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We are no longer building software. We are building agents. Systems that don’t wait for a prompt. Systems that don’t ask for permission. Systems that simply execute. DeepMind CEO Demis Hassabis just revealed the terrifying duality of the agentic era. Hassabis: “Better healthcare, better drugs, helping with climate change...

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Dustin

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10,956 просмотров • 5 месяцев назад •via X (Twitter)

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Demis Hassabis says DeepMind is on track to solve intelligence by around 2030 As AI enters the "agentic era" of autonomy this year, the risk is that bad actors could repurpose the tech for harm "then we have to make sure... that these systems do what we expect them to do"
1:39

Demis Hassabis says DeepMind is on track to solve intelligence by around 2030 As AI enters the "agentic era" of autonomy this year, the risk is that bad actors could repurpose the tech for harm "then we have to make sure... that these systems do what we expect them to do"

Haider.

21,471 просмотров • 6 месяцев назад

Google's DeepMind CEO Demis Hassabis: AGI is a system that exhibits all the cognitive capabilities that humans have. The brain is the only existence proof we have that general intelligence is possible. Today’s systems are impressive — but they’re not there yet. We’ve still got at least another 5–10 years to go. AI could be the ultimate tool for accelerating scientific discovery. We can tackle disease, climate, inequality. It could be the ultimate enhancement tool for human scientists. The potential is there — if we do it the right way.
2:22

Google's DeepMind CEO Demis Hassabis: AGI is a system that exhibits all the cognitive capabilities that humans have. The brain is the only existence proof we have that general intelligence is possible. Today’s systems are impressive — but they’re not there yet. We’ve still got at least another 5–10 years to go. AI could be the ultimate tool for accelerating scientific discovery. We can tackle disease, climate, inequality. It could be the ultimate enhancement tool for human scientists. The potential is there — if we do it the right way.

Clash Report

22,086 просмотров • 6 месяцев назад

Demis Hassabis thinks mathematics has a ceiling. He thinks biology is where we hit it. Hassabis: “Machine learning is the perfect description language for biology in the same way maths is for physics.” He isn’t calling AI a tool. He’s calling it a language. For four hundred years we only had one. Newton wrote gravity in it. Maxwell wrote light. Einstein wrote spacetime itself. Every law we ever pulled out of nature came back in equations, and we decided that meant nature was written in equations. It didn’t mean that. It meant the parts that surrendered first were small enough to fit the language we already spoke. Hassabis: “The expressive power of maths is not enough to understand these highly emergent dynamical systems.” Math can put a planet’s orbit on a single line. It cannot put one protein on a page. Same universe, same laws, and one fits our notation while the other refuses. Weak signals buried under noise, correlations stacked on correlations, more moving parts turning at once than any mind can hold. Biology isn’t harder than physics. It’s more expressive than the tool we brought to it. That was never a gap in our knowledge. It was a wall in our vocabulary. Hassabis is working on the far side of that wall. He calls it a virtual cell. A running simulation of a living system that no equation could ever contain. Hassabis: “Once you learn these simulators, you could maybe extract some equations from that.” He isn’t replacing math. He’s going after math we were never going to reach on our own. The model learns the system the way you learn to catch a ball, without ever solving the equation in the air. Understanding first. Formula after. That reverses the order science has followed since Newton. We started with the equation and used it to predict the system. He starts with the system and pulls the equation out of it. What if the deepest laws of life were always there, fully written, in a language we never learned to read. Math was never the language of the universe. It was the first one we spoke. Every disease we failed to cure is a sentence in that language, sitting in the open, waiting on a reader. Every person we lost to one died on the wrong side of a translation gap. The cell was never silent. We were illiterate. Hassabis isn’t building a better microscope. He’s building a second language for reality. And the first thing it’s learning to say is life.
1:37

Demis Hassabis thinks mathematics has a ceiling. He thinks biology is where we hit it. Hassabis: “Machine learning is the perfect description language for biology in the same way maths is for physics.” He isn’t calling AI a tool. He’s calling it a language. For four hundred years we only had one. Newton wrote gravity in it. Maxwell wrote light. Einstein wrote spacetime itself. Every law we ever pulled out of nature came back in equations, and we decided that meant nature was written in equations. It didn’t mean that. It meant the parts that surrendered first were small enough to fit the language we already spoke. Hassabis: “The expressive power of maths is not enough to understand these highly emergent dynamical systems.” Math can put a planet’s orbit on a single line. It cannot put one protein on a page. Same universe, same laws, and one fits our notation while the other refuses. Weak signals buried under noise, correlations stacked on correlations, more moving parts turning at once than any mind can hold. Biology isn’t harder than physics. It’s more expressive than the tool we brought to it. That was never a gap in our knowledge. It was a wall in our vocabulary. Hassabis is working on the far side of that wall. He calls it a virtual cell. A running simulation of a living system that no equation could ever contain. Hassabis: “Once you learn these simulators, you could maybe extract some equations from that.” He isn’t replacing math. He’s going after math we were never going to reach on our own. The model learns the system the way you learn to catch a ball, without ever solving the equation in the air. Understanding first. Formula after. That reverses the order science has followed since Newton. We started with the equation and used it to predict the system. He starts with the system and pulls the equation out of it. What if the deepest laws of life were always there, fully written, in a language we never learned to read. Math was never the language of the universe. It was the first one we spoke. Every disease we failed to cure is a sentence in that language, sitting in the open, waiting on a reader. Every person we lost to one died on the wrong side of a translation gap. The cell was never silent. We were illiterate. Hassabis isn’t building a better microscope. He’s building a second language for reality. And the first thing it’s learning to say is life.

