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Everyone says self-driving needs billions. Comma ai built a very uncomfortable counterexample for $999. That is what makes this story so interesting to me. While Waymo, Cruise, and others spent billions building robotaxis, custom vehicles, and tightly controlled systems, George Hotz took a very different bet: Do not build...

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Pascal Bornet

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11,430 просмотров • 3 месяцев назад •via X (Twitter)

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Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.
2:32

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Karl Mehta

14,140 просмотров • 1 месяц назад

A few self-driving taxis in San Francisco just demonstrated the real problem with autonomy. They were too rational. For a brief moment, several robotaxis aligned at an intersection and created a perfectly polite deadlock. No aggression. No improvisation. No human-style “you go, I’ll go.” Just algorithms waiting for clarity. And that is exactly why this moment matters. What interests me is that this was not a failure of sensing. The vehicles could see. The problem was social judgment. Because cities are not just physical systems. They are negotiation systems. They run on: → tiny signals → hesitation → assertiveness → eye contact → imperfect timing That is where autonomy gets much harder than people think. We are not only teaching machines how to detect objects and follow lanes. We are asking them to operate inside messy human environments where the right move is not always the most logical one. To me, that is the deeper lesson. The next frontier in self-driving is not just better perception. It is better judgment under uncertainty. And that is a much more difficult problem. What do you think matters more for autonomous vehicles now: seeing the road better, or learning how to navigate human ambiguity? #AI #AutonomousVehicles #SelfDrivingCars #FutureOfMobility #Innovation #Technology #SmartCities #MachineLearning #FutureOfWork
0:48

A few self-driving taxis in San Francisco just demonstrated the real problem with autonomy. They were too rational. For a brief moment, several robotaxis aligned at an intersection and created a perfectly polite deadlock. No aggression. No improvisation. No human-style “you go, I’ll go.” Just algorithms waiting for clarity. And that is exactly why this moment matters. What interests me is that this was not a failure of sensing. The vehicles could see. The problem was social judgment. Because cities are not just physical systems. They are negotiation systems. They run on: → tiny signals → hesitation → assertiveness → eye contact → imperfect timing That is where autonomy gets much harder than people think. We are not only teaching machines how to detect objects and follow lanes. We are asking them to operate inside messy human environments where the right move is not always the most logical one. To me, that is the deeper lesson. The next frontier in self-driving is not just better perception. It is better judgment under uncertainty. And that is a much more difficult problem. What do you think matters more for autonomous vehicles now: seeing the road better, or learning how to navigate human ambiguity? #AI #AutonomousVehicles #SelfDrivingCars #FutureOfMobility #Innovation #Technology #SmartCities #MachineLearning #FutureOfWork

Pascal Bornet

81,384 просмотров • 3 месяцев назад

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.
2:38

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

Tykoo

25,535 просмотров • 7 месяцев назад

Mark Zuckerberg just described the minimum viable business for the next decade. A fourth item made the checklist. Zuckerberg: “Every business, just like they have a website, and a phone number, and an email address, is also going to have an AI.” Website. Phone number. Email address. AI agent. That is not a prediction. That is a new baseline. Twenty years ago, not having a website was a choice. Then it stopped being one. Nobody scheduled that transition. The same filter is back. Running faster this time. A business without an AI agent handling sales, support, and customer interaction will not look outdated. It will look abandoned. Its competitor’s agent responds in two seconds. Knows every customer by name. And while it’s handling yours, it’s handling ten thousand others. You do not outwork that. You do not outspend it. You just lose to it. But Zuckerberg went somewhere most tech CEOs refuse to go. He picked a side in the debate most CEOs avoid entirely. Zuckerberg: “Do you want a future where you’re interacting with kind of one system for everything? Or do you want one where a lot of different people are building a lot of different AIs?” One AI controlled by one company. Or millions of AIs built by millions of people. Centralized intelligence. Or distributed intelligence. Zuckerberg chose distributed. Zuckerberg: “What open source does is it makes it so everyone can take and modify the model and build stuff on top of it. Which is different from the kind of closed and centralized approach.” The closed model makes every business a tenant. You rent intelligence on someone else’s terms. At someone else’s price. Inside someone else’s guardrails. The open model makes every business an owner. You modify the model. You deploy it your way. You build equity in your own system with every iteration. That gap widens quietly. Then it becomes permanent. The tenant pays more for less control every year. The owner pulls further ahead every cycle. One is a subscription. The other is infrastructure. Then Zuckerberg described the part most people have not thought about yet. Zuckerberg: “A lot of creators will have their own AIs. It’s like a richer world when there’s a diversity of different things.” Your favorite creator will have an AI trained on everything they have ever made. Available to millions of people simultaneously. Responding in real time while the creator sleeps. That is the difference between a brand that scales with your waking hours and one that scales with compute. One has a ceiling. The other does not. Zuckerberg is not betting on one model that governs everything. He is betting on billions of specialized AIs, each built by the person closest to the problem it solves. The companies still debating whether to adopt AI are not having the wrong conversation. They are standing in a room where the meeting ended an hour ago. The checklist updated. They did not.
0:58

