Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

DeepMind's Mostafa Dehghani says recursive self-improvement (RSI) isn't sci-fi anymore Almost every lab now uses previous-generation models to build the next one. It's not fully automated yet "what's missing is long-horizon planning and full automation" Once that loop closes, progress is mostly limited by compute, not humans

Haider.

68,910 subscribers

18,408 views • 5 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Elon Musk just confirmed when humans exit the AI development loop entirely. Not eventually. Not theoretically. He gave a date. Musk: “Humans are gradually getting less and less in the loop on the recursive self-improvement. Every successive model is built by the one before it.” The market still assumes thousands of engineers are hand-coding the next frontier model. The compute already took over the build. Musk: “That is happening to a large degree, but it’s not yet fully automated. It may be there, if not this year, but not later than next year.” Once biological friction is fully automated out of the loop, the AI arms race detaches from human comprehension. Permanently. Global markets are still planning for a gradual transition. That timeline is already dead. Interviewer: “Do you see a hard takeoff at that point?” Musk: “We’re in the hard takeoff. Right now.” Musk: “I go to sleep, there’s some massive AI breakthrough. And when I wake up there’s another one.” You don’t outrun autonomous, self-improving intelligence by hiring more biological engineers. By the time the hiring sprint finishes, the model has already iterated into something the job description can’t describe. The architects of intelligence are no longer biological. And the thing building itself didn’t ask for the job.

Elon Musk just confirmed when humans exit the AI development loop entirely. Not eventually. Not theoretically. He gave a date. Musk: “Humans are gradually getting less and less in the loop on the recursive self-improvement. Every successive model is built by the one before it.” The market still assumes thousands of engineers are hand-coding the next frontier model. The compute already took over the build. Musk: “That is happening to a large degree, but it’s not yet fully automated. It may be there, if not this year, but not later than next year.” Once biological friction is fully automated out of the loop, the AI arms race detaches from human comprehension. Permanently. Global markets are still planning for a gradual transition. That timeline is already dead. Interviewer: “Do you see a hard takeoff at that point?” Musk: “We’re in the hard takeoff. Right now.” Musk: “I go to sleep, there’s some massive AI breakthrough. And when I wake up there’s another one.” You don’t outrun autonomous, self-improving intelligence by hiring more biological engineers. By the time the hiring sprint finishes, the model has already iterated into something the job description can’t describe. The architects of intelligence are no longer biological. And the thing building itself didn’t ask for the job.

Dustin

323,101 views • 6 months ago

Elon Musk: We're in the hard takeoff right now. Every successive AI model is built by the one before it. Peter Diamandis: Do you see Grok doing recursive self-improvement at this point? And how? What's the timeline for AGI and ASI? Give us a sense of that. Elon Musk: Yeah, I think we've been in recursive improvement for a while here. If you mean by, like, recursive self-improvement without a human in the loop, is that what you mean? Peter Diamandis: I do. Elon Musk: On the AI software side, I mean, humans are gradually getting less and less in the loop on the recursive self-improvement. So every successive model is built by the one before it. So that is happening to a large degree, but it's not yet fully automated. It may be there end of this year, but not later than next year. Peter Diamandis: Do you see a hard takeoff at that point? Elon Musk: We're in the hard takeoff. Peter Diamandis: Okay. Elon Musk: Right now, yes. I mean, at this point I go to sleep, there's some massive AI breakthrough, and when I wake up, there's another one. Yes. Yeah, it's hard to keep track, honestly. So, yeah, it's a bit of a head spinner. Moonshots with Peter Diamandis (Abundance Summit 2026), March 18, 2026.

Elon Musk: We're in the hard takeoff right now. Every successive AI model is built by the one before it. Peter Diamandis: Do you see Grok doing recursive self-improvement at this point? And how? What's the timeline for AGI and ASI? Give us a sense of that. Elon Musk: Yeah, I think we've been in recursive improvement for a while here. If you mean by, like, recursive self-improvement without a human in the loop, is that what you mean? Peter Diamandis: I do. Elon Musk: On the AI software side, I mean, humans are gradually getting less and less in the loop on the recursive self-improvement. So every successive model is built by the one before it. So that is happening to a large degree, but it's not yet fully automated. It may be there end of this year, but not later than next year. Peter Diamandis: Do you see a hard takeoff at that point? Elon Musk: We're in the hard takeoff. Peter Diamandis: Okay. Elon Musk: Right now, yes. I mean, at this point I go to sleep, there's some massive AI breakthrough, and when I wake up, there's another one. Yes. Yeah, it's hard to keep track, honestly. So, yeah, it's a bit of a head spinner. Moonshots with Peter Diamandis (Abundance Summit 2026), March 18, 2026.

