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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...

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a16z

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35,334 次观看 • 3 天前 •via X (Twitter)

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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

67,827 次观看 • 3 天前

Gavin Baker and a16z's David George on the state of the AI boom: The future doesn't have to be winner-take-all. Labs, open-source, applications, and the clouds can all capture value. Demand for intelligence is still dramatically underestimated. Today's power users number in the millions and will grow to hundreds of millions. Gavin and David argue a compute shortage is a more real risk than an AI bubble, and building through it is an opportunity to reindustrialize America. In this episode, they get into why compute investments pay back so fast, what the data center backlash gets wrong, the case for putting compute in orbit, why enterprises will run several models at once, and how Nvidia ended up at the center of the entire supply chain. 00:00 Intro 01:06 The bear case Gavin couldn't find 05:50 Why a lab would cut its own revenue 75% 08:05 What LPs get wrong about a crash 10:50 Microsoft slowed its capex and regrets it 14:33 The engineers spending 100x the median 17:35 Why 23-year-olds use AI better than Gavin 21:45 How much copper 500M AI users need 23:00 Stop promising to cure cancer 26:00 America's richest county is full of data centers 30:48 Who gets priced out of compute 33:05 The age of Elon and Jensen 34:25 Orbital data centers 44:40 Asteroid mining 48:12 Why Microsoft doesn't need a frontier model 54:02 Who becomes the abstraction layer 55:40 Everyone wanted a deity, Cursor wanted a product 1:00:25 Never take shots at Jensen 1:07:40 What happens when the chip doesn't work 1:12:10 What chip deals reveal about customer demand YouTube: Gavin Baker David George
1:14:25

Gavin Baker and a16z's David George on the state of the AI boom: The future doesn't have to be winner-take-all. Labs, open-source, applications, and the clouds can all capture value. Demand for intelligence is still dramatically underestimated. Today's power users number in the millions and will grow to hundreds of millions. Gavin and David argue a compute shortage is a more real risk than an AI bubble, and building through it is an opportunity to reindustrialize America. In this episode, they get into why compute investments pay back so fast, what the data center backlash gets wrong, the case for putting compute in orbit, why enterprises will run several models at once, and how Nvidia ended up at the center of the entire supply chain. 00:00 Intro 01:06 The bear case Gavin couldn't find 05:50 Why a lab would cut its own revenue 75% 08:05 What LPs get wrong about a crash 10:50 Microsoft slowed its capex and regrets it 14:33 The engineers spending 100x the median 17:35 Why 23-year-olds use AI better than Gavin 21:45 How much copper 500M AI users need 23:00 Stop promising to cure cancer 26:00 America's richest county is full of data centers 30:48 Who gets priced out of compute 33:05 The age of Elon and Jensen 34:25 Orbital data centers 44:40 Asteroid mining 48:12 Why Microsoft doesn't need a frontier model 54:02 Who becomes the abstraction layer 55:40 Everyone wanted a deity, Cursor wanted a product 1:00:25 Never take shots at Jensen 1:07:40 What happens when the chip doesn't work 1:12:10 What chip deals reveal about customer demand YouTube: Gavin Baker David George

a16z

1,924,174 次观看 • 3 天前

“Is there an AI bubble?” Gavin Baker and David George In this conversation from a16z’s Runtime conference, Gavin Baker, Managing Partner and CIO of Atreides Management, joins David George, General Partner at a16z, to unpack the macro view of AI: the trillion-dollar data center buildout, the new economics of GPUs, and what this boom means for investors, founders, and the global economy. They discuss how this cycle compares to the dot-com era, why “there are no dark GPUs,” and what makes today’s infrastructure spending different from past bubbles. Gavin explains why he believes we’re still early in the AI supercycle, the changing margins in SaaS and software, and how business models and consumer interfaces are being rewritten in real time. 00:00 Intro 01:02 Are we in an AI Bubble? 02:41 Dark fiber vs. “no dark GPUs” 05:07 ROI on AI: Why this time is different 08:36 Round-tripping deals 12:22 The application layer: It’s still early 15:24 Margins, scaling laws 23:26 Reasoning models and the flywheel 27:18 The future of business models 29:41 Robotics and Elon’s Optimus vision Gavin Baker David George
31:52

