Helping entrepreneurs build local data businesses for Physical AI. Building the Human Skill Library. YC S26, 3xFounder

San Jose, CA
Joined September 2025
Happy Alumni Demo Day!
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Tim Li retweeted
ReelShort made $1B last year on microdramas. We’re doing it for anime. Today we launch Mochi, the first platform for anime microdramas. Backed by @ycombinator (S26). One minute episodes, new series daily. Produced with AI. In just a few months, we've hit 300M+ views and partnered with top writers from Toei Animation, Marvel, and Cartoon Network. Viewers can also become creators too: anyone can make their own anime in the app. The next hit anime won’t take a studio and 3 years anymore. It’ll take a phone. Try now ⬇️
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How will AI learn about the physical world? We don’t believe the answer is one giant data source. It will be thousands of entrepreneurs building local data businesses around the world. That’s why we built @DeepReach_AI — a platform where entrepreneurs build local data businesses for Physical AI. Today, we’re excited to launch on @ycombinator Join our network today! ycombinator.com/launches/SiT… #yc #robotics #startup #AI #physicalAI #data #BusinessSuccess
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Introducing GEN-1. Our latest milestone in scaling robot learning. We believe it to be the first general-purpose AI model to master simple physical tasks. 99% success rates, 3x faster speeds, adapts in real time to unexpected scenarios, w/ only 1 hour of robot data. More🧵👇
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We built CryptoBench, the world's first expert-level dynamic benchmark for evaluating LLM Agents in cryptocurrency—from real-time retrieval to future predictions, spanning on-chain intel to DeFi risk forecasting. Among 10 top models, #Grok4thinking ranks #1 🏆 @elonmusk, your model leads the pack in the crypto domain 🚀💰 Leaderboard: cryptobench.space/ Arxiv: arxiv.org/pdf/2512.00417
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Tim Li retweeted
My AI investment thesis is that every AI application startup is likely to be crushed by rapid expansion of the foundational model providers. App functionality will be added to the foundational models' offerings, because the big players aren't slow incumbents (it is wrong to apply the analogy of "fast startup, slow incumbent" here), they are just big. Far more so than with any other prior new technology, there is a massive and fast-moving wave that obsoletes every new app almost as fast as it can be invented. There is almost no time to build a company and scale it. There are two ways AI application startup founders can make money: - Make a flash-in-the-pan app that generates a ton of cash and bank the cash (my estimate is that you have about 12-18 months cashflow generation) - Make a good enough app that you get acquired by one of the big players for sufficient equity The situation is highly unstable - we don't know if it's going to crash or go to the moon but both scenarios make it very unlikely that any AI application startup will independently become a generational supercompany (baseline odds are low to begin with). The best odds are finding an application niche in a highly specialized field with extremely unique and specific data barriers, ideally ones relating to real atoms (hardware or world-related) data and not software/finance.
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