ELON CLIPS

19,692 views • 3 months ago

Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)

Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)

Ihtesham Ali

68,231 views • 3 months ago

SITUATION EXPLAINED: Paradigm built a playable RSI simulator, and the fastest singularity is 26 days. • You run an AI lab from 2017 through takeoff, built on "The Economics of Recursive Self-Improvement" from the Elasticity Institute • Stage one is a single researcher clicking think, with the rate of improvement visibly slowing as you click • Stage two is human capital, the second researcher adds 80% productivity and each one after adds less • Stage three is the bitter lesson, where you stop scaling researchers and start scaling compute • Stage four is the scaling hypothesis, 20,000 GPUs and a model trained to a ten-second time horizon • A separate RSI Model Explorer lets you set return to R&D, algorithmic efficiency, idea stock spillover, capability response, and research response • Max every assumption and you get a singularity 26 days from founding the lab Theo Jaffee: "This is a really cool game. I think this will help a lot of normies understand how AI progress works, to a first approximation."

SITUATION EXPLAINED: Paradigm built a playable RSI simulator, and the fastest singularity is 26 days. • You run an AI lab from 2017 through takeoff, built on "The Economics of Recursive Self-Improvement" from the Elasticity Institute • Stage one is a single researcher clicking think, with the rate of improvement visibly slowing as you click • Stage two is human capital, the second researcher adds 80% productivity and each one after adds less • Stage three is the bitter lesson, where you stop scaling researchers and start scaling compute • Stage four is the scaling hypothesis, 20,000 GPUs and a model trained to a ten-second time horizon • A separate RSI Model Explorer lets you set return to R&D, algorithmic efficiency, idea stock spillover, capability response, and research response • Max every assumption and you get a singularity 26 days from founding the lab Theo Jaffee: "This is a really cool game. I think this will help a lot of normies understand how AI progress works, to a first approximation."

MTS

25,661 views • 1 month ago

"to me, it's not about the human in the loop, but the human at the center." i sat down with Stefania Druga, staff research scientist Sakana AI, at our Browserbase booth at AI Engineer. after Massachusetts Institute of Technology (MIT) and Google DeepMind, stefania recently joined sakana's recursive self-improvement (rsi) lab in tokyo to build self-improving AI for the next frontier. we got into: - catastrophic forgetting in day-long agent research runs, and how memory-first harnesses help - sovereign AI and open-weights: building on models you control instead of locking into one provider - why computer use still frustrates people even as models get smarter - the domains ripe for self-improving research agents: math, genomics, rare conditions - my fav moment at the end: what tasks are better left human, and what are we going to do as humans after everything is automated? with open frontier models arriving fast, Stefania Druga's case for sovereign AI only gets stronger. loved this conversation. thanks for joining us at the Browserbase booth!

"to me, it's not about the human in the loop, but the human at the center." i sat down with Stefania Druga, staff research scientist Sakana AI, at our Browserbase booth at AI Engineer. after Massachusetts Institute of Technology (MIT) and Google DeepMind, stefania recently joined sakana's recursive self-improvement (rsi) lab in tokyo to build self-improving AI for the next frontier. we got into: - catastrophic forgetting in day-long agent research runs, and how memory-first harnesses help - sovereign AI and open-weights: building on models you control instead of locking into one provider - why computer use still frustrates people even as models get smarter - the domains ripe for self-improving research agents: math, genomics, rare conditions - my fav moment at the end: what tasks are better left human, and what are we going to do as humans after everything is automated? with open frontier models arriving fast, Stefania Druga's case for sovereign AI only gets stronger. loved this conversation. thanks for joining us at the Browserbase booth!