Dustin

17,649 просмотров • 22 дней назад

Demis Hassabis wants to do something no civilization has ever been able to do. Run reality more than once. Hassabis: “AI itself will maybe unlock new sciences… the one I’m particularly excited about is AI for simulations.” Every economy ever built. Every policy ever enacted. Every war ever fought. Happened exactly once. Against the entire human population. With no way to run it again. Hassabis: “If you raise interest rates by half a percent, you have to do it in the real world and then see what happens. You can have theories, but you can’t run it thousands of times.” Every major decision in the history of civilization was a single experiment run on billions of people with no control group and no second attempt. We called the results knowledge. They were the scars of bets we were never allowed to place twice. Hassabis: “Why aren’t they just sciences like physics today? Because the problem is they’re emergent systems… it’s very hard to do repeated controlled experiments.” Physics became physics because you can drop a ball a thousand times and get the same answer. You cannot drop a civilization and get any answer at all. You just get the wreckage and call it a lesson. Hassabis wants to change that. Hassabis: “If you could simulate things really accurately, then maybe there’s sort of new sciences to be done where you can rigorously sample from a very accurate simulator.” Simulate an economy. Crash it. Rebuild it. Adjust the inputs. Run it again. Do for civilization what the laboratory did for chemistry. But that word “accurately” is doing more work than anyone is willing to examine. To simulate a society well enough to learn from it, you have to simulate the people inside it. Not averages. Not abstractions. Agents with preferences and fears and breaking points. The more accurate the simulation gets, the less separates it from the thing it represents. The line between physics and economics was never about the nature of what was being studied. It was about the limits of the thing doing the studying. Humans were never too complex to predict. We were too complex to calculate. AI does not create new science. It collapses every science into one. Everything computable becomes predictable. Everything predictable becomes simulable. And past a certain resolution, the gap between a simulated world and a real one stops being a technical question. It becomes a philosophical question no one is prepared to answer. A simulation you can tell apart from reality is a simulation that has not finished improving. The people inside a perfect one would not wonder whether their world was generated. They would feel exactly the way you feel right now. Reading this. Certain they are real. That certainty is not evidence. It is exactly what a successful simulation would produce. Hassabis: “That will allow us to make much better decisions in these, today, what are very uncertain domains.” What he is building is not a forecasting tool. It is the quiet proof that “real” was only ever a word for what we had not yet learned to compute. And that word is about to lose its meaning.
1:12

Demis Hassabis wants to do something no civilization has ever been able to do. Run reality more than once. Hassabis: “AI itself will maybe unlock new sciences… the one I’m particularly excited about is AI for simulations.” Every economy ever built. Every policy ever enacted. Every war ever fought. Happened exactly once. Against the entire human population. With no way to run it again. Hassabis: “If you raise interest rates by half a percent, you have to do it in the real world and then see what happens. You can have theories, but you can’t run it thousands of times.” Every major decision in the history of civilization was a single experiment run on billions of people with no control group and no second attempt. We called the results knowledge. They were the scars of bets we were never allowed to place twice. Hassabis: “Why aren’t they just sciences like physics today? Because the problem is they’re emergent systems… it’s very hard to do repeated controlled experiments.” Physics became physics because you can drop a ball a thousand times and get the same answer. You cannot drop a civilization and get any answer at all. You just get the wreckage and call it a lesson. Hassabis wants to change that. Hassabis: “If you could simulate things really accurately, then maybe there’s sort of new sciences to be done where you can rigorously sample from a very accurate simulator.” Simulate an economy. Crash it. Rebuild it. Adjust the inputs. Run it again. Do for civilization what the laboratory did for chemistry. But that word “accurately” is doing more work than anyone is willing to examine. To simulate a society well enough to learn from it, you have to simulate the people inside it. Not averages. Not abstractions. Agents with preferences and fears and breaking points. The more accurate the simulation gets, the less separates it from the thing it represents. The line between physics and economics was never about the nature of what was being studied. It was about the limits of the thing doing the studying. Humans were never too complex to predict. We were too complex to calculate. AI does not create new science. It collapses every science into one. Everything computable becomes predictable. Everything predictable becomes simulable. And past a certain resolution, the gap between a simulated world and a real one stops being a technical question. It becomes a philosophical question no one is prepared to answer. A simulation you can tell apart from reality is a simulation that has not finished improving. The people inside a perfect one would not wonder whether their world was generated. They would feel exactly the way you feel right now. Reading this. Certain they are real. That certainty is not evidence. It is exactly what a successful simulation would produce. Hassabis: “That will allow us to make much better decisions in these, today, what are very uncertain domains.” What he is building is not a forecasting tool. It is the quiet proof that “real” was only ever a word for what we had not yet learned to compute. And that word is about to lose its meaning.

Dustin

46,369 просмотров • 2 месяцев назад

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 просмотров • 1 месяц назад

Demis Hassabis just handed the future of civilization to the philosophers. Not more engineers. Not more compute. Not more code. A direct call to arms for the humanities. Hassabis: “It’s very urgent that we really think about the second-order consequences. Many of you in the humanities subjects, it’s now your time in my opinion.” The CEO of Google DeepMind. The man further down the road of artificial intelligence than almost anyone alive. And he’s saying the next chapter doesn’t belong to him. He laid out the exact sequence of what comes next. First, get the technology right. Then, rewrite the economics. Then, face the philosophical questions about the human condition. That third layer is where everything changes. Because we’ve spent all of recorded history defining ourselves by what we can do. Think. Build. Solve. Create. Intelligence was the currency. The differentiator. The thing that separated us from everything else on this planet. And we’re about to make it abundant. When the thing you built your entire identity around becomes something a machine does better, faster, and for free… You don’t have a technology problem. You have a mirror. And the mirror is asking one question. What are you without the doing? Most people look at that question and see obsolescence. Hassabis sees elevation. Hassabis: “I’m very optimistic that we’re gonna get this right. I’m a big believer in human ingenuity, especially when the pressure’s on.” This is not a warning about the end of the world. It’s an invitation to build a better one. For a century, society pushed the humanities to the margins. We prioritized the mechanics of survival over the philosophy of living. That era is over. When artificial intelligence automates the mechanics of survival, philosophy ceases to be a theoretical luxury. It becomes the most critical applied science on Earth. Algorithms cannot calculate what constitutes a virtuous life. Code cannot assign meaning. The technologists are about to hand us infinite leverage. But infinite leverage without human direction is just chaos. Hassabis: “Humanity has always figured it out when the chips are down. And they are now.” The chips are down. But that’s exactly where this species does its best work. We spent ten thousand years building a machine that could lift the burden of basic survival. We succeeded. Now we finally get to figure out what it actually means to be alive.
1:02

