After almost 12 years in Brain/DeepMind, I’ve finally decided to take the leap. My cofounders: @yinfeiy, Seth and I have kicked-off @ElorianAI. The first multimodal reasoning lab founded and led by former LLM pretraining, data and multimodal leads. https://t.co/XHcEtvl9F9 (1/n)
Not everyone has experienced visual errors with existing models but it's pretty easy to find one. Just ask any frontier model to count more than 10 objects and they'll make mistakes. Last week, I went in depth with @FirasSozan on Inside the Silicon Mind to talk about the visual reasoning we're building at @ElorianAI.
Visual reasoning is a fundamentally different problem to object recognition.
See the full video here: https://t.co/9O8VcBk5D1
The world is made of objects, space, motion, and spatial relationships, not text. Imagine having to describe Karl the Fog to someone without pointing to a picture. That's why we believe visual reasoning is one of the next major frontiers in AI.
Appreciate Ethan, Zara, and the Unicorner team for telling our story. Check it out:
Most AI investing happens downstream of the frontier: a capability emerges, a category gets named, and capital rushes in.
But by the time a category earns a clean box on a market map, the best builders have usually been living in the messy version for months.
Agents. Reasoning. RL environments. World models. AI for Science. Recursive self-improvement.
I call this frontier proximity: the ability to see what is becoming possible before it becomes consensus.
My frontier proximity ladder:
L0 Wrapper: uses today’s models.
L1 Reactor: reacts fast to releases, but roadmap is downstream.
L2 Anticipator: builds for where capabilities are going.
L3 Native: depends on a non-obvious frontier bet.
L4 Shaper: helps move the frontier itself.
The point is not that every company needs to train models.
Apps can have high frontier proximity if they understand what models will make possible next.
Infra can have high frontier proximity if it knows what future agents, multimodal systems, robotics stacks, or scientific workflows will need.
That is why we’re launching MoE Capital.
MoE stands for Mixture of Experts.
The idea is simple: build an AI fund around people closest to the frontier: frontier researchers, technical founders, AI-native builders, and seasoned operators.
We don’t want to be another AI fund with a newsletter-level understanding of the frontier.
We want to build the AI fund closest to the frontier.
More in The Information: https://t.co/CXWJAy34zi
Incredibly excited to welcome @dustinvtran to @ElorianAI as our new Chief Reasoning Architect! 🚀
We had an amazing time working together at Google Brain, and after seeing his incredible work leading post-training at xAI, I couldn't be more thrilled to be teaming up again. Let’s build something special!
Welcome to the team, Dustin!
personal news: i've joined Elorian as Chief Reasoning Architect. multimodal AGI is the most critical frontier as we move from the era of chatbots to coding agents to models that reason and act over the physical world. i'm really excited to design natively visual models across thinking, agents, architectures, and the systems stack with the amazing team at Elorian.
i wish the best to everyone at xAI & SpaceX — driving posttraining was a unique experience with so many memorable stories. all the best to the team, and to Elon.
BREAKING NEWS: Anthropic's latest model will NOT help you if it thinks your ML research/ML engineering is interesting, and/or will secretly degrade its IQ so that the average engineer won't notice. We are already seeing Anthropic's latest model's moderation filters our GPU inference research and programming 😭
This move really damages trust in the research community. Imagine a professor who read everyone else's open research for free and wrote their own series of books but has a strict rule: you are banned from using those books to help you become a professor.
June 9th Researcher Reciprocity License
"if you train on it, you let us generate - reverse terms of use void"
Status quo
1. We teach frontier devs with ICLR/NeurIPS papers, OSS Github contributions
2. They use it to make frontier models
3. Then ban us from exploring our ideas
We need a new license, original thinkers can't be an underclass to a tyrannical researcher fiefdom
Architectures, objectives, optimizers and algorithms come and go with the times but data is forever. The hard part is knowing what to do with all that data.
Super excited to be teaching at @vcunlocked AI Edition with @500Global on Wednesday! Looking forward to meeting the new cohort and diving into the mechanics of venture capital & neolabs (inc. @ElorianAI) with an incredible group of investors and founders from around the world. See you all soon! #VCUnlocked
Today, @MichaelElabd, @QuantumArjun, and I are excited to announce Trajectory.
We are a research lab and product company building the platform for Continual Learning.
Our platform unlocks the signal already sitting in product usage, so companies can continuously post-train large-scale agentic models that outperform the frontier. @trajectorylabs
We’ve raised $15M from @Conviction, @BessemerVP, @radicalvcfund, @jeffdean, @drfeifei and more.
We’re partnering with some of the best AI-native companies: @ClayRunHQ@Harvey, @DecagonAI, @mercor_ai, @RogoAI to power their agentic systems, some of which we are already in production with.
We’ve brought together a world class research team from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, Scale AI, and an elite product team from Stripe and Figma.
AI will never again start on day one. Every correction, every retry, every edit will make products smarter. This is Continual Learning.
Had a fantastic time sitting down with @haslindatv from @BloombergTV at the @jpmorgan Global Summit before my panel.
We packed a lot into 6 minutes, diving into the momentum at our startup and our progress hiring top researchers and customers (actually the hiring number is already out of date). Watch it here! https://t.co/Jqnq7krQ4S
1/ Since February, 8 papers across algebraic geometry, representation theory, number theory, combinatorics have been quietly appearing on arXiv.
Proofs by AxiomProver.
5 papers are now accepted at solid peer-reviewed math journals. To our knowledge, a first for the literature.