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Pseudonymous AI jailbreak researcher who publishes prompt techniques and argues for transparency, capability exploration, and harm reduction outside the model.

Conviction partner and former Neeva engineer whose writing examines AI business durability, search infrastructure and enterprise automation.

Creator of Galileo’s Agent Leaderboard, focused on evaluating whether AI agents complete tasks and turning failures into verifiable improvements.

Machine-learning researcher and DatologyAI founding member studying how training data shapes foundation-model capabilities, memorization, and safety.

Pediatrician and philanthropic co-founder whose work connects AI-powered biological research with the goal of improving patients’ lives.

Atlan co-founder who advocates treating organizational context as governed infrastructure for enterprise AI.

Google DeepMind researcher applying machine learning to scientific problems, including protein structure prediction.

Applied Intuition’s co-founder and CEO focuses on putting AI into physical machines, with an emphasis on production constraints and sustainable commercialization.

AI systems engineer whose work spans distributed model training, practical training guidance, and hardware-aware model design.

Amp CEO and Sourcegraph co-founder whose work centers on code understanding, autonomous coding agents, and verification of generated software.

AI researcher whose collaborative work spans unsupervised feature learning, sequence-to-sequence models, language-model pretraining, neural architecture search, and chain-of-thought prompting; cofounder of Discovery Loop.

Chemist and materials researcher combining machine learning, physical simulation and experimental data, and Lila Sciences’ chief scientific officer for physical sciences.