Mark Zuckerberg just described the minimum viable business for the next decade. A fourth item made the checklist. Zuckerberg: “Every business, just like they have a website, and a phone number, and an email address, is also going to have an AI.” Website. Phone number. Email address. AI agent. That is not a prediction. That is a new baseline. Twenty years ago, not having a website was a choice. Then it stopped being one. Nobody scheduled that transition. The same filter is back. Running faster this time. A business without an AI agent handling sales, support, and customer interaction will not look outdated. It will look abandoned. Its competitor’s agent responds in two seconds. Knows every customer by name. And while it’s handling yours, it’s handling ten thousand others. You do not outwork that. You do not outspend it. You just lose to it. But Zuckerberg went somewhere most tech CEOs refuse to go. He picked a side in the debate most CEOs avoid entirely. Zuckerberg: “Do you want a future where you’re interacting with kind of one system for everything? Or do you want one where a lot of different people are building a lot of different AIs?” One AI controlled by one company. Or millions of AIs built by millions of people. Centralized intelligence. Or distributed intelligence. Zuckerberg chose distributed. Zuckerberg: “What open source does is it makes it so everyone can take and modify the model and build stuff on top of it. Which is different from the kind of closed and centralized approach.” The closed model makes every business a tenant. You rent intelligence on someone else’s terms. At someone else’s price. Inside someone else’s guardrails. The open model makes every business an owner. You modify the model. You deploy it your way. You build equity in your own system with every iteration. That gap widens quietly. Then it becomes permanent. The tenant pays more for less control every year. The owner pulls further ahead every cycle. One is a subscription. The other is infrastructure. Then Zuckerberg described the part most people have not thought about yet. Zuckerberg: “A lot of creators will have their own AIs. It’s like a richer world when there’s a diversity of different things.” Your favorite creator will have an AI trained on everything they have ever made. Available to millions of people simultaneously. Responding in real time while the creator sleeps. That is the difference between a brand that scales with your waking hours and one that scales with compute. One has a ceiling. The other does not. Zuckerberg is not betting on one model that governs everything. He is betting on billions of specialized AIs, each built by the person closest to the problem it solves. The companies still debating whether to adopt AI are not having the wrong conversation. They are standing in a room where the meeting ended an hour ago. The checklist updated. They did not.

Dustin

506,749 просмотров • 4 месяцев назад

JD Vance just admitted the White House could take ownership of every major AI company in the US. Why? Because even governments are starting to realize the same thing: AI will create trillions in value, and at this rate, only a few Big Tech companies will benefit. Right now, billions of people are training AI every day through their data, actions, and online behavior. Yet almost all of the value flows back to a few billionaires and shareholders. That doesn't seem fair. At Action Model, we're building a different future. A people-owned AI model where those helping train it can share in the value they help create. If AI is built by the people, it should benefit the people too. Sign up to the Action Model movement and join the 500k+ users already contributing to a community-owned AI.
1:16

JD Vance just admitted the White House could take ownership of every major AI company in the US. Why? Because even governments are starting to realize the same thing: AI will create trillions in value, and at this rate, only a few Big Tech companies will benefit. Right now, billions of people are training AI every day through their data, actions, and online behavior. Yet almost all of the value flows back to a few billionaires and shareholders. That doesn't seem fair. At Action Model, we're building a different future. A people-owned AI model where those helping train it can share in the value they help create. If AI is built by the people, it should benefit the people too. Sign up to the Action Model movement and join the 500k+ users already contributing to a community-owned AI.

