Simon Batzner

@JohnJumperSci @demishassabis Always greatly enjoyed our conversations, best of luck, John!

A bit of news: After nearly 9 years, I have decided to leave Google DeepMind and join Anthropic (after taking some time to recharge). I am incredibly grateful for my time at GDM. @demishassabis took a real chance letting me lead the AlphaFold team just six months after finishing my PhD, and the entire GDM team taught me so much about how to do great science. GDM is a special place, and I’ll still be excited to hear about what amazing things they discover next.

Very excited about our progress on materials! Super cool work, come join the AI for Science team.

Personal news: I‘ve been promoted to Staff Research Scientist at DeepMind. The past year has been a crazy ride pushing the frontier of physics and AI with the team. Super excited about where we’re headed. 🧑‍🍳

@ProfBuehlerMIT Glückwunsch — und best of luck!

Scientific discovery is reaching the limits of human capacity: too much data, too many disconnected fields, and too few ways to connect ideas fast enough to matter. The next breakthroughs in materials, medicine, energy, and beyond will not come from scaling today’s AI paradigm alone or from relying on serendipity alone. They will require a new kind of AI for knowledge discovery that not only models the world but shapes what it could become. At Unreasonable Labs, we are building superintelligence for knowledge discovery: systems that reason across disciplines, generate novel hypotheses, test them through simulation and experimentation, and help guide real-world discovery. Our AI engine is not confined to what it has seen in training. It creates new data, builds new tools, and maintains a persistent world model that grows more powerful as it reasons. Why now? Even today's most powerful AI models face a core limitation: they are trained on what we already know. True discovery begins when a system encounters something its current model cannot explain. This is why you cannot train your way to a discovery - a system has to reason through new problems, update its beliefs, and revise its understanding of the world as it thinks. Another critical insight is that rich knowledge already exists, but is not yet applied to solve pressing problems. It sits in millions of papers, patents, and datasets, trapped in isolated silos, often in legacy data vaults. What's missing is a way to connect it, scale it, unlock the potential, and synthesize genuine novel predictions. The time is now to build a system that enables practitioners to design, explore, and direct discovery, whether through human guidance or full automation, while capturing the tacit insight that domain experts bring. Steerable reasoning That is why we built an operating system for scientific discovery - one that replaces chance with steerable reasoning. Rather than retrieving static facts, our AI builds and continuously updates a living world model - a representation of knowledge the system can actively reason over, question, and revise. A concrete example: say you want to create "smart concrete" that can flex - a concept that doesn't exist yet. Our AI maps relationships across domains, finds a path from morphable smart materials to concrete, and identifies the most efficient way to bridge those concepts. It then autonomously writes simulations, tests the hypothesis, and refines the idea. Then it interacts with hardware to produce a physical artifact, and the loop expands into the real-world, where the machine becomes world-shaping. Our AI gives users full visibility into how the system arrived at a conclusion. It delineates which existing patents and papers it drew upon versus what is genuinely new - protecting IP and competitive concerns from the start, and offering deep compositional insights into technology advances. It takes unreasonable people to make progress Our team reflects the interdisciplinary expertise required to build this next breakthrough - my co-founder Yuan Cao @caoyuan33 (formerly DeepMind) and Andrew Lew, @HaiqianYang, Matt Insler, Jennifer Kang and Julia McLaughlin. We are backed by $13.5M in seed funding led by @PlaygroundVC with participation from @aixventures, @e14fund, and MS&AD. We are guided by advisors including Robert Langer (1,000+ patents), Kostya Novoselov (Nobel Prize in Physics), and @Thom_Wolf (Co-founder of Hugging Face). We already have multiple pilot programs underway with leading industrial partners in materials science and engineering, with additional engagements developing across energy, logistics, bioengineering, and other strategic domains. The biggest challenges of our time - fusion energy, sustainable materials, new medicines - demand exponentially more innovation than humans alone can produce. We are not replacing scientists, and instead are making every scientist capable of leading their own team of AI-powered researchers. Abundant innovation leads to abundant prosperity. Watch our launch video below to see what we're building @unreasonable_ai 👇

@bilaltwovec @IsomorphicLabs 🔥

very belated update: i joined @isomorphiclabs right after graduating early last year—its been really incredible getting to work on pushing the frontier of scaling ai for science for very very difficult problems (solve all disease) in the real world!

@marceldotsci All three are London-based.

@simonbatzner exciting! i assume these are in person either in mountain view or london?

@TheAndiPenguin @ericzelikman @noahdgoodman @gharik @YuchenHe07 Congrats! :)

I am thrilled to introduce humans& alongside my good friends and co-founders @ericzelikman & @noahdgoodman & @gharik & @YuchenHe07.

@saurabh_shah2 Awesome, congrats m8!

I’ve joined humans&! My last blog post explains why I think a human-centric approach is the missing piece in modern AI systems. I’m super psyched about the technical direction of the company. Perhaps even more important, though, is the team; the humans at humans&. My coworkers are completely and wholly wonderful. They’re brilliant, yes, but they’re also kind, funny, focused, and just about every other good adjective I can think of. Put simply: vibes are goooood. We’re bringing together wonderful people united by a much-needed mission to build something truly different. If that excites you, I’d love to chat.

@khoomeik 😬

proposal: all of sf flies to san diego for a week to hang out with sf ppl (in san diego)

@_arohan_ big fan

I think I found the perfect beans for expresso.

@AriWagen @manntis4 @CorinWagen @Forbes @RowanSci @navvye @tylerarose0 @GabriCorso Congrats, guys!

Honored to have been selected with @manntis4 and @CorinWagen for this year's @Forbes 30 Under 30 Science! And a big congratulations to friends of @RowanSci @navvye & @tylerarose0 (Bindwell) and @GabriCorso & Saro (Boltz) as well!

@sea_snell +1

Downloading for the flight

@pfau david out of hot takes before gta-6

I'm all out of hot takes. The current thing? It's bad. Or it's good. I just don't know any more.

@__tm__157 @PolymathicAI Congrats, Tanya!

Few things bring me as much joy as good science with a great team! Check out Walrus, a new foundation model for continuum dynamics built by @PolymathicAI! The future of AI for Science is very bright and PDEs will play a major role in it. Hope you use our model to push the boundaries even further! Check out the details in @mikemccabe210's thread below!

@katyenko Congrats, super exciting mission!

The smartest AI agents still panic when given tools, and new ones are churning out of labs faster than anyone can test them. After 7 years at the bench, I left to build BioArena to see which models are doing real work and which ones are just doing PR. bioarena.xyz/blog/why-bioar… Please reach out with curiosities, conversations, or collaborations!

@autumn_bujtor 👀

@simonbatzner Other than hanging with your gf!

More from @brendanbc2: x.com/brendanbc2/sta…

Both London-based 🇬🇧 Post 1: job-boards.greenhouse.io/deepmind/jobs/… Post 2: job-boards.greenhouse.io/deepmind/jobs/…

Our team at DeepMind is growing (again). 🚀 We're tackling grand challenges in semiconductors, magnets, energy materials, superconductors, and beyond. Join us! Two positions below.