“Is there an AI bubble?” Gavin Baker and David George In this conversation from a16z’s Runtime conference, Gavin Baker, Managing Partner and CIO of Atreides Management, joins David George, General Partner at a16z, to unpack the macro view of AI: the trillion-dollar data center buildout, the new economics of GPUs, and what this boom means for investors, founders, and the global economy. They discuss how this cycle compares to the dot-com era, why “there are no dark GPUs,” and what makes today’s infrastructure spending different from past bubbles. Gavin explains why he believes we’re still early in the AI supercycle, the changing margins in SaaS and software, and how business models and consumer interfaces are being rewritten in real time. 00:00 Intro 01:02 Are we in an AI Bubble? 02:41 Dark fiber vs. “no dark GPUs” 05:07 ROI on AI: Why this time is different 08:36 Round-tripping deals 12:22 The application layer: It’s still early 15:24 Margins, scaling laws 23:26 Reasoning models and the flywheel 27:18 The future of business models 29:41 Robotics and Elon’s Optimus vision Gavin Baker David George

a16z

92,161 次观看 • 10 个月前

Gavin Baker says Grok Bot feels like another ChatGPT moment because it turns hours of Claude Code work into seconds: "You see these 23-year-old kids and just the way they use AI, they're just fluent and native in it. I just feel like maybe in a way that no matter how hard I try, I will never be, and I'm trying really hard." "We got Claude Code. I built some stuff, did some cool stuff, and in, I don't know, 3 minutes of creating Grok Bots, I had much better versions of everything I created." "I love having a podcast summarizer... It takes 10 seconds in Grok Bot. It's amazing, and it's so good." "A Substack summarizer, an X summarizer, an X sentiment tracker for topics and stocks... All of those would have taken me hours working with Claude Code, and they each took 7 to 12 seconds with Grok Bot. And it's better." "To me, Grok Bot does feel like another ChatGPT moment." Gavin Baker David George
1:39

Gavin Baker says Grok Bot feels like another ChatGPT moment because it turns hours of Claude Code work into seconds: "You see these 23-year-old kids and just the way they use AI, they're just fluent and native in it. I just feel like maybe in a way that no matter how hard I try, I will never be, and I'm trying really hard." "We got Claude Code. I built some stuff, did some cool stuff, and in, I don't know, 3 minutes of creating Grok Bots, I had much better versions of everything I created." "I love having a podcast summarizer... It takes 10 seconds in Grok Bot. It's amazing, and it's so good." "A Substack summarizer, an X summarizer, an X sentiment tracker for topics and stocks... All of those would have taken me hours working with Claude Code, and they each took 7 to 12 seconds with Grok Bot. And it's better." "To me, Grok Bot does feel like another ChatGPT moment." Gavin Baker David George

a16z

6,268,617 次观看 • 2 天前

AI Winners, Token Economics, and Venture Strategy w/ David Clark and David George VenCap CIO David Clark joins a16z GP David George to discuss the AI supercycle and how it's changing the technology industry, including what bigger exits mean for venture capital, why we're not in a bubble, why we're still early in enterprise AI adoption, and more. 00:00 Intro 00:38 The scale shift: Anthropic & OpenAI adding more revenue than hyperscalers 04:20 Skeuomorphic vs native AI applications in the enterprise 06:24 How the best AI companies run themselves differently 08:14 Top 1% exits 10x'd in 24 months 11:17 The half-life problem: Why 40% of AI leaders drop off every year 13:11 Token economics, cost pressure, and who captures value 17:00 Loss ratios, risk, and how to think about early stage 22:51 Are we in an AI bubble? 29:50 The future of venture capital in an AI world David Clark David George
33:09