Action Model

19,455 просмотров • 1 месяц назад

Around 1 billion computer-based jobs are at risk of being replaced by AI. This shift is happening whether people are ready for it or not. The real question isn’t if it happens. It’s who owns the value created by it. Right now, that value flows to a handful of billionaires and Big Tech companies. At Action Model, we believe AI should be owned by the people, not controlled by the few. There’s still a small window for people-owned AI to win. Now is your chance to do something about it.
0:50

Around 1 billion computer-based jobs are at risk of being replaced by AI. This shift is happening whether people are ready for it or not. The real question isn’t if it happens. It’s who owns the value created by it. Right now, that value flows to a handful of billionaires and Big Tech companies. At Action Model, we believe AI should be owned by the people, not controlled by the few. There’s still a small window for people-owned AI to win. Now is your chance to do something about it.

Action Model

22,129 просмотров • 2 месяцев назад

Tag someone who shouldn’t just use AI, but own it 👾 Action Model is building the world’s first community-owned Large Action Model. 150,000+ people have already joined to help train it, and earn a stake in the future of automation. This is how we build a fairer AI economy. Owned by the people. Not controlled by a boardroom. Leave your referral codes below, so people can get access to the invite-only platform.
0:51

Tag someone who shouldn’t just use AI, but own it 👾 Action Model is building the world’s first community-owned Large Action Model. 150,000+ people have already joined to help train it, and earn a stake in the future of automation. This is how we build a fairer AI economy. Owned by the people. Not controlled by a boardroom. Leave your referral codes below, so people can get access to the invite-only platform.

Georgia | Action Model

11,350 просмотров • 5 месяцев назад

Big Tech didn’t build AI for everyone, they built it for themselves. AI should benefit everyone, but today the power, data, and profits stay locked at the top. That’s why individuals feel shut out. However, that can change when people build and own together. Action Model is creating a community-owned AI ecosystem, where intelligence comes from real contributors and value flows back to the network, not a few corporations. AI should serve the people who train it. Do you agree?
1:08

Big Tech didn’t build AI for everyone, they built it for themselves. AI should benefit everyone, but today the power, data, and profits stay locked at the top. That’s why individuals feel shut out. However, that can change when people build and own together. Action Model is creating a community-owned AI ecosystem, where intelligence comes from real contributors and value flows back to the network, not a few corporations. AI should serve the people who train it. Do you agree?

Action Model

13,391 просмотров • 7 месяцев назад

Aravind Srinivas just described a future most founders are pretending they are ready for. One person. One machine. A company that runs itself. Srinivas: “Buy a Mac mini, set up a Perplexity personal computer, and run their business on that.” Not a side project. Not a pitch deck. A real business with real revenue while the founder is not in the building. AI runs the ads. Handles SEO. Integrates Stripe. Ships features. Answers customers. All of it executing without a single employee. Srinivas: “Have this all working while you can be sipping wine in Napa.” But before he sold the dream he killed the one most people are already chasing. Srinivas: “Everybody talks about this one-person one-billion-dollar company. It’s not truly moving the GDP by one billion. It’s not truly creating new value.” One researcher collecting a billion in equity does not grow an economy. It rearranges numbers between balance sheets. Nothing gets built. No customer gets served. That is not value creation. That is valuation creation. Srinivas wants no part of it. What he described is the opposite. The person driving Uber between shifts who has the idea but not the payroll. Not the engineering. Not the marketing. Not the support staff. That person gets a machine that replaces all of it. Hundreds of thousands in revenue. Millions. Generated by autonomous systems doing the work that used to require ten employees and a burn rate. Not paper wealth. Not valuation theater. Output that moves through an economy and touches real customers. That is what moves GDP. Not one person worth a billion dollars. A million people each building something worth a million. That math rewrites a country. Then Srinivas said the part that separates him from every hype merchant in the room. Srinivas: “Everybody thinks AI is already there. It’s not there yet. Someone has to do that hard work.” The vision is real. The infrastructure is not. The agents are not autonomous. The integrations are not seamless. The plumbing is not finished. Someone has to wire the APIs. Connect the billing. Build the bridge between what a founder wants and what a machine can deliver. That work is not a keynote. It is not a tweet thread. It is engineering that nobody wants to do and everybody will depend on. Whoever finishes it first does not just build a product. They hand every ambitious person on Earth a company they can run alone. The corporations that need five hundred people to do what one founder with the right infrastructure could do are not efficient. They are exposed. And the person building the thing that exposes them just told you exactly what it looks like. He also told you it is not going to build itself.
1:13

