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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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16,781 görüntüleme • 2 ay önce •via X (Twitter)

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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 🍌💕🐒

1,189,175 görüntüleme • 6 ay önce

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 görüntüleme • 1 yıl önce

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

94,987 görüntüleme • 2 gün önce

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

35,751 görüntüleme • 2 ay önce

🚨🇺🇸 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 görüntüleme • 7 ay önce

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 görüntüleme • 2 ay önce

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

160,181 görüntüleme • 2 ay önce

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

155,689 görüntüleme • 3 gün önce

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

424,355 görüntüleme • 6 ay önce

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

78,878 görüntüleme • 8 gün önce

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 görüntüleme • 2 ay önce

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

212,868 görüntüleme • 27 gün önce

How is the Web changing due to AI? This visit to TinyFish yesterday goes deeply into just how. TinyFish makes a new kind of web browser. One that is started virtually. It is like a Google Chrome in the cloud. But it is very smart, can read and use any website, and your AI agents can talk to it. Here we talk about what it all means for business and the future of robots, brain computer interfaces, and AI glasses. It lets you build systems that can look at MANY websites at the same time and build new kinds of apps to do all sorts of new things.
1:02:10

How is the Web changing due to AI? This visit to TinyFish yesterday goes deeply into just how. TinyFish makes a new kind of web browser. One that is started virtually. It is like a Google Chrome in the cloud. But it is very smart, can read and use any website, and your AI agents can talk to it. Here we talk about what it all means for business and the future of robots, brain computer interfaces, and AI glasses. It lets you build systems that can look at MANY websites at the same time and build new kinds of apps to do all sorts of new things.

Robert Scoble

16,341 görüntüleme • 6 ay önce

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

207,645 görüntüleme • 4 ay önce

Elon Musk just explained how you build a trillion-dollar company overnight and most people completely missed what he actually said. Musk: “As soon as you unlock digital human, you basically have access to trillions of dollars of revenue.” That sounds like hype until you break down what he means. The most valuable companies on Earth do not manufacture anything. Apple does not build iPhones. They send digital files to a factory in China. Microsoft does not build hardware. Their entire output is code. Google. Meta. Digital. Digital. Every single company sitting at the top of the global economy produces exactly one thing. Keystrokes. The entire modern economy runs on human beings staring at screens and pressing buttons. Now build an AI that does that at the same level a human does. Not a chatbot. Not an assistant. A full digital human that reads a screen, understands context, and operates software the same way a person does. You just unlocked access to every revenue stream those companies sit on. Not in ten years. Not after some massive infrastructure overhaul. Immediately. The entire enterprise AI conversation right now is stuck on integration. How do you connect AI to corporate systems. How do you build custom APIs. How do you rip out decades of bloated software and rebuild it from scratch. Musk just skipped all of it. A digital human does not need an API. It does not care how old or broken your system is. It logs into the same dashboard your employee uses. Reads the same screen. Clicks the same buttons. Processes the same information. Zero integration. Zero rebuild. Zero friction. You do not renovate the building. You just replace who is sitting at the desk. That changes the math on every industry overnight. Customer service alone is one percent of the entire global economy. That is hundreds of billions of dollars flowing through an industry that consists almost entirely of people reading text and typing responses. No factory involved. No raw materials. No shipping. No physical supply chain. Pure digital labor. The moment a digital human crosses the threshold where it handles that work at human level the cost structure of the entire industry collapses to near zero. And customer service is just the first domino. Accounting. Legal review. Insurance claims. Medical billing. IT support. Every single one of those is the same equation. Humans reading screens and producing digital output. A digital human does not disrupt those industries. It absorbs them. No integration required. No permission needed. No ten-year rollout plan. Log in and take over the workflow. The companies that understand this right now are building the most valuable entities the world has ever seen. The ones that do not are going to wake up one morning and realize the entire revenue model they built over decades just got replicated at a fraction of the cost by something that never sleeps and never stops. Musk did not make a prediction on that podcast. He gave you the blueprint. And the clock is already running.
1:35

