A16Z的David George称AI的幂律效应愈发极端，资本投入可放大公司优势

a16z's David George says AI's power law is becoming more extreme because dollars alone can compound ...

a16z's David George says AI's power law is becoming more extreme because dollars alone can compound a company's advantage: "Right now, clearly the power law is more extreme than it has been in the last 10 to 20 years of technology investing, probably going back to the emergence of the network effect-driven consumer companies." "Increasing returns to scale have always been a dynamic in our business... Brand reputation in the market, the accumulation of resources, all provide competitive advantages." "That all still is the case. But right now, especially with the labs, for the first time in my career, you can take capital and throw it at a company, and it compounds their advantage." "How do you screw up a startup? Throw too much money at it and have them hire 1,000 people, and then you create all these coordination issues and overhead issues and dueling priorities... because you can't hire enough people to do enough things fast enough." "Now that's not the case. You can throw dollars at compute, and compute can make products and the businesses better. So to me, it's not terribly surprising that the power law is more extreme. Right now, economies of scale are a very real thing in the AI market, and I think it'll continue to be the case." @DavidGeorge83 Your browser does not support the video tag. 🔗 View on Twitter a16z @a16z Accolade 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: youtube.com/watch?v=bsdJd2… @aramverdi @AccoladePrtnrs @jkhamehl @davidgeorge83 Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 8 🔄 4 ❤️ 54 👀 16996 📊 12 ⚡