one world to asking a model to generate it. That turns synthetic societies from bespoke software projects into a reusable AI primitive. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind, at Semafor Tech

Karl Mehta

80,754 views • 1 month ago

Demis Hassabis, CEO of Google DeepMind and Nobel laureate, says the AGI definition the industry uses is too low a bar. His Einstein test: train a system on a 1901 knowledge cutoff and ask it to invent special relativity. "The human brain is the only existence proof we have that general intelligence is even possible." "That's a pretty high bar, and it means that it's probably higher bar than just being able to do some useful economic work." "The type of test I would have is like an Einstein test of, you know, let's train one of these systems with a 1901 knowledge cutoff. Can it invent special relativity like Einstein did in 1905?" "What you'd actually want is something a future version of AlphaGo to be able to invent Go, right? Invent a game as deep and as complex and elegant and as beautiful as Go, not just come up with a strategy within the game." "I don't think today's systems are yet capable of doing that, but I think they will be able to in the future." - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind, on The Next Big Thing.
2:52

Demis Hassabis, CEO of Google DeepMind and Nobel laureate, says the AGI definition the industry uses is too low a bar. His Einstein test: train a system on a 1901 knowledge cutoff and ask it to invent special relativity. "The human brain is the only existence proof we have that general intelligence is even possible." "That's a pretty high bar, and it means that it's probably higher bar than just being able to do some useful economic work." "The type of test I would have is like an Einstein test of, you know, let's train one of these systems with a 1901 knowledge cutoff. Can it invent special relativity like Einstein did in 1905?" "What you'd actually want is something a future version of AlphaGo to be able to invent Go, right? Invent a game as deep and as complex and elegant and as beautiful as Go, not just come up with a strategy within the game." "I don't think today's systems are yet capable of doing that, but I think they will be able to in the future." - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind, on The Next Big Thing.

Karl Mehta

72,911 views • 1 month ago

Demis Hassabis says he sold DeepMind in 2014 because staying independent needed $7 billion, and he could barely raise $10 million: "He saw Google as an AI company. The reason I sold was I knew it would require 7 billion dollars of investment in order to take on, try and become one of those companies." "And I was barely able to scrape together 10 million dollar rounds at that point." "And the problem was is that we'd done enough, the Atari thing, we had alerted enough of the Silicon Valley titans. They were coming after my people." "We had no business, we didn't even have a product, obviously not, because I was trying to make the core technology." "I was paying myself zero and people were being paid like 100k a year and then they were getting offers of 10 million a year." "And then if that didn't work, they could afford to come back and say, here's 50 million a year." "So at some point, that was going to go wrong." P.S: I'm hosting the AI Assurance & Governance Summit 2026 on October 1 at the Stanford Faculty Club, one day and one track, to work on the part of this that money did not solve. Frontier labs, regulated industries, insurers and investors in the room. Link in the reply. - Demis Hassabis (Demis Hassabis), co-founder and CEO of Google DeepMind, at the Worshipful Company of Information Technologists Annual Lecture.
0:58

Demis Hassabis says he sold DeepMind in 2014 because staying independent needed $7 billion, and he could barely raise $10 million: "He saw Google as an AI company. The reason I sold was I knew it would require 7 billion dollars of investment in order to take on, try and become one of those companies." "And I was barely able to scrape together 10 million dollar rounds at that point." "And the problem was is that we'd done enough, the Atari thing, we had alerted enough of the Silicon Valley titans. They were coming after my people." "We had no business, we didn't even have a product, obviously not, because I was trying to make the core technology." "I was paying myself zero and people were being paid like 100k a year and then they were getting offers of 10 million a year." "And then if that didn't work, they could afford to come back and say, here's 50 million a year." "So at some point, that was going to go wrong." P.S: I'm hosting the AI Assurance & Governance Summit 2026 on October 1 at the Stanford Faculty Club, one day and one track, to work on the part of this that money did not solve. Frontier labs, regulated industries, insurers and investors in the room. Link in the reply. - Demis Hassabis (Demis Hassabis), co-founder and CEO of Google DeepMind, at the Worshipful Company of Information Technologists Annual Lecture.

