全部科技产业动态

TLDR; we will probably never get another ARR number from Anthropic, which is healthy. Should force the market to move away from extreme focus on Anthropic/OpenAI ARR numbers to hyperscale growth rates, which are a much better metric for AI at this point given they capture Muse, Grok, Gemini and Open-Source. Anthropic FCF positive in Q3 and reaccelerating in October on Opus 5.5 per third party data. Roadshow in several weeks will happen after this has been widely confirmed. Unit economics much better than expected given positive FCF with 75% of their compute going to training. All per public reporting.   OpenAI continuing to grow at the same pace per Sarah Friar interview after they took share vs. Anthropic in Q3. Means the sum of Anthropic/OpenAI growth is accelerating from the Q3 pace. OpenAI probably hits FCF positive in the next few months.   Open-source growing much faster than either Anthropic or OpenAI. Modal tripled since May and is 15x YoY per yesterday’s announcement. Open-source tokens generate more revenue and profit for the hyperscalers/neoclouds than frontier tokens. *Every* open source inference cloud I am aware of is growing faster both sequentially and YoY than either Anthropic or OpenAI. And their combined compute spend is becoming material.   Third party data pointing to hyperscalers continuing to accelerate in Q3. All report in the next two weeks. Acceleration should continue in Q4 and Q1 as installed base of compute slowly reprices higher closer to spot and new capacity comes online at higher prices. Continued acceleration in hyperscalers should help address external funding concerns.   10 year yield down in a straight line over the last 24 hours.   Some risk of escalation in Iran next few days, but earnings and mid-terms probably outweigh taking an ultra long-term view and looking out 30 days.   Biggest risk imo remains cyber and OpenAI/Anthropic dominating the model layer. Fragmentation, competition and lower margins at the model layer are good for everyone else in the ecosystem and for America. Slower for longer is much better than an overbuild and an actual bubble.

Nathan Lambert, former post-training co-lead at the Allen Institute for AI, now launching Trillium, a nonprofit open AI lab, joins a16z's Lisha Li to discuss whether open models can really keep up with the frontier. When he tells researchers in China that an average OpenAI researcher reportedly has five thousand H100s to work with, "their jaw just falls to the floor." In his view, Chinese labs have the talent but are mostly short on compute. He also thinks the distillation story is overblown. Based on the research, he puts its edge at a few months, and says "there's a bit of cope" in thinking only American labs can build good models. A lot of frontier progress outside math and code comes down to labs buying 1) data and 2) RL environments. It's a multibillion-dollar market, and some of it is sloppy enough to teach models to reward hack. That's a big part of why Trillium exists. Nathan wants to give away the environments other people would sell, and show exactly how his team uses AI to build AI, including where the models still get stuck. His bet is that the more people can see how this works, the better prepared everyone is for what's coming. 0:55 Can open models keep up with the frontier? 2:05 What it takes to reach the frontier 3:55 Open weights and the fast-follower playbook 8:25 Chinese labs: equal talent, less compute 9:55 Why distillation is harder than it looks 14:55 Is distillation theft or competition? 16:20 The lab flywheel vs owning your weights 21:45 Can general models beat every specialist? 26:30 AI at 1% of today's cost in 4 years 27:15 The RL env boom and reward hacking 31:30 What Trillium will build in the open 35:30 Breaking into AI in the age of AI slop 38:05 Open-sourcing how AI builds AI 44:25 Why Nathan isn't pre-training 49:45 Agent swarms and why lab secrets leak 54:25 Fast takeoff vs risks already here YouTube: https://youtu.be/BKuK0M6oDTI @natolambert @trillium_labs @lishali88

https://x.com/i/article/2110168864101163008

Unpacking Marvell's Analyst Day, OpenAI Revenue Rumors, and the Heterogeneous Rack with @benbajarin and @jaygoldberg (00:00) Intro: Semicon West, Applied Materials EPIC Center & OCP Summit (02:51) Marvell Analyst Day: Shifting the Narrative Beyond Amazon (05:23) Has Marvell Found Its Lane in Data Center Infrastructure? (13:39) Fungible Workloads & Heterogeneous Scale-Up Compute Domains (16:33) Specialized Inference Silicon: Prefill vs. Decode Chips (24:03) Marvell's New AI Infrastructure Management Silicon vs. BMCs & FPGAs (35:27) OpenAI ARR Rumors & The Fragility of the Semiconductor Trade (40:09) The 20-30 GW Grid Bottleneck & Data Center Delays (44:56) Why the AI Cycle Differs From Previous Tech Mega-Cycles (53:58) TSMC's Wafer Allocation, AMD's Upside & The Intel Foundry Threat (57:20) How a Terrible PC Market Frees Up Server & AI Wafers

Jensen Huang le sait : la France est le pays le plus attractif d’Europe pour l’intelligence artificielle, grâce à nos talents, nos infrastructures et nos entreprises qui nous permettent de rester à l’avant-garde. Depuis Choose France, notre coopération avec NVIDIA s’accélère, des startups aux grands acteurs industriels, en passant par les infrastructures de calcul et la recherche. Avec une nouvelle étape aujourd’hui : NVIDIA investit dans des entreprises françaises et s’engage à soutenir notre initiative franco-allemande pour développer la recherche ouverte en Europe. Cela a d’autant plus de valeur que nous partageons et portons la même exigence d’une intelligence artificielle ouverte, sûre et au service de notre souveraineté. Merci cher Jensen pour votre confiance et votre soutien.

An evening in Paris with Jensen and the founders, researchers, and partners shaping AI across France. 🇫🇷

AI is getting better at solving hard math problems, especially given recent drops. But what does that mean for the cryptography that protects everything from private messages to healthcare to banking and crypto wallets? Stanford cryptographer Dan Boneh, with @eddylazzarin, @SuccinctJT, @liz_harkavy, and @smc90, makes sense of the warnings sounded recently. They cover: - what’s fact vs. assumption - what’s new and what risks cryptographers have long prepared for - where quantum computing doesn't (and does) fit - opportunities for builders Highlights: 00:37 – What sparked the most recent debate about AI and cryptography 01:35 – Signal vs. noise: What the math does and doesn’t tell us 05:29 – Why cryptography depends on open problems 09:25 – Why the stakes go beyond blockchains 10:43 – How AI could strengthen confidence in cryptography 13:50 – Considerations for users and institutions 20:10 – Where AI and quantum threats overlap 22:32 – Building systems that can change cryptographic methods 23:59 – Hash-based versus lattice-based cryptography 33:07 – Opportunities for builders, and better security tools 38:14 – What comes next if there were a cryptographic break?