Demis Hassabis just handed the future of civilization to the philosophers. Not more engineers. Not more compute. Not more code. A direct call to arms for the humanities. Hassabis: “It’s very urgent that we really think about the second-order consequences. Many of you in the humanities subjects, it’s now your time in my opinion.” The CEO of Google DeepMind. The man further down the road of artificial intelligence than almost anyone alive. And he’s saying the next chapter doesn’t belong to him. He laid out the exact sequence of what comes next. First, get the technology right. Then, rewrite the economics. Then, face the philosophical questions about the human condition. That third layer is where everything changes. Because we’ve spent all of recorded history defining ourselves by what we can do. Think. Build. Solve. Create. Intelligence was the currency. The differentiator. The thing that separated us from everything else on this planet. And we’re about to make it abundant. When the thing you built your entire identity around becomes something a machine does better, faster, and for free… You don’t have a technology problem. You have a mirror. And the mirror is asking one question. What are you without the doing? Most people look at that question and see obsolescence. Hassabis sees elevation. Hassabis: “I’m very optimistic that we’re gonna get this right. I’m a big believer in human ingenuity, especially when the pressure’s on.” This is not a warning about the end of the world. It’s an invitation to build a better one. For a century, society pushed the humanities to the margins. We prioritized the mechanics of survival over the philosophy of living. That era is over. When artificial intelligence automates the mechanics of survival, philosophy ceases to be a theoretical luxury. It becomes the most critical applied science on Earth. Algorithms cannot calculate what constitutes a virtuous life. Code cannot assign meaning. The technologists are about to hand us infinite leverage. But infinite leverage without human direction is just chaos. Hassabis: “Humanity has always figured it out when the chips are down. And they are now.” The chips are down. But that’s exactly where this species does its best work. We spent ten thousand years building a machine that could lift the burden of basic survival. We succeeded. Now we finally get to figure out what it actually means to be alive.

Dustin

28,498 просмотров • 2 месяцев назад

Demis Hassabis just told a room full of academics that they’re running out of time. Not the engineers. Not the technologists. The economists. The philosophers. The people who are supposed to understand what a civilization actually is. Hassabis: “It’s very urgent that we really think about the second-order consequences.” He wasn’t talking about the technology. He was talking about everything that comes after it. Hassabis: “I’m always a little bit astounded when I talk to economists about what’s happening and it’s sort of, they’re pretty skeptical. ‘Where’s it, where’s it coming in the GDP?’” The architects of the global economy are asking where the biggest economic shift in human history is showing up in a spreadsheet. That’s not skepticism. That’s institutional paralysis dressed up as rigor. Hassabis: “It’s ten times the Industrial Revolution.” The Industrial Revolution didn’t just move capital. It burned the feudal system to the ground and birthed the modern world. Hassabis is telling us to multiply that violence by ten. Hassabis: “We’re going to be in a world for the first time, if we get the technology right, where we’re a non-zero-sum world for the first time in humanity’s existence. How can that not need a new type of economic system?” Every economic model you have ever lived under shares the exact same foundational assumption. Scarcity. Capitalism. Communism. Mercantilism. Feudalism. Four names for the mathematics of starvation. Hassabis: “I don’t think it’s any of the ones we’ve tried, because they were all done under the guise of a zero-sum, a limited, a scarce world.” He’s not saying capitalism failed. He’s saying the premise underneath it is about to dissolve. And nobody has written the replacement. But scarcity didn’t just shape our economies. It shaped our identities. You found meaning in your labor. You found virtue in your utility. You worked so you didn’t die. Every concept of purpose humans have ever constructed was forged in a world where things run out. Where choices cost something. Where suffering had a function. Remove scarcity and you don’t just disrupt markets. You collapse the entire philosophical framework through which human beings have understood what it means to live. Hassabis: “There’s the even harder question of how do we want to evolve our society and what is virtuous, what is meaning, what is purpose.” The technology is solvable. The economics is redesignable. But philosophy itself was built inside scarcity. Ethics is the study of hard choices. Meaning is what we extract from struggle. Purpose is what we build against resistance. Take that away and the entire architecture of human meaning loses its load-bearing wall. Hassabis: “I think that’s going to need lots of great philosophers.” He’s asking for thinkers who don’t exist yet. The engineers are about to automate your survival. And in doing so, they will automate your purpose. We spent all of human history fighting for the right to stop struggling. We have no idea what happens to the human mind when we actually win.
2:01