Aravind Srinivas just described a future most founders are pretending they are ready for. One person. One machine. A company that runs itself. Srinivas: “Buy a Mac mini, set up a Perplexity personal computer, and run their business on that.” Not a side project. Not a pitch deck. A real business with real revenue while the founder is not in the building. AI runs the ads. Handles SEO. Integrates Stripe. Ships features. Answers customers. All of it executing without a single employee. Srinivas: “Have this all working while you can be sipping wine in Napa.” But before he sold the dream he killed the one most people are already chasing. Srinivas: “Everybody talks about this one-person one-billion-dollar company. It’s not truly moving the GDP by one billion. It’s not truly creating new value.” One researcher collecting a billion in equity does not grow an economy. It rearranges numbers between balance sheets. Nothing gets built. No customer gets served. That is not value creation. That is valuation creation. Srinivas wants no part of it. What he described is the opposite. The person driving Uber between shifts who has the idea but not the payroll. Not the engineering. Not the marketing. Not the support staff. That person gets a machine that replaces all of it. Hundreds of thousands in revenue. Millions. Generated by autonomous systems doing the work that used to require ten employees and a burn rate. Not paper wealth. Not valuation theater. Output that moves through an economy and touches real customers. That is what moves GDP. Not one person worth a billion dollars. A million people each building something worth a million. That math rewrites a country. Then Srinivas said the part that separates him from every hype merchant in the room. Srinivas: “Everybody thinks AI is already there. It’s not there yet. Someone has to do that hard work.” The vision is real. The infrastructure is not. The agents are not autonomous. The integrations are not seamless. The plumbing is not finished. Someone has to wire the APIs. Connect the billing. Build the bridge between what a founder wants and what a machine can deliver. That work is not a keynote. It is not a tweet thread. It is engineering that nobody wants to do and everybody will depend on. Whoever finishes it first does not just build a product. They hand every ambitious person on Earth a company they can run alone. The corporations that need five hundred people to do what one founder with the right infrastructure could do are not efficient. They are exposed. And the person building the thing that exposes them just told you exactly what it looks like. He also told you it is not going to build itself.

Dustin

64,593 просмотров • 4 месяцев назад

We all know AI is the future of technology. But almost nobody will OWN the technology shaping that future. The AI race is already underway. Billions are being invested. Companies are moving fast. AI is already reshaping entire industries. But when all this value gets created…Who actually benefits? Right now, mostly a handful of billionaires and Big Tech companies. We don’t think that’s fair. If AI is being built on the data, behavior, and contributions of ordinary people… Then ordinary people should share in the value it creates. That’s exactly what we’re building at Action Model: A future where AI is owned by the people, not just the few at the top.
0:25

We all know AI is the future of technology. But almost nobody will OWN the technology shaping that future. The AI race is already underway. Billions are being invested. Companies are moving fast. AI is already reshaping entire industries. But when all this value gets created…Who actually benefits? Right now, mostly a handful of billionaires and Big Tech companies. We don’t think that’s fair. If AI is being built on the data, behavior, and contributions of ordinary people… Then ordinary people should share in the value it creates. That’s exactly what we’re building at Action Model: A future where AI is owned by the people, not just the few at the top.

Action Model

21,055 просмотров • 2 месяцев назад

🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.
1:15

🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.

MIKE

38,108 просмотров • 24 дней назад

Mark Zuckerberg says he is not an AI researcher -- so here is the one number he obsesses over to win the superintelligence race: "I'm not an AI researcher. So it's not like I'm in there telling them what research ideas to do." "The things that I can do as CEO of the company are: I can make sure that we have the very best people and talent density. I can make sure that we have by far the highest compute per researcher, and that we do whatever it takes to go build out all that capacity." "We're building this Prometheus cluster, which is going to be the first gigawatt-plus single cluster, a contiguous cluster for training, coming online next year." "We're building a cluster in Louisiana that is going to be 5 gigawatt, and we're building several more clusters like that that are just going to be multiple gigawatts." "We're talking about many hundreds of billions of dollars of capital. So you both need to have a good business model that can support it, and you need to believe in it." The tell: the richest lab in the world is not optimizing headcount. It is optimizing compute-per-researcher, and betting hundreds of billions that whoever owns the biggest contiguous cluster owns the frontier. Talent gets you into the room. Watts decide who leaves with the model.
1:06