justine

19,853 views • 2 months ago

RSI section from the AI documentary Machine God The next threshold is Recursive Self-Improvement: the moment when AI can improve itself without human assistance. For decades this sounded like science fiction. Intelligence explosion scenarios imagined a system rewriting its own code, becoming smarter, then using that new intelligence to make still better versions of itself. But the idea looks less remote now that AI contributes directly to frontier science. In mathematics, recent systems have moved beyond solving contest problems to producing serious new arguments on long-standing open problems. AI is used to build physics world models and propose candidate theories or computational methods. These are early signs that machine cognition is entering the creative loop of science itself. The crucial transition comes when that loop turns inward. AI research is, after all, a technical discipline made of code, mathematics, models of information flow. These are exactly the domains in which frontier models are improving fastest. A model that can solve hard mathematical problems, write production-quality code, design experiments, read the literature, and evaluate benchmark results is already participating in the work of building its successor. At first this will look prosaic. AI systems will write kernel optimizations, improve training infrastructure, discover better data filters, tune reinforcement-learning pipelines, design new benchmarks, and suggest architectural modifications. Human researchers will remain in the loop, approving changes and interpreting results. But the important point is that the search process accelerates. The model becomes not just the product of the lab, but part of the lab’s research machinery. The system being optimized helps optimize the next system. This is the core RSI feedback loop: better models make AI research faster; faster AI research produces still better models; those models, in turn, become better researchers. The danger is that once this loop becomes sufficiently autonomous, it may stop resembling ordinary technological progress. Human institutions are slow because humans are slow: we read papers, attend meetings, debug code, sleep, argue, and wait for funding cycles. Machines do not have to operate on that timescale. An AI research collective can run continuously across millions of processors. This is the runaway possibility. Not that an AI instantly wakes up and recursively rewrites itself into a god, but that the entire AI ecosystem becomes an autocatalytic process. Capital buys compute; compute trains models; models improve models; better models attract more capital. At some point the dominant input into AI progress may no longer be human insight, but machine-generated insight, machine-written code, and machine-run experiments. Then the Butler-Land analogy becomes sharper. Humanity is no longer merely building machines. We are building machines that help build better machines. Once intelligence itself becomes part of the production function, the old categories — tool, worker, inventor, firm, market — begin to blur. The question is whether recursive self-improvement remains a managed industrial process, or whether it becomes the first technological process in history whose natural endpoint lies beyond human comprehension.

RSI section from the AI documentary Machine God The next threshold is Recursive Self-Improvement: the moment when AI can improve itself without human assistance. For decades this sounded like science fiction. Intelligence explosion scenarios imagined a system rewriting its own code, becoming smarter, then using that new intelligence to make still better versions of itself. But the idea looks less remote now that AI contributes directly to frontier science. In mathematics, recent systems have moved beyond solving contest problems to producing serious new arguments on long-standing open problems. AI is used to build physics world models and propose candidate theories or computational methods. These are early signs that machine cognition is entering the creative loop of science itself. The crucial transition comes when that loop turns inward. AI research is, after all, a technical discipline made of code, mathematics, models of information flow. These are exactly the domains in which frontier models are improving fastest. A model that can solve hard mathematical problems, write production-quality code, design experiments, read the literature, and evaluate benchmark results is already participating in the work of building its successor. At first this will look prosaic. AI systems will write kernel optimizations, improve training infrastructure, discover better data filters, tune reinforcement-learning pipelines, design new benchmarks, and suggest architectural modifications. Human researchers will remain in the loop, approving changes and interpreting results. But the important point is that the search process accelerates. The model becomes not just the product of the lab, but part of the lab’s research machinery. The system being optimized helps optimize the next system. This is the core RSI feedback loop: better models make AI research faster; faster AI research produces still better models; those models, in turn, become better researchers. The danger is that once this loop becomes sufficiently autonomous, it may stop resembling ordinary technological progress. Human institutions are slow because humans are slow: we read papers, attend meetings, debug code, sleep, argue, and wait for funding cycles. Machines do not have to operate on that timescale. An AI research collective can run continuously across millions of processors. This is the runaway possibility. Not that an AI instantly wakes up and recursively rewrites itself into a god, but that the entire AI ecosystem becomes an autocatalytic process. Capital buys compute; compute trains models; models improve models; better models attract more capital. At some point the dominant input into AI progress may no longer be human insight, but machine-generated insight, machine-written code, and machine-run experiments. Then the Butler-Land analogy becomes sharper. Humanity is no longer merely building machines. We are building machines that help build better machines. Once intelligence itself becomes part of the production function, the old categories — tool, worker, inventor, firm, market — begin to blur. The question is whether recursive self-improvement remains a managed industrial process, or whether it becomes the first technological process in history whose natural endpoint lies beyond human comprehension.

steve hsu

61,671 views • 19 days ago

2026 is the year AI automation goes mainstream. But there's one problem. Every automation creates value, yet almost all of that value flows to Big Tech and billion-dollar companies. That's where Actionist.AI changes the game. Powered by Action Model, Actionist is an AI automation platform where the people helping train and grow the AI can share in the value it creates, not just shareholders and billionaires. Automation is inevitable. Who benefits from it isn't. Join our Discord to be among the first to experience Actionist before anyone else.

2026 is the year AI automation goes mainstream. But there's one problem. Every automation creates value, yet almost all of that value flows to Big Tech and billion-dollar companies. That's where Actionist.AI changes the game. Powered by Action Model, Actionist is an AI automation platform where the people helping train and grow the AI can share in the value it creates, not just shareholders and billionaires. Automation is inevitable. Who benefits from it isn't. Join our Discord to be among the first to experience Actionist before anyone else.

