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Two data points dropped in the last few months that should terrify every software company that thinks its codebase is a moat. First, one engineer at Cloudflare, working with Claude via AI agents, rebuilt 94% of Next.js, one of the most widely used frontend frameworks on the internet, built...

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Milk Road AI

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16,781 просмотров • 3 месяцев назад •via X (Twitter)

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Big Tech's $1 trillion AI moat just got DESTROYED by a free Chinese download. Microsoft, Amazon, Google, and Meta are pouring fortunes into chips and data centers because they have been told that whoever builds the biggest model wins, and that lead becomes a fortress no one can cross. Last week a lab called Zhipu - it trades in Hong Kong as Knowledge Atlas Technology - released a model called GLM-5.2 and destroyed that idea in a single afternoon. It's open weights under an MIT license, which means anyone on earth can download it and build on it for free. On the coding and design benchmarks that actually matter, it went toe to toe with the best models America has - matching even Anthropic's Mythos-class work and beating OpenAI's flagship outright on the coding test everyone watches. And it does the work at roughly one-sixth the price. ONE-SIXTH And barely a year and a half ago a model called DeepSeek did the same thing and wiped the better part of $600 billion off Nvidia in a single session. This was only the first chapter. You cannot dig a moat around something your competitor is happy to give away. If 95% of frontier capability is free, open, and runs at a fraction of the cost, then the hundreds of billions being spent to defend the last 5% is NOT a moat. And now for the irony: The company that just proved the moat is worthless is itself the single most absurd valuation I have seen in a long career of watching absurd valuations. Zhipu did about $105 million in revenue last year and lost more than 4x what it took in. This week the market handed it a value of roughly $128 billion - at the peak, north of a 1,000x sales - on a float so thin that barely 4% of the stock actually trades. THINK about this... A company drowning in losses, doing 9 figures of revenue, priced like it does hundreds of billions, with almost nothing available to sell. So we now have a bubble in China detonating the entire justification for a bubble in America. Two manias pointed straight at each other. This is the lesson I've spent 45 years trying to beat into people. You can ignore valuation for a long time but you cannot ignore it forever. A moat story sold a trillion dollars of spending, a free download just exposed it, and the company that exposed it is priced for a fantasy of its own. When the picks-and-shovels crowd loses its monopoly on the picks, you want to be very careful what you are paying for the shovels. Numbers don't lie. Shoutout to Limitless - they were onto this story before almost anyone on Wall Street. One of the sharpest AI shows out there.
0:46

Big Tech's $1 trillion AI moat just got DESTROYED by a free Chinese download. Microsoft, Amazon, Google, and Meta are pouring fortunes into chips and data centers because they have been told that whoever builds the biggest model wins, and that lead becomes a fortress no one can cross. Last week a lab called Zhipu - it trades in Hong Kong as Knowledge Atlas Technology - released a model called GLM-5.2 and destroyed that idea in a single afternoon. It's open weights under an MIT license, which means anyone on earth can download it and build on it for free. On the coding and design benchmarks that actually matter, it went toe to toe with the best models America has - matching even Anthropic's Mythos-class work and beating OpenAI's flagship outright on the coding test everyone watches. And it does the work at roughly one-sixth the price. ONE-SIXTH And barely a year and a half ago a model called DeepSeek did the same thing and wiped the better part of $600 billion off Nvidia in a single session. This was only the first chapter. You cannot dig a moat around something your competitor is happy to give away. If 95% of frontier capability is free, open, and runs at a fraction of the cost, then the hundreds of billions being spent to defend the last 5% is NOT a moat. And now for the irony: The company that just proved the moat is worthless is itself the single most absurd valuation I have seen in a long career of watching absurd valuations. Zhipu did about $105 million in revenue last year and lost more than 4x what it took in. This week the market handed it a value of roughly $128 billion - at the peak, north of a 1,000x sales - on a float so thin that barely 4% of the stock actually trades. THINK about this... A company drowning in losses, doing 9 figures of revenue, priced like it does hundreds of billions, with almost nothing available to sell. So we now have a bubble in China detonating the entire justification for a bubble in America. Two manias pointed straight at each other. This is the lesson I've spent 45 years trying to beat into people. You can ignore valuation for a long time but you cannot ignore it forever. A moat story sold a trillion dollars of spending, a free download just exposed it, and the company that exposed it is priced for a fantasy of its own. When the picks-and-shovels crowd loses its monopoly on the picks, you want to be very careful what you are paying for the shovels. Numbers don't lie. Shoutout to Limitless - they were onto this story before almost anyone on Wall Street. One of the sharpest AI shows out there.