AI Winners, Token Economics, and Venture Strategy w/ David Clark and David George VenCap CIO David Clark joins a16z GP David George to discuss the AI supercycle and how it's changing the technology industry, including what bigger exits mean for venture capital, why we're not in a bubble, why we're still early in enterprise AI adoption, and more. 00:00 Intro 00:38 The scale shift: Anthropic & OpenAI adding more revenue than hyperscalers 04:20 Skeuomorphic vs native AI applications in the enterprise 06:24 How the best AI companies run themselves differently 08:14 Top 1% exits 10x'd in 24 months 11:17 The half-life problem: Why 40% of AI leaders drop off every year 13:11 Token economics, cost pressure, and who captures value 17:00 Loss ratios, risk, and how to think about early stage 22:51 Are we in an AI bubble? 29:50 The future of venture capital in an AI world David Clark David George

a16z

42,120 次观看 • 3 个月前

VenCap CIO David Clark says top 1% venture exits are 10x-ing: "We always thought that the largest companies were going to continue to be an order of magnitude larger than we'd seen in prior cycles. And if anything, that's accelerating." "Between 2020 and 2024, top 1% exits started at $10 billion. We updated those numbers in February this year, and a top 1% exit for '25 and the first two months of '26 was then $20 billion." "We just updated them yesterday. And if you look at just the exits that have closed, it's now at $32 billion." "And then if you think about OpenAI and Anthropic coming in, potentially we could be north of $100 billion by September... so we've 10x'd over the space of 24 months what a top 1% exit looks like." David Clark with David George
1:01

VenCap CIO David Clark says top 1% venture exits are 10x-ing: "We always thought that the largest companies were going to continue to be an order of magnitude larger than we'd seen in prior cycles. And if anything, that's accelerating." "Between 2020 and 2024, top 1% exits started at $10 billion. We updated those numbers in February this year, and a top 1% exit for '25 and the first two months of '26 was then $20 billion." "We just updated them yesterday. And if you look at just the exits that have closed, it's now at $32 billion." "And then if you think about OpenAI and Anthropic coming in, potentially we could be north of $100 billion by September... so we've 10x'd over the space of 24 months what a top 1% exit looks like." David Clark with David George

a16z

24,581 次观看 • 3 个月前

A client accidentally spent $500M in one month on Claude. $16.6M per day. No usage controls. Chamath Palihapitiya on The All-In Podcast EP275: a Fortune-level company failed to set token limits. The bill arrived at end of month. A separate Fortune 20 company is 6 months into a $1B AI-driven OPEX reduction program. $200M in tokens spent. Minimal results. Microsoft is already canceling Claude enterprise licenses. David Sacks calls it a "meme" forming - that token spend is wasteful and the market is overcorrecting. For enterprise AI buyers: the companies building spend-control and measurable-ROI infrastructure now are positioned to capture budget as the backlash unfolds. Full enterprise spend breakdown and what it means for AI vendor pricing power on The All-In Podcast: Source: All-In Podcast -
10:06

A client accidentally spent $500M in one month on Claude. $16.6M per day. No usage controls. Chamath Palihapitiya on The All-In Podcast EP275: a Fortune-level company failed to set token limits. The bill arrived at end of month. A separate Fortune 20 company is 6 months into a $1B AI-driven OPEX reduction program. $200M in tokens spent. Minimal results. Microsoft is already canceling Claude enterprise licenses. David Sacks calls it a "meme" forming - that token spend is wasteful and the market is overcorrecting. For enterprise AI buyers: the companies building spend-control and measurable-ROI infrastructure now are positioned to capture budget as the backlash unfolds. Full enterprise spend breakdown and what it means for AI vendor pricing power on The All-In Podcast: Source: All-In Podcast -

Podcast Alpha

31,016 次观看 • 3 个月前

🚨RIPPLE CTO DAVID SCHWARTZ HIGHLIGHTS MASSIVE INSTITUTIONAL MOMENTUM HEADING TO THE #XRP LEDGER — MILLIONS OF USERS COULD BE ACTIVATED! THE XRPL INSTITUTIONAL PROGRAM OFFICIALLY LAUNCHES AUGUST 1ST! AT THE CENTER OF IT ALL IS BXE TOKEN — THE POWER BEHIND THIS FINANCIAL EVOLUTION! BXE TOKEN IS STILL ONLY $0.025!! TOTAL SUPPLY IS CAPPED AT 500 MILLION — AND WITH EVERY NEW TOKEN LAUNCH ON 1,000 BXE TOKENS ARE BURNED FOREVER! 🔗 BXE TOKEN TRADE LINK:
0:54