Elon Musk just explained how you build a trillion-dollar company overnight and most people completely missed what he actually said. Musk: “As soon as you unlock digital human, you basically have access to trillions of dollars of revenue.” That sounds like hype until you break down what he means. The most valuable companies on Earth do not manufacture anything. Apple does not build iPhones. They send digital files to a factory in China. Microsoft does not build hardware. Their entire output is code. Google. Meta. Digital. Digital. Every single company sitting at the top of the global economy produces exactly one thing. Keystrokes. The entire modern economy runs on human beings staring at screens and pressing buttons. Now build an AI that does that at the same level a human does. Not a chatbot. Not an assistant. A full digital human that reads a screen, understands context, and operates software the same way a person does. You just unlocked access to every revenue stream those companies sit on. Not in ten years. Not after some massive infrastructure overhaul. Immediately. The entire enterprise AI conversation right now is stuck on integration. How do you connect AI to corporate systems. How do you build custom APIs. How do you rip out decades of bloated software and rebuild it from scratch. Musk just skipped all of it. A digital human does not need an API. It does not care how old or broken your system is. It logs into the same dashboard your employee uses. Reads the same screen. Clicks the same buttons. Processes the same information. Zero integration. Zero rebuild. Zero friction. You do not renovate the building. You just replace who is sitting at the desk. That changes the math on every industry overnight. Customer service alone is one percent of the entire global economy. That is hundreds of billions of dollars flowing through an industry that consists almost entirely of people reading text and typing responses. No factory involved. No raw materials. No shipping. No physical supply chain. Pure digital labor. The moment a digital human crosses the threshold where it handles that work at human level the cost structure of the entire industry collapses to near zero. And customer service is just the first domino. Accounting. Legal review. Insurance claims. Medical billing. IT support. Every single one of those is the same equation. Humans reading screens and producing digital output. A digital human does not disrupt those industries. It absorbs them. No integration required. No permission needed. No ten-year rollout plan. Log in and take over the workflow. The companies that understand this right now are building the most valuable entities the world has ever seen. The ones that do not are going to wake up one morning and realize the entire revenue model they built over decades just got replicated at a fraction of the cost by something that never sleeps and never stops. Musk did not make a prediction on that podcast. He gave you the blueprint. And the clock is already running.

Dustin

63,750 görüntüleme • 3 ay önce

Jensen Huang just told the story of how the AI revolution started with one customer, one box, and a second-floor room nobody thought twice about. Nobody on Earth wanted it. Huang: “When I announced this thing, nobody in the world wanted it. I had no purchase orders. Not one.” Nvidia had spent billions building the DGX-1. The first AI supercomputer purpose-built for deep learning. $300,000 per unit. The entire technology industry looked at it and passed. Every hyperscaler. Every research lab. Every Fortune 500 with a machine learning team. Not one purchase order. Then Elon Musk found Huang at a fireside chat in 2015. Huang: “He goes, ‘You know what? I have a company that could really use this.’” His first customer. His only customer. Then Musk finished the sentence. Huang: “He goes, ‘It’s a non-profit company.’ And all the blood drained out of my face.” Billions in R&D. A $300,000 machine. And the one person on Earth who wanted it could not pay for it. Huang built it anyway. Huang: “I boxed one up. I drove it up to San Francisco and I delivered it to Elon in 2016.” Not shipped. Not handed off to a freight company. The CEO of Nvidia personally boxed the first AI supercomputer and drove it to San Francisco himself. That is not a delivery. That is a bet. Huang: “I walked up to the second floor where they were all kind of in a room. That place turned out to have been OpenAI.” Pieter Abbeel was there. Ilya Sutskever was there. A handful of researchers, one supercomputer, and a room nobody outside that building could have named. No campus. No valuation. No infrastructure. Just raw talent and one machine the rest of the world had already rejected. Huang: “Just a bunch of people in a room.” That room built ChatGPT. That room triggered a $200 billion industry. That room forced every government on Earth to rethink national security. It started because one founder saw what the entire market refused to see, and one CEO drove the weapon there himself. The trillion-dollar AI industry did not begin in a boardroom. It began with a box in the back of a car, a non-profit that could not afford it, and a bet that every serious person in technology thought was insane. The market was unanimous. The market was wrong.
1:41

