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Gavin Baker just posed the trillion dollar question, who's actually going to pay for all this AI spending? Baker frames the whole AI capex debate around one question, since even if hyperscalers are under earning today and compute pricing normalizes, someone has to pay for that recovered cash flow...

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31,050 次观看 • 1 个月前 •via X (Twitter)

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Chamath on why the AI capex spend might end up in a bubble Even though the race is on to adopt this technology as fast as possible, key economic indicators such as productivity and GDP growth are not yet showing any meaningful changes because of AI For the spend on AI to continue, companies need to be able to point to margin expansion or revenue growth that was directly contributed through AI A shoe company must be able to say they sold more shoes because of AI adoption A food or restaurant business must be able to say that they generated higher profits because of AI adoption This is simply not the case today. Until that happens, the AI thesis remains fickle
1:21

Chamath on why the AI capex spend might end up in a bubble Even though the race is on to adopt this technology as fast as possible, key economic indicators such as productivity and GDP growth are not yet showing any meaningful changes because of AI For the spend on AI to continue, companies need to be able to point to margin expansion or revenue growth that was directly contributed through AI A shoe company must be able to say they sold more shoes because of AI adoption A food or restaurant business must be able to say that they generated higher profits because of AI adoption This is simply not the case today. Until that happens, the AI thesis remains fickle

Boring_Business

144,234 次观看 • 4 个月前

THIS IS ABSOLUTELY RIDICULOUS. OpenAI and Anthropic are losing money on every dollar they make. OpenAI generated $20 billion in revenue in 2025 and is projected to lose $14 billion in the same year. Internal forecasts project cumulative losses hitting $44 billion by 2028. The company's own CFO warned executives in April 2026 that OpenAI might struggle to finance upcoming computing deals if revenue growth slows. Anthropic reached $4.3 billion in annualized revenue in April 2026 against $19 billion in total costs. It spends $3 to make $1, and is not expected to stop burning cash until 2027. Now look at what these two companies have committed to spend. OpenAI and Anthropic together have committed $1.05 trillion in cloud spending to Microsoft, Oracle, Google and Amazon, making up 43 to 54% of each provider's entire future revenue backlog. - Microsoft: $627B total backlog. OpenAI and Anthropic account for 49%. - Oracle: $553B total backlog. OpenAI alone accounts for 54%. - Google: $467.6B total backlog. Anthropic accounts for 43%. - Amazon: $464B total backlog. OpenAI and Anthropic account for 51%. The entire cloud industry's future revenue is a bet on two companies losing billions every quarter. Microsoft, Alphabet, Meta and Amazon are collectively expected to spend $725 billion in capex in 2026, almost entirely on AI infrastructure. Combined hyperscaler capex from 2025 to 2027 is projected at $1.15 trillion, more than double what was spent from 2022 to 2024. What is the return on all of this? McKinsey's 2025 State of AI survey found that only a minority of companies reported AI meaningfully increased revenue or reduced costs. Enterprise generative AI spending grew from $1.7 billion in 2023 to $37 billion in 2025 and most CIOs still describe their initiatives as pilots without clear ROI metrics. Microsoft's AI business is running at a $37 billion annual revenue run rate with 123% year over year growth. That sounds impressive until you realize most of the capex funding is justified by expected future AI revenue rather than current AI profit. The internet burned money for years before it became the most profitable industry in history. But right now $1 trillion in committed cloud spend, $725 billion in annual capex, two loss-making customers making up half of every major cloud provider's revenue backlog, and the enterprises writing the checks cannot tell you if any of it is working.
0:22

THIS IS ABSOLUTELY RIDICULOUS. OpenAI and Anthropic are losing money on every dollar they make. OpenAI generated $20 billion in revenue in 2025 and is projected to lose $14 billion in the same year. Internal forecasts project cumulative losses hitting $44 billion by 2028. The company's own CFO warned executives in April 2026 that OpenAI might struggle to finance upcoming computing deals if revenue growth slows. Anthropic reached $4.3 billion in annualized revenue in April 2026 against $19 billion in total costs. It spends $3 to make $1, and is not expected to stop burning cash until 2027. Now look at what these two companies have committed to spend. OpenAI and Anthropic together have committed $1.05 trillion in cloud spending to Microsoft, Oracle, Google and Amazon, making up 43 to 54% of each provider's entire future revenue backlog. - Microsoft: $627B total backlog. OpenAI and Anthropic account for 49%. - Oracle: $553B total backlog. OpenAI alone accounts for 54%. - Google: $467.6B total backlog. Anthropic accounts for 43%. - Amazon: $464B total backlog. OpenAI and Anthropic account for 51%. The entire cloud industry's future revenue is a bet on two companies losing billions every quarter. Microsoft, Alphabet, Meta and Amazon are collectively expected to spend $725 billion in capex in 2026, almost entirely on AI infrastructure. Combined hyperscaler capex from 2025 to 2027 is projected at $1.15 trillion, more than double what was spent from 2022 to 2024. What is the return on all of this? McKinsey's 2025 State of AI survey found that only a minority of companies reported AI meaningfully increased revenue or reduced costs. Enterprise generative AI spending grew from $1.7 billion in 2023 to $37 billion in 2025 and most CIOs still describe their initiatives as pilots without clear ROI metrics. Microsoft's AI business is running at a $37 billion annual revenue run rate with 123% year over year growth. That sounds impressive until you realize most of the capex funding is justified by expected future AI revenue rather than current AI profit. The internet burned money for years before it became the most profitable industry in history. But right now $1 trillion in committed cloud spend, $725 billion in annual capex, two loss-making customers making up half of every major cloud provider's revenue backlog, and the enterprises writing the checks cannot tell you if any of it is working.