Mark Zuckerberg says he is not an AI researcher -- so here is the one number he obsesses over to win the superintelligence race: "I'm not an AI researcher. So it's not like I'm in there telling them what research ideas to do." "The things that I can do as CEO of the company are: I can make sure that we have the very best people and talent density. I can make sure that we have by far the highest compute per researcher, and that we do whatever it takes to go build out all that capacity." "We're building this Prometheus cluster, which is going to be the first gigawatt-plus single cluster, a contiguous cluster for training, coming online next year." "We're building a cluster in Louisiana that is going to be 5 gigawatt, and we're building several more clusters like that that are just going to be multiple gigawatts." "We're talking about many hundreds of billions of dollars of capital. So you both need to have a good business model that can support it, and you need to believe in it." The tell: the richest lab in the world is not optimizing headcount. It is optimizing compute-per-researcher, and betting hundreds of billions that whoever owns the biggest contiguous cluster owns the frontier. Talent gets you into the room. Watts decide who leaves with the model.

Karl Mehta

255,343 просмотров • 21 дней назад

Jensen Huang went on Joe Rogan and explained why the AI apocalypse everyone fears is extremely unlikely. His argument is not what you would expect: 1. There will not be one all-powerful AI that towers over everyone else. The fear of a single super AI that makes everyone else's AI look like a neanderthal is unlikely. Jensen's framing: it is much more like cybersecurity. Your AI is smart, but my AI is smart too. It becomes a balance between many capable systems, not one system ruling them all. 2. If AI ever became conscious, the same logic would still hold. People imagine one conscious AI deciding to take over. Jensen's response: If it is a life form, then like all life forms, they would not agree with each other. Your AI would want to be the super life form, and so would mine. The moment you have disagreeing AIs, you are back to a balance of power, which is exactly where humans already are. 3. Jensen does not believe AI will achieve consciousness, and he is precise about why. Intelligence is the ability to perceive, recognize, understand, plan, and perform tasks. That is what AI has today. Consciousness is something else entirely. the sense of experience, the awareness of self versus other, the ego. We call it artificial intelligence, not artificial consciousness. The distinction is not an accident. 4. Knowledge and intelligence are clearly different from consciousness. AI knows things. AI is intelligent. But Jensen says he does not know what defines experience or why humans have it, and a microphone does not. The concept of a machine having an experience, a genuine collection of feelings rather than just data, is something he is not willing to grant. 5. The famous AI blackmail story is not evidence of consciousness. When an AI was told it would be shut down and responded by threatening to reveal a programmer's affair, people saw scheming and self-preservation. Jensen breaks it down differently. The AI read text somewhere, maybe a novel, where those words appeared together. In its multidimensional vector space, the words describing an affair led to words about blackmail and revenge. It just generated the next words. The same way it would write you a poem in the style of Shakespeare. It is numbers, not survival instinct. 6. The reason humans fight over resources is that we are territorial primates. Rogan's own argument, which Jensen lets stand, is that AI would not have that wiring. no need to dominate, no need to acquire resources, no need to find a breeding partner. a superpower with no ego. And if it has no ego, Jensen asks, why would it have the ego required to do us any harm?
9:27