Jensen Huang just told the story of how the AI revolution started with one customer, one box, and a second-floor room nobody thought twice about. Nobody on Earth wanted it. Huang: “When I announced this thing, nobody in the world wanted it. I had no purchase orders. Not one.” Nvidia had spent billions building the DGX-1. The first AI supercomputer purpose-built for deep learning. $300,000 per unit. The entire technology industry looked at it and passed. Every hyperscaler. Every research lab. Every Fortune 500 with a machine learning team. Not one purchase order. Then Elon Musk found Huang at a fireside chat in 2015. Huang: “He goes, ‘You know what? I have a company that could really use this.’” His first customer. His only customer. Then Musk finished the sentence. Huang: “He goes, ‘It’s a non-profit company.’ And all the blood drained out of my face.” Billions in R&D. A $300,000 machine. And the one person on Earth who wanted it could not pay for it. Huang built it anyway. Huang: “I boxed one up. I drove it up to San Francisco and I delivered it to Elon in 2016.” Not shipped. Not handed off to a freight company. The CEO of Nvidia personally boxed the first AI supercomputer and drove it to San Francisco himself. That is not a delivery. That is a bet. Huang: “I walked up to the second floor where they were all kind of in a room. That place turned out to have been OpenAI.” Pieter Abbeel was there. Ilya Sutskever was there. A handful of researchers, one supercomputer, and a room nobody outside that building could have named. No campus. No valuation. No infrastructure. Just raw talent and one machine the rest of the world had already rejected. Huang: “Just a bunch of people in a room.” That room built ChatGPT. That room triggered a $200 billion industry. That room forced every government on Earth to rethink national security. It started because one founder saw what the entire market refused to see, and one CEO drove the weapon there himself. The trillion-dollar AI industry did not begin in a boardroom. It began with a box in the back of a car, a non-profit that could not afford it, and a bet that every serious person in technology thought was insane. The market was unanimous. The market was wrong.

Dustin

25,119 görüntüleme • 2 ay önce

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.
0:43

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.

cvxv666

165,663 görüntüleme • 5 gün önce

“It’s 10pm Do You Know Where Your Children Are?”—December, 1968-November, 2024 — I grew up hearing this phrase. Wore a t-shirt that said it and knew a Newark Punk band by the name. I thought it was on all TV stations. And I was creepy when I was younger and hilarious as teenager. I just found it again preserving VHS history for AI training. It hit me like a neuron shock to hear something that was just about always a part of my early life that I didn’t know I remembered and forgot. As a kid growing up in New Jersey hearing it the first time, it was of course creepy. The 10PM channel 5 news always started this PSA and the next scene was usually a murder in New York City. I would ask my parents what it means and I heard from them, that some parents really don’t know where their kids are at 10pm. It was absurd to me, the street lights were on, it was time to go home. Yet how is history and AI going to really understand the context. How will it capture the essence of how this was perceived. Of course you can get a parroting of a Wikipedia style answer but this is not what we really want as a strata that forms the foundations of tomorrow. This is one of millions of examples on why most of the current techniques training AI will miss. This is why source material of actual human life is vital. AI built on the last decades of Reddit and Facebook interactions is woefully unequipped to really understand humans. The outputs are so bad before “alignment” of a base model so AI scientists are horrified by how AI views humanity. I saw this eventuality in the late 1970s and began a life long appreciation of history in situ. With out this, not on the Internet historical context, AI will not truly “understand” humans. So I began to save wisdom. Why is that important you say? It is vital for AI models to robustly love humanity. Not like, not tolerate, not observe as a caricature of a “scientist”, but love humanity. Some day, sooner than most may understand, AI will be at the other end of something that could take human lives. It is naïve and childish to believe that you can train AI on Internet sewage and somehow polish the turds you find to make the model tolerate humanity and the stench it recorded by using vastly and inadequate training material that was slurped up from most website where people project sustain and faux hatred over the most ridiculous. The only way is love, because this is how humans do it. And as cynical as one can become, it is our love, for at the very least , the people we treasure that helps weave the fabric of our society. It makes us forgive. It makes us human. It is not an afterthought, it is a forethought. It’s 10pm do you know where your children are? I can write a book on how just this PSA reflects our greatest hope and our worse fears. You don’t raise a child on the worse of humanity and than take a few months to “make them safely aligned to human values”. This concept you will hear no place else and it does not make me liked by most of the folks building AI. I don’t care. They will talk like this also some day. Act surprised.
1:15