George Noble

69,688 просмотров • 1 месяц назад

Hollywood is cooked. A single ai artist just dropped a 7 minute film made 100% with ai tools and it went viral on Chinese social media in a week. The film explores the idea that our world might be a simulation. Built with Nano Banana, Veo3 and Runway, music by Suno. The era of the one person production studio is already here. and it cost ~0.001% of a Hollywood production.
7:10

Hollywood is cooked. A single ai artist just dropped a 7 minute film made 100% with ai tools and it went viral on Chinese social media in a week. The film explores the idea that our world might be a simulation. Built with Nano Banana, Veo3 and Runway, music by Suno. The era of the one person production studio is already here. and it cost ~0.001% of a Hollywood production.

Vera 🍌💕🐒

1,198,101 просмотров • 7 месяцев назад

From Dan Lorenc on the malware attack that almost took down the entire internet last year: “There’s a popular compression library that’s used in almost every piece of software. And it had been maintained by one person in his spare time for the last 20 years. And then a couple years ago, somebody just decided to start helping him. They jumped in, fixed a bunch of bugs, and did a lot of great work. And then that first person got tired of working on it. So he handed the whole project over to this other person. It turned out that other person was just a pseudonym and was not a real person. And within six months of getting control of the project, they had put in a carefully orchestrated set of malware that was really hard to detect and no one noticed. And because it was so widely used, the exploit would've basically given that person remote access to any computer running that piece of software, which was basically everything connected to the Internet. But because it was open source and the code was transparent, some random engineer just happened to be running some benchmarks on a weekend. And he noticed that program was a little bit slower than it used to be, and that it was making a weird cryptographic operation to check something. And right before this thing got widely deployed across every device, he dug in, and discovered that there was a backdoor put in. This was the closest thing to a full-blown internet crisis that we’ve ever had. And they still have no idea who did it. It was just an anonymous email account. No one ever traced it back to an individual. And that's the long game. This person spent years just doing good work and earning the communities trust.”
2:22

From Dan Lorenc on the malware attack that almost took down the entire internet last year: “There’s a popular compression library that’s used in almost every piece of software. And it had been maintained by one person in his spare time for the last 20 years. And then a couple years ago, somebody just decided to start helping him. They jumped in, fixed a bunch of bugs, and did a lot of great work. And then that first person got tired of working on it. So he handed the whole project over to this other person. It turned out that other person was just a pseudonym and was not a real person. And within six months of getting control of the project, they had put in a carefully orchestrated set of malware that was really hard to detect and no one noticed. And because it was so widely used, the exploit would've basically given that person remote access to any computer running that piece of software, which was basically everything connected to the Internet. But because it was open source and the code was transparent, some random engineer just happened to be running some benchmarks on a weekend. And he noticed that program was a little bit slower than it used to be, and that it was making a weird cryptographic operation to check something. And right before this thing got widely deployed across every device, he dug in, and discovered that there was a backdoor put in. This was the closest thing to a full-blown internet crisis that we’ve ever had. And they still have no idea who did it. It was just an anonymous email account. No one ever traced it back to an individual. And that's the long game. This person spent years just doing good work and earning the communities trust.”

The Peel

47,683 просмотров • 1 год назад

Broadcom's CEO just told investors that one senior engineer with Claude Opus can do in one week what ten engineers earning $300,000 a year take three months to produce. Every engineering org in the world is running the wrong headcount math. This was not a prediction. It was a report from inside Broadcom. Hock Tan said his engineers are already using Claude Opus for engineering design and code assistance. Not experimenting with it. Using it. And the output they are getting from a single senior engineer in one week is equivalent to what a team of ten would produce over three months. Do that math slowly. Ten engineers at $300,000 a year is $3 million in annual salary. Three months of their time is $750,000 of labor. One engineer with Claude Opus produces the same result in one week. He also said something that most people in the room let slide past them. The engineers who get good at these tools do not just get faster. They get better. The designs they produce with AI assistance are probably better than what they would produce without it. That is the part nobody wants to say out loud. It was never just about speed. The ceiling on what a single engineer can produce just moved. And the teams that have not figured that out yet are spending $750,000 to do what one person with the right tools can do for a fraction of that. What do you guys think about this? (Watch the full interview at Bloomberg Live on YouTube)
1:15

Broadcom's CEO just told investors that one senior engineer with Claude Opus can do in one week what ten engineers earning $300,000 a year take three months to produce. Every engineering org in the world is running the wrong headcount math. This was not a prediction. It was a report from inside Broadcom. Hock Tan said his engineers are already using Claude Opus for engineering design and code assistance. Not experimenting with it. Using it. And the output they are getting from a single senior engineer in one week is equivalent to what a team of ten would produce over three months. Do that math slowly. Ten engineers at $300,000 a year is $3 million in annual salary. Three months of their time is $750,000 of labor. One engineer with Claude Opus produces the same result in one week. He also said something that most people in the room let slide past them. The engineers who get good at these tools do not just get faster. They get better. The designs they produce with AI assistance are probably better than what they would produce without it. That is the part nobody wants to say out loud. It was never just about speed. The ceiling on what a single engineer can produce just moved. And the teams that have not figured that out yet are spending $750,000 to do what one person with the right tools can do for a fraction of that. What do you guys think about this? (Watch the full interview at Bloomberg Live on YouTube)

