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📁Sam Altman says the real challenge is not AI, but exponential growth itself. The human brain was never built to intuitively grasp curves that keep doubling. That is why, he explains, revenue growth can only continue if compute scales just as fast. Without enough compute, the curve breaks.

Jon Hernandez

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38,785 views • 8 months ago •via X (Twitter)

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Jensen Huang: OpenAI & Anthropic are struggling to keep up with demand. The *exponential* growth in AI compute, adoption, and applications is immense - never seen in humanity. Supporting their scaling is critical.

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Sam Altman says true superintelligence already exists, but it's not a single brain It is the collective "scaffolding" of human civilization If AI discovers new physics that humans later use, is that "AI as superintelligence" or just the scaffold getting taller? I don't know

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The Life AI Biohub is the first scientific infrastructure powered by real humans, not just lab signals. Human network → AI compute → Reusable models → Applications build on top Platform infrastructure that compounds as it grows. This is how healthcare AI scales.

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Sam Altman says forget UBI Give people access to AI itself — they're creative enough to figure out what to do with it But if AI does most of the world's intellectual work, the old system breaks down "you can't keep taxing human labor when the labor is no longer human"

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31,168 views • 4 months ago

If OpenAI actually pulls this off and builds billions of GPUs, every single human on Earth would basically have access to their own GPU. That’s the vision of Universal Basic Compute Sam Altman has been hinting at, compute as abundant and accessible as electricity. Not just AI for the few, but AI for everyone.

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VraserX e/acc

82,484 views • 11 months ago

“There are more companies than ideas by quite a bit. Compute is large enough such that it's not obvious that you need that much more compute to prove some idea. AlexNet was built on 2 GPUs. The transformer was built on 8 to 64 GPUs. Which would be, what, 2 GPUs of today? You could argue that o1 reasoning was not the most compute heavy thing in the world. For research, you definitely need some amount of compute, but it's far from obvious that you need the absolutely largest amount of compute. If everyone is within the same paradigm, then compute becomes one of the big differentiators.” Ilya Sutskever

“There are more companies than ideas by quite a bit. Compute is large enough such that it's not obvious that you need that much more compute to prove some idea. AlexNet was built on 2 GPUs. The transformer was built on 8 to 64 GPUs. Which would be, what, 2 GPUs of today? You could argue that o1 reasoning was not the most compute heavy thing in the world. For research, you definitely need some amount of compute, but it's far from obvious that you need the absolutely largest amount of compute. If everyone is within the same paradigm, then compute becomes one of the big differentiators.” Ilya Sutskever

Dwarkesh Patel

203,605 views • 9 months ago

The CEO of Anthropic just said we are 1 to 3 years away from AGI. On camera. With 90% confidence. Almost nobody watched it. INSTEAD OF ARGUING ABOUT AI TAKING JOBS. Spend 2 hours watching the CEO of Anthropic explain exactly when and how it happens. The AI compute arms race just got a reality check nobody expected. Dario Amodei, CEO of Anthropic just did the math out loud: → Revenue growing 10x per year means $100B by end of 2026 and $1T by end of 2027 → At that trajectory you could theoretically buy $5T in compute over 5 years → But if revenue comes in at even $800B instead of $1T, bankruptcy. No exceptions → And if growth slows from 10x to just 5x per year, the entire model collapses by one year This is the most honest thing anyone in AI has said publicly in years. The companies building AI infrastructure are making trillion-dollar bets on growth curves that have never existed before in history. One year off. One multiplier wrong. The whole thing unravels. The AI compute boom is real. The risk underneath it is equally real. The question is not whether AI is the future. It is whether anyone can survive long enough to get there

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Dami-Defi

16,080 views • 3 months ago

Ilya Sutskever says the real challenge of AI is how fast and extreme its growth will be it still has limits, but it already speaks, codes, and interacts in ways once impossible soon, perhaps within 3 to 10 years, AI will be able to do everything humans can do the reason: "the human brain is a biological computer, so why can't a digital brain do the same?"

