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3 people · 14 quotes · 23 Apr 2024 to 17 Aug 2026
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Gurley calls foundational model companies' hundred-million-dollar annual burn rates poor capital allocation but possibly unavoidable competitive traps.
“200,000,000 a year? There's no way that's high quality capital allocation from my point of view. There's no chance. But maybe they don't have the alternative.”
Baker argues the ultimate AI winner must be the lowest cost compute provider, which requires owning rather than renting compute.
“the ultimate AI winner will be the one with the lowest cost of infrastructure cost, the lowest cost of compute.”
Gurley warns AI companies trading equity for server capacity don't recognize true COGS and get warped decision-making.
“They're trading equity for server capacity. And so those companies are running on a day to day basis with a high COGS, but they don't know it because they're not paying cash for it.”
Patel argues open source AI lacks software's feedback loops because reusing models requires significant compute and expertise.
“fundamentally, I would say that that's because open source AI does not have the same feedback loops as open source software.”
Patel argues most AI value accrues to users, not to companies building or deploying AI.
“But most of the AI value that's been generated does not accrue to the companies building the AI or deploying it. It actually deploy it actually accrues to the user.”
Baker says AI is the first time in his career that being the low-cost producer matters in tech.
“And this is really important because AI is the first time in my career as a tech investor that being the low cost producers ever matter.”
Baker says AI is the first time in tech where being the low-cost producer matters economically.
“AI is the first time in my career as a tech investor that being the low cost producers ever matter. Apple is not worth trillions because they're a low cost producer of phones.”
Baker notes AI companies generate cash earlier than SaaS despite lower margins due to fewer employees.
“The crazy thing is because of those efficiency gains, they're generating cash way earlier than SaaS companies did historically, but they're generating cash earlier, not because they have high gross margins, but because they have very few human employees.”
Baker says AI companies generate cash earlier than SaaS despite lower margins because they have very few human employees.
“And it's just tragic to watch all of these companies. Like you want to have an agent, It's never going to succeed.”
Patel calculates $100B AI revenue requires $250B infrastructure with five-year depreciation at 50% margins.
“That $50,000,000,000 of COGS needs to burn on infra, which cost roughly with if a five if you're talking about five year depreciation, call it $250,000,000,000,”
Gurley argues AI productivity gains won't lead to 70% margins because competition will lower prices instead.
“I don't think there's any scenario where you just do more for less and all of a sudden everyone has 70% operating margins.”
Patel reports Anthropic achieved profitability and positive free cash flow in April and May 2025.
“Anthropic is free cash flow positive, and they are profitable in q two. Even in April. In April, they closed April's books. They were profitable.”
Gurley suspects some AI companies are reselling tokens below cost, creating unsustainable growth.
“I suspect there are companies that are selling, reselling tokens from Amazon or Anthropic or whoever at a price lower than they're paying for them. And which looks like growth, but it is unsustainable.”
Patel argues AI business transformation has severe upfront spend spike then major cost efficiency gains.
“Like you spike up on spend a lot for the one time and then you spike down a lot and your cost efficiency is way better.”