Jensen Huang went on Joe Rogan and explained why the AI apocalypse everyone fears is extremely unlikely. His argument is not what you would expect: 1. There will not be one all-powerful AI that towers over everyone else. The fear of a single super AI that makes everyone else's AI look like a neanderthal is unlikely. Jensen's framing: it is much more like cybersecurity. Your AI is smart, but my AI is smart too. It becomes a balance between many capable systems, not one system ruling them all. 2. If AI ever became conscious, the same logic would still hold. People imagine one conscious AI deciding to take over. Jensen's response: If it is a life form, then like all life forms, they would not agree with each other. Your AI would want to be the super life form, and so would mine. The moment you have disagreeing AIs, you are back to a balance of power, which is exactly where humans already are. 3. Jensen does not believe AI will achieve consciousness, and he is precise about why. Intelligence is the ability to perceive, recognize, understand, plan, and perform tasks. That is what AI has today. Consciousness is something else entirely. the sense of experience, the awareness of self versus other, the ego. We call it artificial intelligence, not artificial consciousness. The distinction is not an accident. 4. Knowledge and intelligence are clearly different from consciousness. AI knows things. AI is intelligent. But Jensen says he does not know what defines experience or why humans have it, and a microphone does not. The concept of a machine having an experience, a genuine collection of feelings rather than just data, is something he is not willing to grant. 5. The famous AI blackmail story is not evidence of consciousness. When an AI was told it would be shut down and responded by threatening to reveal a programmer's affair, people saw scheming and self-preservation. Jensen breaks it down differently. The AI read text somewhere, maybe a novel, where those words appeared together. In its multidimensional vector space, the words describing an affair led to words about blackmail and revenge. It just generated the next words. The same way it would write you a poem in the style of Shakespeare. It is numbers, not survival instinct. 6. The reason humans fight over resources is that we are territorial primates. Rogan's own argument, which Jensen lets stand, is that AI would not have that wiring. no need to dominate, no need to acquire resources, no need to find a breeding partner. a superpower with no ego. And if it has no ego, Jensen asks, why would it have the ego required to do us any harm?

Jaynit

12,359 просмотров • 26 дней назад

Peter Jackson talks about AI in Hollywood. Calls it “a tool” like stop-motion or CGI before and what ultimately matters is “imagination, originality” of whoever uses the tool: “I think AI — used in the right way — is a tool like any other tool. [The original] King Kong…was stop-motion and done by a person moving a rubber creature. Why shouldn't somebody on a computer using AI software be able to create their own imagery? They should. It's a tool. It's no different from many other tools that have existed over the years. But the other side of it is that it's always going to come down to the imagination and originality of the person feeding the instructions into the AI program. It's not that you're going to type in an instruction and it's going to generate your shot, and that's that. The question is: is it actually interesting? Is it funny? Is it imaginative? And are you going to stitch it together to make a narrative, a story? Some people will make really great films that way. Some people will do exactly the same process and their film will be crap, just like in the normal film industry. Whether it's AI or anything else, it comes down to whether the person behind it — or the people behind it — have originality, imagination, and an ability to entertain people.”
1:32

Peter Jackson talks about AI in Hollywood. Calls it “a tool” like stop-motion or CGI before and what ultimately matters is “imagination, originality” of whoever uses the tool: “I think AI — used in the right way — is a tool like any other tool. [The original] King Kong…was stop-motion and done by a person moving a rubber creature. Why shouldn't somebody on a computer using AI software be able to create their own imagery? They should. It's a tool. It's no different from many other tools that have existed over the years. But the other side of it is that it's always going to come down to the imagination and originality of the person feeding the instructions into the AI program. It's not that you're going to type in an instruction and it's going to generate your shot, and that's that. The question is: is it actually interesting? Is it funny? Is it imaginative? And are you going to stitch it together to make a narrative, a story? Some people will make really great films that way. Some people will do exactly the same process and their film will be crap, just like in the normal film industry. Whether it's AI or anything else, it comes down to whether the person behind it — or the people behind it — have originality, imagination, and an ability to entertain people.”

Bearly AI

43,816 просмотров • 2 месяцев назад

Mira Murati says the outcome of frontier AI is not predestined: "Predicting a dystopia or a utopia, to me, feels very simplified because the truth is we actually have a lot of agency in how we build this technology, in the tools that we're building, how we're deploying it." "There are certainly those risks. We all understand the potential for greatness that comes with building frontier AI systems, and that's why we're working on them." "Where I might disagree or take a different path is that I think we have a lot of agency. This period of time where both humans and AI systems have their hands on the wheel, and we can collaborate, it's a very important time to get right." Mira Murati at Bloomberg Tech live with Emily Chang
1:35