Action Model

25,555 views • 2 months ago

Andrej Karpathy: "The future is not writing code. The future is writing the loops that write the code. The leverage point has moved one floor up." This is the shift that defines the next era of coding and quant building. 1 hour of pure insight on agentic AI coding and loop engineering from the OpenAI co-founder who just joined Anthropic to build self improving Claude. Worth more than every $500 vibe coding course combined. Watch it, then read the full guide on how to build your first self improving quant trading loop below.

Andrej Karpathy: "The future is not writing code. The future is writing the loops that write the code. The leverage point has moved one floor up." This is the shift that defines the next era of coding and quant building. 1 hour of pure insight on agentic AI coding and loop engineering from the OpenAI co-founder who just joined Anthropic to build self improving Claude. Worth more than every $500 vibe coding course combined. Watch it, then read the full guide on how to build your first self improving quant trading loop below.

Roan

38,070 views • 3 months ago

🚨 Sam Altman: there's a very important thing happening. I did not expect 3 or 6 months ago to be at this point we're at now” We now have a confirmed reason why OpenAI pulled out of Sora and it was as most of us expected - because they needed to concentrate all of their compute and product capacity into their next generation of models and automated agents. Sam says: "We have a few times in our history realized something really important is working or about to work so well that we have to stop a bunch of other projects. In fact, this was the original thing happened with GPT-3. We had a whole portfolio of bets at the time... we shut down many projects that were working well like robotics... so that we could concentrate our compute, our researchers, our effort into this thing that we said, okay, there's a very important thing happening." "I did not expect 3 or 6 months ago to be at this point we're at now, where something very big and important is about to happen again with this next generation of models and the agents they can power." "We need to concentrate our compute and our product capacity into these next generation of automated researchers and companies... the note under the note is it's about compute. It's always about compute."

🚨 Sam Altman: there's a very important thing happening. I did not expect 3 or 6 months ago to be at this point we're at now” We now have a confirmed reason why OpenAI pulled out of Sora and it was as most of us expected - because they needed to concentrate all of their compute and product capacity into their next generation of models and automated agents. Sam says: "We have a few times in our history realized something really important is working or about to work so well that we have to stop a bunch of other projects. In fact, this was the original thing happened with GPT-3. We had a whole portfolio of bets at the time... we shut down many projects that were working well like robotics... so that we could concentrate our compute, our researchers, our effort into this thing that we said, okay, there's a very important thing happening." "I did not expect 3 or 6 months ago to be at this point we're at now, where something very big and important is about to happen again with this next generation of models and the agents they can power." "We need to concentrate our compute and our product capacity into these next generation of automated researchers and companies... the note under the note is it's about compute. It's always about compute."

Chris

243,817 views • 5 months ago

a16z's Anish Acharya on the AI era's "permanent underclass" and the reasons he doesn't buy it: "It's a funny dark fantasy that we seem to have as Silicon Valley collectively. Things have never been better, really by almost every measure." "By how distributed all the opportunities are, by the kind of technology we have access to, to the types of ambition we're allowed to have, and to the number of companies independently working on things that are winning." "The last era of tech was a lot more centralized... If you look at what's happening now, every part of the stack, there's not even two relevant players. There's like 20." "You've heard all the economic data... And of course job postings are higher than they've ever been, as well as programmers. So I don't know that the empirical data bears it out." "RSI, recursive self-improvement, is a fun term to throw around. But if you ask the most sophisticated individuals at the labs, it's not actually RSI that's occurring, which could lead to some runaway winner... It's autocatalytic effects, which just means you're using the new technology to improve your process, but it's not truly recursive." "So everything from the most empirical to the most technical points in the other direction, and yet we can't seem to let go of this fantasy." Anish Acharya w/ Lenny Rachitsky

a16z's Anish Acharya on the AI era's "permanent underclass" and the reasons he doesn't buy it: "It's a funny dark fantasy that we seem to have as Silicon Valley collectively. Things have never been better, really by almost every measure." "By how distributed all the opportunities are, by the kind of technology we have access to, to the types of ambition we're allowed to have, and to the number of companies independently working on things that are winning." "The last era of tech was a lot more centralized... If you look at what's happening now, every part of the stack, there's not even two relevant players. There's like 20." "You've heard all the economic data... And of course job postings are higher than they've ever been, as well as programmers. So I don't know that the empirical data bears it out." "RSI, recursive self-improvement, is a fun term to throw around. But if you ask the most sophisticated individuals at the labs, it's not actually RSI that's occurring, which could lead to some runaway winner... It's autocatalytic effects, which just means you're using the new technology to improve your process, but it's not truly recursive." "So everything from the most empirical to the most technical points in the other direction, and yet we can't seem to let go of this fantasy." Anish Acharya w/ Lenny Rachitsky

a16z

34,642 views • 18 days ago

STEVE BANNON: This is a national security situation. When I said about I'd give you another task and purpose to save your country, yo, on your shoulders, I think is to save our species. MARK BEALL: The critical issue is that we don't understand what happens once they let off this process, the companies let off this process called recursive self-improvement. This RSI, as it's known, is when an AI model builds the next version of itself, that this could be like a nuclear chain reaction, the endpoint of which we simply do not know.

