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“If AI is technologically possible, it is inevitable.” - Dwarkesh Patel We’re mid-Cambrian Explosion: • 4× annual jump in frontier-model spend • Energy demand will hit nation-state scale by 2028 • Chips & compute racing toward $ 1Tn+ industries This is the Scaling Era—a new normal written in real... show more

Limitless

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18,823 views • 1 year ago •via X (Twitter)

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Limitless1 year ago

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Na1 year ago

@dwarkesh_sp 31:25 models have to feel it in their bones (weights), just like humans tie their identity to a job and develop instuition and familiar routine of working paths. So my guess is there will be countless individually trained models distilled from largest models.

PIN AI1 year ago

@dwarkesh_sp AGI seems inevitable, but who it serves is uncertain. The Scaling Era will shape both its abilities and values. PIN AI aims for a future where your AI responds to you, not big tech.

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Dwarkesh says the Age of Scaling isn’t over. Companies will keep pouring exponentially more compute into training because the value of intelligence is so high, it’s worth even $1 trillion if it gets us to AGI. Right now, compute usage for frontier models grows ~4x per year, but by 2028–2030, physical and economic limits (chips, energy, GDP) will take progress slower. A 160x increase in four years can’t go on.

Dwarkesh says the Age of Scaling isn’t over. Companies will keep pouring exponentially more compute into training because the value of intelligence is so high, it’s worth even $1 trillion if it gets us to AGI. Right now, compute usage for frontier models grows ~4x per year, but by 2028–2030, physical and economic limits (chips, energy, GDP) will take progress slower. A 160x increase in four years can’t go on.

Wes Roth

18,531 views • 1 year 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 • 4 months ago

🚀 Ron Baron now has roughly $25 billion invested in SpaceX, making it the largest position across his funds. After speaking with Elon Musk, Baron says Musk believes SpaceX is still "just getting started." The long-term vision is enormous: Starlink, reusable rockets, AI infrastructure, compute, chips, and a global communications network that few competitors can match. Ron Baron believes SpaceX is years ahead in several key industries and expects the investment to generate hundreds of billions of dollars over time. The most interesting part? SpaceX generated about $19 billion in revenue last year, yet some projections see a path toward $1 trillion in annual revenue by 2030! That's the kind of scale investors are trying to wrap their heads around. 👀 $SPCX

🚀 Ron Baron now has roughly $25 billion invested in SpaceX, making it the largest position across his funds. After speaking with Elon Musk, Baron says Musk believes SpaceX is still "just getting started." The long-term vision is enormous: Starlink, reusable rockets, AI infrastructure, compute, chips, and a global communications network that few competitors can match. Ron Baron believes SpaceX is years ahead in several key industries and expects the investment to generate hundreds of billions of dollars over time. The most interesting part? SpaceX generated about $19 billion in revenue last year, yet some projections see a path toward $1 trillion in annual revenue by 2030! That's the kind of scale investors are trying to wrap their heads around. 👀 $SPCX

Herbert Ong

8,766,233 views • 1 month ago

If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.

If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.

Satya Nadella

2,020,601 views • 10 months ago

Jensen Huang explains how blocking China from Nvidia does not mean blocking China from AI. The usual export-control story assumes scarcity: deny the best chips, and the rival falls behind. China is no longer merely waiting at the door of American compute. Huawei’s rise is showing how a sanction is turning into an industrial stimulus: absence creates a market, and a market teaches domestic suppliers how to harden, scale, and export. That does not mean that the gap with Nvidia chips has vanished. It means the real contest is no longer just about who owns the fastest accelerator, but who sets the operating layer for intelligence: chips, energy, infrastructure, models, applications, and the standards others build upon. The mistake is to treat chip policy as a valve that can simply open or close. Every restriction slows one flow but strengthens another, and the long-term danger may be a world where American technology is absent from the very systems America wants to influence. ---- From "Fox Business" YouTube channel, (full video link in comment)