Crypto Rover

58,862 次观看 • 4 个月前

a16z's David George says the real economy has barely begun adopting AI, while the top 1% in one dataset already spend $7,000 per employee per month: "Part of the thing that we're monitoring, which makes us extremely bullish about AI, is just actual diffusion into the real economy." "The median company in the US is spending $12 per employee on AI per month. The top 1% of the dataset that we've seen is spending $7,000 per employee on AI per month." "Not only have we had limited diffusion beyond coding, but if you just look at the shape of who is consuming tokens and actually getting real value out of AI today, we're super early. The most cutting-edge banks are probably doing 1% of headcount cost on AI tools." "The reason this makes me very bullish is these are the fastest-growing companies we've ever seen, of all time. They're adding more revenue per month than the mega-cap tech companies." "And yet it's probably on the back of adoption of 10 million users, maybe 20, maybe 30 max. And I think it's going to transform the way we do a lot of work." David George
1:36

a16z's David George says the real economy has barely begun adopting AI, while the top 1% in one dataset already spend $7,000 per employee per month: "Part of the thing that we're monitoring, which makes us extremely bullish about AI, is just actual diffusion into the real economy." "The median company in the US is spending $12 per employee on AI per month. The top 1% of the dataset that we've seen is spending $7,000 per employee on AI per month." "Not only have we had limited diffusion beyond coding, but if you just look at the shape of who is consuming tokens and actually getting real value out of AI today, we're super early. The most cutting-edge banks are probably doing 1% of headcount cost on AI tools." "The reason this makes me very bullish is these are the fastest-growing companies we've ever seen, of all time. They're adding more revenue per month than the mega-cap tech companies." "And yet it's probably on the back of adoption of 10 million users, maybe 20, maybe 30 max. And I think it's going to transform the way we do a lot of work." David George

a16z

110,972 次观看 • 14 天前

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?
1:33

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Ricardo

2,977,042 次观看 • 4 个月前

Morgan Stanley just raised their 2027 AI capex forecast to $1.1 trillion and that number still doesn't include SpaceX or a lot of the other AI companies (Save this). When you factor those in, the real 2027 figure is probably closer to $1.5 trillion and AI lab inference revenue combined is tracking toward $300 billion in 2027. On its surface that ratio sounds alarming, spending $1.5 trillion in capex to generate $300 billion in revenue. But the framing collapses the moment you examine two things the bears consistently ignore, gross margins and the revenue trajectory. Gross margins on inference revenue are running at 60 to 70 percent. That means the $300 billion in inference revenue generates $180 to $210 billion in gross profit and that number compounds rapidly as utilization scales on infrastructure that is already built and paid for. The Capex is not being deployed against today's revenue but rather being deployed against a revenue trajectory that has shown no signs of decelerating. To understand how aggressive that trajectory actually is, consider that Morgan Stanley's $1.1 trillion hyperscaler forecast is nearly double what analysts projected for the same year just twelve months ago And they described the demand as inelastic, meaning it is not slowing down regardless of rising costs, tighter financing conditions or geopolitical risk. The AI industry ended 2025 tracking well over $200 billion in combined inference revenue and the growth rate since then has continued to accelerate rather than flatten. Anthropic alone scaled from negligible revenue to a $30 billion annualized run rate in approximately 18 months while OpenAI is tracking toward $280 billion in annual revenue by 2030 from $13 billion in 2025. There is also a structural reality in the capex number that the bears never account for. Roughly 35 percent of total AI spending goes toward training, building the next model generation which is not revenue-generating in the current period. That means only about 65 percent of the $1.5 trillion in capex is actually deployed against the inference infrastructure that earns revenue today. When you apply the 60 to 70 percent gross margin to the revenue that sits on top of that 65 percent figure, the economics look substantially better than the headline capex to revenue ratio implies. Every CEO who has been closest to this buildout has consistently underestimated it and Jensen Huang projected $1 trillion in AI capex two years ago and was called delusional. Dario Amodei said in early 2026 that AI revenues would reach the low hundreds of billions by 2028 and trillions before 2030 and given where Anthropic's own revenue trajectory is today, he is likely revising those numbers upward. The pattern here is consistent, every time someone models the revenue ceiling, the actual number breaks through it faster than expected. Come join Milk Road Pro for our full breakdown, the real unit economics of the AI inference buildout, how the capex to revenue ratio evolves over the next three years, and our entire AI thesis! Link below!
4:04

Morgan Stanley just raised their 2027 AI capex forecast to $1.1 trillion and that number still doesn't include SpaceX or a lot of the other AI companies (Save this). When you factor those in, the real 2027 figure is probably closer to $1.5 trillion and AI lab inference revenue combined is tracking toward $300 billion in 2027. On its surface that ratio sounds alarming, spending $1.5 trillion in capex to generate $300 billion in revenue. But the framing collapses the moment you examine two things the bears consistently ignore, gross margins and the revenue trajectory. Gross margins on inference revenue are running at 60 to 70 percent. That means the $300 billion in inference revenue generates $180 to $210 billion in gross profit and that number compounds rapidly as utilization scales on infrastructure that is already built and paid for. The Capex is not being deployed against today's revenue but rather being deployed against a revenue trajectory that has shown no signs of decelerating. To understand how aggressive that trajectory actually is, consider that Morgan Stanley's $1.1 trillion hyperscaler forecast is nearly double what analysts projected for the same year just twelve months ago And they described the demand as inelastic, meaning it is not slowing down regardless of rising costs, tighter financing conditions or geopolitical risk. The AI industry ended 2025 tracking well over $200 billion in combined inference revenue and the growth rate since then has continued to accelerate rather than flatten. Anthropic alone scaled from negligible revenue to a $30 billion annualized run rate in approximately 18 months while OpenAI is tracking toward $280 billion in annual revenue by 2030 from $13 billion in 2025. There is also a structural reality in the capex number that the bears never account for. Roughly 35 percent of total AI spending goes toward training, building the next model generation which is not revenue-generating in the current period. That means only about 65 percent of the $1.5 trillion in capex is actually deployed against the inference infrastructure that earns revenue today. When you apply the 60 to 70 percent gross margin to the revenue that sits on top of that 65 percent figure, the economics look substantially better than the headline capex to revenue ratio implies. Every CEO who has been closest to this buildout has consistently underestimated it and Jensen Huang projected $1 trillion in AI capex two years ago and was called delusional. Dario Amodei said in early 2026 that AI revenues would reach the low hundreds of billions by 2028 and trillions before 2030 and given where Anthropic's own revenue trajectory is today, he is likely revising those numbers upward. The pattern here is consistent, every time someone models the revenue ceiling, the actual number breaks through it faster than expected. Come join Milk Road Pro for our full breakdown, the real unit economics of the AI inference buildout, how the capex to revenue ratio evolves over the next three years, and our entire AI thesis! Link below!

