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The concentration of AI returns

An a16z investor argues that AI labs can turn more capital into better products by buying computing power. He contrasts that with the overhead and conflicting priorities of hiring huge teams.

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3 Sources, 21d ago, first seen 21d ago

TLDR

In a conversation shared by a16z, an investor at the firm argues that spending on computing power can compound AI companies’ advantages, helping concentrate returns. On the fund side, a16z says Accolade Partners examined 3,000 US venture firms and found only 20 that delivered consistent 3x net returns over two decades. According to a16z, their common feature was access to category-defining companies, fund after fund.

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217.8K

3 Sources, first seen 21d ago

355 likes56 comments178 saves39 reposts

Combined views

217.8K

3 Sources, first seen 21d ago

355 likes56 comments178 saves39 reposts

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Sentiment

Positive——Negative

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3 Sources

@a16zAccolade Partners' Aram Verdiyan with a16z's Jen Kha and David George on AI's extreme power law and where the next trillion dollars gets made: The classic way to blow up a startup was throwing too much money at it. Hire a thousand people, create dueling priorities, and kill what was working. AI turned spending into a vending machine. You put dollars in and you get something out. Money buys compute and compute alone can improve a product. Nothing has concentrated returns like this since the social networks. The same concentration runs through the funds. Accolade looked at 3,000 US venture firms and found only 20 delivered consistent 3x net returns over two decades. What they have in common is access to the category-defining company, fund after fund. In this conversation, Aram, Jen, and David get into why AI is being sold against labor budgets rather than software budgets, why an LP gets fired for the opposite reasons a GP does, and why getting the fund right but sizing it wrong is the same as missing it. 00:00 Intro 01:44 The compute vending machine 04:02 AI hit $100B in 4 years, SaaS took 15 05:55 Why nobody can size the TAM of AI 07:44 "Which layer wins" is the wrong question 08:44 The category winner takes it, second place takes scraps 11:09 3,000 venture firms, 20 with 3x returns 13:40 Mid-sized venture is getting squeezed out 18:58 Why a late stage fund needs an early stage fund 21:28 Why AI made picking companies harder 23:53 What Harvey looked like pre-reasoning 25:33 An LP gets fired for the opposite reason a GP does 28:10 Why 60 funds is too many 32:02 Software companies without a buyer 35:14 Is AI coding a head fake? 36:25 $12 per employee, or $7,000 39:32 Where bolting on AI backfires 40:56 The liquidity case against venture 44:18 The first $100 trillion company YouTube: https://www.youtube.com/watch?v=bsdJd2VeLvg @aramverdi @AccoladePrtnrs @jkhamehl @davidgeorge8321d
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    3 Sources

    @a16zAccolade Partners' Aram Verdiyan with a16z's Jen Kha and David George on AI's extreme power law and where the next trillion dollars gets made: The classic way to blow up a startup was throwing too much money at it. Hire a thousand people, create dueling priorities, and kill what was working. AI turned spending into a vending machine. You put dollars in and you get something out. Money buys compute and compute alone can improve a product. Nothing has concentrated returns like this since the social networks. The same concentration runs through the funds. Accolade looked at 3,000 US venture firms and found only 20 delivered consistent 3x net returns over two decades. What they have in common is access to the category-defining company, fund after fund. In this conversation, Aram, Jen, and David get into why AI is being sold against labor budgets rather than software budgets, why an LP gets fired for the opposite reasons a GP does, and why getting the fund right but sizing it wrong is the same as missing it. 00:00 Intro 01:44 The compute vending machine 04:02 AI hit $100B in 4 years, SaaS took 15 05:55 Why nobody can size the TAM of AI 07:44 "Which layer wins" is the wrong question 08:44 The category winner takes it, second place takes scraps 11:09 3,000 venture firms, 20 with 3x returns 13:40 Mid-sized venture is getting squeezed out 18:58 Why a late stage fund needs an early stage fund 21:28 Why AI made picking companies harder 23:53 What Harvey looked like pre-reasoning 25:33 An LP gets fired for the opposite reason a GP does 28:10 Why 60 funds is too many 32:02 Software companies without a buyer 35:14 Is AI coding a head fake? 36:25 $12 per employee, or $7,000 39:32 Where bolting on AI backfires 40:56 The liquidity case against venture 44:18 The first $100 trillion company YouTube: https://www.youtube.com/watch?v=bsdJd2VeLvg @aramverdi @AccoladePrtnrs @jkhamehl @davidgeorge8321d
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