Ihtesham Ali

96,980 просмотров • 1 месяц назад

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

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

Milk Road AI

36,062 просмотров • 3 месяцев назад

🚨🇺🇸 TRUMP: WE’RE BUILDING ONE OF THE GREATEST BALLROOMS IN THE WORLD "The East Wing was a beautiful little tiny structure that was built many years ago that was renovated and expanded and disbanded, and columns ripped out. And it had nothing to do with the original building. It was a poor, sad sight, and I could have built the ballroom around it, but it would not have been. We're building one of the greatest ballrooms in the world, by the way. Zero money spent by the government, zero. The East Wing is being spent by private donors and it's a $250 to $300 million building." Source: Laura Ingraham
1:28

🚨🇺🇸 TRUMP: WE’RE BUILDING ONE OF THE GREATEST BALLROOMS IN THE WORLD "The East Wing was a beautiful little tiny structure that was built many years ago that was renovated and expanded and disbanded, and columns ripped out. And it had nothing to do with the original building. It was a poor, sad sight, and I could have built the ballroom around it, but it would not have been. We're building one of the greatest ballrooms in the world, by the way. Zero money spent by the government, zero. The East Wing is being spent by private donors and it's a $250 to $300 million building." Source: Laura Ingraham

Mario Nawfal

30,755 просмотров • 9 месяцев назад

What if I told you the most remarkable feat of engineering occurred over 4,500 years ago? The stepped pyramid of Djoser built circa ~2,600 BCE is widely recognized as the first pyramid in Ancient Egypt. But did you know it had a “moat” a dry moat they call it, that surrounded the pyramid. A study published not that long ago proved it was not remotely dry, water from a check dam was diverted from a “wadi” (a natural valley that collects rain water) directly into the moat. As luck would have it this was once excavated. And with that the publishers of the study proved something they didn’t even realize. The moat is identical to a modern water filtration system.
0:23

What if I told you the most remarkable feat of engineering occurred over 4,500 years ago? The stepped pyramid of Djoser built circa ~2,600 BCE is widely recognized as the first pyramid in Ancient Egypt. But did you know it had a “moat” a dry moat they call it, that surrounded the pyramid. A study published not that long ago proved it was not remotely dry, water from a check dam was diverted from a “wadi” (a natural valley that collects rain water) directly into the moat. As luck would have it this was once excavated. And with that the publishers of the study proved something they didn’t even realize. The moat is identical to a modern water filtration system.

Ancient Hypotheses

25,682 просмотров • 4 месяцев назад

Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.
0:56

Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.

Dustin

160,573 просмотров • 3 месяцев назад

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.
0:41

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.

cvxv666

103,359 просмотров • 1 месяц назад

This is one of the coolest open-source AI agent projects I've seen in a while: 'Understand Anything' It's a plugin for Claude Code, Codex, OpenCode etc. that analyzes your codebase and turns it into a knowledge base that you can interact with. It explains the codebase to you, rather than showing you the structure. It seems like it's designed for code but I opened my Obsidian vault of podcast highlights in Claude Code, then ran /understand. The result is a knowledge graph that I can search of highlights from 888 podcast episodes and 144K lines of markdown text.
0:22

This is one of the coolest open-source AI agent projects I've seen in a while: 'Understand Anything' It's a plugin for Claude Code, Codex, OpenCode etc. that analyzes your codebase and turns it into a knowledge base that you can interact with. It explains the codebase to you, rather than showing you the structure. It seems like it's designed for code but I opened my Obsidian vault of podcast highlights in Claude Code, then ran /understand. The result is a knowledge graph that I can search of highlights from 888 podcast episodes and 144K lines of markdown text.

Dan McAteer

174,045 просмотров • 1 месяц назад

American shows that the city of Palo Alto in San Francisco built this pedestrian bridge that not a single person uses - The original cost was $6 million - The cost more the doubled to $15 million by completion - It took the city 12 years to build this one bridge “What the hell is going on here” “We have massive problems of our infrastructure in California and we're spending 12 years and $15 million building a pedestrian bridge that nobody is using. What the hell is going on here? This was first contemplated back in 2013. It's now 2025 and it opened last week. So it took 12 years to build this bridge.”
1:14

American shows that the city of Palo Alto in San Francisco built this pedestrian bridge that not a single person uses - The original cost was $6 million - The cost more the doubled to $15 million by completion - It took the city 12 years to build this one bridge “What the hell is going on here” “We have massive problems of our infrastructure in California and we're spending 12 years and $15 million building a pedestrian bridge that nobody is using. What the hell is going on here? This was first contemplated back in 2013. It's now 2025 and it opened last week. So it took 12 years to build this bridge.”