STEVE BANNON: This is a national security situation. When I said about I'd give you another task and purpose to save your country, yo, on your shoulders, I think is to save our species. MARK BEALL: The critical issue is that we don't understand what happens once they let off this process, the companies let off this process called recursive self-improvement. This RSI, as it's known, is when an AI model builds the next version of itself, that this could be like a nuclear chain reaction, the endpoint of which we simply do not know.

Bannon’s WarRoom

14,161 views • 1 month ago

Ex-OpenAI safety researcher Steven Adler says AI labs are monitoring their models like a bank that lets the robber turn off the security cameras: "Imagine you ran a bank, and you want the bank to not get robbed. The way you do this is not setting up a security camera and every hour you check the feed to see if the bank was robbed in that time, and then you try to respond." "You certainly don't leave it where a criminal could walk into the bank and turn off the camera, and that's the equivalent of what's happening at AI labs today, not just in terms of automated AI R&D and recursive self-improvement, but broadly across the board."

Ex-OpenAI safety researcher Steven Adler says AI labs are monitoring their models like a bank that lets the robber turn off the security cameras: "Imagine you ran a bank, and you want the bank to not get robbed. The way you do this is not setting up a security camera and every hour you check the feed to see if the bank was robbed in that time, and then you try to respond." "You certainly don't leave it where a criminal could walk into the bank and turn off the camera, and that's the equivalent of what's happening at AI labs today, not just in terms of automated AI R&D and recursive self-improvement, but broadly across the board."

MTS

16,470 views • 1 month ago

Vals AI CEO Rayan Krishnan on how they score a model's ability to build its own successor: "One benchmark we released recently is our Recursive Self-Improvement Index. It's a topic which a lot of the big labs have been talking about and starting to report on in their model cards." "There isn't a shared language to talk about the RSI potential of models. So we created this as an apples-to-apples way to actually benchmark across the models." "In an ideal world, what you want to do is take a frontier model and have it train the next version of itself and see where the delta comes from. But obviously that's very expensive and slow." "What we're doing is forming a set of proxies for every part of the process it takes to build the next version of the model." "There's work around pre-training, post-training, harness-level engineering, and then seeing in which mechanisms and behaviors the models are able to do very good research work and build something new, and where they're struggling." Rayan Krishnan Jennifer Li

Vals AI CEO Rayan Krishnan on how they score a model's ability to build its own successor: "One benchmark we released recently is our Recursive Self-Improvement Index. It's a topic which a lot of the big labs have been talking about and starting to report on in their model cards." "There isn't a shared language to talk about the RSI potential of models. So we created this as an apples-to-apples way to actually benchmark across the models." "In an ideal world, what you want to do is take a frontier model and have it train the next version of itself and see where the delta comes from. But obviously that's very expensive and slow." "What we're doing is forming a set of proxies for every part of the process it takes to build the next version of the model." "There's work around pre-training, post-training, harness-level engineering, and then seeing in which mechanisms and behaviors the models are able to do very good research work and build something new, and where they're struggling." Rayan Krishnan Jennifer Li

a16z

34,852 views • 17 days ago

Read the actual Anthropic piece. It's wilder than the hype precisely because it's so measured. As of last month, Claude writes more than 80% of the code Anthropic merges into its own codebase. Their engineers now ship roughly 8x the code per day they did in 2024. On a fixed test they run every release (take some training code, make it run faster), the model went from ~3x a year ago to ~52x. A skilled human gets about 4x in a few hours. And when a researcher hits a wrong turn mid-investigation, the newest model now picks the better next step 64% of the time. The honest part the hype skips is that Anthropic says we are NOT at self-improvement yet. Claude still can't decide which problems are worth solving. That research taste is the last thing keeping a human in the loop. But they openly map the moment it closes, when agents can build and train models themselves and Claude starts improving Claude. And the loudest signal in the whole thing sits at the end: the company building this is the one asking for a working pause button before the loop closes.