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📁 Sam Altman says that even though building software is now dramatically easier, building a real business is still just as hard. AI removes technical barriers, but it does not solve the scarcest resource, human attention. Even in a world of radical abundance, distribution and focus remain the bottleneck. The edge will not be speed, but creativity and building things that truly matter.

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Sam Altman just explained how OpenAI’s revenue could line up with $1.4T in compute commitments, the debt situation, and what happens if the models do not get better. A must watch, since everyone is dying to know the answer.

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📁 Sam Altman, CEO of OpenAI, says the problem with AI is not if it works. It is what happens when it does. Because curing disease is not enough. The real question is: what do you do when you are no longer needed?

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Sam Altman says OpenAI has reached another GPT-3-style moment Something important is working so well that other strong projects have to give way That is why OpenAI is focusing its compute on next-generation models and the automated researchers and companies they can power

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59,715 views • 5 months ago

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

Karl Mehta

16,619 views • 2 months ago

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 views • 1 month ago

The cleanest test of the AI cycle is what $NVDA is actually shipping, and current expectations still point to revenue climbing from ~$95B this quarter toward ~$110B next quarter. If revenue keeps rising while gross margins hold around 75% then it becomes much harder to argue the compute boom is already rolling over. That demand base is still expanding too with $SPCX potentially growing from ~4% of Nvidia revenue today toward 10% by the end of 2027 as its compute buildout scales.

The cleanest test of the AI cycle is what $NVDA is actually shipping, and current expectations still point to revenue climbing from ~$95B this quarter toward ~$110B next quarter. If revenue keeps rising while gross margins hold around 75% then it becomes much harder to argue the compute boom is already rolling over. That demand base is still expanding too with $SPCX potentially growing from ~4% of Nvidia revenue today toward 10% by the end of 2027 as its compute buildout scales.

Shay Boloor

103,065 views • 6 days ago

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

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

Dustin

214,158 views • 3 months ago

Jonathan Ross just revealed why AI companies aren’t growing faster. Not demand. Not competition. Physics. Ross: “The demand for compute is insatiable.” There isn’t enough compute in the world. Not a temporary shortage. A fundamental gap between what the market wants and what the infrastructure can deliver. Ross: “Right now, one of the biggest complaints of Anthropic is the rate limits. People can’t get enough tokens.” Rate limits aren’t product decisions. They’re rationing. Companies forced to regulate access because infrastructure cannot meet demand. Slower services. Token caps. The only things standing between these companies and a revenue surge they can’t access. Every token cap is a revenue cap. Every slowdown is a sale that didn’t happen. Ross: “If Anthropic was given twice the inference compute, within one month their revenue would almost double.” Read that again. Double the compute. Double the revenue. Within thirty days. That’s not a growth projection. That’s a measurement of how deep the backlog already is. The demand exists right now. It’s sitting in a queue. The only thing between these companies and that revenue is physical hardware they don’t have. This breaks every assumption about how tech companies scale. Usually you scale by finding customers. AI companies have infinite customers. They scale by finding hardware. The constraint isn’t market fit. It isn’t distribution. It isn’t competition. It’s processing power. This is why Jensen Huang is the most important person in the world right now. NVIDIA doesn’t just make chips. It makes the thing every government, every AI lab, and every company racing for this future needs more of and can’t get enough of. The compute bottleneck isn’t a tech industry problem. It’s a civilizational one. The winner of this era isn’t determined by who builds the smartest model. Every major lab has a frontier model. The winner is whoever secures the most compute fastest while everyone else rations what’s left. The race isn’t for intelligence. It’s for infrastructure. And right now there isn’t enough to go around.