Wall Street Apes

424,578 просмотров • 8 месяцев назад

Elon Musk was asked how fast AI is moving, and answered by correcting the most aggressive forecaster in technology for not being aggressive enough. Musk: “I have to give credit to Ray Kurzweil in being actually remarkably accurate in his predictions. If anything, I think he was perhaps a bit conservative in his predictions.” Kurzweil spent thirty years publishing timelines that were treated as proof he had lost the plot. He hit nearly all of them. The word Musk used for that record was conservative. Kurzweil built his forecasts by reading the rate of change. Musk is reading it now. Musk: “The dedicated AI compute appears to be growing by a factor of 10 every six months.” That is not a projection. “Appears to be growing” describes something that has already happened. Musk: “Almost a 100x improvement per year, at least for the next few years.” Moore’s Law was 2x every two years. That one curve produced the internet, the smartphone, the cloud, and every instinct you have about how fast the world can change. Your sense of normal was calibrated on 2x. The system is running at 100x. The new world is not being built next to the old one. Musk: “Probably a lot of the data centers, maybe most of the data centers that currently do conventional compute, will transition to AI compute.” The infrastructure holding up the current economy is being converted into what replaces it. The old world is the raw material for the next one. Every generation assumes the speed it grew up with is the speed of history. Ours is the first to be handed the actual number and still round it down. Everything you build takes time. A child, a trade, a life’s work. All of it rests on the assumption that the world will still be recognizable when the work is finished. That assumption has held for every human who has ever lived. It was never a law of nature. It was a side effect of how slowly things moved. The most extreme forecast on record came in low. Reasonable is no longer the safe position. It is only the comfortable one.
1:01

Elon Musk was asked how fast AI is moving, and answered by correcting the most aggressive forecaster in technology for not being aggressive enough. Musk: “I have to give credit to Ray Kurzweil in being actually remarkably accurate in his predictions. If anything, I think he was perhaps a bit conservative in his predictions.” Kurzweil spent thirty years publishing timelines that were treated as proof he had lost the plot. He hit nearly all of them. The word Musk used for that record was conservative. Kurzweil built his forecasts by reading the rate of change. Musk is reading it now. Musk: “The dedicated AI compute appears to be growing by a factor of 10 every six months.” That is not a projection. “Appears to be growing” describes something that has already happened. Musk: “Almost a 100x improvement per year, at least for the next few years.” Moore’s Law was 2x every two years. That one curve produced the internet, the smartphone, the cloud, and every instinct you have about how fast the world can change. Your sense of normal was calibrated on 2x. The system is running at 100x. The new world is not being built next to the old one. Musk: “Probably a lot of the data centers, maybe most of the data centers that currently do conventional compute, will transition to AI compute.” The infrastructure holding up the current economy is being converted into what replaces it. The old world is the raw material for the next one. Every generation assumes the speed it grew up with is the speed of history. Ours is the first to be handed the actual number and still round it down. Everything you build takes time. A child, a trade, a life’s work. All of it rests on the assumption that the world will still be recognizable when the work is finished. That assumption has held for every human who has ever lived. It was never a law of nature. It was a side effect of how slowly things moved. The most extreme forecast on record came in low. Reasonable is no longer the safe position. It is only the comfortable one.

Dustin

163,648 просмотров • 3 дней назад

Satya Nadella, CEO of Microsoft, says every company now runs on two kinds of capital, and leaking one of them is a one-way door: "This economy is going to be shaped going forward by both human capital and, let's call it, this token capital. That is true for Microsoft, that is true for a new startup, that's going to be true for any bank that's been in existence for 100 years." "The interplay between human capital and token capital, and compounding the returns of it, is what you need to do." "What is the tacit knowledge of an enterprise or a firm? It's the unique ways that you're able to operate, pass judgment, have taste." "The model, in some sense, is able to extract that tacit knowledge through human trajectories, and then code it in a set of weights in a model." "Every enterprise now needs to be more mindful about that interplay of humans and their digital estate working together, those trajectories training the models that they keep as IP versus leak it." "Because if you leak it, it's a one-way door. You're done in some sense." The moat was never the data sitting in your warehouse. It was the judgment your people apply to it, and AI now turns that judgment into weights you either own or hand away. Treat your tacit knowledge like a patent, not a prompt. The firms that pipe a hundred years of taste into someone else's frontier model are trading a permanent edge for a quarter of convenience.
1:34