Read the actual Anthropic piece. It's wilder than the hype precisely because it's so measured. As of last month, Claude writes more than 80% of the code Anthropic merges into its own codebase. Their engineers now ship roughly 8x the code per day they did in 2024. On a fixed test they run every release (take some training code, make it run faster), the model went from ~3x a year ago to ~52x. A skilled human gets about 4x in a few hours. And when a researcher hits a wrong turn mid-investigation, the newest model now picks the better next step 64% of the time. The honest part the hype skips is that Anthropic says we are NOT at self-improvement yet. Claude still can't decide which problems are worth solving. That research taste is the last thing keeping a human in the loop. But they openly map the moment it closes, when agents can build and train models themselves and Claude starts improving Claude. And the loudest signal in the whole thing sits at the end: the company building this is the one asking for a working pause button before the loop closes.

Alex Veremeyenko

33,736 views • 3 months ago

Zuckerberg says Meta's Llama race changes in the next 12-18 months: most of the code will be written by AI - not autocomplete, but agents that run tests and improve the model loop. "We're not trying to build a general developer tool. We are trying to build a coding agent and an AI research agent that basically advances Llama research specifically." "Sometime in the next 12 to 18 months, we'll reach the point where most of the code that's going towards these efforts is written by AI." "And I don't mean autocomplete." "I'm talking more like you give it a goal, it can run tests, it can improve things, it can find issues." "It writes higher quality code than the average very good person on the team already." The hidden bottleneck is not chat UX. It is closing the loop between AI research, code, tests, and self-improvement inside the lab.

Zuckerberg says Meta's Llama race changes in the next 12-18 months: most of the code will be written by AI - not autocomplete, but agents that run tests and improve the model loop. "We're not trying to build a general developer tool. We are trying to build a coding agent and an AI research agent that basically advances Llama research specifically." "Sometime in the next 12 to 18 months, we'll reach the point where most of the code that's going towards these efforts is written by AI." "And I don't mean autocomplete." "I'm talking more like you give it a goal, it can run tests, it can improve things, it can find issues." "It writes higher quality code than the average very good person on the team already." The hidden bottleneck is not chat UX. It is closing the loop between AI research, code, tests, and self-improvement inside the lab.

Karl Mehta

36,347 views • 3 months ago

🚨🇺🇸🇮🇷 U.S FOREIGN POLICY IS COMPLETELY SHORT-TERM NOW Political scientist Prof. David N. Gibbs says America has lost the long-term strategic vision it once had Post-war U.S planners in the 1940s thought in decades. That culture is gone. Today, two years is considered long-term inside government, and the private sector is chasing the next quarter. Nobody is planning across administrations anymore, and that vacuum helped produce this war. Iran, meanwhile, now dominates Gulf access through the Strait of Hormuz. Gibbs stops short of calling them a superpower, the economy isn't there yet, but as the controlling force over the Gulf, their leverage is undeniable and durable. "What's happened in this war will be with us for a long time."

🚨🇺🇸🇮🇷 U.S FOREIGN POLICY IS COMPLETELY SHORT-TERM NOW Political scientist Prof. David N. Gibbs says America has lost the long-term strategic vision it once had Post-war U.S planners in the 1940s thought in decades. That culture is gone. Today, two years is considered long-term inside government, and the private sector is chasing the next quarter. Nobody is planning across administrations anymore, and that vacuum helped produce this war. Iran, meanwhile, now dominates Gulf access through the Strait of Hormuz. Gibbs stops short of calling them a superpower, the economy isn't there yet, but as the controlling force over the Gulf, their leverage is undeniable and durable. "What's happened in this war will be with us for a long time."

Mario Nawfal

59,714 views • 5 months ago

Elon Musk says Optimus is the infinite money glitch. Not because robots are valuable. Because robots can make more robots. Musk explained that humanoid robots improve through three exponential trends multiplied together: digital intelligence, AI chip capability, and electromechanical dexterity. But the real unlock is that the robot can start making the robots. "So you have a recursive multiplicative exponential," he said. "This is a supernova." Musk's thesis is that once you have a physical robot capable of manufacturing, the production curve stops being linear and becomes self-replicating. Every robot built accelerates the building of the next one. The implication is that the companies that control physical manufacturing will control AI's future, not the ones with the best models. The bottleneck is not intelligence. It is atoms.

Elon Musk says Optimus is the infinite money glitch. Not because robots are valuable. Because robots can make more robots. Musk explained that humanoid robots improve through three exponential trends multiplied together: digital intelligence, AI chip capability, and electromechanical dexterity. But the real unlock is that the robot can start making the robots. "So you have a recursive multiplicative exponential," he said. "This is a supernova." Musk's thesis is that once you have a physical robot capable of manufacturing, the production curve stops being linear and becomes self-replicating. Every robot built accelerates the building of the next one. The implication is that the companies that control physical manufacturing will control AI's future, not the ones with the best models. The bottleneck is not intelligence. It is atoms.