🚨RIPPLE CTO DAVID SCHWARTZ HIGHLIGHTS MASSIVE INSTITUTIONAL MOMENTUM HEADING TO THE #XRP LEDGER — MILLIONS OF USERS COULD BE ACTIVATED! THE XRPL INSTITUTIONAL PROGRAM OFFICIALLY LAUNCHES AUGUST 1ST! AT THE CENTER OF IT ALL IS BXE TOKEN — THE POWER BEHIND THIS FINANCIAL EVOLUTION! BXE TOKEN IS STILL ONLY $0.025!! TOTAL SUPPLY IS CAPPED AT 500 MILLION — AND WITH EVERY NEW TOKEN LAUNCH ON 1,000 BXE TOKENS ARE BURNED FOREVER! 🔗 BXE TOKEN TRADE LINK:

JackTheRippler ©️

23,884 次观看 • 1 年前

Gavin Baker, CIO of the $6.5 billion fund Atreides Management, revealed at Sohn 2026 how he makes money on tech and AI the man who previously managed $17 billion at Fidelity and posted one of the best returns in the industry in the interview he explains why the AI cycle is only just beginning, which chip is the most underrated right now, and why the shortage of power and chips is actually a plus for investors bookmark it if you follow tech and AI, real insights here from a man who manages billions ↓
25:18

Gavin Baker, CIO of the $6.5 billion fund Atreides Management, revealed at Sohn 2026 how he makes money on tech and AI the man who previously managed $17 billion at Fidelity and posted one of the best returns in the industry in the interview he explains why the AI cycle is only just beginning, which chip is the most underrated right now, and why the shortage of power and chips is actually a plus for investors bookmark it if you follow tech and AI, real insights here from a man who manages billions ↓

Damir Akaza

728,763 次观看 • 2 个月前

If you hold $NET, this one changes an input. Gavin Baker Gavin Baker of Atreides Management told Jordi Hays on TBPN: CDNs like Cloudflare and Akamai deliver under 1% of global tokens today. Possibly under 10 basis points. The bulk of token generation happens inside hyperscaler and neocloud data centers. CDNs handle the final delivery hop - real, but small. Cloudflare is trading at all-time highs on AI narrative. Baker's framework says the stock is pricing in infrastructure upside that isn't flowing through token throughput yet. Token factory = yes. Token deliverer = maybe. The gap between those two labels is a valuation gap. Who passes Baker's token path test and who doesn't: Source: TBPN -
1:12

If you hold $NET, this one changes an input. Gavin Baker Gavin Baker of Atreides Management told Jordi Hays on TBPN: CDNs like Cloudflare and Akamai deliver under 1% of global tokens today. Possibly under 10 basis points. The bulk of token generation happens inside hyperscaler and neocloud data centers. CDNs handle the final delivery hop - real, but small. Cloudflare is trading at all-time highs on AI narrative. Baker's framework says the stock is pricing in infrastructure upside that isn't flowing through token throughput yet. Token factory = yes. Token deliverer = maybe. The gap between those two labels is a valuation gap. Who passes Baker's token path test and who doesn't: Source: TBPN -

Podcast Alpha

44,033 次观看 • 2 个月前

Neil on why AI spending today is different from the dot-com bubble: "A lot of the investment in networking equipment historically was speculative. We anticipated this future demand for users that never came. What's interesting about token consumption is that it's no longer speculative. People buy tokens because they're immediately valuable to them. You don't hoard tokens. You use them immediately. This is even different from what we had two years ago, where there was a supply crunch for Hopper generation chips in 2023, 2024. In that period it was all training oriented spend, and training is inherently speculative. I do think inference spend monotonically increases. There's no speculation on inference spend."
0:59

Neil on why AI spending today is different from the dot-com bubble: "A lot of the investment in networking equipment historically was speculative. We anticipated this future demand for users that never came. What's interesting about token consumption is that it's no longer speculative. People buy tokens because they're immediately valuable to them. You don't hoard tokens. You use them immediately. This is even different from what we had two years ago, where there was a supply crunch for Hopper generation chips in 2023, 2024. In that period it was all training oriented spend, and training is inherently speculative. I do think inference spend monotonically increases. There's no speculation on inference spend."