Karl Mehta

302,818 views • 1 month ago

Demis Hassabis says AI won’t just accelerate drug discovery. It will replace the process entirely. The pharmaceutical industry finds drugs the same way it has for decades. Synthesize a compound. Test it on animals. Test it on humans. Wait years for approval. Hope the molecule doesn’t kill someone along the way. Every step is physical. Every step is slow. Every step is expensive enough to make most diseases not worth curing. Hassabis: “We’re focusing on solving the rest of the drug discovery process, which is a lot of chemistry, designing the compounds, checking it’s not toxic, and all the different properties you need for drugs to be safe.” That sounds incremental. It isn’t. AlphaFold solved protein folding. Isomorphic Labs is now working through the rest of the chain. Compound design. Toxicity screening. Safety profiling. All computational. None of it requires a lab. Hassabis: “I think we’ll have that whole drug design engine ready in the next five to 10 years.” Not a tool that assists chemists. A system that replaces the chemistry. But designing the drug was never the bottleneck that killed people. Clinical trials were. A single drug takes over a decade to move from lab to patient. Most of that time isn’t science. It’s bureaucracy, logistics, and the blunt reality of testing molecules on living tissue one dose at a time. Hassabis: “Simulating parts of the human metabolism, also stratifying patients to make sure that certain patients get exactly the right type of drug that’s suitable for their genomic makeup.” Simulate the patient before you treat the patient. Map individual DNA. Model personal metabolism. Test the drug on a digital replica before it touches a vein. Not personalized medicine as a marketing phrase. Personalized medicine as an engineering output. The final wall is regulatory. The FDA exists because humans make mistakes with molecules. Every approval gate was built to catch errors that cost lives. The entire structure assumes the process is fallible. What happens when the process stops being fallible. Hassabis: “Perhaps like the animal testing is not needed anymore, maybe we can go up the dosage ladder quicker, because you can rely on these models.” He’s not speculating. He’s describing a sequence. AI-designed drugs enter the existing pipeline. A dozen compounds go through full traditional trials. Regulators collect data. They back-test model predictions against real outcomes. Hassabis: “Then the government and the regulatory bodies see that and they have enough data to sort of back-test the predictions of those models.” When the models prove more accurate than the trials they’re meant to replace, the trials become the bottleneck. Not the science. The paperwork. Animal testing shortened. Dosage ladders compressed. Entire stages of the pipeline collapsed into computation. The drug doesn’t get discovered faster. The drug gets discovered differently. The laboratory moves from a building to a server. The clinical trial moves from a hospital ward to a simulation. The patient moves from a statistic to a genome. Hassabis isn’t promising a cure for one disease. He’s describing the architecture that makes curing disease an engineering problem with a known solution path. The bottleneck was never biology. It was the speed at which humans were allowed to solve it. That speed limit is about to be revoked.
1:37

Demis Hassabis says AI won’t just accelerate drug discovery. It will replace the process entirely. The pharmaceutical industry finds drugs the same way it has for decades. Synthesize a compound. Test it on animals. Test it on humans. Wait years for approval. Hope the molecule doesn’t kill someone along the way. Every step is physical. Every step is slow. Every step is expensive enough to make most diseases not worth curing. Hassabis: “We’re focusing on solving the rest of the drug discovery process, which is a lot of chemistry, designing the compounds, checking it’s not toxic, and all the different properties you need for drugs to be safe.” That sounds incremental. It isn’t. AlphaFold solved protein folding. Isomorphic Labs is now working through the rest of the chain. Compound design. Toxicity screening. Safety profiling. All computational. None of it requires a lab. Hassabis: “I think we’ll have that whole drug design engine ready in the next five to 10 years.” Not a tool that assists chemists. A system that replaces the chemistry. But designing the drug was never the bottleneck that killed people. Clinical trials were. A single drug takes over a decade to move from lab to patient. Most of that time isn’t science. It’s bureaucracy, logistics, and the blunt reality of testing molecules on living tissue one dose at a time. Hassabis: “Simulating parts of the human metabolism, also stratifying patients to make sure that certain patients get exactly the right type of drug that’s suitable for their genomic makeup.” Simulate the patient before you treat the patient. Map individual DNA. Model personal metabolism. Test the drug on a digital replica before it touches a vein. Not personalized medicine as a marketing phrase. Personalized medicine as an engineering output. The final wall is regulatory. The FDA exists because humans make mistakes with molecules. Every approval gate was built to catch errors that cost lives. The entire structure assumes the process is fallible. What happens when the process stops being fallible. Hassabis: “Perhaps like the animal testing is not needed anymore, maybe we can go up the dosage ladder quicker, because you can rely on these models.” He’s not speculating. He’s describing a sequence. AI-designed drugs enter the existing pipeline. A dozen compounds go through full traditional trials. Regulators collect data. They back-test model predictions against real outcomes. Hassabis: “Then the government and the regulatory bodies see that and they have enough data to sort of back-test the predictions of those models.” When the models prove more accurate than the trials they’re meant to replace, the trials become the bottleneck. Not the science. The paperwork. Animal testing shortened. Dosage ladders compressed. Entire stages of the pipeline collapsed into computation. The drug doesn’t get discovered faster. The drug gets discovered differently. The laboratory moves from a building to a server. The clinical trial moves from a hospital ward to a simulation. The patient moves from a statistic to a genome. Hassabis isn’t promising a cure for one disease. He’s describing the architecture that makes curing disease an engineering problem with a known solution path. The bottleneck was never biology. It was the speed at which humans were allowed to solve it. That speed limit is about to be revoked.