Satya Nadella, CEO of Microsoft, says every company now runs on two kinds of capital, and leaking one of them is a one-way door: "This economy is going to be shaped going forward by both human capital and, let's call it, this token capital. That is true for Microsoft, that is true for a new startup, that's going to be true for any bank that's been in existence for 100 years." "The interplay between human capital and token capital, and compounding the returns of it, is what you need to do." "What is the tacit knowledge of an enterprise or a firm? It's the unique ways that you're able to operate, pass judgment, have taste." "The model, in some sense, is able to extract that tacit knowledge through human trajectories, and then code it in a set of weights in a model." "Every enterprise now needs to be more mindful about that interplay of humans and their digital estate working together, those trajectories training the models that they keep as IP versus leak it." "Because if you leak it, it's a one-way door. You're done in some sense." The moat was never the data sitting in your warehouse. It was the judgment your people apply to it, and AI now turns that judgment into weights you either own or hand away. Treat your tacit knowledge like a patent, not a prompt. The firms that pipe a hundred years of taste into someone else's frontier model are trading a permanent edge for a quarter of convenience.

Karl Mehta

34,573 просмотров • 1 месяц назад

Jeff Bezos looked at a government waiting room and priced it at a hundred billion dollars. Bezos: “This is a $100 billion business, by the way. This is huge business.” That number is not ambition. It is a measurement. A company that size does not create a hundred billion dollars of new value here. It recovers a hundred billion that is already being destroyed, on schedule, in public, by a process everyone has agreed to call normal. The size of the opportunity is the size of the wound. Bezos: “They almost always say yes, they just make you wait a long time.” So the verdict was never in question. Only the delivery date. Which means the months bought nothing. They were storage. Your project sat in a room while the word yes waited its turn to be said. Nobody in there was deciding. Someone was scheduling. And the room bills by the day. Suarez: “The daily carrying cost, one day of interest, 200 to $400,000 per day.” Now find the recipient. There isn’t one. No school funded, no inspector paid, no safety review performed. A tax at least moves money into someone’s hands. This deletes it at four hundred thousand a day, and every dollar was real before the queue touched it. Bezos: “And that doesn’t count the frustration.” Count it anyway. It lands on a body long before it lands on a spreadsheet. Suarez: “Which is infinite. I have some white hairs as a result of this stuff.” That is the invoice. The money has a rate. The rest gets charged to the man, and he pays it whether the answer comes back yes or no. No one has ever been refunded a year. Every other cost in this economy can be hedged, insured, refinanced, or written off. Waiting is the only one that is final. A man will forgive being told no. He will never get back the time he spent standing there waiting to be told yes. We built a civilization that can move a decision at the speed of light and still schedule it at the speed of a man’s hair turning white. Bezos: “It should give you a yes or a no in 10 seconds. And if the answer is no, it should tell you the 6 things you have to change to get a yes.” For four thousand years the wait was honest. Someone had to hold the entire code in one head and walk it line by line against your drawings. Reading was slow because reading was human. That stopped being true in less time than it takes to approve one building. Bezos: “Maybe with AI it can just read all the plans. And it knows all the codes. Spit it out.” The constraint is gone. The wait stayed. Anything still moving at the old speed after the cost of thinking collapsed is not slow. It is choosing. The queue never protected you from a bad building. It protected a signature from a bad outcome. Nobody gets fired for the wait. They get fired for the answer. An institution has no lifespan, so time costs it nothing. You have one, so time is the only thing it can take. Every queue is a trade between something that dies and something that doesn’t, and only one side is ever charged. That trade held for the whole of recorded history because the mortal side had no leverage. It does now. Somebody is going to build this. A hundred billion does not sit in a waiting room forever. And the day it exists, the wait stops being a fact of the world and becomes a decision with a name attached. The hundred billion was never the prize. It is the receipt for how long we agreed to stand there. We are the first people alive who get to stop signing it.
1:55

