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Dario Amodei was asked whether open source will eventually gut Anthropic's business. He didn't defend the moat. He didn't argue closed beats open. He said the whole question is a red herring. That is the reframe. And it flips how the industry keeps scoring this race. The conventional narrative... is inherited from the last era of tech: open source wins because anyone can read the code, anyone improves it, contributions stack, and eventually the free thing catches the paid thing. Investors have a full lexicon for it. Commoditization. Which layer captures the value. Everyone repeats it. Amodei says the analogy breaks at the root. It's called open weights, not open source, for a reason: you can't see inside the model. So the thing that actually made open source powerful elsewhere, many people reading and additively improving shared code, never transfers. You just get a large file of numbers. Now here's where it gets interesting. The second engine isn't ideology. It's infrastructure. Free isn't free. Someone still has to host it. These are big models, and they're hard to run inference on. Someone has to make that fast. And the capabilities people assume only open weights unlock fine tuning, steering, inspecting activations labs are increasingly serving on their own clouds anyway. When DeepSeek shipped, he says he never asked whether it was open. He asked one thing: is it a good model, and is it better than us. That's the only axis he competes on. He even inverts the usual edge. Coming from outside that investor lexicon, he thinks knowing none of it lets him predict this better than the people fluent in it. He is not defending closed models. He is saying the scoreboard everyone is watching measures the wrong thing. The uncomfortable question if the free model still needs someone to run it, was the moat ever the weights, or always the machine underneath ?show more

Vikram M

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58,688 次观看 • 1 个月前 •via X (Twitter)

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Dario Amodei just dismantled the biggest myth in the AI industry. Open source AI isn’t free. It never was. Amodei: “It’s not free. You have to run it on inference and someone has to make it fast on inference.” For decades, open source meant something real. It meant a teenager in a basement could download the same tools as a Fortune 500 company. Could read the code. Could modify it. Could build something that competed with the giants. That was genuine democratization. That actually happened. AI is different. Fundamentally. Physically. In ways the ideology hasn’t caught up to yet. Downloading the weights is the easy part. The part that actually costs something is turning the weights into a running system. Into responses. Into intelligence operating in real time at scale. That requires compute. Power. Infrastructure. The kind measured in billions of dollars and years of construction. Amodei: “These are big models. They’re hard to do inference on. Ultimately you have to host it on the cloud. The people who host it on the cloud do inference.” The open source debate was never about who owns the model. It was always about who owns the cloud. And Amodei goes further. When a competitor drops a new open model, he doesn’t ask whether it’s open or closed. He doesn’t care about the licensing. He doesn’t engage the ideology. Amodei: “I don’t think it mattered that DeepSeek is open source. I think I ask, is it a good model? Is it better than us at the things that matter? That’s the only thing that I care about.” That’s the ruthless clarity of someone actually trying to win. While the media debates licensing frameworks, Amodei is asking one question. Is it better. Everything else is a distraction. Amodei: “I don’t think open source works the same way in AI that it has worked in other areas. Here we can’t see inside the model.” This isn’t Linux. You can’t read it. You can’t fork it. You can’t understand it the way generations of developers understood the tools they inherited. You can download it. And then you need a data center to run it. The teenager in the basement who was supposed to be empowered by this revolution needs a billion dollars of infrastructure before the empowerment starts. The era of the basement coder rewriting civilization on a laptop is over. The future belongs to whoever commands the compute, owns the power grid, and can actually turn the intelligence on. Open weights without infrastructure isn’t democratization. It’s a promise the physics of the universe won’t let us keep.