Jonathan Ross just revealed why AI companies aren’t growing faster. Not demand. Not competition. Physics. Ross: “The demand for compute is insatiable.” There isn’t enough compute in the world. Not a temporary shortage. A fundamental gap between what the market wants and what the infrastructure can deliver. Ross: “Right now, one of the biggest complaints of Anthropic is the rate limits. People can’t get enough tokens.” Rate limits aren’t product decisions. They’re rationing. Companies forced to regulate access because infrastructure cannot meet demand. Slower services. Token caps. The only things standing between these companies and a revenue surge they can’t access. Every token cap is a revenue cap. Every slowdown is a sale that didn’t happen. Ross: “If Anthropic was given twice the inference compute, within one month their revenue would almost double.” Read that again. Double the compute. Double the revenue. Within thirty days. That’s not a growth projection. That’s a measurement of how deep the backlog already is. The demand exists right now. It’s sitting in a queue. The only thing between these companies and that revenue is physical hardware they don’t have. This breaks every assumption about how tech companies scale. Usually you scale by finding customers. AI companies have infinite customers. They scale by finding hardware. The constraint isn’t market fit. It isn’t distribution. It isn’t competition. It’s processing power. This is why Jensen Huang is the most important person in the world right now. NVIDIA doesn’t just make chips. It makes the thing every government, every AI lab, and every company racing for this future needs more of and can’t get enough of. The compute bottleneck isn’t a tech industry problem. It’s a civilizational one. The winner of this era isn’t determined by who builds the smartest model. Every major lab has a frontier model. The winner is whoever secures the most compute fastest while everyone else rations what’s left. The race isn’t for intelligence. It’s for infrastructure. And right now there isn’t enough to go around.

Dustin

28,395 views • 6 months ago

$INTC earns its victory lap after a quarter that beats expectations across revenue, margins, earnings, manufacturing execution and guidance. Agentic AI is creating a second growth curve for server CPUs because every accelerator requires significant general-purpose compute around it giving Intel immediate exposure through its large server market share. That opportunity also extends into purpose-built silicon and advanced packaging allowing Intel to benefit from custom AI processors even when it does not control the accelerator itself.

$INTC earns its victory lap after a quarter that beats expectations across revenue, margins, earnings, manufacturing execution and guidance. Agentic AI is creating a second growth curve for server CPUs because every accelerator requires significant general-purpose compute around it giving Intel immediate exposure through its large server market share. That opportunity also extends into purpose-built silicon and advanced packaging allowing Intel to benefit from custom AI processors even when it does not control the accelerator itself.

Shay Boloor

145,525 views • 1 month ago

⚡Why Edge Compute Matters for the Future of Agentic AI AI isn’t just about smarter models, it’s about where they run. Centralized clouds are costly, slow, and controlled by a few. That’s not the future. 👉Join the OptimAI Network: With OptimAI Edge Compute and the upcoming Core Node release, every device becomes part of a decentralized supercomputer: + AI models run directly on your devices (phones, desktops, IoT devices) + Data stays local, secure, and real-time + Compute scales with the community, not corporations This is how we power the next generation of Agentic AI - AI systems that don’t just think, but act like you. Lightweight, fast, everywhere, for everyone. Stay tuned. The Edge is coming.

⚡Why Edge Compute Matters for the Future of Agentic AI AI isn’t just about smarter models, it’s about where they run. Centralized clouds are costly, slow, and controlled by a few. That’s not the future. 👉Join the OptimAI Network: With OptimAI Edge Compute and the upcoming Core Node release, every device becomes part of a decentralized supercomputer: + AI models run directly on your devices (phones, desktops, IoT devices) + Data stays local, secure, and real-time + Compute scales with the community, not corporations This is how we power the next generation of Agentic AI - AI systems that don’t just think, but act like you. Lightweight, fast, everywhere, for everyone. Stay tuned. The Edge is coming.

OptimAI Network

43,634 views • 11 months ago

📁 Sarah Friar, CFO of OpenAI, says AI growth is measured in megawatts. 200 MW turned into 2 billion in revenue. 600 MW into 6 billion. 2 GW into more than 20 billion. The relationship is direct, more compute means more revenue. The real constraint is time. Decisions made today define capacity for 2028, 2029 and 2030. The industry has already moved, global hardware investment jumped by over 220 billion.

📁 Sarah Friar, CFO of OpenAI, says AI growth is measured in megawatts. 200 MW turned into 2 billion in revenue. 600 MW into 6 billion. 2 GW into more than 20 billion. The relationship is direct, more compute means more revenue. The real constraint is time. Decisions made today define capacity for 2028, 2029 and 2030. The industry has already moved, global hardware investment jumped by over 220 billion.

Jon Hernandez

59,693 views • 7 months ago