Jeff Bezos looked at a government waiting room and priced it at a hundred billion dollars. Bezos: “This is a $100 billion business, by the way. This is huge business.” That number is not ambition. It is a measurement. A company that size does not create a hundred billion dollars of new value here. It recovers a hundred billion that is already being destroyed, on schedule, in public, by a process everyone has agreed to call normal. The size of the opportunity is the size of the wound. Bezos: “They almost always say yes, they just make you wait a long time.” So the verdict was never in question. Only the delivery date. Which means the months bought nothing. They were storage. Your project sat in a room while the word yes waited its turn to be said. Nobody in there was deciding. Someone was scheduling. And the room bills by the day. Suarez: “The daily carrying cost, one day of interest, 200 to $400,000 per day.” Now find the recipient. There isn’t one. No school funded, no inspector paid, no safety review performed. A tax at least moves money into someone’s hands. This deletes it at four hundred thousand a day, and every dollar was real before the queue touched it. Bezos: “And that doesn’t count the frustration.” Count it anyway. It lands on a body long before it lands on a spreadsheet. Suarez: “Which is infinite. I have some white hairs as a result of this stuff.” That is the invoice. The money has a rate. The rest gets charged to the man, and he pays it whether the answer comes back yes or no. No one has ever been refunded a year. Every other cost in this economy can be hedged, insured, refinanced, or written off. Waiting is the only one that is final. A man will forgive being told no. He will never get back the time he spent standing there waiting to be told yes. We built a civilization that can move a decision at the speed of light and still schedule it at the speed of a man’s hair turning white. Bezos: “It should give you a yes or a no in 10 seconds. And if the answer is no, it should tell you the 6 things you have to change to get a yes.” For four thousand years the wait was honest. Someone had to hold the entire code in one head and walk it line by line against your drawings. Reading was slow because reading was human. That stopped being true in less time than it takes to approve one building. Bezos: “Maybe with AI it can just read all the plans. And it knows all the codes. Spit it out.” The constraint is gone. The wait stayed. Anything still moving at the old speed after the cost of thinking collapsed is not slow. It is choosing. The queue never protected you from a bad building. It protected a signature from a bad outcome. Nobody gets fired for the wait. They get fired for the answer. An institution has no lifespan, so time costs it nothing. You have one, so time is the only thing it can take. Every queue is a trade between something that dies and something that doesn’t, and only one side is ever charged. That trade held for the whole of recorded history because the mortal side had no leverage. It does now. Somebody is going to build this. A hundred billion does not sit in a waiting room forever. And the day it exists, the wait stops being a fact of the world and becomes a decision with a name attached. The hundred billion was never the prize. It is the receipt for how long we agreed to stand there. We are the first people alive who get to stop signing it.

Dustin

16,735 просмотров • 5 дней назад

Chamath said AI is not like the internet. Every new user costs real money. And the infrastructure making it possible was built by everyone. His argument was the clearest case for government ownership of AI labs I have ever heard. And it had nothing to do with Bernie Sanders. Start with the internet comparison. Google and Facebook became the most profitable companies in human history because of one number. The marginal cost of adding a new user was effectively zero. One more search query cost Google nothing. One more Facebook profile cost Meta nothing. They could serve a billion people and the incremental cost of that billion person was rounding error. That is the money printer. Infinite scale at zero marginal cost. AI breaks that model completely. Every single user taxes a GPU. Every query costs electricity. Every response requires memory and compute. The marginal cost of AI is real, significant, and does not disappear at scale. You cannot print money the same way. Then Chamath made the point that landed hardest. The infrastructure these companies depend on, the power grid, the land, the data centers, the permitting, the national security apparatus that protects their chips from being stolen, none of that was built by Anthropic or OpenAI. It was built by the public. By taxpayers. By decades of government investment in the physical and legal foundation these companies are now running on. He compared it to the interstate highway system. If the federal government built the roads and two companies transported all the goods on them, a logical question at that point would be how much of that should I own? You are riding on my rails. His conclusion was direct. If he were running a sovereign wealth fund and had the negotiating leverage of the US government, he would own 75% of these companies when he was done. The internet had zero marginal cost. That is why the founders captured almost all of the value. AI has real marginal cost and runs on public infrastructure. That changes who has a claim on what gets built. WATCH THE FULL PODCAST ON The All-In Podcast
2:26

Chamath said AI is not like the internet. Every new user costs real money. And the infrastructure making it possible was built by everyone. His argument was the clearest case for government ownership of AI labs I have ever heard. And it had nothing to do with Bernie Sanders. Start with the internet comparison. Google and Facebook became the most profitable companies in human history because of one number. The marginal cost of adding a new user was effectively zero. One more search query cost Google nothing. One more Facebook profile cost Meta nothing. They could serve a billion people and the incremental cost of that billion person was rounding error. That is the money printer. Infinite scale at zero marginal cost. AI breaks that model completely. Every single user taxes a GPU. Every query costs electricity. Every response requires memory and compute. The marginal cost of AI is real, significant, and does not disappear at scale. You cannot print money the same way. Then Chamath made the point that landed hardest. The infrastructure these companies depend on, the power grid, the land, the data centers, the permitting, the national security apparatus that protects their chips from being stolen, none of that was built by Anthropic or OpenAI. It was built by the public. By taxpayers. By decades of government investment in the physical and legal foundation these companies are now running on. He compared it to the interstate highway system. If the federal government built the roads and two companies transported all the goods on them, a logical question at that point would be how much of that should I own? You are riding on my rails. His conclusion was direct. If he were running a sovereign wealth fund and had the negotiating leverage of the US government, he would own 75% of these companies when he was done. The internet had zero marginal cost. That is why the founders captured almost all of the value. AI has real marginal cost and runs on public infrastructure. That changes who has a claim on what gets built. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

79,066 просмотров • 1 месяц назад

every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.
0:48

every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.