Dario Amodei just dismantled the biggest myth in the AI industry. Open source AI isn’t free. It never was. Amodei: “It’s not free. You have to run it on inference and someone has to make it fast on inference.” For decades, open source meant something real. It meant a teenager in a basement could download the same tools as a Fortune 500 company. Could read the code. Could modify it. Could build something that competed with the giants. That was genuine democratization. That actually happened. AI is different. Fundamentally. Physically. In ways the ideology hasn’t caught up to yet. Downloading the weights is the easy part. The part that actually costs something is turning the weights into a running system. Into responses. Into intelligence operating in real time at scale. That requires compute. Power. Infrastructure. The kind measured in billions of dollars and years of construction. Amodei: “These are big models. They’re hard to do inference on. Ultimately you have to host it on the cloud. The people who host it on the cloud do inference.” The open source debate was never about who owns the model. It was always about who owns the cloud. And Amodei goes further. When a competitor drops a new open model, he doesn’t ask whether it’s open or closed. He doesn’t care about the licensing. He doesn’t engage the ideology. Amodei: “I don’t think it mattered that DeepSeek is open source. I think I ask, is it a good model? Is it better than us at the things that matter? That’s the only thing that I care about.” That’s the ruthless clarity of someone actually trying to win. While the media debates licensing frameworks, Amodei is asking one question. Is it better. Everything else is a distraction. Amodei: “I don’t think open source works the same way in AI that it has worked in other areas. Here we can’t see inside the model.” This isn’t Linux. You can’t read it. You can’t fork it. You can’t understand it the way generations of developers understood the tools they inherited. You can download it. And then you need a data center to run it. The teenager in the basement who was supposed to be empowered by this revolution needs a billion dollars of infrastructure before the empowerment starts. The era of the basement coder rewriting civilization on a laptop is over. The future belongs to whoever commands the compute, owns the power grid, and can actually turn the intelligence on. Open weights without infrastructure isn’t democratization. It’s a promise the physics of the universe won’t let us keep.

Dustin

687,652 次观看 • 6 个月前

Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.

Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.

Vikram M

12,078 次观看 • 1 个月前

Dario Amodei keeps warning that open-source AI models are dangerous and need to be restricted. David Sacks David Sacks answered with a rhetorical question: "Dangerous to whom?" It was the sharpest thing said on the latest All-In The All-In Podcast. Not dangerous to the enterprise that wants to keep control of its own data. Open weights are what finally give that enterprise a choice. It is dangerous to Anthropic's business model. That model only works if customers have no real alternative at the model layer. So the safety argument starts to look like a moat argument wearing a lab coat. Line it up with the rest of the strategy. Dominate the layer everyone builds on. Launch Claude into science, security, legal, finance, and design, the verticals your own customers used to own. And campaign to restrict the open models that would hand those customers the exit. Here is the giveaway. When the person selling you safety also needs you to have no other option, you are not who is being kept safe.

Dario Amodei keeps warning that open-source AI models are dangerous and need to be restricted. David Sacks David Sacks answered with a rhetorical question: "Dangerous to whom?" It was the sharpest thing said on the latest All-In The All-In Podcast. Not dangerous to the enterprise that wants to keep control of its own data. Open weights are what finally give that enterprise a choice. It is dangerous to Anthropic's business model. That model only works if customers have no real alternative at the model layer. So the safety argument starts to look like a moat argument wearing a lab coat. Line it up with the rest of the strategy. Dominate the layer everyone builds on. Launch Claude into science, security, legal, finance, and design, the verticals your own customers used to own. And campaign to restrict the open models that would hand those customers the exit. Here is the giveaway. When the person selling you safety also needs you to have no other option, you are not who is being kept safe.

Fireside Alpha

10,037 次观看 • 2 个月前

Jensen Huang was asked what NVIDIA's single biggest moat is. The most valuable company in the world. He didn't point to the chips. He said a competitor could clone CUDA exactly and it wouldn't matter. "if somebody came up with a GUDA or TUDA it wouldn't make any difference at all." That is the reframe. And it changes what NVIDIA is actually defending. The conventional story is that NVIDIA wins on silicon. Faster GPU, better transistors, more FLOPS. Which means the day someone ships a faster chip, the game is over. Every competitor is racing on that assumption. Jensen is defending something else entirely. He said it wasn't three people who made CUDA win. It was 43,000 people and several million developers who bet their software on it. The moat isn't the hardware. It's the install base. Now here's where it gets interesting. Put yourself in a developer's seat. Target CUDA and you reach a few hundred million machines: every cloud, every computer maker, every industry, every country. And the platform gets roughly 10x better every six months, for free, while you wait. A rival could ship a chip that is genuinely faster and still lose, because no rational developer ports a mountain of software off a platform that already owns the install base and improves itself every two quarters. He is not selling the best chip. He is renting out the largest install base in computing. The open question is whether better silicon can ever beat that, or whether the only way past a moat like this is to make the whole category obsolete.