Dustin

43,980 views • 4 months ago

“We think we have a competitive advantage.” - Demis Hassibis, Head of Google DeepMind / AI efforts While Anthropic and OpenAI seek to win via training on “text” (written language), Demis Hassabis says Google Gemini is attempting a different path: Multimodal. Gemini aims to be the most effective model in the real world: physics, robotics, voice, visual, sensory, & text. The bet is multimodal unlocks more of what humanity really cares about.
2:47

“We think we have a competitive advantage.” - Demis Hassibis, Head of Google DeepMind / AI efforts While Anthropic and OpenAI seek to win via training on “text” (written language), Demis Hassabis says Google Gemini is attempting a different path: Multimodal. Gemini aims to be the most effective model in the real world: physics, robotics, voice, visual, sensory, & text. The bet is multimodal unlocks more of what humanity really cares about.

Compound248 💰

17,731 views • 3 months ago

Demis Hassabis just described the exact architecture he is building to cure all human disease and said the breakthrough will not come gradually. It will hit all at once. He is building a full platform to do it. Think of it as a dozen AlphaFold-level models, each one covering a different step in the drug discovery process, all stitched together into a single engine that can be pointed at almost any disease area on earth. AlphaFold solved protein structure. It then folded 200 million proteins in a single year. Hassabis is now building the equivalent of that for every other step like protein interactions, molecular binding, toxicity prediction, drug design, and clinical stratification. Each one its own AlphaFold. All of them connected. He is already testing it on disease profiles in pre-clinical stages right now. Most biotech analysts still model drug discovery timelines in decades. McKinsey published a report estimating AI could reduce discovery timelines by 25 to 50 percent. Hassabis is not describing a 25 percent improvement. He is describing a complete replacement of the process. Do not expect a slow drip of results. Expect nothing for a few years and then a sudden explosion where everything works at once. Demis is running the same play. At a scale that makes AlphaFold look like a warm-up act. (Watch the full interview on YouTube at Two Minute Papers channel)
2:29

Demis Hassabis just described the exact architecture he is building to cure all human disease and said the breakthrough will not come gradually. It will hit all at once. He is building a full platform to do it. Think of it as a dozen AlphaFold-level models, each one covering a different step in the drug discovery process, all stitched together into a single engine that can be pointed at almost any disease area on earth. AlphaFold solved protein structure. It then folded 200 million proteins in a single year. Hassabis is now building the equivalent of that for every other step like protein interactions, molecular binding, toxicity prediction, drug design, and clinical stratification. Each one its own AlphaFold. All of them connected. He is already testing it on disease profiles in pre-clinical stages right now. Most biotech analysts still model drug discovery timelines in decades. McKinsey published a report estimating AI could reduce discovery timelines by 25 to 50 percent. Hassabis is not describing a 25 percent improvement. He is describing a complete replacement of the process. Do not expect a slow drip of results. Expect nothing for a few years and then a sudden explosion where everything works at once. Demis is running the same play. At a scale that makes AlphaFold look like a warm-up act. (Watch the full interview on YouTube at Two Minute Papers channel)