Milk Road AI

21,141 次观看 • 3 个月前

David George and Gavin Baker on why AI demand is just getting started, and the numbers they're seeing across power users: David: "If you actually look at the power law of the actual engineers in those companies, the highest-spending engineers are spending 10 or sometimes 100x more than the median engineer." "And so there's this question of, where are we at in diffusion? There's one and a half billion knowledge workers. It feels like we're nowhere on the demand side and we're massively supply constrained." Gavin: "At Atreides, our internal token consumption has gone up 100x from the month of March. March through August, 100x our token spend." "And we just got access to Grok Bot Enterprise, and with two people using it, it looks like token spend might 10 or 20x in a month from August." David: "It's actually extremely valuable. We have some heavy Grok Bot users here, and it is very productive use. This is not wasteful token spend." David George Gavin Baker
1:38

David George and Gavin Baker on why AI demand is just getting started, and the numbers they're seeing across power users: David: "If you actually look at the power law of the actual engineers in those companies, the highest-spending engineers are spending 10 or sometimes 100x more than the median engineer." "And so there's this question of, where are we at in diffusion? There's one and a half billion knowledge workers. It feels like we're nowhere on the demand side and we're massively supply constrained." Gavin: "At Atreides, our internal token consumption has gone up 100x from the month of March. March through August, 100x our token spend." "And we just got access to Grok Bot Enterprise, and with two people using it, it looks like token spend might 10 or 20x in a month from August." David: "It's actually extremely valuable. We have some heavy Grok Bot users here, and it is very productive use. This is not wasteful token spend." David George Gavin Baker

a16z

35,767 次观看 • 25 天前

a16z GP Julie Yoo breaks down 3 massive shifts reshaping healthcare: "Some of the fastest growing companies in health tech are cash pay. Because of AI, the cost structure to deliver an actual medically credible service is like 100 times lower than it used to be." "New marketplace of products designed for the consumer, not insurance company and doctor with consumers as an afterthought. Consumers are now at the top of that pile. We wrote a piece called Consumers Are the New Payer." "Another post argued the best companies right now are AI native and AI proof. Companies that on the outside look like a retailer, but on the inside are highly AI native. Service area not achievable historically, disruptive cost structure, high margins to keep investing in innovation." "Third: payment rails. It's why healthcare seems so screwed up. It's actually designing exactly as planned. Really ambitious companies saying: what if we blew up that current healthcare payment modality and built a new one from scratch? Companies like Devoted Health and others are doing this." a16z Kyruus Health
3:54

a16z GP Julie Yoo breaks down 3 massive shifts reshaping healthcare: "Some of the fastest growing companies in health tech are cash pay. Because of AI, the cost structure to deliver an actual medically credible service is like 100 times lower than it used to be." "New marketplace of products designed for the consumer, not insurance company and doctor with consumers as an afterthought. Consumers are now at the top of that pile. We wrote a piece called Consumers Are the New Payer." "Another post argued the best companies right now are AI native and AI proof. Companies that on the outside look like a retailer, but on the inside are highly AI native. Service area not achievable historically, disruptive cost structure, high margins to keep investing in innovation." "Third: payment rails. It's why healthcare seems so screwed up. It's actually designing exactly as planned. Really ambitious companies saying: what if we blew up that current healthcare payment modality and built a new one from scratch? Companies like Devoted Health and others are doing this." a16z Kyruus Health

MTS

17,583 次观看 • 28 天前

Jack Dorsey, co-founder of Twitter (now X) and Block, on why treating AI as a "copilot" is a losing strategy: jack argues that most companies are approaching AI in a way that will make it nearly impossible for them to survive. "I think most of the industry is thinking about AI as like a co-pilot, as something that is augmented onto, rather than like how do you just rebuild our whole company with this as the core." His concern is that bolting AI onto existing structures produces companies that look indistinguishable from each other, and from the AI labs themselves. "If it doesn't make sense for your business to do that and you end up being or looking very similar or rhyming too closely with the frontier labs, then I think it's going to be very, very challenging to differentiate and survive." This thinking has been driving his decisions since early 2024, when these tools "really came to bear." That's when his team began building Goose, an agent coding harness, as part of a broader effort to rebuild around AI rather than layer it on top. The core insight? Speeding up old workflows with AI is a short-term gain every competitor will match. Real differentiation comes from rebuilding the company itself around intelligence.
0:46

Jack Dorsey, co-founder of Twitter (now X) and Block, on why treating AI as a "copilot" is a losing strategy: jack argues that most companies are approaching AI in a way that will make it nearly impossible for them to survive. "I think most of the industry is thinking about AI as like a co-pilot, as something that is augmented onto, rather than like how do you just rebuild our whole company with this as the core." His concern is that bolting AI onto existing structures produces companies that look indistinguishable from each other, and from the AI labs themselves. "If it doesn't make sense for your business to do that and you end up being or looking very similar or rhyming too closely with the frontier labs, then I think it's going to be very, very challenging to differentiate and survive." This thinking has been driving his decisions since early 2024, when these tools "really came to bear." That's when his team began building Goose, an agent coding harness, as part of a broader effort to rebuild around AI rather than layer it on top. The core insight? Speeding up old workflows with AI is a short-term gain every competitor will match. Real differentiation comes from rebuilding the company itself around intelligence.

Big Brain AI

863,100 次观看 • 5 个月前

Khosla Ventures' Keith Rabois explains how a mere blip in the Anthropic or OpenAI IPOs would be catastrophic for the venture-funded AI ecosystem: "The most devastating thing to the frothy hype, traction, and funding of AI from VCs would be if Anthropic or OpenAI has a blip in their IPOs." "Do they have to show metrics over the next month to first few quarters that cause people to value either company less than the current belief that they will trade in the trillions? They could have a miss on revenue traction, a miss on margins, something like that." "That would be catastrophic. It would immediately reset virtually all the valuations in the AI world. And probably deprive half the companies that are raising money from access to capital." "I'm not saying that is going to happen, or is likely. And I think if they miss, it would be more on the margin side than on the revenue side." "But if they do, everybody is predicating their new investments in dollars on the idea that you can build a trillion-dollar company within the lifecycle of a venture fund. There are 2 examples, currently, and there are several companies that people believe have the potential to get there." "If that changes, and you cannot predict that more trillion-dollar companies can be created, then the valuations that people are investing at, and the dollars that are being invested, do not make that much sense."
2:36

Khosla Ventures' Keith Rabois explains how a mere blip in the Anthropic or OpenAI IPOs would be catastrophic for the venture-funded AI ecosystem: "The most devastating thing to the frothy hype, traction, and funding of AI from VCs would be if Anthropic or OpenAI has a blip in their IPOs." "Do they have to show metrics over the next month to first few quarters that cause people to value either company less than the current belief that they will trade in the trillions? They could have a miss on revenue traction, a miss on margins, something like that." "That would be catastrophic. It would immediately reset virtually all the valuations in the AI world. And probably deprive half the companies that are raising money from access to capital." "I'm not saying that is going to happen, or is likely. And I think if they miss, it would be more on the margin side than on the revenue side." "But if they do, everybody is predicating their new investments in dollars on the idea that you can build a trillion-dollar company within the lifecycle of a venture fund. There are 2 examples, currently, and there are several companies that people believe have the potential to get there." "If that changes, and you cannot predict that more trillion-dollar companies can be created, then the valuations that people are investing at, and the dollars that are being invested, do not make that much sense."