Jensen Huang was asked what NVIDIA's single biggest moat is. The most valuable company in the world. He didn't point to the chips. He said a competitor could clone CUDA exactly and it wouldn't matter. "if somebody came up with a GUDA or TUDA it wouldn't make any difference at all." That is the reframe. And it changes what NVIDIA is actually defending. The conventional story is that NVIDIA wins on silicon. Faster GPU, better transistors, more FLOPS. Which means the day someone ships a faster chip, the game is over. Every competitor is racing on that assumption. Jensen is defending something else entirely. He said it wasn't three people who made CUDA win. It was 43,000 people and several million developers who bet their software on it. The moat isn't the hardware. It's the install base. Now here's where it gets interesting. Put yourself in a developer's seat. Target CUDA and you reach a few hundred million machines: every cloud, every computer maker, every industry, every country. And the platform gets roughly 10x better every six months, for free, while you wait. A rival could ship a chip that is genuinely faster and still lose, because no rational developer ports a mountain of software off a platform that already owns the install base and improves itself every two quarters. He is not selling the best chip. He is renting out the largest install base in computing. The open question is whether better silicon can ever beat that, or whether the only way past a moat like this is to make the whole category obsolete.

Vikram M

367,768 次观看 • 1 个月前

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 次观看 • 1 个月前

America is about to lose the AI race, and it will not happen at the frontier. It will happen at the floor. Everyone is watching who ships the smartest model. The actual war is over the 80% of tokens nobody posts about: the routine inference that quietly runs the world. On current trajectory, that fight is already lost. Watch what people do, not what they say. Coinbase just defaulted its own engineers off frontier models onto open weights and cut AI spend nearly in half while usage kept climbing. Even NVIDIA runs a closed frontier model as an orchestrator and pushes the volume to its own open weights. The frontier is becoming a router. The volume goes open. That part is settled. Here is the part that should terrify Washington: the only credible open tier today is Chinese. GLM. Kimi. And the US answer is to tighten export controls and freeze its own labs in place, as if you can embargo a file that is already downloaded, or price-match free. So China hands the Global South Huawei hardware and free open models, and a generation in Africa and Southeast Asia learns to reason through a model that will not tell them what happened at Tiananmen Square. That is not a cost story. That is influence through inference. You do not have to win hearts and minds when you supply the mind. Open source is not a nice-to-have for America. It is the whole ballgame for the 80%, and right now the US is barely on the field. We need American open weights. Not eventually. Now.

America is about to lose the AI race, and it will not happen at the frontier. It will happen at the floor. Everyone is watching who ships the smartest model. The actual war is over the 80% of tokens nobody posts about: the routine inference that quietly runs the world. On current trajectory, that fight is already lost. Watch what people do, not what they say. Coinbase just defaulted its own engineers off frontier models onto open weights and cut AI spend nearly in half while usage kept climbing. Even NVIDIA runs a closed frontier model as an orchestrator and pushes the volume to its own open weights. The frontier is becoming a router. The volume goes open. That part is settled. Here is the part that should terrify Washington: the only credible open tier today is Chinese. GLM. Kimi. And the US answer is to tighten export controls and freeze its own labs in place, as if you can embargo a file that is already downloaded, or price-match free. So China hands the Global South Huawei hardware and free open models, and a generation in Africa and Southeast Asia learns to reason through a model that will not tell them what happened at Tiananmen Square. That is not a cost story. That is influence through inference. You do not have to win hearts and minds when you supply the mind. Open source is not a nice-to-have for America. It is the whole ballgame for the 80%, and right now the US is barely on the field. We need American open weights. Not eventually. Now.