Ihtesham Ali

101,488 views • 2 months ago

$GOOGL has an anti-aging asset nobody is pricing. Friedberg david friedberg walked through a Calico paper that should be on Alphabet holders' radar. Calico is Google's longevity lab. Working with Revel Pharma, it used AlphaFold plus directed evolution to design a novel enzyme that degrades CML, the glycation end-product that stiffens tissue and drives aging. The result is not a slide. Across five recursive design cycles the enzyme degraded **52-97%** of CML on human proteins, and on donated skin from patients over 70 it eliminated about **55%** of CML - reversing the skin's profile toward that of a 31-year-old. It is AI-designed proteins doing something genuinely new in biology, not a benchmark score. Chamath Chamath Palihapitiya frames the first market as cosmetic: a workable anti-aging cream is a multi-trillion-dollar category. This is the AlphaFold thesis paying out in a product line, and the AlphaFold-to-drugs bridge is exactly what Demis Hassabis Demis Hassabis has argued is the real near-term return on Google's AI - not chatbots, but designed molecules. Same company, same stack, a second revenue engine the market files under "research." Implication: this is deep-optionality inside a name you already hold on search and cloud. It does not move the $GOOGL model this year. But it is a reminder that Alphabet's AI edge shows up in drug and materials design, an option the Street assigns roughly zero value. If a Calico or Isomorphic asset reaches a real market, that is upside no analyst is underwriting. Watch for Calico or Isomorphic Labs to move a designed molecule toward a commercial or clinical path.
5:52

$GOOGL has an anti-aging asset nobody is pricing. Friedberg david friedberg walked through a Calico paper that should be on Alphabet holders' radar. Calico is Google's longevity lab. Working with Revel Pharma, it used AlphaFold plus directed evolution to design a novel enzyme that degrades CML, the glycation end-product that stiffens tissue and drives aging. The result is not a slide. Across five recursive design cycles the enzyme degraded **52-97%** of CML on human proteins, and on donated skin from patients over 70 it eliminated about **55%** of CML - reversing the skin's profile toward that of a 31-year-old. It is AI-designed proteins doing something genuinely new in biology, not a benchmark score. Chamath Chamath Palihapitiya frames the first market as cosmetic: a workable anti-aging cream is a multi-trillion-dollar category. This is the AlphaFold thesis paying out in a product line, and the AlphaFold-to-drugs bridge is exactly what Demis Hassabis Demis Hassabis has argued is the real near-term return on Google's AI - not chatbots, but designed molecules. Same company, same stack, a second revenue engine the market files under "research." Implication: this is deep-optionality inside a name you already hold on search and cloud. It does not move the $GOOGL model this year. But it is a reminder that Alphabet's AI edge shows up in drug and materials design, an option the Street assigns roughly zero value. If a Calico or Isomorphic asset reaches a real market, that is upside no analyst is underwriting. Watch for Calico or Isomorphic Labs to move a designed molecule toward a commercial or clinical path.

Podcast Alpha

124,118 views • 1 month ago

Demis Hassabis CEO of DeepMind $GOOG: No one ever has enough chips … and there just aren’t enough compute chips in the world for the demand There’s a reason why $AMZN and $GOOG are running their own semiconductor labs and why we’re building a ridiculous number of semiconductor fabs. AI is projected to have more than 5 billion users by 2030 and the world still not ready for that day Al resources are gonna continue to be undersupplied
0:58

Demis Hassabis CEO of DeepMind $GOOG: No one ever has enough chips … and there just aren’t enough compute chips in the world for the demand There’s a reason why $AMZN and $GOOG are running their own semiconductor labs and why we’re building a ridiculous number of semiconductor fabs. AI is projected to have more than 5 billion users by 2030 and the world still not ready for that day Al resources are gonna continue to be undersupplied