TBPN

78,950 次观看 • 16 天前

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
2:22:19

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

Dami-Defi

16,105 次观看 • 4 个月前

Chamath just asked the question nobody in AI wants to answer (Save this). "Okay guys, you've spent $3 trillion in the last four years. What is the ROI of these tokens?" It is the most important question in technology right now and the data suggests most of the people being asked cannot answer it. A PwC CEO survey published in January 2026 found that 56% of CEOs report no increase in revenue and no decrease in costs attributable to AI over the past year meaning the majority of companies deploying AI tools have not yet produced a single dollar of auditable return. And only 12% reported experiencing both benefits. Hyperscalers alone are on track to spend $675 billion on AI infrastructure in 2026, up 63% year over year, with total global AI investment approaching $2.5 trillion this year alone against a backdrop where most enterprise buyers cannot yet quantify what any of it produced. Chamath's answer to the question is the real insight. He said what happens next is that enterprises go to guys like Mark Benioff and say: "please sell my tokens." In other words, the AI labs built the capability but the enterprise software giants are the ones who have the customer relationships, the distribution, the workflows and the trust to actually convert token consumption into measurable business outcomes and therefore into revenue that justifies the spend. Mark Benioff was sitting in the same conversation and confirmed exactly that, he said Salesforce is about to spend $300 million on Anthropic. But listen to what Benioff did with Salesforce's own balance sheet at the same time. He announced the largest stock buyback in enterprise software history $50 billion, or 28% of Salesforce's entire market cap while simultaneously admitting the stock has fallen 36% over the past year. In March, Salesforce launched the largest accelerated share repurchase in history to execute $25 billion of it immediately, financed in part with debt it will be carrying until 2066. Chamath is pointing at the underlying structural problem that has triggered the SaaS rout of 2026, software forward P/E multiples have now fallen below the S&P 500 for the first time in history, the iShares software ETF is down over 21% year to date and 30% from its September 2025 peak, and companies like Adobe, and Workday have seen their valuation multiples drop 47-54% in a single year. The core fear is not that AI does not work but rather that AI is breaking the seat based model that built the entire B2B software industry. If one AI agent can do the work of five employees, enterprises stop buying 500 seats and start buying 100, or renegotiate entirely and the recurring revenue that made SaaS stocks trade at 40 times forward earnings simply evaporates. Chamath's prediction is that AI multiples come way back down while infrastructure plays go back up and find a balance is essentially already happening in real time.
1:06

Chamath just asked the question nobody in AI wants to answer (Save this). "Okay guys, you've spent $3 trillion in the last four years. What is the ROI of these tokens?" It is the most important question in technology right now and the data suggests most of the people being asked cannot answer it. A PwC CEO survey published in January 2026 found that 56% of CEOs report no increase in revenue and no decrease in costs attributable to AI over the past year meaning the majority of companies deploying AI tools have not yet produced a single dollar of auditable return. And only 12% reported experiencing both benefits. Hyperscalers alone are on track to spend $675 billion on AI infrastructure in 2026, up 63% year over year, with total global AI investment approaching $2.5 trillion this year alone against a backdrop where most enterprise buyers cannot yet quantify what any of it produced. Chamath's answer to the question is the real insight. He said what happens next is that enterprises go to guys like Mark Benioff and say: "please sell my tokens." In other words, the AI labs built the capability but the enterprise software giants are the ones who have the customer relationships, the distribution, the workflows and the trust to actually convert token consumption into measurable business outcomes and therefore into revenue that justifies the spend. Mark Benioff was sitting in the same conversation and confirmed exactly that, he said Salesforce is about to spend $300 million on Anthropic. But listen to what Benioff did with Salesforce's own balance sheet at the same time. He announced the largest stock buyback in enterprise software history $50 billion, or 28% of Salesforce's entire market cap while simultaneously admitting the stock has fallen 36% over the past year. In March, Salesforce launched the largest accelerated share repurchase in history to execute $25 billion of it immediately, financed in part with debt it will be carrying until 2066. Chamath is pointing at the underlying structural problem that has triggered the SaaS rout of 2026, software forward P/E multiples have now fallen below the S&P 500 for the first time in history, the iShares software ETF is down over 21% year to date and 30% from its September 2025 peak, and companies like Adobe, and Workday have seen their valuation multiples drop 47-54% in a single year. The core fear is not that AI does not work but rather that AI is breaking the seat based model that built the entire B2B software industry. If one AI agent can do the work of five employees, enterprises stop buying 500 seats and start buying 100, or renegotiate entirely and the recurring revenue that made SaaS stocks trade at 40 times forward earnings simply evaporates. Chamath's prediction is that AI multiples come way back down while infrastructure plays go back up and find a balance is essentially already happening in real time.

Milk Road AI

115,726 次观看 • 4 个月前

🚨 THE WORLD’S CENTRAL BANKERS ARE STARTING TO PANIC ABOUT AI. Not because AI is failing. Because the entire AI boom is now being built on debt, leverage, shadow banking, and financial structures that are starting to look disturbingly similar to 2008. The BIS just warned that an “AI bust” could trigger serious global financial instability. And once you look at the numbers, the concern starts making sense very quickly. Amazon, Microsoft, Meta, Google and Oracle are expected to spend more than $1 TRILLION on AI capex between 2025 and 2026 alone. For 2026 itself, hyperscaler AI spending is tracking around $750 BILLION. That is a 77% jump from already record levels last year. The problem? A massive part of this expansion is being financed through debt. Morgan Stanley estimates hyperscalers and AI joint ventures alone could generate $250–300 BILLION in debt issuance by 2026. AI-focused companies already secured at least $200 BILLION through debt financing in 2025. And some companies are now literally borrowing money using Nvidia GPUs as collateral. That is where things start getting dangerous. CoreWeave, one of the biggest AI infrastructure companies, now has liabilities above $21 BILLION after pioneering GPU-backed debt financing. Its entire structure depends on Nvidia chips holding value long enough for the debt to get repaid. But GPUs depreciate extremely fast. Every new Nvidia generation immediately weakens the value of the previous one. Which means billions in loans are now backed by hardware that can lose relevance before the debt even matures. And according to the BIS, the financing structures around AI are becoming increasingly opaque. The same assets may be pledged multiple times across different financing vehicles. That is exactly the type of circular leverage that made the 2008 system so fragile. At the same time, the actual economic returns from AI still remain largely unproven. Goldman Sachs found “basically zero” economy-wide productivity gains from AI in 2025 despite hundreds of billions already being spent. Only 1% of S&P 500 companies were even able to quantify any earnings impact from AI. Yet markets are behaving like the returns are already guaranteed. Now combine this with: • Global debt at a record $353 TRILLION • Inflation jumping back to 4.2% • Private credit stress already appearing • AI chip shortages • Data center bottlenecks • And AI valuations approaching dot-com bubble extremes according to the IMF, ECB, BIS and Bank of England. This is why central banks are suddenly sounding alarmed. Because if hyperscalers ever slow AI spending, the debt chain behind the entire boom starts getting tested immediately. And right now, the global financial system is more leveraged than ever.
0:28

