"I think half the co-founders of Anthropic were theoretical physicists. Jared Kaplan was exactly in the same area of physics as I was. Dario was in physics back in the day, also at Princeton, but he was more on the bio side. There's been an exodus of physicists to more interesting problems, and I think that's for a good reason" Why @matanSF dropped out of his physics PhD at Princeton

"The mathematics of string theory is probably one of the most beautiful artifacts humanity has created. In order to even produce something in theoretical physics, there's hundreds of years of literature that you need to catch up on, and also every day there's, 100 new papers that come out. And before you can contribute to theoretical physics, you need to know basically all of math, like algebraic topology, differential equations, algebraic geometry, and these are all things that people will spend their careers going in on, and yet to do the physics, you need to at least have, a pretty deep understanding of these things. And so it really teaches you how to have, good judgment with minimal information." @matanSF studied physics for a decade, eventually dropping out of his PhD focused on string theory to found @FactoryAI.

New episode with Matan Grinberg (@matanSF), CEO & co-founder of @FactoryAI !! We get into so many good topics: Studying physics for a decade, how to measure ROI of AI spend, who to hire in a world where we don't code, policy, the risk of Chinese open models vs. closed-model monopoly, what he thinks about recent security incidents from large labs, and why he believes we’re already living in a post-AGI world. ------ TIMESTAMPS: (00:00) Intro (02:00) Young Matan (5:30) String Theory (11:10) Leaving academia, becoming cultured, and getting nerd-sniped by code generation (16:53) Cold-emailing @shaunmmaguire and founding Factory (18:27) The desert years, and what made autonomous engineering work (20:55) The new bottleneck is deciding what to do (23:30) The three phases of enterprise AI adoption, and the end of token maxing (28:18) Chinese open models, and the monopoly to end all monopolies (30:30) Sleeper agents, poisoned code, and the labs cybersecurity incidents (34:30) The White House open-weights policy, @mkratsios47, and the distillation problem (37:30) The coming billion-dollar agent incident (42:30) "We're already post-AGI" and AI taking on math (49:30) Building your own software (52:20) Reflections & best advisors

So much discussion about whether we should be using Chinese open-source models in the US. But how could they actually hurt us? Matan Grimberg @matanSF breaks down the biggest risks- 1. Intentional vulnerabilities. A model could contain backdoors, sleeper behavior, or other weaknesses deliberately introduced into the weights, potentially sitting dormant until the right conditions trigger them. 2. Unintentional vulnerabilities. A model built in China will naturally be trained and optimized around Chinese users, codebases, languages, and data. It could perform incredibly well overall while having subtle blind spots or security weaknesses in Western systems that nobody intentionally put there. The third we may not know for years. The most concerning failures might not show up in today’s benchmarks or security evaluations. If models become deeply embedded across companies and infrastructure, vulnerabilities could emerge only after we’ve become dependent on them.

2021: I start a podcast senior year of Stanford. @alexatallah is the very first guest. His frame of thinking back then rings even truer today. "Any time information is being transferred in the internet, value can be transferred too. Just imagine every website getting marketplace-ified, every community on the internet has members that want to exchange value. And it's just a question of figuring out what that value is. Now that we have a really easy way of of building that value, it's just about creativity now and figuring out what's gonna stick. And there is so much incentive to answering that question. "

This week in SF with award-winning filmmaker Darren Aronofsky @DarrenAronofsky, @openai researcher Noam Brown @polynoamial, and Dylan Golden president of Primordial Soup We discussed the future of filmmaking, building creative models, and Hollywood and Silicon Valley’s long partnership & recent tension Put on the by the one & only @eladgil, & @emmzaoui

"We have this long term vision of turning biology from something which is trial and error and experimental and make it something that looks more like an engineering discipline in the next century." New Episode with @jackdent ! 0:00 Intro 02:28 Inside @stripe w/ @patrickc, at <100 People 08:45 @sama role in @chaidiscovery origin 16:20 Chai-2 Antibody Breakthrough 19:24 Approaching biology as an engineering problem 24:45 Using AI models to treat people on an individual scale 26:52 Robotics in labs 27:22 Pharma industry today 29:49 The clinical trial bottleneck 32:53 Personalized biology models & cancer vaccines 36:24 Longevity, peptides, and trillion-dollar drugs 39:02 Does it matter that the government cut exploratory science grants? 41:00 What next-gen Chai models will do 42:08 Can frontier AI labs build this themselves? 43:26 Biology is hard