Bourbon Capital

163,842 views • 7 months ago

HUGE NEWS: CEOs of 3 of the 4 leading AI companies all said they would pause/slow down if others agreed Anthropic CEO: If we don't sell chips to China, it slows them down. And then? "I'm very confident that [Demis and I] can work something out." EMILY CHANG: Some folks have advocated for a pause to give regulation time to catch up, to give society time to adjust to some of these changes. In a perfect world, if you knew that every other company would pause, if every country would pause, would you advocate for that? DEMIS HASSABIS: I think so. [DAVOS INTERVIEW] DEMIS HASSABIS: And if we can, maybe it would be good to have a bit of slow, a slightly slower pace than we're currently predicting, even my timelines, so that we can get this right societally. But that would require some coordination that is hard. DARIO AMODEI: I prefer your [longer] timelines DEMIS HASSABIS: Yes. Absolutely. DARIO AMODEI: I'll concede, yes... I said before, I prefer Demis' timeline. I wish we had 5 to 10 years. It's possible he's just right and I'm just wrong. But assume I'm right and it can be done in one to two years. Why can't we slow down to Demis' timeline? INTERVIEWER: You could just slow down. DARIO AMODEI: We can't do that because we have geopolitical adversaries building the same technology at a similar pace. It's very hard to have an enforceable agreement where they slow down and we slow down. So if we can just not sell the chips [to China], then this isn't a question of competition between the US and China. This is a question of competition between me and Demis, which I'm very confident that we can work out.
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HUGE NEWS: CEOs of 3 of the 4 leading AI companies all said they would pause/slow down if others agreed Anthropic CEO: If we don't sell chips to China, it slows them down. And then? "I'm very confident that [Demis and I] can work something out." EMILY CHANG: Some folks have advocated for a pause to give regulation time to catch up, to give society time to adjust to some of these changes. In a perfect world, if you knew that every other company would pause, if every country would pause, would you advocate for that? DEMIS HASSABIS: I think so. [DAVOS INTERVIEW] DEMIS HASSABIS: And if we can, maybe it would be good to have a bit of slow, a slightly slower pace than we're currently predicting, even my timelines, so that we can get this right societally. But that would require some coordination that is hard. DARIO AMODEI: I prefer your [longer] timelines DEMIS HASSABIS: Yes. Absolutely. DARIO AMODEI: I'll concede, yes... I said before, I prefer Demis' timeline. I wish we had 5 to 10 years. It's possible he's just right and I'm just wrong. But assume I'm right and it can be done in one to two years. Why can't we slow down to Demis' timeline? INTERVIEWER: You could just slow down. DARIO AMODEI: We can't do that because we have geopolitical adversaries building the same technology at a similar pace. It's very hard to have an enforceable agreement where they slow down and we slow down. So if we can just not sell the chips [to China], then this isn't a question of competition between the US and China. This is a question of competition between me and Demis, which I'm very confident that we can work out.

AI Notkilleveryoneism Memes ⏸️

522,984 views • 7 months ago

Demis Hassabis reveals Google's three-stage stack rewrite: AI first, model first, agent first. Bolting agents onto normal software does not scale to billions. "We've got to, we've done a lot of engineering work under the hood in the last year, which you'll see, you see the surface of when we announced things like this." "But actually, you can almost think that whole stack had to be rewritten to be sort of AI first, model first, and now agent first." "So that's quite a big change from like normal software." "And obviously there's all this huge infrastructure that serves billions of users every day at lightning speed and that's all gonna work." "And I think we've made massive improvements on that over the past year. And that's why you can see all the goodness of the AI models flowing through into our product surfaces so quickly and in such compelling ways." Most companies are adding an agent interface to yesterday's software. Demis says Google changed the substrate itself. The moat may not be the model. It may be the infrastructure that lets the model reach billions of users every day without breaking. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind, with Rowan Cheung at Google I/O 2026
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Demis Hassabis reveals Google's three-stage stack rewrite: AI first, model first, agent first. Bolting agents onto normal software does not scale to billions. "We've got to, we've done a lot of engineering work under the hood in the last year, which you'll see, you see the surface of when we announced things like this." "But actually, you can almost think that whole stack had to be rewritten to be sort of AI first, model first, and now agent first." "So that's quite a big change from like normal software." "And obviously there's all this huge infrastructure that serves billions of users every day at lightning speed and that's all gonna work." "And I think we've made massive improvements on that over the past year. And that's why you can see all the goodness of the AI models flowing through into our product surfaces so quickly and in such compelling ways." Most companies are adding an agent interface to yesterday's software. Demis says Google changed the substrate itself. The moat may not be the model. It may be the infrastructure that lets the model reach billions of users every day without breaking. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind, with Rowan Cheung at Google I/O 2026