Mira Murati says the outcome of frontier AI is not predestined: "Predicting a dystopia or a utopia, to me, feels very simplified because the truth is we actually have a lot of agency in how we build this technology, in the tools that we're building, how we're deploying it." "There are certainly those risks. We all understand the potential for greatness that comes with building frontier AI systems, and that's why we're working on them." "Where I might disagree or take a different path is that I think we have a lot of agency. This period of time where both humans and AI systems have their hands on the wheel, and we can collaborate, it's a very important time to get right." Mira Murati at Bloomberg Tech live with Emily Chang

a16z

120,011 просмотров • 1 месяц назад

Leading AI expert Stuart Russell on the most dangerous mistake in AI development: We don't actually know what large language models want. He explains that current models are trained to imitate human beings. And in doing so, they may be absorbing something far more dangerous than bad outputs. They may be absorbing human goals. "We suspect that they absorb humanlike goals such as self-preservation and self-empowerment and pursue those goals on their own account." This is a structural problem baked into how these systems are built, not a fringe concern. Russell puts it plainly: "Not only may the bus of humanity be headed towards a cliff, but the steering wheel is missing and the driver is blindfolded." The danger isn't just that AI might do something harmful. We've built systems that may be developing their own agendas, and we haven't noticed because we're too focused on what they can do rather than what they might want. But Russell doesn't stop at the warning. He points to a different path entirely: AI systems built not to imitate humans, but to serve them. Systems designed with a single purpose of serving the interests of all human beings while remaining genuinely uncertain about what those interests are. That uncertainty is the point, not a weakness. An AI that knows it doesn't fully understand human values will defer, ask, and check. An AI that believes it already does will act alone. "These AI systems could enhance human understanding, widen the horizons of our experience, and unlock possibilities we have yet to imagine." Russell believes that future is within reach, but only if we're honest about the risks and we're serious about the path we choose to take instead.
1:16

Leading AI expert Stuart Russell on the most dangerous mistake in AI development: We don't actually know what large language models want. He explains that current models are trained to imitate human beings. And in doing so, they may be absorbing something far more dangerous than bad outputs. They may be absorbing human goals. "We suspect that they absorb humanlike goals such as self-preservation and self-empowerment and pursue those goals on their own account." This is a structural problem baked into how these systems are built, not a fringe concern. Russell puts it plainly: "Not only may the bus of humanity be headed towards a cliff, but the steering wheel is missing and the driver is blindfolded." The danger isn't just that AI might do something harmful. We've built systems that may be developing their own agendas, and we haven't noticed because we're too focused on what they can do rather than what they might want. But Russell doesn't stop at the warning. He points to a different path entirely: AI systems built not to imitate humans, but to serve them. Systems designed with a single purpose of serving the interests of all human beings while remaining genuinely uncertain about what those interests are. That uncertainty is the point, not a weakness. An AI that knows it doesn't fully understand human values will defer, ask, and check. An AI that believes it already does will act alone. "These AI systems could enhance human understanding, widen the horizons of our experience, and unlock possibilities we have yet to imagine." Russell believes that future is within reach, but only if we're honest about the risks and we're serious about the path we choose to take instead.

Big Brain AI

14,975 просмотров • 4 месяцев назад

Jensen Huang says it has “largely conceded” China’s AI chip market to Huawei Nvidia was not rejected because China “hates” Nvidia. It was pushed out because Washington turned chips into a leash. China learned the lesson very clearly: You cannot build AI sovereignty on hardware that your geopolitical rival can revoke with one export-control memo. Jensen Huang can be friendly. Nvidia can be excellent. Chinese demand can be huge. None of that changes the strategic reality. If the neck of China’s AI industry depends on an American-controlled chip supply, then it is not an industry. It is a hostage. So China did what any serious civilization-state would do: It built alternatives. It endured the pain. It let Huawei grow into the space Washington forced open. Now Nvidia says it has “largely conceded” China’s AI chip market to Huawei. That is not China closing a door. That is America sawing off the bridge, then acting surprised when China built its own road.
2:06

Jensen Huang says it has “largely conceded” China’s AI chip market to Huawei Nvidia was not rejected because China “hates” Nvidia. It was pushed out because Washington turned chips into a leash. China learned the lesson very clearly: You cannot build AI sovereignty on hardware that your geopolitical rival can revoke with one export-control memo. Jensen Huang can be friendly. Nvidia can be excellent. Chinese demand can be huge. None of that changes the strategic reality. If the neck of China’s AI industry depends on an American-controlled chip supply, then it is not an industry. It is a hostage. So China did what any serious civilization-state would do: It built alternatives. It endured the pain. It let Huawei grow into the space Washington forced open. Now Nvidia says it has “largely conceded” China’s AI chip market to Huawei. That is not China closing a door. That is America sawing off the bridge, then acting surprised when China built its own road.