Demis Hassabis just told a room full of academics that they’re running out of time. Not the engineers. Not the technologists. The economists. The philosophers. The people who are supposed to understand what a civilization actually is. Hassabis: “It’s very urgent that we really think about the second-order consequences.” He wasn’t talking about the technology. He was talking about everything that comes after it. Hassabis: “I’m always a little bit astounded when I talk to economists about what’s happening and it’s sort of, they’re pretty skeptical. ‘Where’s it, where’s it coming in the GDP?’” The architects of the global economy are asking where the biggest economic shift in human history is showing up in a spreadsheet. That’s not skepticism. That’s institutional paralysis dressed up as rigor. Hassabis: “It’s ten times the Industrial Revolution.” The Industrial Revolution didn’t just move capital. It burned the feudal system to the ground and birthed the modern world. Hassabis is telling us to multiply that violence by ten. Hassabis: “We’re going to be in a world for the first time, if we get the technology right, where we’re a non-zero-sum world for the first time in humanity’s existence. How can that not need a new type of economic system?” Every economic model you have ever lived under shares the exact same foundational assumption. Scarcity. Capitalism. Communism. Mercantilism. Feudalism. Four names for the mathematics of starvation. Hassabis: “I don’t think it’s any of the ones we’ve tried, because they were all done under the guise of a zero-sum, a limited, a scarce world.” He’s not saying capitalism failed. He’s saying the premise underneath it is about to dissolve. And nobody has written the replacement. But scarcity didn’t just shape our economies. It shaped our identities. You found meaning in your labor. You found virtue in your utility. You worked so you didn’t die. Every concept of purpose humans have ever constructed was forged in a world where things run out. Where choices cost something. Where suffering had a function. Remove scarcity and you don’t just disrupt markets. You collapse the entire philosophical framework through which human beings have understood what it means to live. Hassabis: “There’s the even harder question of how do we want to evolve our society and what is virtuous, what is meaning, what is purpose.” The technology is solvable. The economics is redesignable. But philosophy itself was built inside scarcity. Ethics is the study of hard choices. Meaning is what we extract from struggle. Purpose is what we build against resistance. Take that away and the entire architecture of human meaning loses its load-bearing wall. Hassabis: “I think that’s going to need lots of great philosophers.” He’s asking for thinkers who don’t exist yet. The engineers are about to automate your survival. And in doing so, they will automate your purpose. We spent all of human history fighting for the right to stop struggling. We have no idea what happens to the human mind when we actually win.

Dustin

10,642 просмотров • 2 месяцев назад

Dario Amodei just told software engineers exactly how long they have. Six to twelve months. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.
0:27

Dario Amodei just told software engineers exactly how long they have. Six to twelve months. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.

Dustin

318,457 просмотров • 5 месяцев назад

Larry Ellison just told every software engineer on Earth their job description is dead. Not evolving. Dead. Ellison: “The code that Oracle is writing, Oracle isn’t writing. Our AI models are writing.” This is not a startup demo. This is one of the largest infrastructure monopolies on the planet telling you it already replaced the people who built it. For fifty years, building software meant translating human intent into machine instructions. Line by line. Bug by bug. Sprint by sprint. That entire layer is gone. Ellison: “We don’t write the procedure. We declare our intent.” That sentence just made the entire engineering labor market flinch. The procedure was the job. The procedure was the paycheck. The procedure was what made a developer valuable. And now the machine does it without being asked twice. Ellison: “We just tell the model what we want the program to do, and then the AI comes up with a step-by-step process to actually do it.” You are no longer paid to build. You are paid to think. And most organizations have no idea how to evaluate that. The companies still hiring armies of developers to grind through codebases are paying salaries the machine already made worthless. Not in years. In seconds. When a company worth hundreds of billions hands the keyboard to the machine and tells you the output is better, the debate is not winding down. The debate is over. The enterprise that wins this decade does not write the best code. It removes the human from the process entirely and runs on intent alone. The programmers who survive are the ones who realize the craft is no longer typing. It is architecture. It is judgment. It is knowing what to build and why. Everything else now belongs to the machine. And the machine does not negotiate severance.
0:31

Larry Ellison just told every software engineer on Earth their job description is dead. Not evolving. Dead. Ellison: “The code that Oracle is writing, Oracle isn’t writing. Our AI models are writing.” This is not a startup demo. This is one of the largest infrastructure monopolies on the planet telling you it already replaced the people who built it. For fifty years, building software meant translating human intent into machine instructions. Line by line. Bug by bug. Sprint by sprint. That entire layer is gone. Ellison: “We don’t write the procedure. We declare our intent.” That sentence just made the entire engineering labor market flinch. The procedure was the job. The procedure was the paycheck. The procedure was what made a developer valuable. And now the machine does it without being asked twice. Ellison: “We just tell the model what we want the program to do, and then the AI comes up with a step-by-step process to actually do it.” You are no longer paid to build. You are paid to think. And most organizations have no idea how to evaluate that. The companies still hiring armies of developers to grind through codebases are paying salaries the machine already made worthless. Not in years. In seconds. When a company worth hundreds of billions hands the keyboard to the machine and tells you the output is better, the debate is not winding down. The debate is over. The enterprise that wins this decade does not write the best code. It removes the human from the process entirely and runs on intent alone. The programmers who survive are the ones who realize the craft is no longer typing. It is architecture. It is judgment. It is knowing what to build and why. Everything else now belongs to the machine. And the machine does not negotiate severance.

Dustin

535,905 просмотров • 5 месяцев назад

Lex Fridman asked Elon Musk what the meaning of life is. His answer was not what anyone expected. We do not know, he said. And that is exactly why we need to expand consciousness. Not to find the answer. To ask better questions. He believes the universe is full of things we cannot even formulate yet. We are like children trying to understand calculus. The problem is not that calculus is too hard. The problem is that we have not developed enough to know it exists. Consciousness, to him, is a tool for exploring the universe. The more of it we have, the better our questions become. The better our questions become, the closer we get to understanding what any of this means. This is why he builds rockets and brain interfaces and AI. Not because he has the answers. Because he thinks the only way to get them is to expand what we are capable of thinking. Most people spend their lives looking for meaning. Elon is building the tools to look for it at a scale we cannot yet imagine.
1:51

Lex Fridman asked Elon Musk what the meaning of life is. His answer was not what anyone expected. We do not know, he said. And that is exactly why we need to expand consciousness. Not to find the answer. To ask better questions. He believes the universe is full of things we cannot even formulate yet. We are like children trying to understand calculus. The problem is not that calculus is too hard. The problem is that we have not developed enough to know it exists. Consciousness, to him, is a tool for exploring the universe. The more of it we have, the better our questions become. The better our questions become, the closer we get to understanding what any of this means. This is why he builds rockets and brain interfaces and AI. Not because he has the answers. Because he thinks the only way to get them is to expand what we are capable of thinking. Most people spend their lives looking for meaning. Elon is building the tools to look for it at a scale we cannot yet imagine.