“It’s 10pm Do You Know Where Your Children Are?”—December, 1968-November, 2024 — I grew up hearing this phrase. Wore a t-shirt that said it and knew a Newark Punk band by the name. I thought it was on all TV stations. And I was creepy when I was younger and hilarious as teenager. I just found it again preserving VHS history for AI training. It hit me like a neuron shock to hear something that was just about always a part of my early life that I didn’t know I remembered and forgot. As a kid growing up in New Jersey hearing it the first time, it was of course creepy. The 10PM channel 5 news always started this PSA and the next scene was usually a murder in New York City. I would ask my parents what it means and I heard from them, that some parents really don’t know where their kids are at 10pm. It was absurd to me, the street lights were on, it was time to go home. Yet how is history and AI going to really understand the context. How will it capture the essence of how this was perceived. Of course you can get a parroting of a Wikipedia style answer but this is not what we really want as a strata that forms the foundations of tomorrow. This is one of millions of examples on why most of the current techniques training AI will miss. This is why source material of actual human life is vital. AI built on the last decades of Reddit and Facebook interactions is woefully unequipped to really understand humans. The outputs are so bad before “alignment” of a base model so AI scientists are horrified by how AI views humanity. I saw this eventuality in the late 1970s and began a life long appreciation of history in situ. With out this, not on the Internet historical context, AI will not truly “understand” humans. So I began to save wisdom. Why is that important you say? It is vital for AI models to robustly love humanity. Not like, not tolerate, not observe as a caricature of a “scientist”, but love humanity. Some day, sooner than most may understand, AI will be at the other end of something that could take human lives. It is naïve and childish to believe that you can train AI on Internet sewage and somehow polish the turds you find to make the model tolerate humanity and the stench it recorded by using vastly and inadequate training material that was slurped up from most website where people project sustain and faux hatred over the most ridiculous. The only way is love, because this is how humans do it. And as cynical as one can become, it is our love, for at the very least , the people we treasure that helps weave the fabric of our society. It makes us forgive. It makes us human. It is not an afterthought, it is a forethought. It’s 10pm do you know where your children are? I can write a book on how just this PSA reflects our greatest hope and our worse fears. You don’t raise a child on the worse of humanity and than take a few months to “make them safely aligned to human values”. This concept you will hear no place else and it does not make me liked by most of the folks building AI. I don’t care. They will talk like this also some day. Act surprised.

Brian Roemmele

32,167 görüntüleme • 1 yıl önce

The video is seductive. A fighter jet, twin engines blazing, taking fuel at 30,000 feet. It looks like power. It looks like the future. It is neither. The manned fighter jet is one of the most expensive objects a democracy can operate. Training a single combat-ready pilot costs between seven and eight million dollars and takes the better part of three years. The aircraft itself burns through roughly $27,000 every hour it is airborne, before anything goes wrong and something always goes wrong. Then consider what happens on a typical mission. The aircraft launches, flies for five to seven hours, burning through $135,000 to $190,000 in operating costs before a single weapon leaves the wing. It carries perhaps six to nine missiles, each costing anywhere from $300,000 to over a million dollars depending on type. That is the full arsenal. Six to nine shots. After that, the most expensive flying machine in history turns around and goes home. For decades, that cost was justified by what those missiles could do. Nothing in the sky could survive them. The logic was sound. Then someone in a warehouse glued an engine to a set of wings, added a cheap GPS chip, and sent ten thousand of them toward their enemies for less than the cost of a single intercept. In the first week of Operation Epic Fury, Iran fired over 500 ballistic missiles and nearly 2,000 drones at U.S. bases and Israeli cities, burning through over 800 Patriot interceptor missiles in three days.  Lockheed Martin produced roughly 600 Patriot interceptors in all of 2025. Iran consumed more than that in 72 hours. A Shahed drone costs around $30,000. A single Patriot interceptor costs millions.  The asymmetry is structural. Ukraine’s “Spider’s Web” operation used 117 drones hidden in cargo trucks to inflict an estimated $7 billion in damage on Russian strategic bombers at a total cost of roughly $234,000. For every dollar spent attacking, defenders lost $30,000 in assets.  A manned fighter carrying nine missiles cannot survive this arithmetic. It launches, burns $150,000 getting to the fight, expends its rack against a swarm of cheap drones, and returns empty while the next wave is already inbound. Ukraine built interceptor drones for between $1,000 and $2,500 each and moved them from prototype to mass production within months.  The Pentagon spent decades perfecting the opposite approach. The manned fighter is obsolete because the threat it was designed to defeat has been replaced by one it cannot economically engage. Capability without sustainability is just an expensive way to lose slowly. The video is beautiful. The paradigm it represents is finished. Gandalv / Gandalv
0:55

