A16Z 分析师称 AI 在真实经济中的采用率仍低，顶尖公司人均月支出达 7000 美元

a16z's David George says the real economy has barely begun adopting AI, while the top 1% in one data...

a16z's David George says the real economy has barely begun adopting AI, while the top 1% in one dataset already spend $7,000 per employee per month: "Part of the thing that we're monitoring, which makes us extremely bullish about AI, is just actual diffusion into the real economy." "The median company in the US is spending $12 per employee on AI per month. The top 1% of the dataset that we've seen is spending $7,000 per employee on AI per month." "Not only have we had limited diffusion beyond coding, but if you just look at the shape of who is consuming tokens and actually getting real value out of AI today, we're super early. The most cutting-edge banks are probably doing 1% of headcount cost on AI tools." "The reason this makes me very bullish is these are the fastest-growing companies we've ever seen, of all time. They're adding more revenue per month than the mega-cap tech companies." "And yet it's probably on the back of adoption of 10 million users, maybe 20, maybe 30 max. And I think it's going to transform the way we do a lot of work." @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 💬 25 🔄 22 ❤️ 186 👀 48409 📊 43 ⚡