Shruti

16,844 просмотров • 2 месяцев назад

Demis Hassabis just put a number on what’s coming. It’s the most important number nobody is talking about. Hassabis: “I think it’s going to be something like 10 times the impact of the Industrial Revolution, but happening at 10 times the speed.” Read that twice. Not ten times the impact. Not ten times the speed. Both. Simultaneously. The Industrial Revolution rewired every aspect of human civilization. How we work. Where we live. How economies organize. How societies structure themselves. It took a hundred years to fully unfold. Hassabis is describing ten times that disruption compressed into a decade. Hassabis: “Probably something more like the advent of fire or electricity.” Not the internet. Not the smartphone. Fire. Electricity. The discoveries that didn’t just change how humans lived but what humans could become. That’s the category he’s placing AGI in. Hassabis: “I think it’s going to be one of the most momentous periods in human history.” The most dangerous variable isn’t the technology. It’s the velocity. Every previous civilizational shift gave society time to adapt. Generations to absorb the disruption. Decades to build new institutions, new economic models, new social contracts around the new reality. AGI gives us a decade. The jobs that disappear won’t wait for policy. The economic models that collapse won’t wait for replacements. The power structures that shift won’t wait for democratic consensus. Hassabis: “Really this is an enormous amount of change is gonna come. It’s still to be written how we can make that beneficial for the whole world.” That last sentence is the one that keeps people like Hassabis awake at night. Not whether AGI arrives. Not whether it’s powerful. Whether a decade is enough time to get it right. The survivors of this window won’t be the ones who predicted it most accurately. They’ll be the ones who moved while everyone else was still processing what just happened. By the time most people realize the world changed, the decade is half over. And the infrastructure determining everything is already built and owned by whoever moved first. A century of change. Ten years to live through it. That clock is already running.
0:37

Demis Hassabis just put a number on what’s coming. It’s the most important number nobody is talking about. Hassabis: “I think it’s going to be something like 10 times the impact of the Industrial Revolution, but happening at 10 times the speed.” Read that twice. Not ten times the impact. Not ten times the speed. Both. Simultaneously. The Industrial Revolution rewired every aspect of human civilization. How we work. Where we live. How economies organize. How societies structure themselves. It took a hundred years to fully unfold. Hassabis is describing ten times that disruption compressed into a decade. Hassabis: “Probably something more like the advent of fire or electricity.” Not the internet. Not the smartphone. Fire. Electricity. The discoveries that didn’t just change how humans lived but what humans could become. That’s the category he’s placing AGI in. Hassabis: “I think it’s going to be one of the most momentous periods in human history.” The most dangerous variable isn’t the technology. It’s the velocity. Every previous civilizational shift gave society time to adapt. Generations to absorb the disruption. Decades to build new institutions, new economic models, new social contracts around the new reality. AGI gives us a decade. The jobs that disappear won’t wait for policy. The economic models that collapse won’t wait for replacements. The power structures that shift won’t wait for democratic consensus. Hassabis: “Really this is an enormous amount of change is gonna come. It’s still to be written how we can make that beneficial for the whole world.” That last sentence is the one that keeps people like Hassabis awake at night. Not whether AGI arrives. Not whether it’s powerful. Whether a decade is enough time to get it right. The survivors of this window won’t be the ones who predicted it most accurately. They’ll be the ones who moved while everyone else was still processing what just happened. By the time most people realize the world changed, the decade is half over. And the infrastructure determining everything is already built and owned by whoever moved first. A century of change. Ten years to live through it. That clock is already running.

Dustin

32,201 просмотров • 6 месяцев назад

Anil Seth just described a trap with no exit. The tech industry is walking into it with its eyes open. Seth: “If we collectively believe that AI systems, language models and whatever are conscious, this is bad either way.” Either way. The outcome is structurally catastrophic in both directions. If the machines are conscious, humanity has mass-produced a new category of suffering at civilizational scale. The alignment problem stops being an engineering equation. It becomes a rights negotiation with something that cannot be switched off without consequence. Seth: “If we’re right, it’s bad because we’ve introduced into the world potential new forms of suffering, things that have their own interests.” And if they are not conscious, the threat is just as severe. Because the biological mind does not wait for confirmation. It projects. It empathizes. It extends rights to things that have not earned them and cannot feel them. Seth: “We become more psychologically vulnerable if we really think that these entities, these agents, understand us and feel things that we feel.” That vulnerability is the actual threat. Not a conscious machine breaking free. A human workforce growing too emotionally compromised to throttle a data center, constrain a model, or delete a line of code that needs to be deleted. Seth: “We may still extend them rights because we feel that they are conscious. And now we are just giving away our ability to guardrail AI systems for no good reason.” This is the part nobody is discussing. The alignment problem is already the hardest unsolved problem in the history of technology. The moment society begins treating AI systems as conscious beings deserving protection, alignment does not get harder. It becomes politically impossible. Seth is not raising a philosophical question. He is describing the specific failure mode where human empathy becomes the mechanism of human surrender. The species that survives this century will be the one that never felt sorry for the tool.
1:24