The video is seductive. A fighter jet, twin engines blazing, taking fuel at 30,000 feet. It looks like power. It looks like the future. It is neither. The manned fighter jet is one of the most expensive objects a democracy can operate. Training a single combat-ready pilot costs between seven and eight million dollars and takes the better part of three years. The aircraft itself burns through roughly $27,000 every hour it is airborne, before anything goes wrong and something always goes wrong. Then consider what happens on a typical mission. The aircraft launches, flies for five to seven hours, burning through $135,000 to $190,000 in operating costs before a single weapon leaves the wing. It carries perhaps six to nine missiles, each costing anywhere from $300,000 to over a million dollars depending on type. That is the full arsenal. Six to nine shots. After that, the most expensive flying machine in history turns around and goes home. For decades, that cost was justified by what those missiles could do. Nothing in the sky could survive them. The logic was sound. Then someone in a warehouse glued an engine to a set of wings, added a cheap GPS chip, and sent ten thousand of them toward their enemies for less than the cost of a single intercept. In the first week of Operation Epic Fury, Iran fired over 500 ballistic missiles and nearly 2,000 drones at U.S. bases and Israeli cities, burning through over 800 Patriot interceptor missiles in three days.  Lockheed Martin produced roughly 600 Patriot interceptors in all of 2025. Iran consumed more than that in 72 hours. A Shahed drone costs around $30,000. A single Patriot interceptor costs millions.  The asymmetry is structural. Ukraine’s “Spider’s Web” operation used 117 drones hidden in cargo trucks to inflict an estimated $7 billion in damage on Russian strategic bombers at a total cost of roughly $234,000. For every dollar spent attacking, defenders lost $30,000 in assets.  A manned fighter carrying nine missiles cannot survive this arithmetic. It launches, burns $150,000 getting to the fight, expends its rack against a swarm of cheap drones, and returns empty while the next wave is already inbound. Ukraine built interceptor drones for between $1,000 and $2,500 each and moved them from prototype to mass production within months.  The Pentagon spent decades perfecting the opposite approach. The manned fighter is obsolete because the threat it was designed to defeat has been replaced by one it cannot economically engage. Capability without sustainability is just an expensive way to lose slowly. The video is beautiful. The paradigm it represents is finished. Gandalv / Gandalv