Patrick OShaughnessy

59,906 次观看 • 9 天前

David Friedberg: Government Spending is Making Everything More Expensive… and Driving America Toward Socialism david friedberg: “California's got tens of billions of dollars it spent on this stupid f**king railway that goes nowhere, where there's no f**king tracks…” David Sacks: “And there's no political will to even stop that. It'd be the easiest cut in the world.” Friedberg: “Just think about the idiocy. Like, no one is saying we shouldn't do this. Why is no one saying we shouldn't do this? A quarter trillion dollars for a train from San Francisco to Fresno that costs more than an airline ticket is so idiotic. It's like the movie ‘Don't Look Up’. Like, look up. It tells you everything you need to know. Across the board, these government programs cause more harm than good. When the government intervenes in underwriting student loans, and gives everyone a loan, administrative costs went up by 6x and tuition skyrocketed 8% a year, compounding for 30 years, because the government said, ‘We'll underwrite any student loan.’ The same with housing, and now no young people can afford a house. The same with healthcare, where they said, ‘We'll pay people to stay home and not work to take care of people, and we won't check on whether or not they're actually taking care of people,’ and the cost of healthcare skyrocketed. You go across every one of these government programs, and every one of them has the adverse effect of driving up costs and inflating everything. And the core root of inflation in this country is government spending. And the reason we are going to end up becoming a socialist country is because we aren't reining in government spending and looking it in the mirror and saying, ‘Guys, this is idiotic. What are we doing?’”
1:29

David Friedberg: Government Spending is Making Everything More Expensive… and Driving America Toward Socialism david friedberg: “California's got tens of billions of dollars it spent on this stupid f**king railway that goes nowhere, where there's no f**king tracks…” David Sacks: “And there's no political will to even stop that. It'd be the easiest cut in the world.” Friedberg: “Just think about the idiocy. Like, no one is saying we shouldn't do this. Why is no one saying we shouldn't do this? A quarter trillion dollars for a train from San Francisco to Fresno that costs more than an airline ticket is so idiotic. It's like the movie ‘Don't Look Up’. Like, look up. It tells you everything you need to know. Across the board, these government programs cause more harm than good. When the government intervenes in underwriting student loans, and gives everyone a loan, administrative costs went up by 6x and tuition skyrocketed 8% a year, compounding for 30 years, because the government said, ‘We'll underwrite any student loan.’ The same with housing, and now no young people can afford a house. The same with healthcare, where they said, ‘We'll pay people to stay home and not work to take care of people, and we won't check on whether or not they're actually taking care of people,’ and the cost of healthcare skyrocketed. You go across every one of these government programs, and every one of them has the adverse effect of driving up costs and inflating everything. And the core root of inflation in this country is government spending. And the reason we are going to end up becoming a socialist country is because we aren't reining in government spending and looking it in the mirror and saying, ‘Guys, this is idiotic. What are we doing?’”

The All-In Podcast

95,610 次观看 • 4 天前

"I'm talking to founders like, things look really good for you, what's going on, and it's like, yeah we have users, we just don't make any money on them" Offchain CEO Steven Goldfeder on why having users is no longer enough "We have a strong ecosystem team tracking projects. There's a lot, so not every project, but certainly a bunch. We're seeing massive consolidation everywhere. What I track probably more than that is one level up, Arbitrum versus its competitors, where we've also seen massive consolidation. I think it's healthy, probably a good thing ultimately" "There was so much capital in crypto, and the idea was you don't have to think about revenues, just put this thing out, make it open source, get people to use it, launch a token, and that's going to save everyone. I think we're coming very much back to reality. Some of the projects we're seeing shut down, I'm talking to founders like, things look really good for you, what's going on, and it's like, yeah we have users, we just don't make any money on them" "Back in the day having users was sufficient for the token price, your users would buy the token, that was how they paid you. Those times are well gone. Now revenue is actually important, and ultimately I think it's healthy"
1:37

