On the latest Radical Talks podcast: @GeoffreyHinton X @JeffDean - a friendship that defined modern AI as breakthrough theories met massive scale. Recorded at #NeurIPS2025, Radical Co-Founder @JordanJacobs10 sits down with these two icons of AI to discuss one of history’s most productive collaborations.
Together, they're working on AI that doesn't just generate video — it understands physics, simulates motion, and enables robots to act in the real world. Looking forward to seeing what these teams accomplish together.
@RekaAILabs has joined forces with @moonvalley, adding researchers and engineers behind some of the most advanced video generation and multimodal models in the field, including former DeepMind scientists who were core contributors to Google's Veo. https://t.co/rznm0V36SE
Yann LeCun is Executive Chairman of @amilabs and a Professor at @nyuniversity. A Turing Award winner, he was the Chief AI Scientist of Meta, the founding Director of Meta-FAIR, and of the NYU Center for Data Science. His foundational work on convolutional neural networks and deep learning has helped shape the field of modern AI.
In this week's #RadicalReads, we announce our lead investment in @GeneralistAI 's $400M Series B. The company is building general-purpose foundation models to power the next generation of robotics: https://t.co/kSaPD2ineS
If you're an AI researcher considering commercialization or an early-stage founder building in AI, this program was made for you. Stay tuned for more announcements — we have a remarkable speaker lineup coming.
The Masterclass brings together AI researchers from Stanford, MIT, Oxford, Cambridge, and premier institutes like Vector and Mila, alongside entrepreneurs who are already building the future.
@genesismolai's Pearl system has beaten every other cofolding model tested — reaching 78% on OpenBind's primary success metric. This was achieved with no target-specific tuning, demonstrating Pearl's ability to generalize to new targets. Congratulations to the team.
Today we're sharing new breakthrough results for Pearl, our foundation model for protein–ligand cofolding.
The OpenBind Consortium recently released the first public structure-affinity benchmark for molecular AI, evaluating six prominent cofolding models on the EV-A71 2A protease. We ran our full Pearl system against the same target.
Zero-shot, with no binding-site information and no tuning, the Pearl system reaches 78% on OpenBind's primary success criteria, far ahead of every cofolding model tested by OpenBind. We also assessed a stricter sub-1 Å accuracy threshold, which is more relevant for real-world R&D usage – the Pearl system’s success is still 60%, versus 1–27% for the other models.
What matters most to us: this is the same system setup our scientists use on live drug discovery programs, not a benchmark-specific configuration.
Thanks to the OpenBind Consortium for building a rigorous public benchmark, and to @NVIDIAHealth for the support on optimizations that enabled model scaling.
In this week's #RadicalReads, Radical Partner Aaron Rosenberg explores our Seed investment in @inherent_labs. Inherent is building an AI lab designed around recursive self-improvement: https://t.co/8NBdAH50SB
4️⃣ How founders compete: your first hires are cultural co-founders, not just employees. The companies winning on talent right now are the ones with the clearest vision, the most intentional early teams, and a compelling answer to the frontier lab threat.
Our latest episode of Radical Talks is live! Tune in to hear from @YLikomanova, Senior Director of Talent at Radical Ventures, in conversation with Radical Partner @molwelch on the forces shaping AI talent flows.
3️⃣ The forward deployed engineer: why FDEs have become one of the most sought-after and least understood roles in applied AI, what the ideal background looks like, and why supply remains critically short.