🚨 THE WORLD’S CENTRAL BANKERS ARE STARTING TO PANIC ABOUT AI. Not because AI is failing. Because the entire AI boom is now being built on debt, leverage, shadow banking, and financial structures that are starting to look disturbingly similar to 2008. The BIS just warned that an “AI bust” could trigger serious global financial instability. And once you look at the numbers, the concern starts making sense very quickly. Amazon, Microsoft, Meta, Google and Oracle are expected to spend more than $1 TRILLION on AI capex between 2025 and 2026 alone. For 2026 itself, hyperscaler AI spending is tracking around $750 BILLION. That is a 77% jump from already record levels last year. The problem? A massive part of this expansion is being financed through debt. Morgan Stanley estimates hyperscalers and AI joint ventures alone could generate $250–300 BILLION in debt issuance by 2026. AI-focused companies already secured at least $200 BILLION through debt financing in 2025. And some companies are now literally borrowing money using Nvidia GPUs as collateral. That is where things start getting dangerous. CoreWeave, one of the biggest AI infrastructure companies, now has liabilities above $21 BILLION after pioneering GPU-backed debt financing. Its entire structure depends on Nvidia chips holding value long enough for the debt to get repaid. But GPUs depreciate extremely fast. Every new Nvidia generation immediately weakens the value of the previous one. Which means billions in loans are now backed by hardware that can lose relevance before the debt even matures. And according to the BIS, the financing structures around AI are becoming increasingly opaque. The same assets may be pledged multiple times across different financing vehicles. That is exactly the type of circular leverage that made the 2008 system so fragile. At the same time, the actual economic returns from AI still remain largely unproven. Goldman Sachs found “basically zero” economy-wide productivity gains from AI in 2025 despite hundreds of billions already being spent. Only 1% of S&P 500 companies were even able to quantify any earnings impact from AI. Yet markets are behaving like the returns are already guaranteed. Now combine this with: • Global debt at a record $353 TRILLION • Inflation jumping back to 4.2% • Private credit stress already appearing • AI chip shortages • Data center bottlenecks • And AI valuations approaching dot-com bubble extremes according to the IMF, ECB, BIS and Bank of England. This is why central banks are suddenly sounding alarmed. Because if hyperscalers ever slow AI spending, the debt chain behind the entire boom starts getting tested immediately. And right now, the global financial system is more leveraged than ever.

Crypto Rover

86,404 次观看 • 2 个月前

Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.
1:24

Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.

Aish

240,743 次观看 • 1 年前

What happens when intelligence becomes a line item? For the first time, companies can treat AI token spending almost like headcount: allocate more money, deploy more intelligence, and potentially get more work done. That changes the economics of building a company. It could reshape how companies form, how teams are managed, how businesses are financed, and who is best positioned to build them. a16z crypto General Partner Guy Wuollet and Head of Engineering ncitron.eth join Robert Hackett to explore why engineering leaders may soon manage token budgets like P&Ls, the emergence of software-engineering “pod shops”; and whether the future belongs to smaller, leaner businesses run by people who are unusually good at directing agents. They also ask why AI agents may naturally transact using stablecoins, and confront a larger question: If AI has created so much new intelligence, why hasn’t it produced an obvious jump in economic growth? And in a world where everyone can access powerful models, will intelligence matter less than grit, judgment, and agency? 0:00 Intro 4:01 Why token spending is starting to resemble headcount 4:51 Deciding how much spend is too much spend 9:04 The agentic A/B test: How to quantify "return on tokens" 12:05 The software-engineering "pod shop" 25:13 Paying deference to the Machine God 26:06 The rise (or not) of lean, AI-native microbusinesses 28:05 Everyone's a manager? The new style of thinking for engineers 30:48 Why AI agents may naturally use stablecoins 34:33 When AI productivity will appear in GDP 47:49 Why grit and agency may matter more than IQ 51:38 How AI could create new paths for startups 54:53 Innovation, commoditization, and creative destruction
58:14

What happens when intelligence becomes a line item? For the first time, companies can treat AI token spending almost like headcount: allocate more money, deploy more intelligence, and potentially get more work done. That changes the economics of building a company. It could reshape how companies form, how teams are managed, how businesses are financed, and who is best positioned to build them. a16z crypto General Partner Guy Wuollet and Head of Engineering ncitron.eth join Robert Hackett to explore why engineering leaders may soon manage token budgets like P&Ls, the emergence of software-engineering “pod shops”; and whether the future belongs to smaller, leaner businesses run by people who are unusually good at directing agents. They also ask why AI agents may naturally transact using stablecoins, and confront a larger question: If AI has created so much new intelligence, why hasn’t it produced an obvious jump in economic growth? And in a world where everyone can access powerful models, will intelligence matter less than grit, judgment, and agency? 0:00 Intro 4:01 Why token spending is starting to resemble headcount 4:51 Deciding how much spend is too much spend 9:04 The agentic A/B test: How to quantify "return on tokens" 12:05 The software-engineering "pod shop" 25:13 Paying deference to the Machine God 26:06 The rise (or not) of lean, AI-native microbusinesses 28:05 Everyone's a manager? The new style of thinking for engineers 30:48 Why AI agents may naturally use stablecoins 34:33 When AI productivity will appear in GDP 47:49 Why grit and agency may matter more than IQ 51:38 How AI could create new paths for startups 54:53 Innovation, commoditization, and creative destruction

a16z crypto

60,168 次观看 • 2 个月前

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."
3:06

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."