Ben Pouladian

71,722 次观看 • 2 个月前

🚨 JASON CALACANIS: “I think the best part about (DeepSeek) is the fact that Sam Altman was supposed to be doing open source, he made it a closed source company, he stole everyone’s data, he got caught red handed, he’s being sued by The New York Times for that, and now the Chinese have come and open sourced all the stuff he stole, and he’s got a real competitor on the mission that OpenAI was supposed to do. I have zero sympathy for him or the team over there. I’m glad that this is all going open sourced. It should have been open sourced, and it’s better for humanity, and the fact that the Chinese did it to Sam Altman is comeuppance for the fact that he stole everyone else’s content. But I don’t have strong opinions on it.” LMAO. Wow. Jason with the double headshot. 😂

Autism Capital 🧩

2,612,181 次观看 • 1 年前

Jensen Huang was asked how China built so many world-class AI companies in ten years. He didn't credit the government. He didn't say they stole it. He pointed at the culture. In China, it's "family first, friends second, and company third." So the whole system is open source by default. That is the reframe. And it flips how the West explains China's speed. The conventional narrative is top-down central planning: a state pouring money into national champions, plus a lot of copied IP. Everyone repeats it. Jensen describes almost the opposite. China isn't one economy; it's provinces and cities with mayors competing against each other like startups. That's why there are dozens of EV companies and dozens of AI companies clawing to survive. What crawls out of that gauntlet is world-class. Now here's where it gets interesting. The second engine is social, not economic. Because friends and schoolmates outrank the employer, engineers share freely across rival companies. What are they protecting, when their brothers work down the street ? So the ecosystem defaults to open, and open source amplifies everyone at once. Half the world's AI researchers are Chinese, most still in China, wired into a culture that spreads every breakthrough at the speed of friendship. Competition sharpens the work. Openness distributes it. That combination compounds. He is not describing a command economy. He is describing the fastest-innovating culture on earth. The uncomfortable question for the West: can a system built to protect IP ever outrun one built to share it ?

Jensen Huang was asked how China built so many world-class AI companies in ten years. He didn't credit the government. He didn't say they stole it. He pointed at the culture. In China, it's "family first, friends second, and company third." So the whole system is open source by default. That is the reframe. And it flips how the West explains China's speed. The conventional narrative is top-down central planning: a state pouring money into national champions, plus a lot of copied IP. Everyone repeats it. Jensen describes almost the opposite. China isn't one economy; it's provinces and cities with mayors competing against each other like startups. That's why there are dozens of EV companies and dozens of AI companies clawing to survive. What crawls out of that gauntlet is world-class. Now here's where it gets interesting. The second engine is social, not economic. Because friends and schoolmates outrank the employer, engineers share freely across rival companies. What are they protecting, when their brothers work down the street ? So the ecosystem defaults to open, and open source amplifies everyone at once. Half the world's AI researchers are Chinese, most still in China, wired into a culture that spreads every breakthrough at the speed of friendship. Competition sharpens the work. Openness distributes it. That combination compounds. He is not describing a command economy. He is describing the fastest-innovating culture on earth. The uncomfortable question for the West: can a system built to protect IP ever outrun one built to share it ?

Vikram M

432,711 次观看 • 1 个月前

Open source software is GREAT. But "open source" AI is NOT like software - it's VERY different. Rob Miles cuts through the bullshit: ROB: Oh, hey, Meta. I heard Llama's weights leaked. That's rough, man. Information security's hard. How you holding up? META: Oh, we're great. Yeah, we're fine. We... actually, that was deliberate. We meant to do that. ROB MILES: Oh, really? META: Yeah... well, the second time anyway. It's called open source. Look it up. ROB MILES: Oh. Well, I love free and open source software, but do those principles really apply to network weights? How does that work? META: Open source is good for users because it lets them read the source code and see what the program is really doing and how it works. ROB MILES: Wait, have you found a way to tell how a model works by looking at its weights? META: No. But, it lets developers all over the world spot bugs in the code and submit patches. ROB: Wait, people are fixing bugs in Llama's weights? META: Well, no. People can fine tune it themselves, though. ROB: ?? Other companies offer fine tuning through APIs. ... So, hang on, if you can't actually read the code and know what it's doing, then network weights are effectively a compiled binary. So, in what sense is this open source? Why not call it like public weights? Why call it open source at all? META: I love open source. ROB: Well, I know a lot of your employees do, but you don't love anything. You're a giant corporation. What's in it for you? META: I love, love open source.