Jensen Huang explains how blocking China from Nvidia does not mean blocking China from AI. The usual export-control story assumes scarcity: deny the best chips, and the rival falls behind. China is no longer merely waiting at the door of American compute. Huawei’s rise is showing how a sanction is turning into an industrial stimulus: absence creates a market, and a market teaches domestic suppliers how to harden, scale, and export. That does not mean that the gap with Nvidia chips has vanished. It means the real contest is no longer just about who owns the fastest accelerator, but who sets the operating layer for intelligence: chips, energy, infrastructure, models, applications, and the standards others build upon. The mistake is to treat chip policy as a valve that can simply open or close. Every restriction slows one flow but strengthens another, and the long-term danger may be a world where American technology is absent from the very systems America wants to influence. ---- From "Fox Business" YouTube channel, (full video link in comment)

Rohan Paul

41,215 views • 18 days ago

Distilled recap of the back-and-forth with Jensen on export controls: Dwarkesh: Wouldn’t selling Nvidia chips to China enable them to train models like Claude Mythos with cyber offensive capabilities that would be threats to American companies and national security? Jensen: First of all, Mythos was trained on fairly mundane capacity and a fairly mundane amount of it by an extraordinary company. The amount of capacity and the type of compute it was trained on is abundantly available in China. Dwarkesh: With that, could they eventually train a model like Mythos? Yes. But the question is, because we have more FLOPs, American labs are able to get to this level of capabilities first. Furthermore, even if they trained a model like this, the ability to deploy it at scale matters. If you had a cyber hacker, it's much more dangerous if they have a million of them versus a thousand of them. Jensen: Your premise is just wrong. The fact of the matter is their AI development is going just fine. The best AI researchers in the world, because they are limited in compute, also come up with extremely smart algorithms. DeepSeek is not an inconsequential advance. The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation. Dwarkesh: Currently, you can have a model like DeepSeek that can run on any accelerator if it's open source. Why would that stop being the case in the future? Jensen: Suppose it optimizes for Huawei. Suppose it optimizes for their architecture. It would put others at a disadvantage. As AI diffuses out into the rest of the world, their standards and their tech stack will become superior to ours because their models are open. Dwarkesh: Tesla sold extremely good electric vehicles to China for a long time. iPhones are sold in China. They didn't cause some lock-in. China will still make their version of EVs, and they're dominating, or smartphones, they're dominating. Jensen: We are not a car. The fact that I can buy this car brand one day and use another car brand another day is easy. Computing is not like that. There's a reason why x86 still exists. There's a reason why Arm is so sticky. These ecosystems are hard to replace. Dwarkesh: It's just hard to imagine that there's a long-term lock-in to the Chinese ecosystem, even if they have this slightly better open-source model for a while. American labs port across accelerators constantly. Anthropic's models are run on GPUs, they're run on Trainium, they're run on TPUs. There are so many things you can do, from distilling to a model that's well fit for your chips. Jensen: China is the largest contributor to open source software in the world. China's the largest contributor to open models in the world. Today it's built on the American tech stack, Nvidia’s. Fact. All five layers of the tech stack for AI are important. The United States ought to go win all five of them. in a few years time, I'm making you the prediction that when we want American technology to be diffused around the world—out to India, out to the Middle East, out to Africa, out to Southeast Asia—on that day, I will tell you exactly about today's conversation, about how your policy ... caused the United States to concede the second largest market in the world for no good reason at all.

Dwarkesh Patel

1,250,786 views • 3 months ago

When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!

When we first met Tom, his obsession with energy markets came through immediately. Seemed random, but it's "actually" central to what he's building at Actual Computer. The thesis is that local inference is inevitable. Open source models are getting good enough, people don't want to feed their thoughts to AI labs, and demand for compute will continue to be insatiable. But the main forcing function is that chips aren't the bottleneck anymore - power is. Compute is derivative of energy, and the massive data center buildout has created an energy shortage where GPUs sit idle waiting for power to come online. Yet one place that already has power, and always will, is everywhere else. Homes. Businesses. Universities. Collectively, they have more compute than all the data centers combined. Tom Lynch goal is to build a distributed network of people plugging in their local compute (devices like Mac minis to consumer Nvidia cards) to serve inference demand. The substrate he's chosen is Bittensor, on Subnet 95. Actual Computer has an exceptional team behind it, and this is one of the projects that's gotten me most excited in a long time. Enjoy this conversation with the two of us!