Patrick OShaughnessy

394,361 次观看 • 7 个月前

Big Tech just ran out of money building AI and what they're doing to cover it up should be illegal. Google, Amazon, Microsoft, and Meta are spending a combined $700 BILLION this year on AI infrastructure. This eats up 94% of their total operating cash flow. The richest companies in human history are almost broke. And instead of slowing down, they're covering it up with the biggest financial engineering operation since 2008: Google just sold $80 billion in stock to fund AI infrastructure. That was their first equity raise in 20 YEARS. The last time Google needed to sell stock, YouTube didn't even exist. Sundar Pichai admitted the thing keeping him up at night is "compute capacity." The company that prints $100 billion a year in ad revenue just told Wall Street it isn't enough anymore. Amazon's free cash flow is projected to go NEGATIVE this year for the first time ever. Morgan Stanley estimates a $17 billion deficit and Bank of America says $28 billion. The most profitable logistics machine on Earth is about to burn more cash than it generates, and they quietly filed with the SEC saying they may need to raise even more debt and equity to keep building. All four hyperscalers are now borrowing hundreds of billions in bonds to keep the AI buildout alive. These were the most cash-rich companies in human history, and they're leveraging themselves to the teeth to build infrastructure that nobody has proven will generate enough revenue to pay for itself. And the cracks are already starting to show: Broadcom makes the custom AI chips that power Google, Meta, OpenAI, and Anthropic. This week their AI revenue TRIPLED year over year, sales grew 48%, and profits smashed every Wall Street estimate. The reward for all of that was $320 billion in value erased in a single trading session. Their CEO Hock Tan went on the earnings call and exposed three things about the AI industry: Google is already shopping for cheaper AI chip alternatives, broadcom abandoned its strategy of selling complete AI systems and is now retreating to selling bare chips at lower margins. And despite supposedly "unprecedented demand," Tan refused to raise his full-year forecast, which tells you everything about what he's actually seeing behind the curtain. Wall Street heard all three and hit the sell button so hard it dragged AMD, Intel, and the entire chip sector down with it. When a company triples its AI revenue and gets punished because tripling isn't fast enough, the expectations have left the atmosphere entirely. And here's the really scary part... These companies ARE your retirement account. Apple, Microsoft, Amazon, Google, Meta, and Nvidia make up roughly 30% of the S&P 500. If you have a 401k or an index fund, you are already exposed to this bet whether you chose to be or not. Every single one of these companies is telling you AI will generate trillions in revenue. But right now the math says they're spending trillions FIRST and hoping the revenue shows up later. If the revenue catches up, this becomes the greatest infrastructure buildout in human history. Bigger than railroads and bigger than the internet. If it doesn't, the companies that make up a third of the American stock market just leveraged their balance sheets into the largest write-down cycle since 2000. And unlike the dot-com crash, this time the bubble companies aren't random startups with no revenue. They're the backbone of the entire global economy.
2:08

Big Tech just ran out of money building AI and what they're doing to cover it up should be illegal. Google, Amazon, Microsoft, and Meta are spending a combined $700 BILLION this year on AI infrastructure. This eats up 94% of their total operating cash flow. The richest companies in human history are almost broke. And instead of slowing down, they're covering it up with the biggest financial engineering operation since 2008: Google just sold $80 billion in stock to fund AI infrastructure. That was their first equity raise in 20 YEARS. The last time Google needed to sell stock, YouTube didn't even exist. Sundar Pichai admitted the thing keeping him up at night is "compute capacity." The company that prints $100 billion a year in ad revenue just told Wall Street it isn't enough anymore. Amazon's free cash flow is projected to go NEGATIVE this year for the first time ever. Morgan Stanley estimates a $17 billion deficit and Bank of America says $28 billion. The most profitable logistics machine on Earth is about to burn more cash than it generates, and they quietly filed with the SEC saying they may need to raise even more debt and equity to keep building. All four hyperscalers are now borrowing hundreds of billions in bonds to keep the AI buildout alive. These were the most cash-rich companies in human history, and they're leveraging themselves to the teeth to build infrastructure that nobody has proven will generate enough revenue to pay for itself. And the cracks are already starting to show: Broadcom makes the custom AI chips that power Google, Meta, OpenAI, and Anthropic. This week their AI revenue TRIPLED year over year, sales grew 48%, and profits smashed every Wall Street estimate. The reward for all of that was $320 billion in value erased in a single trading session. Their CEO Hock Tan went on the earnings call and exposed three things about the AI industry: Google is already shopping for cheaper AI chip alternatives, broadcom abandoned its strategy of selling complete AI systems and is now retreating to selling bare chips at lower margins. And despite supposedly "unprecedented demand," Tan refused to raise his full-year forecast, which tells you everything about what he's actually seeing behind the curtain. Wall Street heard all three and hit the sell button so hard it dragged AMD, Intel, and the entire chip sector down with it. When a company triples its AI revenue and gets punished because tripling isn't fast enough, the expectations have left the atmosphere entirely. And here's the really scary part... These companies ARE your retirement account. Apple, Microsoft, Amazon, Google, Meta, and Nvidia make up roughly 30% of the S&P 500. If you have a 401k or an index fund, you are already exposed to this bet whether you chose to be or not. Every single one of these companies is telling you AI will generate trillions in revenue. But right now the math says they're spending trillions FIRST and hoping the revenue shows up later. If the revenue catches up, this becomes the greatest infrastructure buildout in human history. Bigger than railroads and bigger than the internet. If it doesn't, the companies that make up a third of the American stock market just leveraged their balance sheets into the largest write-down cycle since 2000. And unlike the dot-com crash, this time the bubble companies aren't random startups with no revenue. They're the backbone of the entire global economy.

Ricardo

228,416 次观看 • 3 个月前

Jensen Huang just said the most dangerous thing about AI that no one is sitting with. Huang: “AI basically does most of our coding. And yet we’re hiring more engineers than ever. We have more challenges than ever. We have bigger dreams than ever.” Every engineer at NVIDIA uses AI. AI writes most of their code. This is the company building the infrastructure behind every major AI system on Earth. Closer to this technology than any organization alive. They’re hiring more people. Not fewer. Every conversation about AI is built around subtraction. Fewer jobs. Fewer workers. Fewer humans in the loop. Jensen just told you the opposite is true. Huang: “Suppose we infused AI into this country, and as a result of that, we are doing things faster than ever before. Our ambition is greater than ever before. Our expectations are greater than ever before. How is that a bad condition for our country?” He’s not defending AI. He’s describing what happens inside the organizations that actually use it. It doesn’t make them leaner. It makes them hungrier. More ambition. More speed. More appetite for problems no one would have touched five years ago. The car didn’t make humans travel less. The internet didn’t make humans communicate less. No tool in human history has ever made humans want less. AI will not be the exception. Huang: “Prior to that, it’s been incredible but not useful. Now it’s useful and incredible.” Six months. That’s how fast AI crossed from impressive demo to daily weapon. The companies that adopted it didn’t shrink. They expanded. Compressed timelines. Started chasing problems they never would have attempted. The companies that ignored it stayed exactly where they were. That gap compounds. Every day a company uses AI to move faster, it learns something the one standing still never will. That knowledge stacks. That speed stacks. That ambition stacks. Jensen isn’t warning about a future where machines take your job. He’s describing a present where the companies using AI are becoming so fast and so hungry that standing still is already fatal. By the time you notice, it’s over. You were never going to be replaced by AI. You were going to be erased by someone it made hungrier than you.
1:23