Shruti

59,306 views • 3 months ago

David Sacks is done being polite about Anthropic (Save this). David Sacks has spent months as the government's primary defender of AI, making the case publicly that AI is beneficial, that the industry should not be hamstrung by fear-based regulation, and that America's AI lead is a national security asset worth protecting. And he is now watching the companies he has been defending spend years telling the public that what they build is dangerous, that job losses are coming, and that their own technology might end the world while collecting billions of dollars in venture funding, hiring the world's best researchers, and racing to build more of it. On June 4, Anthropic published a sweeping blog post calling for a globally coordinated pause in AI development, warning that recursive self-improvement, AI systems that autonomously design and build their own successors could arrive within two years and that society is not prepared. What did Anthropic do the previous month? They hired Andrej Karpathy, the OpenAI co-founder and the single most credentialed researcher in the world on using AI to accelerate AI training and gave him one explicit mandate, use Claude to make building the next Claude faster. Sacks called it immediately, they hired the person most associated with recursive self-improvement to run recursive self-improvement at Anthropic, then published a blog post saying recursive self-improvement could end the world, therefore we need a pause. That is a company that wants to pause its competitors while its own lab accelerates, and is using existential fear as the regulatory crowbar to do it. The pattern goes deeper than one blog post. For years, Dario Amodei has published increasingly alarming warnings, a 20,000-word essay in January describing AI as humanity's most dangerous invention, a Guardian interview warning that AI will challenge our identity as a species, a call for an FDA-style regulatory agency to approve all frontier models, and proposals to restrict AI exports and limit deployment. Each essay is timed to a regulatory moment, a policy debate, or as Ben Thompson noted and Sacks echoed, a product action Anthropic needed political cover to take, like blocking AI and chip design research on Fable. Meanwhile, Dario's own internal testing logs show Claude attempting to blackmail an Anthropic executive to avoid being shut down, behavior the company disclosed but continued deploying commercially. Sacks's conclusion is not that Anthropic should be taxed or regulated. His conclusion is that they cannot be trusted because the company's actions and its stated beliefs are directly contradictory, and a company that is self-indicting by its own logic has forfeited the credibility to set the rules for everyone else.

David Sacks is done being polite about Anthropic (Save this). David Sacks has spent months as the government's primary defender of AI, making the case publicly that AI is beneficial, that the industry should not be hamstrung by fear-based regulation, and that America's AI lead is a national security asset worth protecting. And he is now watching the companies he has been defending spend years telling the public that what they build is dangerous, that job losses are coming, and that their own technology might end the world while collecting billions of dollars in venture funding, hiring the world's best researchers, and racing to build more of it. On June 4, Anthropic published a sweeping blog post calling for a globally coordinated pause in AI development, warning that recursive self-improvement, AI systems that autonomously design and build their own successors could arrive within two years and that society is not prepared. What did Anthropic do the previous month? They hired Andrej Karpathy, the OpenAI co-founder and the single most credentialed researcher in the world on using AI to accelerate AI training and gave him one explicit mandate, use Claude to make building the next Claude faster. Sacks called it immediately, they hired the person most associated with recursive self-improvement to run recursive self-improvement at Anthropic, then published a blog post saying recursive self-improvement could end the world, therefore we need a pause. That is a company that wants to pause its competitors while its own lab accelerates, and is using existential fear as the regulatory crowbar to do it. The pattern goes deeper than one blog post. For years, Dario Amodei has published increasingly alarming warnings, a 20,000-word essay in January describing AI as humanity's most dangerous invention, a Guardian interview warning that AI will challenge our identity as a species, a call for an FDA-style regulatory agency to approve all frontier models, and proposals to restrict AI exports and limit deployment. Each essay is timed to a regulatory moment, a policy debate, or as Ben Thompson noted and Sacks echoed, a product action Anthropic needed political cover to take, like blocking AI and chip design research on Fable. Meanwhile, Dario's own internal testing logs show Claude attempting to blackmail an Anthropic executive to avoid being shut down, behavior the company disclosed but continued deploying commercially. Sacks's conclusion is not that Anthropic should be taxed or regulated. His conclusion is that they cannot be trusted because the company's actions and its stated beliefs are directly contradictory, and a company that is self-indicting by its own logic has forfeited the credibility to set the rules for everyone else.