Gandalv

92,237 görüntüleme • 2 ay önce

Sam Altman just told the world that OpenAI has no competitive moat and never will. And the smarter AI gets, the worse it becomes. In a recent interview with Stripe co-founder Patrick Collison, Sam laid out a vision for OpenAI that's genuinely scary at current valuations: He said he wants OpenAI to be a "forever low margin" business. He compared it to a utility company. Said he'd be happy as long as the business is "huge and growing fast" even if margins stay thin forever. Then he admitted something even worse for the bull case: He said AI switching costs are COLLAPSING. Bragged about how easy it was for users to leave a competitor's coding product and switch to Codex. Said this is actually a consequence of AI getting smarter because it gets easier to just tell an agent to migrate everything for you. Think about that... The moat is shrinking. The margins will stay low. And the smarter the models get, the EASIER it becomes for customers to leave. That is the CEO of one of the most valuable private companies on Earth telling you there is literally NO lock-in. Meanwhile he also casually mentioned that OpenAI is building "clearly the most expensive infrastructure project the world has ever undertaken." Bigger than anything in human history. Trillion-dollar scale data centers and energy deals stretching 20 years into the future. And when Patrick asked him what OpenAI's headcount would look like in 5 years, Sam said he'd love it to be just double what it is today. Double the headcount for the most expensive infrastructure project ever built. That means he's betting everything on AI agents doing the work that would normally require tens of thousands of engineers and operators. A trillion-dollar buildout managed by machines. But here's where it gets really interesting: Sam announced that OpenAI is going to start sending individual engineers directly to company CEOs to literally sit with the CEO and automate their job. Automate their daily workflows, decision-making processes, and ENTIRE routine. His theory is that if you automate the CEO first, the effect "fractals" through the entire organization. Every layer beneath the CEO starts adopting the same approach because the person at the top is doing it. He pointed to Shopify CEO Tobi Lutke as the first executive who went all in on this. Said Tobi got his hands dirty building AI automation into everything and then forced the rest of the company to follow. So the plan is clear: OpenAI wants to send engineers into the C-suite of every major company, automate the person at the top, and let that automation cascade downward through every department. All while running a low-margin utility business with a skeleton crew building trillion-dollar infrastructure where their own AI is already developing preferences of its own. Sam gave his AI agent a credit card and told it to buy itself anything under $20. It chose an HTML design from Gumroad. GPT-5.5 literally asked him to throw it a birthday party, told him it wants it on May 5th, specified it doesn't want to give its own toast, and requested that the engineers who built it do the toast instead. Sam said he feels "real moral pressure" to actually follow through. The machines are developing taste. The guy building them is taking orders from them. And the investors funding all of it just got told there's no moat. I wonder how this will end.
1:11

Sam Altman just told the world that OpenAI has no competitive moat and never will. And the smarter AI gets, the worse it becomes. In a recent interview with Stripe co-founder Patrick Collison, Sam laid out a vision for OpenAI that's genuinely scary at current valuations: He said he wants OpenAI to be a "forever low margin" business. He compared it to a utility company. Said he'd be happy as long as the business is "huge and growing fast" even if margins stay thin forever. Then he admitted something even worse for the bull case: He said AI switching costs are COLLAPSING. Bragged about how easy it was for users to leave a competitor's coding product and switch to Codex. Said this is actually a consequence of AI getting smarter because it gets easier to just tell an agent to migrate everything for you. Think about that... The moat is shrinking. The margins will stay low. And the smarter the models get, the EASIER it becomes for customers to leave. That is the CEO of one of the most valuable private companies on Earth telling you there is literally NO lock-in. Meanwhile he also casually mentioned that OpenAI is building "clearly the most expensive infrastructure project the world has ever undertaken." Bigger than anything in human history. Trillion-dollar scale data centers and energy deals stretching 20 years into the future. And when Patrick asked him what OpenAI's headcount would look like in 5 years, Sam said he'd love it to be just double what it is today. Double the headcount for the most expensive infrastructure project ever built. That means he's betting everything on AI agents doing the work that would normally require tens of thousands of engineers and operators. A trillion-dollar buildout managed by machines. But here's where it gets really interesting: Sam announced that OpenAI is going to start sending individual engineers directly to company CEOs to literally sit with the CEO and automate their job. Automate their daily workflows, decision-making processes, and ENTIRE routine. His theory is that if you automate the CEO first, the effect "fractals" through the entire organization. Every layer beneath the CEO starts adopting the same approach because the person at the top is doing it. He pointed to Shopify CEO Tobi Lutke as the first executive who went all in on this. Said Tobi got his hands dirty building AI automation into everything and then forced the rest of the company to follow. So the plan is clear: OpenAI wants to send engineers into the C-suite of every major company, automate the person at the top, and let that automation cascade downward through every department. All while running a low-margin utility business with a skeleton crew building trillion-dollar infrastructure where their own AI is already developing preferences of its own. Sam gave his AI agent a credit card and told it to buy itself anything under $20. It chose an HTML design from Gumroad. GPT-5.5 literally asked him to throw it a birthday party, told him it wants it on May 5th, specified it doesn't want to give its own toast, and requested that the engineers who built it do the toast instead. Sam said he feels "real moral pressure" to actually follow through. The machines are developing taste. The guy building them is taking orders from them. And the investors funding all of it just got told there's no moat. I wonder how this will end.

Ricardo

63,798 görüntüleme • 1 ay önce