Jensen Huang just said the most dangerous thing about AI that no one is sitting with. Huang: “AI basically does most of our coding. And yet we’re hiring more engineers than ever. We have more challenges than ever. We have bigger dreams than ever.” Every engineer at NVIDIA uses AI. AI writes most of their code. This is the company building the infrastructure behind every major AI system on Earth. Closer to this technology than any organization alive. They’re hiring more people. Not fewer. Every conversation about AI is built around subtraction. Fewer jobs. Fewer workers. Fewer humans in the loop. Jensen just told you the opposite is true. Huang: “Suppose we infused AI into this country, and as a result of that, we are doing things faster than ever before. Our ambition is greater than ever before. Our expectations are greater than ever before. How is that a bad condition for our country?” He’s not defending AI. He’s describing what happens inside the organizations that actually use it. It doesn’t make them leaner. It makes them hungrier. More ambition. More speed. More appetite for problems no one would have touched five years ago. The car didn’t make humans travel less. The internet didn’t make humans communicate less. No tool in human history has ever made humans want less. AI will not be the exception. Huang: “Prior to that, it’s been incredible but not useful. Now it’s useful and incredible.” Six months. That’s how fast AI crossed from impressive demo to daily weapon. The companies that adopted it didn’t shrink. They expanded. Compressed timelines. Started chasing problems they never would have attempted. The companies that ignored it stayed exactly where they were. That gap compounds. Every day a company uses AI to move faster, it learns something the one standing still never will. That knowledge stacks. That speed stacks. That ambition stacks. Jensen isn’t warning about a future where machines take your job. He’s describing a present where the companies using AI are becoming so fast and so hungry that standing still is already fatal. By the time you notice, it’s over. You were never going to be replaced by AI. You were going to be erased by someone it made hungrier than you.

Dustin

12,200 次观看 • 4 个月前

David Friedberg: The AI Jobs Panic Is a Crock of Sh*t Why? The revenue potential outweighs the cost savings by 100x. “There is no job loss with AI. I've said it a thousand times, and I will say it again, and again, and again. What I see on the ground, and what I've seen at dozens of companies, including my company that I run, there are two sides to a business. There is revenue and there’s costs. On the cost side of the equation, AI can be used to reduce humans doing things that cost money, to some extent. The effect there, I would argue, is nominal. The real opportunity with AI is on the revenue side, where suddenly one engineer can do 100x or 1000x what they used to be able to do, meaning you can make more products at your company, whether those are agricultural seed products, or boats and ships, or software for companies, or clothing, or what have you. Because of AI, everyone has the ability to expand their revenue base to create more products, and that is the foundation of good economic prosperity. It is called productivity. We can grow productivity in this country with AI. So where I see AI being used is on the revenue side 100x more than the cost side. And in that equation, people are hiring like crazy. We cannot hire enough people. I just had a review meeting with my product and engineering team two days ago, and they're like, ‘We want to add an extra 15 headcount to our engineering squads because we have all this opportunity to do stuff that we couldn't otherwise do.’ So we are going to hire more people. And to Sacks' point, we are seeing that show up in the jobs numbers. The idea that AI is going to destroy jobs is a Luddite idea that is being disproven every single day, and I see it on the ground. It is only a matter of time before people wake up to this and they realize that this narrative that they've all been sold is a crock of sh*t.”
1:42

David Friedberg: The AI Jobs Panic Is a Crock of Sh*t Why? The revenue potential outweighs the cost savings by 100x. “There is no job loss with AI. I've said it a thousand times, and I will say it again, and again, and again. What I see on the ground, and what I've seen at dozens of companies, including my company that I run, there are two sides to a business. There is revenue and there’s costs. On the cost side of the equation, AI can be used to reduce humans doing things that cost money, to some extent. The effect there, I would argue, is nominal. The real opportunity with AI is on the revenue side, where suddenly one engineer can do 100x or 1000x what they used to be able to do, meaning you can make more products at your company, whether those are agricultural seed products, or boats and ships, or software for companies, or clothing, or what have you. Because of AI, everyone has the ability to expand their revenue base to create more products, and that is the foundation of good economic prosperity. It is called productivity. We can grow productivity in this country with AI. So where I see AI being used is on the revenue side 100x more than the cost side. And in that equation, people are hiring like crazy. We cannot hire enough people. I just had a review meeting with my product and engineering team two days ago, and they're like, ‘We want to add an extra 15 headcount to our engineering squads because we have all this opportunity to do stuff that we couldn't otherwise do.’ So we are going to hire more people. And to Sacks' point, we are seeing that show up in the jobs numbers. The idea that AI is going to destroy jobs is a Luddite idea that is being disproven every single day, and I see it on the ground. It is only a matter of time before people wake up to this and they realize that this narrative that they've all been sold is a crock of sh*t.”