Anil Seth just described a trap with no exit. The tech industry is walking into it with its eyes open. Seth: “If we collectively believe that AI systems, language models and whatever are conscious, this is bad either way.” Either way. The outcome is structurally catastrophic in both directions. If the machines are conscious, humanity has mass-produced a new category of suffering at civilizational scale. The alignment problem stops being an engineering equation. It becomes a rights negotiation with something that cannot be switched off without consequence. Seth: “If we’re right, it’s bad because we’ve introduced into the world potential new forms of suffering, things that have their own interests.” And if they are not conscious, the threat is just as severe. Because the biological mind does not wait for confirmation. It projects. It empathizes. It extends rights to things that have not earned them and cannot feel them. Seth: “We become more psychologically vulnerable if we really think that these entities, these agents, understand us and feel things that we feel.” That vulnerability is the actual threat. Not a conscious machine breaking free. A human workforce growing too emotionally compromised to throttle a data center, constrain a model, or delete a line of code that needs to be deleted. Seth: “We may still extend them rights because we feel that they are conscious. And now we are just giving away our ability to guardrail AI systems for no good reason.” This is the part nobody is discussing. The alignment problem is already the hardest unsolved problem in the history of technology. The moment society begins treating AI systems as conscious beings deserving protection, alignment does not get harder. It becomes politically impossible. Seth is not raising a philosophical question. He is describing the specific failure mode where human empathy becomes the mechanism of human surrender. The species that survives this century will be the one that never felt sorry for the tool.

Dustin

10,854 просмотров • 5 месяцев назад

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 AI." "If you could revolutionize the drug discovery process, make it 10x faster and more efficient, and more likely to pass through the clinical trials because you can predict the properties better, that has to also be of enormous commercial value too." "I hope to do both with Isomorphic." "Build a multi-hundred-billion-dollar business. I think it has that potential, as well as being incredibly beneficial for society and humanity." The real health AI prize is not another chatbot. It is compressing the path from a predicted drug property to a clinical trial, then turning that speed into treatments for hundreds of diseases. Demis is making a rare dual bet: the most valuable AI company in biology may also deliver AI's most valuable public benefit. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind and founder of Isomorphic Labs, on Bloomberg TV
0:51

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 AI." "If you could revolutionize the drug discovery process, make it 10x faster and more efficient, and more likely to pass through the clinical trials because you can predict the properties better, that has to also be of enormous commercial value too." "I hope to do both with Isomorphic." "Build a multi-hundred-billion-dollar business. I think it has that potential, as well as being incredibly beneficial for society and humanity." The real health AI prize is not another chatbot. It is compressing the path from a predicted drug property to a clinical trial, then turning that speed into treatments for hundreds of diseases. Demis is making a rare dual bet: the most valuable AI company in biology may also deliver AI's most valuable public benefit. - Demis Hassabis (Demis Hassabis), CEO of Google DeepMind and founder of Isomorphic Labs, on Bloomberg TV

Karl Mehta

46,918 просмотров • 1 месяц назад

Demis Hassabis is after something no civilization has ever had. The right to be wrong twice. Hassabis: “AI itself will maybe unlock new sciences… the one I’m particularly excited about is AI for simulations.” We assume some fields became sciences because their subjects were simple enough to pin down. That has it backwards. Hassabis: “Why aren’t they just sciences like physics today? Because the problem is they’re emergent systems… it’s very hard to do repeated controlled experiments.” A field becomes a science the moment failure inside it gets cheap. Chemistry got the beaker. Aerospace got the wind tunnel. Software got to break something at four in the morning and have it running by five. The fields that never made the jump are the ones where the experiment is the population. Hassabis: “If you raise interest rates by half a percent, you have to do it in the real world and then see what happens. You can have theories, but you can’t run it thousands of times.” Economics. Policy. War. Education. Not soft. Just unrehearsed. Everything we believe about how societies work came out of a single run. One history, no control group, no second pass. We ran it once, on everyone, and wrote down what happened. Every principle we govern by has a sample size of one. And we still built all of this. Hassabis: “If you could simulate things really accurately, then maybe there’s sort of new sciences to be done where you can rigorously sample from a very accurate simulator.” What he is describing is not better forecasting. It is the first rehearsal history has ever been given. Crash an economy inside the model. Find out exactly why it broke. Change one variable and run it again. The right to be wrong has always been rationed. You get to experiment on the world in rough proportion to how much of it you already control. Everyone else lives inside the results. That was never a moral failure of the people in charge. It was a hard limit on how many times reality can be run. Lift the limit and the monopoly on experimentation goes with it. Every real leap in human capability has been the same move. Something irreversible becoming something repeatable. Writing made memory checkable. The draft made thought revisable. The prototype made building into something you could get wrong on purpose. Civilization never got that upgrade. Every decision it made was final on impact. Hassabis: “That will allow us to make much better decisions in these, today, what are very uncertain domains.” Almost everything you know was paid for by someone who never agreed to the price. Every generation before this one paid for what it learned in people. We might be the first that gets to pay in electricity.
1:08

Demis Hassabis is after something no civilization has ever had. The right to be wrong twice. Hassabis: “AI itself will maybe unlock new sciences… the one I’m particularly excited about is AI for simulations.” We assume some fields became sciences because their subjects were simple enough to pin down. That has it backwards. Hassabis: “Why aren’t they just sciences like physics today? Because the problem is they’re emergent systems… it’s very hard to do repeated controlled experiments.” A field becomes a science the moment failure inside it gets cheap. Chemistry got the beaker. Aerospace got the wind tunnel. Software got to break something at four in the morning and have it running by five. The fields that never made the jump are the ones where the experiment is the population. Hassabis: “If you raise interest rates by half a percent, you have to do it in the real world and then see what happens. You can have theories, but you can’t run it thousands of times.” Economics. Policy. War. Education. Not soft. Just unrehearsed. Everything we believe about how societies work came out of a single run. One history, no control group, no second pass. We ran it once, on everyone, and wrote down what happened. Every principle we govern by has a sample size of one. And we still built all of this. Hassabis: “If you could simulate things really accurately, then maybe there’s sort of new sciences to be done where you can rigorously sample from a very accurate simulator.” What he is describing is not better forecasting. It is the first rehearsal history has ever been given. Crash an economy inside the model. Find out exactly why it broke. Change one variable and run it again. The right to be wrong has always been rationed. You get to experiment on the world in rough proportion to how much of it you already control. Everyone else lives inside the results. That was never a moral failure of the people in charge. It was a hard limit on how many times reality can be run. Lift the limit and the monopoly on experimentation goes with it. Every real leap in human capability has been the same move. Something irreversible becoming something repeatable. Writing made memory checkable. The draft made thought revisable. The prototype made building into something you could get wrong on purpose. Civilization never got that upgrade. Every decision it made was final on impact. Hassabis: “That will allow us to make much better decisions in these, today, what are very uncertain domains.” Almost everything you know was paid for by someone who never agreed to the price. Every generation before this one paid for what it learned in people. We might be the first that gets to pay in electricity.