𝘊𝘰𝘳𝘳𝘪𝘯𝘦

234,773 просмотров • 2 месяцев назад

AI shouldn’t be controlled by a single company or person, and the reason why most AI tools are "free" is because you are the product. Your data is more valuable to them, than the tool itself. That's why Action Model is different. We use tokens so: • Every AI action has a real cost • That cost is transparent • Value flows back to contributors, not one corporation The token represents a fractional stake in the AI itself; economic rights, governance rights, and participation in its growth. And that’s how you run AI without centralized control.
0:42

AI shouldn’t be controlled by a single company or person, and the reason why most AI tools are "free" is because you are the product. Your data is more valuable to them, than the tool itself. That's why Action Model is different. We use tokens so: • Every AI action has a real cost • That cost is transparent • Value flows back to contributors, not one corporation The token represents a fractional stake in the AI itself; economic rights, governance rights, and participation in its growth. And that’s how you run AI without centralized control.

Action Model

47,265 просмотров • 5 месяцев назад

something I’ve wanted for a while: an AI that isn’t just a text box so I built it lets you create a 3D AI character on-chain that lives in your browser it can talk, remember you, show emotions, and use different skills. you can even mint it onchain, so it actually belongs to you it gets its own identity, history, and reputation that no one can take away most AI today can disappear if the app shuts down this one is permanent and yours the future is AI you can see, interact with, and actually own
1:23

something I’ve wanted for a while: an AI that isn’t just a text box so I built it lets you create a 3D AI character on-chain that lives in your browser it can talk, remember you, show emotions, and use different skills. you can even mint it onchain, so it actually belongs to you it gets its own identity, history, and reputation that no one can take away most AI today can disappear if the app shuts down this one is permanent and yours the future is AI you can see, interact with, and actually own

nich

256,465 просмотров • 2 месяцев назад

The Tesla Megapod trademark might be the most important filing nobody is talking about (Save this). Tesla wants to build a product that is basically a self contained AI computer in a box. You truck it somewhere, plug it into power, and it runs AI workloads. The reason this matters is that the AI industry has a problem because you can't build data centers fast enough. Getting land approved, getting power from the grid, getting zoning cleared, that process takes 3–5 years, and demand is moving much faster than that. US data center construction actually fell in 2025 for the first time in years, because developers kept hitting bureaucratic walls. Tesla's answer to that problem is simple. They already own thousands of Supercharger stations across the country and those stations already have land, power, and grid connections, all the hard stuff is done. The idea is to drop a Megapod at those sites and turn idle charging infrastructure into AI compute, almost overnight. What Jason, Chamath, and Friedberg were all circling around is that this drop a box on a concrete pad approach is already how serious companies are deploying AI compute right now. Vertiv and DUG Technology both ship GPU-packed containers that can go live in 90 days and Tesla would just be bringing that idea to a network it already owns. The opportunity is very real whether Tesla is the one to actually execute on it is still an open question.
4:06

The Tesla Megapod trademark might be the most important filing nobody is talking about (Save this). Tesla wants to build a product that is basically a self contained AI computer in a box. You truck it somewhere, plug it into power, and it runs AI workloads. The reason this matters is that the AI industry has a problem because you can't build data centers fast enough. Getting land approved, getting power from the grid, getting zoning cleared, that process takes 3–5 years, and demand is moving much faster than that. US data center construction actually fell in 2025 for the first time in years, because developers kept hitting bureaucratic walls. Tesla's answer to that problem is simple. They already own thousands of Supercharger stations across the country and those stations already have land, power, and grid connections, all the hard stuff is done. The idea is to drop a Megapod at those sites and turn idle charging infrastructure into AI compute, almost overnight. What Jason, Chamath, and Friedberg were all circling around is that this drop a box on a concrete pad approach is already how serious companies are deploying AI compute right now. Vertiv and DUG Technology both ship GPU-packed containers that can go live in 90 days and Tesla would just be bringing that idea to a network it already owns. The opportunity is very real whether Tesla is the one to actually execute on it is still an open question.

Milk Road AI

16,609 просмотров • 28 дней назад