The All-In Podcast

152,633 次观看 • 3 个月前

🚨 WARNING: SOMETHING VERY WIERD IS HAPPENING. The S&P 500 keeps printing new highs. Everyone is celebrating. Nobody is looking at what's actually holding it up. Semiconductor stocks are now worth $13.4 trillion. 19.7% of the entire index. Five years ago that number was closer to 5%. It quadrupled in a single cycle on a single bet. AI. And here's the number that should concern everyone. AI chips generate 50% of all semiconductor revenue. They represent less than 0.2% of total chip shipments. Half the revenue, a fraction of the volume, trillions in market cap sitting on top of a sliver of actual production. Three companies are carrying all of it. Nvidia. Broadcom. TSMC. The same names in every major institutional portfolio simultaneously. Everyone owns them, nobody can afford to be the first one out. And the way the money moves inside this system should sound familiar. Big players fund AI startups. AI startups spend that money on Big Tech infrastructure. Big Tech reports record AI revenue. Valuations justify the next round, the same dollar completes the loop and gets counted as growth every time. We watched this exact dynamic play out once before. 2000. A handful of tech companies carried the entire market. Valuations made no sense, narratives did the work that fundamentals couldn't. Then one company missed earnings, then another, then the S&P lost 50% and the Nasdaq lost 78%. The current setup is more concentrated, the valuations are more extreme. And one cut in AI spending is all it takes to start the unwind. I called the $16K Bitcoin bottom. I called the $126K top. Every major turn for 15 years public, before the move. The next call is already forming. Follow now and turn on notifications, you'll understand why that matters sooner than you think.
0:34

🚨 WARNING: SOMETHING VERY WIERD IS HAPPENING. The S&P 500 keeps printing new highs. Everyone is celebrating. Nobody is looking at what's actually holding it up. Semiconductor stocks are now worth $13.4 trillion. 19.7% of the entire index. Five years ago that number was closer to 5%. It quadrupled in a single cycle on a single bet. AI. And here's the number that should concern everyone. AI chips generate 50% of all semiconductor revenue. They represent less than 0.2% of total chip shipments. Half the revenue, a fraction of the volume, trillions in market cap sitting on top of a sliver of actual production. Three companies are carrying all of it. Nvidia. Broadcom. TSMC. The same names in every major institutional portfolio simultaneously. Everyone owns them, nobody can afford to be the first one out. And the way the money moves inside this system should sound familiar. Big players fund AI startups. AI startups spend that money on Big Tech infrastructure. Big Tech reports record AI revenue. Valuations justify the next round, the same dollar completes the loop and gets counted as growth every time. We watched this exact dynamic play out once before. 2000. A handful of tech companies carried the entire market. Valuations made no sense, narratives did the work that fundamentals couldn't. Then one company missed earnings, then another, then the S&P lost 50% and the Nasdaq lost 78%. The current setup is more concentrated, the valuations are more extreme. And one cut in AI spending is all it takes to start the unwind. I called the $16K Bitcoin bottom. I called the $126K top. Every major turn for 15 years public, before the move. The next call is already forming. Follow now and turn on notifications, you'll understand why that matters sooner than you think.

Hanzo ㊗️

18,356 次观看 • 2 个月前

The last time Gavin Baker and I sat down to record a podcast, it was almost exactly a year ago and we started with the immediate question: is AI a bubble? Gavin's answer focused on utilization and returns (and perhaps unsurprisingly, his answer was: No). Unlike the dark fiber of 2000, there were, and still are, no idle GPUs. The largest buyers of compute were funding the buildout from some of the strongest balance sheets in the world, and their AI investment was already producing real returns. I sat down with Gavin again last week, and to chart just exactly where we are in the cycle. He has spent the summer asking operators for one quantitative measure in their business that is getting worse and has yet to find one. At the same time, public AI stocks have gone through meaningful drawdowns. Still, if you look at the way people are using AI today, there’s a good chance that demand diffusion has barely begun. AI revenue rests on fewer than 10m heavy users, against roughly 1.5b knowledge workers. At the most AI-native startups, token spend is approaching or exceeding 10% of human compensation, which suggests that there’s a long way to go before even the earliest adopters fully integrate agentic capabilities. Within Atreides, Gavin said token consumption rose 100x from March through August, and even further once the team started using products like GrokBot. As Gavin put it, once people begin approving automations, token consumption starts to feel “sort of endless.” This dynamic would be reason enough alone to believe that we’re massively undersupplied at the moment. But there are other reasons on the supply side too, namely that there are a near-unbounded number of potential winners in the space: - Frontier labs can keep winning because on the highest-value tasks, marginal improvements in intelligence are worth far more than marginal differences in price. - Open models illustrate that most work doesn’t require frontier performance, creating a much larger market for intelligence that is cheaper and customizable. - Nvidia, hyperscalers, neoclouds, and inference providers can simultaneously win because every additional token still requires physical compute, even if models become more efficient. - Enterprises can win as proprietary data, workflows, and institutional knowledge become more valuable. - Application companies can win by capturing services budgets and owning the customer relationship. This doesn’t suggest everyone will be successful, but it does mean the market itself is positive-sum and we will look back on zero-sum thinking as far too limiting. Better models create better products, better products create more users, more users create more token demand, and more demand supports continued investment in models and infrastructure. Check out the whole conversation below: a16z
2:31

The last time Gavin Baker and I sat down to record a podcast, it was almost exactly a year ago and we started with the immediate question: is AI a bubble? Gavin's answer focused on utilization and returns (and perhaps unsurprisingly, his answer was: No). Unlike the dark fiber of 2000, there were, and still are, no idle GPUs. The largest buyers of compute were funding the buildout from some of the strongest balance sheets in the world, and their AI investment was already producing real returns. I sat down with Gavin again last week, and to chart just exactly where we are in the cycle. He has spent the summer asking operators for one quantitative measure in their business that is getting worse and has yet to find one. At the same time, public AI stocks have gone through meaningful drawdowns. Still, if you look at the way people are using AI today, there’s a good chance that demand diffusion has barely begun. AI revenue rests on fewer than 10m heavy users, against roughly 1.5b knowledge workers. At the most AI-native startups, token spend is approaching or exceeding 10% of human compensation, which suggests that there’s a long way to go before even the earliest adopters fully integrate agentic capabilities. Within Atreides, Gavin said token consumption rose 100x from March through August, and even further once the team started using products like GrokBot. As Gavin put it, once people begin approving automations, token consumption starts to feel “sort of endless.” This dynamic would be reason enough alone to believe that we’re massively undersupplied at the moment. But there are other reasons on the supply side too, namely that there are a near-unbounded number of potential winners in the space: - Frontier labs can keep winning because on the highest-value tasks, marginal improvements in intelligence are worth far more than marginal differences in price. - Open models illustrate that most work doesn’t require frontier performance, creating a much larger market for intelligence that is cheaper and customizable. - Nvidia, hyperscalers, neoclouds, and inference providers can simultaneously win because every additional token still requires physical compute, even if models become more efficient. - Enterprises can win as proprietary data, workflows, and institutional knowledge become more valuable. - Application companies can win by capturing services budgets and owning the customer relationship. This doesn’t suggest everyone will be successful, but it does mean the market itself is positive-sum and we will look back on zero-sum thinking as far too limiting. Better models create better products, better products create more users, more users create more token demand, and more demand supports continued investment in models and infrastructure. Check out the whole conversation below: a16z

David George

70,565 次观看 • 25 天前