Adeniyi.sui

13,419 просмотров • 1 месяц назад

There is a prediction circulating in AI circles right now that most people are not taking seriously enough and the data says they should be. Within the next year or two, if you work remotely, your company will be able to create a digital twin of you. A model that speaks like you, writes like you, has learned from everything you have done right and wrong, your tone, your judgment calls, your workflow. It will be you on the other side of Zoom or Slack and no one could tell the difference. The harder question, the one nobody wants to sit with is whether it will actually be worse at your job than you are. Probably not. It will never sleep and it will always learn from its mistakes and it will cost 10 to 100 times less than you do and is tax deductible on top of that. The data is not speculative at this point. Anthropic's own labor market report pulled from millions of real Claude conversations found that AI can already theoretically automate 94% of tasks in computer and math occupations, 60-80% across law, office work, and tech. Actual usage is still at 10-20% of that potential which means we are in the early innings of the gap closing. Companies already know what direction this is headed. One in five companies replaced specific roles with AI in 2025 and by end of 2026, 30-37% plan to do so. Amazon cut 14,000 corporate jobs citing AI, Klarna replaced 700 customer service workers, Duolingo offboarded 10% of its contractor workforce. Anthropic's own first internal role eliminated was the engineer who reviewed Claude Code releases before they went to production. The argument from the clip is that the human in the loop is approaching the point of being a liability, the dumbest person on a team that is otherwise AI. That inflection point, by this estimate, is somewhere in the next 900 days.
1:13

There is a prediction circulating in AI circles right now that most people are not taking seriously enough and the data says they should be. Within the next year or two, if you work remotely, your company will be able to create a digital twin of you. A model that speaks like you, writes like you, has learned from everything you have done right and wrong, your tone, your judgment calls, your workflow. It will be you on the other side of Zoom or Slack and no one could tell the difference. The harder question, the one nobody wants to sit with is whether it will actually be worse at your job than you are. Probably not. It will never sleep and it will always learn from its mistakes and it will cost 10 to 100 times less than you do and is tax deductible on top of that. The data is not speculative at this point. Anthropic's own labor market report pulled from millions of real Claude conversations found that AI can already theoretically automate 94% of tasks in computer and math occupations, 60-80% across law, office work, and tech. Actual usage is still at 10-20% of that potential which means we are in the early innings of the gap closing. Companies already know what direction this is headed. One in five companies replaced specific roles with AI in 2025 and by end of 2026, 30-37% plan to do so. Amazon cut 14,000 corporate jobs citing AI, Klarna replaced 700 customer service workers, Duolingo offboarded 10% of its contractor workforce. Anthropic's own first internal role eliminated was the engineer who reviewed Claude Code releases before they went to production. The argument from the clip is that the human in the loop is approaching the point of being a liability, the dumbest person on a team that is otherwise AI. That inflection point, by this estimate, is somewhere in the next 900 days.

Milk Road AI

17,209 просмотров • 3 месяцев назад

The future of design is… engineering. All designers at Vercel now also build, thanks to tools like v0, Claude Code, and Cursor. They've been contributing to our frontends and apps for a while now. But over the past few months, the leap they've made is engineering the design process itself by building agents. A big part of shipping is getting the word out in a compelling way, especially on the X platform, the everything app. In the past, we used to spend a bunch of time hand-crafting images and illustrations for social cards. Our design team built an internal agent and web ui using v0 and Claude Code that makes this process fully self-serve. It even includes a previewer of what the final artifact will look like on X. It's called Leap. It's probably saved us hundreds of hours of work but also massively raised our quality bar. The artifacts it produces are beautiful. If you had asked me even 12 months ago whether our design team would be building their own design tools, let alone be this good, I would call bs. There was no master plan, or God forbid, a "sprint" to make this happen. It just took a handful of prompts to build and it propagated on Slack. Leap is now one of the many agents that helps us run our company more smoothly, built and securely deployed on Vercel for our internal use.
1:08

The future of design is… engineering. All designers at Vercel now also build, thanks to tools like v0, Claude Code, and Cursor. They've been contributing to our frontends and apps for a while now. But over the past few months, the leap they've made is engineering the design process itself by building agents. A big part of shipping is getting the word out in a compelling way, especially on the X platform, the everything app. In the past, we used to spend a bunch of time hand-crafting images and illustrations for social cards. Our design team built an internal agent and web ui using v0 and Claude Code that makes this process fully self-serve. It even includes a previewer of what the final artifact will look like on X. It's called Leap. It's probably saved us hundreds of hours of work but also massively raised our quality bar. The artifacts it produces are beautiful. If you had asked me even 12 months ago whether our design team would be building their own design tools, let alone be this good, I would call bs. There was no master plan, or God forbid, a "sprint" to make this happen. It just took a handful of prompts to build and it propagated on Slack. Leap is now one of the many agents that helps us run our company more smoothly, built and securely deployed on Vercel for our internal use.