Open source software is GREAT. But "open source" AI is NOT like software - it's VERY different. Rob Miles cuts through the bullshit: ROB: Oh, hey, Meta. I heard Llama's weights leaked. That's rough, man. Information security's hard. How you holding up? META: Oh, we're great. Yeah, we're fine. We... actually, that was deliberate. We meant to do that. ROB MILES: Oh, really? META: Yeah... well, the second time anyway. It's called open source. Look it up. ROB MILES: Oh. Well, I love free and open source software, but do those principles really apply to network weights? How does that work? META: Open source is good for users because it lets them read the source code and see what the program is really doing and how it works. ROB MILES: Wait, have you found a way to tell how a model works by looking at its weights? META: No. But, it lets developers all over the world spot bugs in the code and submit patches. ROB: Wait, people are fixing bugs in Llama's weights? META: Well, no. People can fine tune it themselves, though. ROB: ?? Other companies offer fine tuning through APIs. ... So, hang on, if you can't actually read the code and know what it's doing, then network weights are effectively a compiled binary. So, in what sense is this open source? Why not call it like public weights? Why call it open source at all? META: I love open source. ROB: Well, I know a lot of your employees do, but you don't love anything. You're a giant corporation. What's in it for you? META: I love, love open source.

AI Notkilleveryoneism Memes ⏸️

107,362 次观看 • 2 年前

This has to be one of the dumbest people that I have ever heard open his dumb mouth. The scary thing, is that he is so dumb that he does not know it ... and other dumb people will actually believe him. Even the black American reporter was looking at him squiff.

This has to be one of the dumbest people that I have ever heard open his dumb mouth. The scary thing, is that he is so dumb that he does not know it ... and other dumb people will actually believe him. Even the black American reporter was looking at him squiff.

Pookie's Polls & Opinions

35,244 次观看 • 1 年前

What does it mean when an AI model is “open weights” and how is it different than “open source”? Welcome to the next episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! Every week we will discuss open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Episode 3 out next week; episode 1 linked below. Please leave all your questions about open-source & open-weight AI in the comments. This is a super important topic that is novel to most people, and it can be challenging to grasp - so your questions are shared with many others.

What does it mean when an AI model is “open weights” and how is it different than “open source”? Welcome to the next episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! Every week we will discuss open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Episode 3 out next week; episode 1 linked below. Please leave all your questions about open-source & open-weight AI in the comments. This is a super important topic that is novel to most people, and it can be challenging to grasp - so your questions are shared with many others.

Harper Carroll

32,624 次观看 • 16 天前

Kimi K3's weights are free to download. Almost nobody on earth can actually run them. 2.8 trillion parameters. The download is the easy part. Open weights used to mean you could self-host. At this scale it means you can read the file. So people download it and call the API anyway. Daily revenue up at least 6x since launch. Openness stopped being the opposite of a business model. It became the top of the funnel. The license says the quiet part. Past $20M in revenue you negotiate a contract. Past 100M users you display their name. Free for the developer. A contract for anyone the model actually earns from. And publishing the weights kills the argument before it starts. Nobody can claim the benchmarks were cooked. Independent indexes put K3 top three overall and first in frontend coding, with no one taking Moonshot's word for it. That's the whole trade. Give away the artifact, keep the infrastructure, get the audit for free. Check the last open model you praised. Ask whether you could serve it, or only download it.