Sami Kassab

35,706 views • 5 months ago

.Dylan Patel gives a deep dive on the 3 big bottlenecks to scaling AI compute: logic, memory, and power. And walks through the economics of labs, hyperscalers, foundries, and fab equipment manufacturers. Learned a ton about every single level of the stack. 0:00:00 – Why an H100 is worth more today than 3 years ago 0:24:52 – Nvidia secured TSMC allocation early; Google is getting squeezed 0:34:34 – ASML will be the #1 constraint for AI compute scaling by 2030 0:56:06 – Can’t we just use TSMC’s older fabs? 1:05:56 – When will China outscale the West in semis? 1:16:20 – The enormous incoming memory crunch 1:42:53 – Scaling power in the US will not be a problem 1:55:03 – Space GPUs aren't happening this decade 2:14:26 – Why aren’t more hedge funds making the AGI trade? 2:18:49 – Will TSMC kick Apple out from N2? 2:24:35 – Robots and Taiwan risk Look up Dwarkesh Podcast on YouTube, Apple Podcasts, or Spotify. Enjoy!

.Dylan Patel gives a deep dive on the 3 big bottlenecks to scaling AI compute: logic, memory, and power. And walks through the economics of labs, hyperscalers, foundries, and fab equipment manufacturers. Learned a ton about every single level of the stack. 0:00:00 – Why an H100 is worth more today than 3 years ago 0:24:52 – Nvidia secured TSMC allocation early; Google is getting squeezed 0:34:34 – ASML will be the #1 constraint for AI compute scaling by 2030 0:56:06 – Can’t we just use TSMC’s older fabs? 1:05:56 – When will China outscale the West in semis? 1:16:20 – The enormous incoming memory crunch 1:42:53 – Scaling power in the US will not be a problem 1:55:03 – Space GPUs aren't happening this decade 2:14:26 – Why aren’t more hedge funds making the AGI trade? 2:18:49 – Will TSMC kick Apple out from N2? 2:24:35 – Robots and Taiwan risk Look up Dwarkesh Podcast on YouTube, Apple Podcasts, or Spotify. Enjoy!

Dwarkesh Patel

489,541 views • 4 months ago

We’re no longer just scaling computing power. We’re using compute to scale intelligence itself. That’s what makes this moment historically significant. For sixty years, progress in computing followed Moore’s Law—transistor density doubling roughly every two years. But AI is advancing on a far steeper curve. Today, frontier model capabilities are improving on a cadence closer to every six months—an order of magnitude faster than classical hardware scaling. The underlying principle is both simple and radical: when you increase data, compute, and model complexity, intelligence emerges. Scaling laws show that larger models—given sufficient compute and high-quality data—become predictably more capable. In just over a decade, we’ve gone from neural nets that could identify cats to systems that can draft legal briefs, write production-grade code, generate scientific hypotheses, and outperform top human competitors in mathematics, strategy, and reasoning tasks. This is no longer “software” in the traditional sense. It is a new form of intelligence—synthetic, scalable, rapidly compounding, and increasingly able to take meaningful action in the real world. The geopolitical, economic, and societal implications of this shift are only beginning to unfold—and they will redefine global power in the decades ahead.

