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Arrived right in time for the @EthenaPay event in Singapore tomorrow ​I've received a lot of merch over the years, but hands down this sets a new benchmark Columbia fleece jacket ​Secrid wallet (I am using one myself) ​JBL speaker ​2 quality cotton t-shirts ​Handwritten letter by @gdog97_ himself Thank you team! @miguelweb3 @litocoen

Callina Liang says she felt a lot of pressure after seeing the reaction to her casting as Chun-Li in the new Street Fighter movie. After Deadline announced her casting, Liang came across a Reddit thread criticizing her physique: “Callina Liang’s thighs are nowhere as big as Chun-Li’s.” Liang admitted that the comments got into her head, but she also understood where fans were coming from: “As a die-hard fan, I also wanted the person playing Chun-Li to look like the Chun-Li in my head, and the Chun-Li from the video games and the anime.” Instead of ignoring the criticism, she used it as motivation. Liang went through an intense two-month preparation period, training in Taekwondo, Muay Thai and Wushu while following a strict diet. She even ate 12 eggs a day and had a meal every two hours while bulking. By the end of filming, her thighs had grown by 6.5 cm.

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: @natolambert @trillium_labs @lishali88

AI agents can reason over enormous amounts of information, but much of the world's actual work is still trapped in PDFs, spreadsheets, scans, and other formats that models struggle to reliably understand. Reducto co-founder and CEO Adit Abraham argues that solving this document layer is essential if agents are going to take on consequential work in healthcare, legal, finance, and insurance. Adit joins a16z General Partner Jennifer Li to explain why traditional OCR breaks down on messy real-world documents, how Reducto combines vision-language models with specialized infrastructure to reach high accuracy without sacrificing cost or latency, and why frontier models can silently drop information on long extraction tasks. Reducto now processes billions of pages each month, and crossed its 7 billionth page the morning of this recording. 0:00 Why AI agents need to read PDFs 2:12 From 1990s OCR to vision models 5:00 Training R1 on the messiest docs 7:46 When agents beat humans at extraction 9:45 A penny a page for 7 billion pages 12:47 Why legal and finance lag coding AI 15:01 The stakes of AI errors in healthcare 16:25 Agents that can edit Word and Excel 17:58 Why the labs won't go deep on PDFs 20:14 Frontier models silently drop data 22:21 Benchmarks have become marketing 25:45 Why charts and Excel stump models 28:46 Infra for 10M documents at once 31:03 How "chet pain" gets claims denied 34:32 The paperwork AI should kill first YouTube: @aditabrm @reductoai @JenniferHli