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    a16z investor argues compute spending can compound AI companies’ advantage

    a16z says Accolade Partners found only 20 of 3,000 US venture firms delivered consistent 3x net returns over two decades—a finding shared in its discussion of AI’s concentrated rewards.

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    TLDR

    An a16z investor argues that AI’s rewards are more concentrated than in the past 10 to 20 years of technology investing. His explanation: especially at AI labs, capital can buy computing power that improves products and compounds a company’s advantage. He contrasts that with spending on rapid hiring, which can create coordination problems and competing priorities. On venture funds, a16z says Accolade Partners found only 20 of 3,000 US firms delivered consistent 3x net returns over two decades. Their common trait, according to a16z’s account, was access to category-defining companies, fund after fund.

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    3 Sources, first seen 19d ago

    Combined views

    278.7K

    3 Sources, first seen 19d ago

    655 likes
    19d ago
    first seen 19d ago
    655 likes
    88 comments
    401 saves
    81 reposts

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    88 comments
    401 saves
    81 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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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 @davidgeorge83

    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 @davidgeorge83