We’re no longer just scaling computing power. We’re using compute to scale intelligence itself. That’s what makes this moment historically significant. For sixty years, progress in computing followed Moore’s Law—transistor density doubling roughly every two years. But AI is advancing on a far steeper curve. Today, frontier model capabilities are improving on a cadence closer to every six months—an order of magnitude faster than classical hardware scaling. The underlying principle is both simple and radical: when you increase data, compute, and model complexity, intelligence emerges. Scaling laws show that larger models—given sufficient compute and high-quality data—become predictably more capable. In just over a decade, we’ve gone from neural nets that could identify cats to systems that can draft legal briefs, write production-grade code, generate scientific hypotheses, and outperform top human competitors in mathematics, strategy, and reasoning tasks. This is no longer “software” in the traditional sense. It is a new form of intelligence—synthetic, scalable, rapidly compounding, and increasingly able to take meaningful action in the real world. The geopolitical, economic, and societal implications of this shift are only beginning to unfold—and they will redefine global power in the decades ahead.

Nina Schick

17,728 views • 6 months ago

Demis Hassabis just explained why the real AI bottleneck has nothing to do with training runs. Most people picture the AI arms race as who can build the biggest model. GPT-4 or Gemini Ultra style training runs, a few hundred million in compute, fired once or twice a year. The constraint sits somewhere else. Every time a researcher has a new algorithmic idea, a new architecture, a new training technique, they can't just test it on a laptop. They have to run it at the scale where it would actually be deployed, because ideas that look promising at small scale fall apart completely when you put them into a real system. Every research hypothesis burns significant compute before a single line of production code gets written. At a lab like DeepMind, hundreds of researchers are running hundreds of ideas simultaneously. The demand for experimental compute is continuous. It never stops. Now layer the hardware reality on top. GPU lead times are currently 36 to 52 weeks for data center hardware. Global AI data centers are already drawing 29.6 gigawatts, equivalent to the peak power demand of the entire state of New York, and they still can't meet demand. Companies willing to pay any price can't just buy more compute. They wait in line. The speed of scientific discovery in AI is now gated by hardware availability. The next breakthrough is sitting in a researcher's head right now. Whether it gets validated fast enough to matter depends entirely on whether the compute is there when they need it. The AI race gets won by whoever can run the most experiments per month.

Demis Hassabis just explained why the real AI bottleneck has nothing to do with training runs. Most people picture the AI arms race as who can build the biggest model. GPT-4 or Gemini Ultra style training runs, a few hundred million in compute, fired once or twice a year. The constraint sits somewhere else. Every time a researcher has a new algorithmic idea, a new architecture, a new training technique, they can't just test it on a laptop. They have to run it at the scale where it would actually be deployed, because ideas that look promising at small scale fall apart completely when you put them into a real system. Every research hypothesis burns significant compute before a single line of production code gets written. At a lab like DeepMind, hundreds of researchers are running hundreds of ideas simultaneously. The demand for experimental compute is continuous. It never stops. Now layer the hardware reality on top. GPU lead times are currently 36 to 52 weeks for data center hardware. Global AI data centers are already drawing 29.6 gigawatts, equivalent to the peak power demand of the entire state of New York, and they still can't meet demand. Companies willing to pay any price can't just buy more compute. They wait in line. The speed of scientific discovery in AI is now gated by hardware availability. The next breakthrough is sitting in a researcher's head right now. Whether it gets validated fast enough to matter depends entirely on whether the compute is there when they need it. The AI race gets won by whoever can run the most experiments per month.

Aakash Gupta

31,285 views • 2 months ago

Jensen Huang open-sourced NVIDIA's flagship AI model, its weights, its data, AND how they created it. "We open sourced the models," Huang says. "We open sourced the weights." "We open sourced the data." "We open sourced how we created it." Four layers of openness in one model release. "Open source is fundamentally necessary for many industries to join the AI revolution." "NVIDIA has the scale and the motivation to build and continue to build these AI models for as long as we shall live." The chip seller benefits when every model is open. NVIDIA is the chip seller. P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Grab a free copy here: If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