"I'm talking to founders like, things look really good for you, what's going on, and it's like, yeah we have users, we just don't make any money on them" Offchain CEO Steven Goldfeder on why having users is no longer enough "We have a strong ecosystem team tracking projects. There's a lot, so not every project, but certainly a bunch. We're seeing massive consolidation everywhere. What I track probably more than that is one level up, Arbitrum versus its competitors, where we've also seen massive consolidation. I think it's healthy, probably a good thing ultimately" "There was so much capital in crypto, and the idea was you don't have to think about revenues, just put this thing out, make it open source, get people to use it, launch a token, and that's going to save everyone. I think we're coming very much back to reality. Some of the projects we're seeing shut down, I'm talking to founders like, things look really good for you, what's going on, and it's like, yeah we have users, we just don't make any money on them" "Back in the day having users was sufficient for the token price, your users would buy the token, that was how they paid you. Those times are well gone. Now revenue is actually important, and ultimately I think it's healthy"

Laura Shin

12,659 次观看 • 1 个月前

Gavin Baker says the physics PhDs calling orbital data centers impossible on X have not out-thought the 10,000 SpaceX engineers who treat it as solved "It's very hard for me to engage. There are all these people on X and they're like, I am a physics PhD and this is impossible." "There's a friend, another investor, who actually is a physics PhD, who I had many arguments about it with him, and he's like, I am a PhD and this is impossible. And then he goes to the SpaceX day and talks to the SpaceX engineers, and he's like, well, I was wrong." "So let's say you're an astrophysics PhD. You are brilliant, you're hanging 100 IQ points on me. Have you thought about this for an hour? Have you thought about it for 10 hours?" "Because you have 10,000 of the world's smartest engineers at SpaceX who've thought about this each for hundreds if not thousands of hours. And the sum of that, working with very sophisticated engineering tools, is it's a solved problem." _______ For more on Gavin Baker:
1:00

Gavin Baker says the physics PhDs calling orbital data centers impossible on X have not out-thought the 10,000 SpaceX engineers who treat it as solved "It's very hard for me to engage. There are all these people on X and they're like, I am a physics PhD and this is impossible." "There's a friend, another investor, who actually is a physics PhD, who I had many arguments about it with him, and he's like, I am a PhD and this is impossible. And then he goes to the SpaceX day and talks to the SpaceX engineers, and he's like, well, I was wrong." "So let's say you're an astrophysics PhD. You are brilliant, you're hanging 100 IQ points on me. Have you thought about this for an hour? Have you thought about it for 10 hours?" "Because you have 10,000 of the world's smartest engineers at SpaceX who've thought about this each for hundreds if not thousands of hours. And the sum of that, working with very sophisticated engineering tools, is it's a solved problem." _______ For more on Gavin Baker:

Fireside Alpha

1,550,524 次观看 • 3 天前

AI is extremely good at spending your money very quietly. our own token spend went from a rounding error to more than 10% of payroll in a year. one week in May we burned through $1.5 m. our CFO didn't love telling me that number, and he really didn't love telling the internet. but every finance leader we talk to is living the same story. the bill keeps going up and teams can't answer basic questions about it. which team is driving it? which models they're using? what changed this month? whether a cheaper model would do the same job? so finance gets two bad options: keep paying and hope, or cut broadly and slow down the work that's actually compounding. we built a third one. Ramp now connects to OpenAI, Anthropic, Gemini, Cursor and pulls it all into one place. see it, understand it, control it - down to a single API key. built with 1,000+ companies managing 100T+ tokens a month, and now spending less than they expected too! try it today at
1:26

