language-models
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Andreas Stuhlmüller
Andreas Stuhlmüller is the cofounder and CEO of Elicit, an AI research assistant that automates evidence synthesis for scientists and analysts. A cognitive scientist by training (PhD, MIT), he built probabilistic programming languages before turning to a single question: can AI help people reason well about hard problems? His answer is process supervision, the idea that you get trustworthy machines by watching how they think, not just grading what they produce. Elicit, which spun out of his nonprofit Ought, raised a $22M Series A in 2025 and serves hundreds of thousands of researchers.
Eric Zelikman
Eric Zelikman is the co-founder and CEO of humans&, a human-centric frontier AI lab in Redwood City building foundation models designed for communication and collaboration rather than automation. A former early researcher at xAI, he helped shape Grok's pretraining data and scaled reinforcement learning for reasoning across Grok 3 and Grok 4. Before that, as a Stanford PhD candidate, he authored STaR and Quiet-STaR, foundational papers that taught language models to reason in natural language using their own rationales. In January 2026, humans& emerged from stealth with a $480M seed round at a roughly $4.48B valuation, backed by Nvidia, Jeff Bezos, GV, SV Angel and Emerson Collective.
Suman Kanuganti
Suman Kanuganti is the Co-Founder and CEO of Personal AI, a San Francisco-based platform building memory-first AI for enterprise workforce transformation. A two-time venture-backed immigrant founder, he previously built Aira - an AI-powered accessibility company serving the blind and low-vision community that was recognized by TIME Magazine and Fast Company - before turning his attention to giving everyone a permanent, personalized AI trained on their own knowledge. He holds 10 patents in emerging technologies and has raised $16M for Personal AI, with customers including Microsoft, NVIDIA, Verizon, AT&T, and T-Mobile.
Nathan Lambert
Nathan Lambert is a Senior Research Scientist and Post-Training Lead at the Allen Institute for AI (Ai2), where he leads open-source language model development on the OLMo and Tulu series. A UC Berkeley PhD, he previously led the RLHF team at Hugging Face, co-building the TRL library and the Zephyr model. He runs Interconnects AI, a Substack newsletter read by tens of thousands covering post-training, open models, and AI policy, and is the author of The RLHF Book (Manning Publications). With roughly 8,000 academic citations and a reputation for demystifying the hardest parts of modern AI, Lambert is one of the most trusted voices at the intersection of open-source AI research and public education.