Jensen Huang open-sourced NVIDIA's flagship AI model, its weights, its data, AND how they created it. "We open sourced the models," Huang says. "We open sourced the weights." "We open sourced the data." "We open sourced how we created it." Four layers of openness in one model release. "Open source is fundamentally necessary for many industries to join the AI revolution." "NVIDIA has the scale and the motivation to build and continue to build these AI models for as long as we shall live." The chip seller benefits when every model is open. NVIDIA is the chip seller. P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Grab a free copy here: If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

GeniusThinking

14,769 views • 1 month ago

Satya is Out TLDR: MSFT doesn’t believe in AGI, wary of overinvestment, OpenAI partnership is over A. AI and AGI are overhyped - “Us self-claiming some AGI milestone, that's just nonsensical benchmark hacking to me. The real benchmark is: the world growing at 10%.” B. Very negative on more capex spend from MSFT - “[If you look at the Industrial Revolution] there was a lot of money lost” - “…countries are going to deploy capital… I'm so excited to be a leaser… I build a lot, I lease a lot.“ C. Value in AI is in infra where there is overbuild, and B2C apps which OpenAI has won, negative on model layer - “in consumer, in some categories, there may be some winner-take-all network effect. ChatGPT is a great example [of a] at-scale consumer property that has already got real escape velocity” - “[in enterprise] buyers will not tolerate winner-take-all. … where the buyer is a corporation, an enterprise, an IT department, they will want multiple suppliers.. That is what will happen even on the model side.” - “In the model layer, one is models need ultimately to run on some hyperscale compute.” Opinion ———— 1. Satya is calling the bubble in buildout. The crazy people like govts are entering the game. He’s happy to lease from them when they overbuild. His own capex spend is capped 2. He’s disappointed in the OpenAI partnership - he sees them as having built a great consumer app for themselves with dominance in the category, but models for enterprise usage have gotten commoditized. 3. He’s sooo done with the AGI talk. If you can’t get to 10% global growth your AGI talk is meaningless to him 4. He’s backing MSFT from the capex precipice. It’s funny because he certainly did make Google dance, and now they’re committed to insane capex. But he’s outta here One of the most meaningful Dwarkesh Patel interviews so far

Satya is Out TLDR: MSFT doesn’t believe in AGI, wary of overinvestment, OpenAI partnership is over A. AI and AGI are overhyped - “Us self-claiming some AGI milestone, that's just nonsensical benchmark hacking to me. The real benchmark is: the world growing at 10%.” B. Very negative on more capex spend from MSFT - “[If you look at the Industrial Revolution] there was a lot of money lost” - “…countries are going to deploy capital… I'm so excited to be a leaser… I build a lot, I lease a lot.“ C. Value in AI is in infra where there is overbuild, and B2C apps which OpenAI has won, negative on model layer - “in consumer, in some categories, there may be some winner-take-all network effect. ChatGPT is a great example [of a] at-scale consumer property that has already got real escape velocity” - “[in enterprise] buyers will not tolerate winner-take-all. … where the buyer is a corporation, an enterprise, an IT department, they will want multiple suppliers.. That is what will happen even on the model side.” - “In the model layer, one is models need ultimately to run on some hyperscale compute.” Opinion ———— 1. Satya is calling the bubble in buildout. The crazy people like govts are entering the game. He’s happy to lease from them when they overbuild. His own capex spend is capped 2. He’s disappointed in the OpenAI partnership - he sees them as having built a great consumer app for themselves with dominance in the category, but models for enterprise usage have gotten commoditized. 3. He’s sooo done with the AGI talk. If you can’t get to 10% global growth your AGI talk is meaningless to him 4. He’s backing MSFT from the capex precipice. It’s funny because he certainly did make Google dance, and now they’re committed to insane capex. But he’s outta here One of the most meaningful Dwarkesh Patel interviews so far

Prakash

1,447,479 views • 1 year ago

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

Vikram M

21,463 views • 12 days ago

We have officially submitted our project to the Pump.fun Hackathon. Through this initiative, our goal is to test Grok in real world conditions and push it to its full operational limits. We want to explore the complete range of it's capabilities, observe how it performs under continuous and complex decision making scenarios, and clearly understand where it's strengths end and it's boundaries begin. This is not a theoretical experiment. We are applying Grok across hardware, software, and physical systems, using live data and real world constraints. Our long term vision is to scale this into something much bigger a fully automated, real world farm powered by robotics, intelligent systems, and AI driven optimization. By building in public and testing without shortcuts, we aim to learn fast, iterate openly, and discover how far this technology can truly go.