AI is extremely good at spending your money very quietly. our own token spend went from a rounding error to more than 10% of payroll in a year. one week in May we burned through $1.5 m. our CFO didn't love telling me that number, and he really didn't love telling the internet. but every finance leader we talk to is living the same story. the bill keeps going up and teams can't answer basic questions about it. which team is driving it? which models they're using? what changed this month? whether a cheaper model would do the same job? so finance gets two bad options: keep paying and hope, or cut broadly and slow down the work that's actually compounding. we built a third one. Ramp now connects to OpenAI, Anthropic, Gemini, Cursor and pulls it all into one place. see it, understand it, control it - down to a single API key. built with 1,000+ companies managing 100T+ tokens a month, and now spending less than they expected too! try it today at

Eric Glyman

334,819 次观看 • 1 个月前

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 and that money comes from only two places, faster economic growth or labor substitution. The scale of the number itself is what makes the debate matter. There's roughly $25 trillion in global knowledge work spending and even a conservative 20% token spend ratio against that total comp base implies $5 trillion in potential AI spend, an amount that has to be funded by either productivity gains or replaced headcount. Real company data shows token spend already climbing toward that range. Ramp's own economist reported AI adoption crossing 50% of US businesses by March 2026, up from 35% a year earlier, and average monthly AI token spend across Ramp's customer base has grown 13 times since January 2025. Among the most aggressive adopters, that spend is becoming a serious share of total labor cost rather than a rounding error. SemiAnalysis reportedly runs AI token spend at 30% of total compensation spend and some companies have pushed that ratio as high as 50%, putting them well ahead of the 20-25% range typical even among heavily AI-pilled firms. The labor substitution side of Gavin Baker framework is already visible in company financials. Gross profit dollars per full-time employee at AI native companies tracked by a16z and Carta are running meaningfully higher than prior startup generations, showing these companies are staying lean rather than scaling headcount alongside revenue. Founder led companies are sending a different signal than the substitution story alone would suggest. Rather than replacing existing staff, most founder controlled companies aren't running large-scale layoffs even as they adopt AI heavily, which points toward AI spend layering on top of existing headcount to chase new growth rather than simply cutting costs. That distinction matters because it's exactly the bull case Baker is describing. Ramp's Spring 2026 report found companies investing the most in AI more than doubled their revenue since ChatGPT launched, while companies with zero AI spend grew only 15% and Ramp separately found heavy AI spenders growing revenue roughly twice as fast as low spend peers. Put together, the evidence leans toward growth rather than pure substitution funding this spend.
3:00

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 and that money comes from only two places, faster economic growth or labor substitution. The scale of the number itself is what makes the debate matter. There's roughly $25 trillion in global knowledge work spending and even a conservative 20% token spend ratio against that total comp base implies $5 trillion in potential AI spend, an amount that has to be funded by either productivity gains or replaced headcount. Real company data shows token spend already climbing toward that range. Ramp's own economist reported AI adoption crossing 50% of US businesses by March 2026, up from 35% a year earlier, and average monthly AI token spend across Ramp's customer base has grown 13 times since January 2025. Among the most aggressive adopters, that spend is becoming a serious share of total labor cost rather than a rounding error. SemiAnalysis reportedly runs AI token spend at 30% of total compensation spend and some companies have pushed that ratio as high as 50%, putting them well ahead of the 20-25% range typical even among heavily AI-pilled firms. The labor substitution side of Gavin Baker framework is already visible in company financials. Gross profit dollars per full-time employee at AI native companies tracked by a16z and Carta are running meaningfully higher than prior startup generations, showing these companies are staying lean rather than scaling headcount alongside revenue. Founder led companies are sending a different signal than the substitution story alone would suggest. Rather than replacing existing staff, most founder controlled companies aren't running large-scale layoffs even as they adopt AI heavily, which points toward AI spend layering on top of existing headcount to chase new growth rather than simply cutting costs. That distinction matters because it's exactly the bull case Baker is describing. Ramp's Spring 2026 report found companies investing the most in AI more than doubled their revenue since ChatGPT launched, while companies with zero AI spend grew only 15% and Ramp separately found heavy AI spenders growing revenue roughly twice as fast as low spend peers. Put together, the evidence leans toward growth rather than pure substitution funding this spend.