Guillermo Rauch

208,844 просмотров • 5 месяцев назад

Elon Musk was asked how fast AI is moving. His answer wasn’t about the technology. It was about the one man who got it all right and was still too conservative. Musk: “I have to give credit to Ray Kurzweil in being actually remarkably accurate in his predictions. If anything, I think he was perhaps a bit conservative in his predictions.” Kurzweil spent 30 years making forecasts that made serious people uncomfortable. He predicted timelines that sounded impossible. He was mocked for it. He was right about nearly all of them. And Musk just called him conservative. Musk: “The dedicated AI compute appears to be growing by a factor of 10 every six months.” 10x every six months. Musk: “Almost a 100x improvement per year, at least for the next few years.” Moore’s Law was a 2x improvement every two years. That single curve drove every technological shift of the last 50 years. The internet. Smartphones. Cloud computing. All of it rode a 2x curve. AI is on a 100x curve. And the current infrastructure isn’t running beside the new one. It’s becoming it. Musk: “Probably a lot of the data centers, maybe most of the data centers that currently do conventional compute, will transition to AI compute.” Everything that runs the world you know is being rewired for the world that comes next. Human beings process the future in straight lines. We take the speed of the last decade and project it forward. Exponential growth doesn’t work that way. It’s invisible until it’s everywhere. The most aggressive forecaster in the history of technology was too conservative. That’s not about Kurzweil being wrong about the direction. That’s about the human brain being wrong about the speed. The limit was never the technology. It was the organ we use to comprehend it. And that organ hasn’t been upgraded in 200,000 years.
1:01

Elon Musk was asked how fast AI is moving. His answer wasn’t about the technology. It was about the one man who got it all right and was still too conservative. Musk: “I have to give credit to Ray Kurzweil in being actually remarkably accurate in his predictions. If anything, I think he was perhaps a bit conservative in his predictions.” Kurzweil spent 30 years making forecasts that made serious people uncomfortable. He predicted timelines that sounded impossible. He was mocked for it. He was right about nearly all of them. And Musk just called him conservative. Musk: “The dedicated AI compute appears to be growing by a factor of 10 every six months.” 10x every six months. Musk: “Almost a 100x improvement per year, at least for the next few years.” Moore’s Law was a 2x improvement every two years. That single curve drove every technological shift of the last 50 years. The internet. Smartphones. Cloud computing. All of it rode a 2x curve. AI is on a 100x curve. And the current infrastructure isn’t running beside the new one. It’s becoming it. Musk: “Probably a lot of the data centers, maybe most of the data centers that currently do conventional compute, will transition to AI compute.” Everything that runs the world you know is being rewired for the world that comes next. Human beings process the future in straight lines. We take the speed of the last decade and project it forward. Exponential growth doesn’t work that way. It’s invisible until it’s everywhere. The most aggressive forecaster in the history of technology was too conservative. That’s not about Kurzweil being wrong about the direction. That’s about the human brain being wrong about the speed. The limit was never the technology. It was the organ we use to comprehend it. And that organ hasn’t been upgraded in 200,000 years.

Dustin

213,892 просмотров • 2 месяцев назад

This is one of the wildest things I have built with AI so far 😱 Asked Claude Fable 5 to create a fully interactive 3D crime investigation board using Three.js and it did it in ONE Prompt. It took one more prompt to give it a polished UI inspired by a reference design that I created with GPT Image. What surprised me most was not how it looked. It was how it worked. Fable built a complete Verlet rope physics system with 46 particles in every thread. It handled gravity, damping, distance constraints and all other features of file upload, text and everything. The threads/ropes even lift naturally with pinned evidence instead of clipping through it. I never asked it to implement any of that. I only described the experience I wanted. Maybe that's how we should work with AI. Instead of writing long long specs, we should explain what we want as an experience and let AI do its thing. Anyway, every day Fable 5 raises the bar for what I think AI can build. More to this Live: Code:
0:49

This is one of the wildest things I have built with AI so far 😱 Asked Claude Fable 5 to create a fully interactive 3D crime investigation board using Three.js and it did it in ONE Prompt. It took one more prompt to give it a polished UI inspired by a reference design that I created with GPT Image. What surprised me most was not how it looked. It was how it worked. Fable built a complete Verlet rope physics system with 46 particles in every thread. It handled gravity, damping, distance constraints and all other features of file upload, text and everything. The threads/ropes even lift naturally with pinned evidence instead of clipping through it. I never asked it to implement any of that. I only described the experience I wanted. Maybe that's how we should work with AI. Instead of writing long long specs, we should explain what we want as an experience and let AI do its thing. Anyway, every day Fable 5 raises the bar for what I think AI can build. More to this Live: Code:

The Bugged Dev

103,552 просмотров • 28 дней назад