AI

Abhishek Saxena’s work at Sentient targets the gap where open-source AI keeps losing: not capability, but economics—and his answer is infrastructure that automatically pays builders, maintainers, and evaluators every time their artifact is used, enforced by smart contracts rather than legal goodwill. By combining cryptographic fingerprinting, on-chain attribution, and grant funding with no equity attached, Sentient is building the coordination layer that would make open-source development financially rational enough to compete with a corporate salary.

Apeksha Jain’s human-centered approach to AI design at Adobe is driven by a conviction that the most consequential design decisions in generative systems aren’t visual—they’re structural: how uncertainty is surfaced, how control is preserved, and how professionals are kept authoring rather than just approving what a model produced. Her work treats accessibility, transparency, and recovery not as compliance considerations but as the foundational architecture of trust, arguing that enterprise AI tools which ask users to blindly accept outputs without visibility into the process will always fail in high-stakes professional environments.