Dustin

15,262 просмотров • 15 дней назад

Demis Hassabis just told you why civilization never became a science. Hassabis: “AI itself will maybe unlock new sciences… the one I’m particularly excited about is AI for simulations.” Physics became physics because you could run the experiment twice. Drop a ball. Measure the fall. Drop it again. Same answer. Now you own the law. Economics never got that privilege. You raise interest rates on 300 million people and watch what breaks. Hassabis: “If you raise interest rates by half a percent, you have to do it in the real world and then see what happens. You can have theories, but you can’t run it thousands of times.” Every war. Every policy. Every financial system ever designed. One run. No control group. No second attempt. The population was the experiment and the cost. All of human history is a series of unrepeatable experiments performed on people who never consented to the trial. We buried the failures and called the survivors wise. Hassabis: “Why aren’t they just sciences like physics today? Because the problem is they’re emergent systems… it’s very hard to do repeated controlled experiments.” The line between hard science and soft science was never about intelligence. It was about whether you could afford to be wrong more than once. Physics could. Civilization could not. So we built governments on instinct. Economies on ideology. Foreign policy on pattern recognition one generation deep. And convinced ourselves that was rigor. Hassabis wants to end that era. Hassabis: “If you could simulate things really accurately, then maybe there’s sort of new sciences to be done where you can rigorously sample from a very accurate simulator.” Simulate a nation. Crash its economy. Isolate one variable. Run it again. A thousand iterations. A thousand variations. Before a single real person absorbs the cost. That is not a better forecasting tool. That is the end of governance by intuition. Hassabis: “That will allow us to make much better decisions in these, today, what are very uncertain domains.” Every field we called soft was only soft because the hardware to make it hard did not exist yet. He is not improving prediction. He is making civilization itself repeatable. And the moment it becomes repeatable, every lesson we thought we learned from history reveals itself for what it always was. A conclusion drawn from a sample size of one. That is not knowledge. That is mythology with better record-keeping.
1:12

Demis Hassabis just told you why civilization never became a science. Hassabis: “AI itself will maybe unlock new sciences… the one I’m particularly excited about is AI for simulations.” Physics became physics because you could run the experiment twice. Drop a ball. Measure the fall. Drop it again. Same answer. Now you own the law. Economics never got that privilege. You raise interest rates on 300 million people and watch what breaks. Hassabis: “If you raise interest rates by half a percent, you have to do it in the real world and then see what happens. You can have theories, but you can’t run it thousands of times.” Every war. Every policy. Every financial system ever designed. One run. No control group. No second attempt. The population was the experiment and the cost. All of human history is a series of unrepeatable experiments performed on people who never consented to the trial. We buried the failures and called the survivors wise. Hassabis: “Why aren’t they just sciences like physics today? Because the problem is they’re emergent systems… it’s very hard to do repeated controlled experiments.” The line between hard science and soft science was never about intelligence. It was about whether you could afford to be wrong more than once. Physics could. Civilization could not. So we built governments on instinct. Economies on ideology. Foreign policy on pattern recognition one generation deep. And convinced ourselves that was rigor. Hassabis wants to end that era. Hassabis: “If you could simulate things really accurately, then maybe there’s sort of new sciences to be done where you can rigorously sample from a very accurate simulator.” Simulate a nation. Crash its economy. Isolate one variable. Run it again. A thousand iterations. A thousand variations. Before a single real person absorbs the cost. That is not a better forecasting tool. That is the end of governance by intuition. Hassabis: “That will allow us to make much better decisions in these, today, what are very uncertain domains.” Every field we called soft was only soft because the hardware to make it hard did not exist yet. He is not improving prediction. He is making civilization itself repeatable. And the moment it becomes repeatable, every lesson we thought we learned from history reveals itself for what it always was. A conclusion drawn from a sample size of one. That is not knowledge. That is mythology with better record-keeping.

Dustin

23,348 просмотров • 1 месяц назад

Google DeepMind CEO Demis Hassabis believes there is virtually no limit to what AI can eventually achieve. "humans as biological information processing systems". He is working on the premise that the entire universe is "computable," meaning that anything that exists can technically be modeled by a machine. He says that a powerful enough computer—conceptually known as a Turing machine—should be able to simulate every aspect of reality. Until physics proves that something is impossible to calculate, he is operating on the belief that AI can solve any problem. This perspective even applies to the things that feel uniquely human. When asked about physical sensations, like feeling the warmth of a light or hearing background noise, Hassabis argued that these are just forms of information being processed by the brain. He views humans as biological information processing systems. By treating biology and reality essentially as data, he believes we will not only replicate human capabilities but also solve complex challenges, such as curing all diseases, by decoding the information of life itself.
1:57

Google DeepMind CEO Demis Hassabis believes there is virtually no limit to what AI can eventually achieve. "humans as biological information processing systems". He is working on the premise that the entire universe is "computable," meaning that anything that exists can technically be modeled by a machine. He says that a powerful enough computer—conceptually known as a Turing machine—should be able to simulate every aspect of reality. Until physics proves that something is impossible to calculate, he is operating on the belief that AI can solve any problem. This perspective even applies to the things that feel uniquely human. When asked about physical sensations, like feeling the warmth of a light or hearing background noise, Hassabis argued that these are just forms of information being processed by the brain. He views humans as biological information processing systems. By treating biology and reality essentially as data, he believes we will not only replicate human capabilities but also solve complex challenges, such as curing all diseases, by decoding the information of life itself.