Kimi K3's weights are free to download. Almost nobody on earth can actually run them. 2.8 trillion parameters. The download is the easy part. Open weights used to mean you could self-host. At this scale it means you can read the file. So people download it and call the API anyway. Daily revenue up at least 6x since launch. Openness stopped being the opposite of a business model. It became the top of the funnel. The license says the quiet part. Past $20M in revenue you negotiate a contract. Past 100M users you display their name. Free for the developer. A contract for anyone the model actually earns from. And publishing the weights kills the argument before it starts. Nobody can claim the benchmarks were cooked. Independent indexes put K3 top three overall and first in frontend coding, with no one taking Moonshot's word for it. That's the whole trade. Give away the artifact, keep the infrastructure, get the audit for free. Check the last open model you praised. Ask whether you could serve it, or only download it.

wast3

62,272 次观看 • 18 天前

Sam Alman was asked to justify $1.4 trillion in infrastructure against roughly $20 billion in revenue. He didn't defend the number. He didn't promise the demand would arrive. He said the problem is us. "exponential growth is usually very hard for people." That is the reframe. And it flips what the bubble argument is actually about. The conventional narrative is a spending crisis: a company burning capital far ahead of revenue, chasing demand that may never show up, heading for the bust every cycle ends in. Everyone repeats it. Alman describes almost the opposite problem. Revenue roughly tracks the compute fleet. OpenAI tripled compute in a year and plans to triple it again. His blunt version: double the compute, and they'd be at double the revenue today. They have never once found compute they couldn't monetize. The constraint was never customers. It's silicon, and they have never had enough of it. Now here's where it gets interesting. The second engine isn't financial. It's arithmetic. He runs a rough thought experiment. A frontier AI company outputs maybe 10 trillion tokens a day. Eight billion people, call it 20,000 tokens each. Run those numbers and one company is already doing something like a sixteenth of humanity's total output. Then 10x that. Then 100x. And the thing he most wants to spend it on isn't chat. It's science. He says the small discoveries were supposed to start in 2026. They started in late 2025. Mathematicians are publicly saying 5.2 crossed a line for them. Tiny results, but qualitatively different from nothing, and once the curve lifts off the x-axis, this field knows how to climb it. He is not spending ahead of demand. He is saying he has never in his life met it. The uncomfortable question if none of us can intuit an exponential, how would you know from the inside whether $1.4 trillion is reckless or already late ?

Sam Alman was asked to justify $1.4 trillion in infrastructure against roughly $20 billion in revenue. He didn't defend the number. He didn't promise the demand would arrive. He said the problem is us. "exponential growth is usually very hard for people." That is the reframe. And it flips what the bubble argument is actually about. The conventional narrative is a spending crisis: a company burning capital far ahead of revenue, chasing demand that may never show up, heading for the bust every cycle ends in. Everyone repeats it. Alman describes almost the opposite problem. Revenue roughly tracks the compute fleet. OpenAI tripled compute in a year and plans to triple it again. His blunt version: double the compute, and they'd be at double the revenue today. They have never once found compute they couldn't monetize. The constraint was never customers. It's silicon, and they have never had enough of it. Now here's where it gets interesting. The second engine isn't financial. It's arithmetic. He runs a rough thought experiment. A frontier AI company outputs maybe 10 trillion tokens a day. Eight billion people, call it 20,000 tokens each. Run those numbers and one company is already doing something like a sixteenth of humanity's total output. Then 10x that. Then 100x. And the thing he most wants to spend it on isn't chat. It's science. He says the small discoveries were supposed to start in 2026. They started in late 2025. Mathematicians are publicly saying 5.2 crossed a line for them. Tiny results, but qualitatively different from nothing, and once the curve lifts off the x-axis, this field knows how to climb it. He is not spending ahead of demand. He is saying he has never in his life met it. The uncomfortable question if none of us can intuit an exponential, how would you know from the inside whether $1.4 trillion is reckless or already late ?