We have officially submitted our project to the Pump.fun Hackathon. Through this initiative, our goal is to test Grok in real world conditions and push it to its full operational limits. We want to explore the complete range of it's capabilities, observe how it performs under continuous and complex decision making scenarios, and clearly understand where it's strengths end and it's boundaries begin. This is not a theoretical experiment. We are applying Grok across hardware, software, and physical systems, using live data and real world constraints. Our long term vision is to scale this into something much bigger a fully automated, real world farm powered by robotics, intelligent systems, and AI driven optimization. By building in public and testing without shortcuts, we aim to learn fast, iterate openly, and discover how far this technology can truly go.

Grok Potato

32,252 views • 5 months ago

Economics of AGI episode w Alex Imas and Phil Trammell. There's a bunch of important questions about how we deal with AI that only economics can answer. What is the optimal way to tax and redistribute the wealth that will be generated? How should countries not in the AI supply chain index into the gains? Is there any world where inequality doesn't explode? It might seem like these questions have obvious answers, but the first thing economics teaches you is that your intuitions can often be entirely wrong. It was very helpful to chat through these things with Alex and Phil. Look up Dwarkesh Podcast on Apple Podcasts, YouTube, or Spotify. Enjoy! 00:00:00 – Will capital share increase? 00:19:36 – Messy Middle scenario 00:25:57 – How to tax and redistribute AI wealth 00:30:02 – Why demand collapse is unlikely 00:39:26 – Human employees would be hard to integrate into the machine economy 00:43:08 – What if some humans (or AIs) value wealth accumulation intrinsically? 01:01:28 – What should developing countries do?

Economics of AGI episode w Alex Imas and Phil Trammell. There's a bunch of important questions about how we deal with AI that only economics can answer. What is the optimal way to tax and redistribute the wealth that will be generated? How should countries not in the AI supply chain index into the gains? Is there any world where inequality doesn't explode? It might seem like these questions have obvious answers, but the first thing economics teaches you is that your intuitions can often be entirely wrong. It was very helpful to chat through these things with Alex and Phil. Look up Dwarkesh Podcast on Apple Podcasts, YouTube, or Spotify. Enjoy! 00:00:00 – Will capital share increase? 00:19:36 – Messy Middle scenario 00:25:57 – How to tax and redistribute AI wealth 00:30:02 – Why demand collapse is unlikely 00:39:26 – Human employees would be hard to integrate into the machine economy 00:43:08 – What if some humans (or AIs) value wealth accumulation intrinsically? 01:01:28 – What should developing countries do?

Dwarkesh Patel

299,626 views • 1 month ago

German Chancellor Merz: German business model was clear for quite a long time. Strong exports, global markets, reliable supply chains, cheap energy, security above all through the Americans. And with all of that, we wanted the greatest political restraint. That will no longer work in the new world. The competition has become harder, costs have risen and it is fought with hard bandages. The epochal break affects us all. The decisive question is therefore not whether we change our economy, but how quickly we manage these changes and how successful we will be in the process.

German Chancellor Merz: German business model was clear for quite a long time. Strong exports, global markets, reliable supply chains, cheap energy, security above all through the Americans. And with all of that, we wanted the greatest political restraint. That will no longer work in the new world. The competition has become harder, costs have risen and it is fought with hard bandages. The epochal break affects us all. The decisive question is therefore not whether we change our economy, but how quickly we manage these changes and how successful we will be in the process.