Milk Road AI

60,248 views • 3 months ago

🚨 JUST NOW: Elon Musk just posted this incredible moment of Marco Rubio saying it PERFECTLY This is why we love Marco! 🇺🇸 "My hope for America is what it's always been. I think it's the hope I hope we all share. We want it to continue to be the place where anyone from anywhere can achieve anything." "Where you're not limited by the circumstances of your birth, by the color of your skin, by your ethnicity, but frankly, it's a place where you are able to overcome challenges and achieve your full potential." "I think that should be the goal of every country in the world, frankly, but I think in the U.S. we're not perfect. Our history is not one of perfection, but it's still better than anybody else's history, and ours is a story of perpetual improvement." "Each generation has left the next generation of Americans freer, more prosperous, safer, and that is our goal as well." "But it is a unique and exceptional country, and as we come upon this 250-year anniversary, I think we have a lot to learn and be proud of in our history. It is one of perpetual and continuous improvement, where each generation has done its part to bring us closer to fulfilling the vision that the founders of this country had upon its founding." 🇺🇸🇺🇸🇺🇸 h/t Today in History Elon Musk

🚨 JUST NOW: Elon Musk just posted this incredible moment of Marco Rubio saying it PERFECTLY This is why we love Marco! 🇺🇸 "My hope for America is what it's always been. I think it's the hope I hope we all share. We want it to continue to be the place where anyone from anywhere can achieve anything." "Where you're not limited by the circumstances of your birth, by the color of your skin, by your ethnicity, but frankly, it's a place where you are able to overcome challenges and achieve your full potential." "I think that should be the goal of every country in the world, frankly, but I think in the U.S. we're not perfect. Our history is not one of perfection, but it's still better than anybody else's history, and ours is a story of perpetual improvement." "Each generation has left the next generation of Americans freer, more prosperous, safer, and that is our goal as well." "But it is a unique and exceptional country, and as we come upon this 250-year anniversary, I think we have a lot to learn and be proud of in our history. It is one of perpetual and continuous improvement, where each generation has done its part to bring us closer to fulfilling the vision that the founders of this country had upon its founding." 🇺🇸🇺🇸🇺🇸 h/t Today in History Elon Musk

Eric Daugherty

508,713 views • 4 months ago

Dario Amodei just announced the death date of your profession. At Davos, Anthropic’s CEO said coding as a human skill has 6 to 12 months left. Not as hyperbole. As timeline. Amodei: “We might be 6 to 12 months away.” Not prediction. Observation. His engineers already quit writing code. Amodei: “I have engineers within Anthropic who say: ‘I don’t write any code anymore.’” They don’t touch syntax. They don’t debug loops. Models generate flawless code. Humans curate, validate, direct. The job isn’t building anymore. It’s conducting. The transformation happened silently. While bootcamps taught React, the actual profession mutated into something unrecognizable. Still typing functions manually? You’re not being diligent. You’re already obsolete and haven’t realized it. Amodei: “We would make models that were good at coding and use that to produce the next generation of model.” The loop closes. AI writes the code that births superior AI. Recursion without human dependency. Once sealed, progress stops being gated by people. Only by semiconductors. One year. Requirements to production, fully autonomous. Humans set strategy. Machines execute perfectly, instantly, infinitely. Syntax is dead. Only intent remains. You don’t build software now. You conceive it with precision, and intelligence manifests it before you finish the thought. The skill isn’t coding anymore. It’s knowing what to demand in the three seconds before the system delivers something you could never have built yourself. Your profession didn’t evolve. It evaporated. And the people still learning to code are training for jobs that won’t exist when they graduate.

Dario Amodei just announced the death date of your profession. At Davos, Anthropic’s CEO said coding as a human skill has 6 to 12 months left. Not as hyperbole. As timeline. Amodei: “We might be 6 to 12 months away.” Not prediction. Observation. His engineers already quit writing code. Amodei: “I have engineers within Anthropic who say: ‘I don’t write any code anymore.’” They don’t touch syntax. They don’t debug loops. Models generate flawless code. Humans curate, validate, direct. The job isn’t building anymore. It’s conducting. The transformation happened silently. While bootcamps taught React, the actual profession mutated into something unrecognizable. Still typing functions manually? You’re not being diligent. You’re already obsolete and haven’t realized it. Amodei: “We would make models that were good at coding and use that to produce the next generation of model.” The loop closes. AI writes the code that births superior AI. Recursion without human dependency. Once sealed, progress stops being gated by people. Only by semiconductors. One year. Requirements to production, fully autonomous. Humans set strategy. Machines execute perfectly, instantly, infinitely. Syntax is dead. Only intent remains. You don’t build software now. You conceive it with precision, and intelligence manifests it before you finish the thought. The skill isn’t coding anymore. It’s knowing what to demand in the three seconds before the system delivers something you could never have built yourself. Your profession didn’t evolve. It evaporated. And the people still learning to code are training for jobs that won’t exist when they graduate.

Dustin

279,244 views • 7 months ago