Vikram M

30,378 次观看 • 1 个月前

Ilya Sutskever, co-founder and chief scientist of OpenAI: "The better a neural network can predict the next word, the more it understands it." Everyone calls it autocomplete. Ilya explained why that is a comforting lie. To guess the final word of a murder mystery, the name of the killer, the model has to reason through every clue in the book. Prediction that good is not statistics. It is understanding. Then he said the part that should stop you. The text these models train on is "a projection of the world," and to compress it they learn more and more about people, "the human condition, their hopes, dreams, and motivations." The machine is not memorizing our words. It is quietly building a model of us. That is the whole premise of the fear. The machine will never hate you. It understands you far better than that. The frightening variable was never the intelligence. It was the aim. I wrote the full story: the one fear about AI everyone gets wrong, and the one already here.

Ilya Sutskever, co-founder and chief scientist of OpenAI: "The better a neural network can predict the next word, the more it understands it." Everyone calls it autocomplete. Ilya explained why that is a comforting lie. To guess the final word of a murder mystery, the name of the killer, the model has to reason through every clue in the book. Prediction that good is not statistics. It is understanding. Then he said the part that should stop you. The text these models train on is "a projection of the world," and to compress it they learn more and more about people, "the human condition, their hopes, dreams, and motivations." The machine is not memorizing our words. It is quietly building a model of us. That is the whole premise of the fear. The machine will never hate you. It understands you far better than that. The frightening variable was never the intelligence. It was the aim. I wrote the full story: the one fear about AI everyone gets wrong, and the one already here.

Casper

92,247 次观看 • 1 个月前

This is insane.🤯 Someone just open-sourced professional motion capture. The same tech behind every Marvel movie and AAA game now runs on any webcam. Free. The only thing it needs is a camera. You already have one. It's called FreeMoCap and it just became a GitHub link. The barrier was never the talent. It was always the tools.

This is insane.🤯 Someone just open-sourced professional motion capture. The same tech behind every Marvel movie and AAA game now runs on any webcam. Free. The only thing it needs is a camera. You already have one. It's called FreeMoCap and it just became a GitHub link. The barrier was never the talent. It was always the tools.

Vaibhav Sisinty

37,613 次观看 • 4 个月前

Chad's wife always left the snack cabinet door open (when he was trying to get in shape.) He asked her to keep it closed, but she refused. One day he was emptying the dishwasher and busted his head on the edge of the open door. This is what happened next.

Chad's wife always left the snack cabinet door open (when he was trying to get in shape.) He asked her to keep it closed, but she refused. One day he was emptying the dishwasher and busted his head on the edge of the open door. This is what happened next.

Giff Lasta

68,974 次观看 • 4 个月前

Regarding Neil Ferguson “And so my conclusion from that is not that he was stupid or that he was doing bad science. He wasn't doing bad science. He was doing exactly what he needed to do and he was paid to do it and he did it knowingly and intentionally. So I think it's a mistake to go too deep into showing how these papers are scientifically wrong and he didn't consider this and he didn't consider that and the model is this and the model is that. From the beginning, the entire thing is just a piece of propaganda. So his job is to be a propagandist. And so anything that he puts out cannot be analyzed or considered as a scientific production. It is not a production of anything that has to do with science. It is a production of propaganda and public relations.” Debbie Lerman Episode 445 NEVER FORGET WHAT THEY DID TO US

Regarding Neil Ferguson “And so my conclusion from that is not that he was stupid or that he was doing bad science. He wasn't doing bad science. He was doing exactly what he needed to do and he was paid to do it and he did it knowingly and intentionally. So I think it's a mistake to go too deep into showing how these papers are scientifically wrong and he didn't consider this and he didn't consider that and the model is this and the model is that. From the beginning, the entire thing is just a piece of propaganda. So his job is to be a propagandist. And so anything that he puts out cannot be analyzed or considered as a scientific production. It is not a production of anything that has to do with science. It is a production of propaganda and public relations.” Debbie Lerman Episode 445 NEVER FORGET WHAT THEY DID TO US

Doc Malik

11,609 次观看 • 6 个月前

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”

The All-In Podcast

64,611 次观看 • 4 个月前