Clash Report

13,664 views • 4 months ago

🔹Interviewer: How long can Cuba survive like this? 🇨🇺 President Díaz-Canel: Cuba has the right to import oil. The U.S. energy blockade is unfair and part of a broader economic war lasting decades. The Russian shipment only covers about one-third of our monthly needs. It helps, but it does not solve the crisis. No country can function without fuel. Blocking energy supply is a severe form of pressure. But we are not passive. We are increasing domestic production, exploring new reserves, and investing in renewables. We are open to foreign investment, including from American companies, but the blockade prevents it. We will endure, but this situation is difficult. Our strength comes from our people. Their resistance and creativity sustain us.

🔹Interviewer: How long can Cuba survive like this? 🇨🇺 President Díaz-Canel: Cuba has the right to import oil. The U.S. energy blockade is unfair and part of a broader economic war lasting decades. The Russian shipment only covers about one-third of our monthly needs. It helps, but it does not solve the crisis. No country can function without fuel. Blocking energy supply is a severe form of pressure. But we are not passive. We are increasing domestic production, exploring new reserves, and investing in renewables. We are open to foreign investment, including from American companies, but the blockade prevents it. We will endure, but this situation is difficult. Our strength comes from our people. Their resistance and creativity sustain us.

Drop Site

18,338 views • 3 months ago

"The AI Doc: or How I Became an Apocaloptimist" is out in theaters across the US this Friday! Tristan Harris went on Real Time with Bill Maher with Bill Maher to talk about why this film will help create the clarity we desperately need to shift the direction of AI. You can watch the full episode here: Go see The AI Doc this weekend and bring your friends! We need everyone to understand the stakes of the challenge in front of us and the amazing world we can build — if we meet it. Buy your tix today:

"The AI Doc: or How I Became an Apocaloptimist" is out in theaters across the US this Friday! Tristan Harris went on Real Time with Bill Maher with Bill Maher to talk about why this film will help create the clarity we desperately need to shift the direction of AI. You can watch the full episode here: Go see The AI Doc this weekend and bring your friends! We need everyone to understand the stakes of the challenge in front of us and the amazing world we can build — if we meet it. Buy your tix today:

Center For Humane Technology

106,916 views • 3 months ago

The smartest physicist quit physics to build AI. Here’s why that matters. In a recent interview, Demis Hassabis lays out one of the clearest origin stories for modern AI I’ve heard. He starts as a physics kid obsessed with the big stuff: reality, consciousness, why anything exists at all. Feynman was his hero. But then comes the uncomfortable realization: for decades, fundamental physics barely moved. The deepest questions stayed stuck. So he pivots. Not away from truth, but toward leverage. If the human brain is the bottleneck, don’t push harder on the same brain. Build a new one. AI, in his framing, isn’t a product. It’s the ultimate scientific instrument. A tool to smash through the ceiling that human cognition hit in physics, biology, and beyond. And the twist is beautiful: By trying to build intelligence, we’re forced to understand our own. AI becomes both microscope and mirror. This isn’t about apps or chatbots. It’s about finishing the questions physics couldn’t.

The smartest physicist quit physics to build AI. Here’s why that matters. In a recent interview, Demis Hassabis lays out one of the clearest origin stories for modern AI I’ve heard. He starts as a physics kid obsessed with the big stuff: reality, consciousness, why anything exists at all. Feynman was his hero. But then comes the uncomfortable realization: for decades, fundamental physics barely moved. The deepest questions stayed stuck. So he pivots. Not away from truth, but toward leverage. If the human brain is the bottleneck, don’t push harder on the same brain. Build a new one. AI, in his framing, isn’t a product. It’s the ultimate scientific instrument. A tool to smash through the ceiling that human cognition hit in physics, biology, and beyond. And the twist is beautiful: By trying to build intelligence, we’re forced to understand our own. AI becomes both microscope and mirror. This isn’t about apps or chatbots. It’s about finishing the questions physics couldn’t.

VraserX e/acc

77,489 views • 6 months ago