Karl Mehta

36,402 views • 1 month ago

I had a chance to sit down with Demis Hassabis to discuss his goal of solving intelligence and using it to help cure all disease. But what I found most compelling wasn't the scale of the mission, but Demis Hassabis himself. His level of focus is extraordinary. We talked about the personal cost of pursuing a mission this ambitious, and the tension between the two sides of his work: the CEO of a commercial entity and the scientist driven by a humanitarian vision. It was an absolutely fascinating conversation, and a deep honor to speak with one of the most important minds of our time. Thank you to the Google DeepMind team for having me. (0:00) Curing Every Disease in a Decade (1:04) Gemini for Science (2:01) Changing Science Forever with AlphaFold (4:04) Can Humans Trust AI In The Medical Field? (6:02) Generative AI vs. Practical AI (7:03) The Tension Between CEO & Scientist (8:28) AGI by 2030 (10:07) The Chess Prodigy Turned CEO (11:20) Ender's Game & What This Mission Has Cost
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I had a chance to sit down with Demis Hassabis to discuss his goal of solving intelligence and using it to help cure all disease. But what I found most compelling wasn't the scale of the mission, but Demis Hassabis himself. His level of focus is extraordinary. We talked about the personal cost of pursuing a mission this ambitious, and the tension between the two sides of his work: the CEO of a commercial entity and the scientist driven by a humanitarian vision. It was an absolutely fascinating conversation, and a deep honor to speak with one of the most important minds of our time. Thank you to the Google DeepMind team for having me. (0:00) Curing Every Disease in a Decade (1:04) Gemini for Science (2:01) Changing Science Forever with AlphaFold (4:04) Can Humans Trust AI In The Medical Field? (6:02) Generative AI vs. Practical AI (7:03) The Tension Between CEO & Scientist (8:28) AGI by 2030 (10:07) The Chess Prodigy Turned CEO (11:20) Ender's Game & What This Mission Has Cost

Roberto Nickson

51,426 views • 2 months ago

Demis Hassabis, CEO of Google DeepMind: "some jobs will disappear, but better and more meaningful ones will take their place" and the advice that followed: "if I was talking to a class of students right now, I'd tell them to get really good with AI, even better than I am" 31 minutes on what AI does to your job, your work, your next move Demis Hassabis and the CEO of Anthropic in one room probably the best conversation you'll find on this topic the ones who listen won't be the ones replaced, they'll be the ones building and shipping that's exactly why I put together a step-by-step guide on building your own AI agent agents are the part of AI moving fastest right now, the full walkthrough is in the article below
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Demis Hassabis, CEO of Google DeepMind: "some jobs will disappear, but better and more meaningful ones will take their place" and the advice that followed: "if I was talking to a class of students right now, I'd tell them to get really good with AI, even better than I am" 31 minutes on what AI does to your job, your work, your next move Demis Hassabis and the CEO of Anthropic in one room probably the best conversation you'll find on this topic the ones who listen won't be the ones replaced, they'll be the ones building and shipping that's exactly why I put together a step-by-step guide on building your own AI agent agents are the part of AI moving fastest right now, the full walkthrough is in the article below

Anatoli Kopadze

15,625 views • 2 months ago

THE SMARTEST MAN IN AI EXPOSED THE ENTIRE AGI NARRATIVE AS A LIE. GOOGLE DEEPMIND CEO DEMIS HASSABIS SAYS TODAY'S AI STILL CAN'T DO THE ONE THING THAT WOULD PROVE TRUE GENERAL INTELLIGENCE: CREATE A TRULY ORIGINAL IDEA. HIS TEST? TRAIN AN AI ONLY ON HUMAN KNOWLEDGE FROM 1901. THEN ASK IT TO DISCOVER EINSTEIN'S THEORY OF RELATIVITY ON ITS OWN. NO INTERNET. NO MEMORIZED ANSWER. NO HUMAN SOLUTION TO COPY. JUST PURE REASONING. NOT A SINGLE AI TODAY CAN DO IT. AGI MAY BE CLOSER THAN EVER... BUT HASSABIS SAYS WE'RE NOT THERE YET 🤯
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THE SMARTEST MAN IN AI EXPOSED THE ENTIRE AGI NARRATIVE AS A LIE. GOOGLE DEEPMIND CEO DEMIS HASSABIS SAYS TODAY'S AI STILL CAN'T DO THE ONE THING THAT WOULD PROVE TRUE GENERAL INTELLIGENCE: CREATE A TRULY ORIGINAL IDEA. HIS TEST? TRAIN AN AI ONLY ON HUMAN KNOWLEDGE FROM 1901. THEN ASK IT TO DISCOVER EINSTEIN'S THEORY OF RELATIVITY ON ITS OWN. NO INTERNET. NO MEMORIZED ANSWER. NO HUMAN SOLUTION TO COPY. JUST PURE REASONING. NOT A SINGLE AI TODAY CAN DO IT. AGI MAY BE CLOSER THAN EVER... BUT HASSABIS SAYS WE'RE NOT THERE YET 🤯

Vivek Sen

58,397 views • 17 days ago