Milk Road AI

30,966 次观看 • 26 天前

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

152,399 次观看 • 2 个月前

YC Managing Partner Harj Taggar says many young founders are still too conservative with token spend and advises them to not skimp on token budget. "Token spend is more like rent. You don't say, 'I'm not moving to San Francisco to build my tech startup because the rent's expensive.' Yes, it's expensive, but it's the place to be. You don't conserve on that. You actually want to spend money on that. There's other things you save money on." "Many of the founders, especially if they're younger, just don't have the budgets to use the latest models and be cost insensitive and token-max. They're just used to conserving and trying to do more with less." "Internally, we wonder if there's some training to push the founders to spend more on tokens because maybe they're thinking about tokens as something you should conserve spend on."
1:42

YC Managing Partner Harj Taggar says many young founders are still too conservative with token spend and advises them to not skimp on token budget. "Token spend is more like rent. You don't say, 'I'm not moving to San Francisco to build my tech startup because the rent's expensive.' Yes, it's expensive, but it's the place to be. You don't conserve on that. You actually want to spend money on that. There's other things you save money on." "Many of the founders, especially if they're younger, just don't have the budgets to use the latest models and be cost insensitive and token-max. They're just used to conserving and trying to do more with less." "Internally, we wonder if there's some training to push the founders to spend more on tokens because maybe they're thinking about tokens as something you should conserve spend on."

TBPN

66,654 次观看 • 2 个月前

Dara (CEO of Uber) on their AI spend: "We blew through our AI budget in a quarter, for the whole year. It is forcing us to adjust. We are going to meter headcount increases because to the extent that my engineers are getting much more efficient, their throughput is increasing. There's a cost to that, and it's a significant cost. AI adoption has been occurring in all parts of the business –– whether it's engineers and how they scope projects, how they build, debugging, platform migrations. I'm pushing the teams to fundamentally use the power of AI to rebuild systems and processes from the bottoms up. I do think it's a combination for us right now of encouraging adoption, but then driving efficiency. We're using the more expensive models to explore. Once we scale some of these experiences, we'll look to bring in more efficient models that are more efficient on a token basis or are open source."
2:19

Dara (CEO of Uber) on their AI spend: "We blew through our AI budget in a quarter, for the whole year. It is forcing us to adjust. We are going to meter headcount increases because to the extent that my engineers are getting much more efficient, their throughput is increasing. There's a cost to that, and it's a significant cost. AI adoption has been occurring in all parts of the business –– whether it's engineers and how they scope projects, how they build, debugging, platform migrations. I'm pushing the teams to fundamentally use the power of AI to rebuild systems and processes from the bottoms up. I do think it's a combination for us right now of encouraging adoption, but then driving efficiency. We're using the more expensive models to explore. Once we scale some of these experiences, we'll look to bring in more efficient models that are more efficient on a token basis or are open source."

Patrick OShaughnessy

616,953 次观看 • 3 个月前

$OGPU token is no longer waiting for utility. It is live. Most people still haven’t understood what this means. $OGPU is now connected to some of the most powerful AI models in the world. Claude. GPT. DeepSeek. Kling 3.0. Gemini And more. Top up Relay with $OGPU and unlock up to +20% extra AI credits. That means more Claude usage for the same spend than going direct. Not vaporware. Not a future promise. Live token utility. Once people actually use it, the power of $OGPU token becomes very hard to ignore. $OGPU = AI credits = model usage = real demand Powered by OpenGPU.
0:34

$OGPU token is no longer waiting for utility. It is live. Most people still haven’t understood what this means. $OGPU is now connected to some of the most powerful AI models in the world. Claude. GPT. DeepSeek. Kling 3.0. Gemini And more. Top up Relay with $OGPU and unlock up to +20% extra AI credits. That means more Claude usage for the same spend than going direct. Not vaporware. Not a future promise. Live token utility. Once people actually use it, the power of $OGPU token becomes very hard to ignore. $OGPU = AI credits = model usage = real demand Powered by OpenGPU.

OpenGPU Network

1,705,347 次观看 • 3 个月前