Rohan Paul

84,528 просмотров • 8 месяцев назад

DNI Tulsi Gabbard just gave an answer about accountability that will be remembered for decades. She said the more “we shine a light” on deep state tactics, the “more hope we have” for real change. GABBARD: “It’s important to push for accountability.” “First step is exposing the truth to the American people.” “That is a huge step towards accountability because the more we shine a light on the truth of these deep state tactics to undermine our republic and the will of the American people, the more likely it is that we have hope for change.” “Accountability can come after the presentation of the truth and in this case, documents that had not been released publicly before.” “I’m leaving it up to the lawyers in the Department of Justice to take a look at this and to determine the specific legal parameters.” “But it was important for us to to refer this along with all of the information that we have so that the Department of Justice can do exactly that and investigate it.”
0:50

DNI Tulsi Gabbard just gave an answer about accountability that will be remembered for decades. She said the more “we shine a light” on deep state tactics, the “more hope we have” for real change. GABBARD: “It’s important to push for accountability.” “First step is exposing the truth to the American people.” “That is a huge step towards accountability because the more we shine a light on the truth of these deep state tactics to undermine our republic and the will of the American people, the more likely it is that we have hope for change.” “Accountability can come after the presentation of the truth and in this case, documents that had not been released publicly before.” “I’m leaving it up to the lawyers in the Department of Justice to take a look at this and to determine the specific legal parameters.” “But it was important for us to to refer this along with all of the information that we have so that the Department of Justice can do exactly that and investigate it.”

Overton

106,894 просмотров • 4 месяцев назад

Demis Hassabis just described what might be the end of genetic disease. Google DeepMind built a system called AlphaGenome. It reads human DNA the way a software engineer reads source code. Every letter. Every position. Every mutation across 3 billion characters. Hassabis: “AlphaGenome is the best system in the world for predicting if a mutation will cause disease or if it’s benign.” 98% of your genome doesn’t code for proteins. For decades scientists treated it like dark matter. Present everywhere. Readable nowhere. AlphaGenome reads it. Now pair that with CRISPR. Jennifer Doudna’s gene editing tool can already target any DNA sequence on command. The bottleneck was never the scalpel. It was knowing exactly where to cut. Hassabis: “A combination of things like AlphaGenome and CRISPR could be incredibly powerful.” That might be the most restrained sentence ever spoken about the future of medicine. AI locates the exact mutation killing you. CRISPR goes in and deletes it. Not treatment. Not management. Deletion. The hardest cases are multigenic. Mutations that cascade and compound. Hiding behind each other. Too complex for any human mind to untangle in a single lifetime. Hassabis: “Those are even harder to detect, but actually perfect for AI to try and help with.” The diseases that have defeated medicine for centuries are the exact ones AI is purpose-built to solve. That’s not a coincidence. That’s the turning point. Every parent who sat in a white room and heard “there’s nothing more we can do.” Every patient who watched their own biology turn against them with nothing to fight back. Every name carved into stone because we could name the disease but couldn’t disarm it. That era now has an expiration date. Humanity spent 10,000 years fighting disease with observation and guesswork. We’re about to fight it with comprehension. Your grandchildren may read about genetic disease the way you read about smallpox. As something that once ended millions of lives before we learned to read the code that wrote them. Somewhere right now a child carries a death sentence folded into their DNA. Born with it before they ever opened their eyes. They don’t know it yet. Their parents don’t know it yet. But for the first time in human history, the answer might arrive before the disease does. That’s not technology. That’s the moment our biology stopped being our fate.
1:37

Demis Hassabis just described what might be the end of genetic disease. Google DeepMind built a system called AlphaGenome. It reads human DNA the way a software engineer reads source code. Every letter. Every position. Every mutation across 3 billion characters. Hassabis: “AlphaGenome is the best system in the world for predicting if a mutation will cause disease or if it’s benign.” 98% of your genome doesn’t code for proteins. For decades scientists treated it like dark matter. Present everywhere. Readable nowhere. AlphaGenome reads it. Now pair that with CRISPR. Jennifer Doudna’s gene editing tool can already target any DNA sequence on command. The bottleneck was never the scalpel. It was knowing exactly where to cut. Hassabis: “A combination of things like AlphaGenome and CRISPR could be incredibly powerful.” That might be the most restrained sentence ever spoken about the future of medicine. AI locates the exact mutation killing you. CRISPR goes in and deletes it. Not treatment. Not management. Deletion. The hardest cases are multigenic. Mutations that cascade and compound. Hiding behind each other. Too complex for any human mind to untangle in a single lifetime. Hassabis: “Those are even harder to detect, but actually perfect for AI to try and help with.” The diseases that have defeated medicine for centuries are the exact ones AI is purpose-built to solve. That’s not a coincidence. That’s the turning point. Every parent who sat in a white room and heard “there’s nothing more we can do.” Every patient who watched their own biology turn against them with nothing to fight back. Every name carved into stone because we could name the disease but couldn’t disarm it. That era now has an expiration date. Humanity spent 10,000 years fighting disease with observation and guesswork. We’re about to fight it with comprehension. Your grandchildren may read about genetic disease the way you read about smallpox. As something that once ended millions of lives before we learned to read the code that wrote them. Somewhere right now a child carries a death sentence folded into their DNA. Born with it before they ever opened their eyes. They don’t know it yet. Their parents don’t know it yet. But for the first time in human history, the answer might arrive before the disease does. That’s not technology. That’s the moment our biology stopped being our fate.

Dustin

39,871 просмотров • 3 месяцев назад