Excited for the imminent health system research/ops work on local 1) post-training (to improve hospital-specific clinical context), 2) benchmarking/uncertainty evaluation (to track population-specific accuracy/drift), and 3) continuous model monitoring and rapid on-prem patching

Love that AI labs are prioritizing biomedical research. Between Skills and MCP, the near-term future of computational biology might not necessarily be *better prediction models* but rather *better integration layers* that let general intelligence operate across the full research

AWS is down. Government is shut down. Epic is still up.

My latest in Health Affairs on the AI training data challenge health systems shouldn't ignore: Amidst AI labs moving towards model training on chat history with multi-year retention windows, ~2.6% of Claude & 5.7% of ChatGPT conversations contain clinical content, often as PHI

AI has given venture capital a new way to repeat an old mistake: kingmaking. The pattern from 2021 is back: a category becomes "obvious," a top-tier firm anoints its winner, and everyone else acts like the decision is final. Sierra for support. Harvey for legal. Applied Compute

If you’re starting an autonomy company today, public roads are the hardest place to begin. We sat down with Boris Sofman (CEO Bedrock Robotics) & Malhar Patel (Special Projects Head Applied Intuition) to discuss picking the right problem for physical AI.

How long until this is a standard ask for health systems fielding agent vendors? cc Ariel Dora Stern

Healthcare is one of the few sectors where policy has arguably *driven* tech innovation and diffusion rather than vice-versa. With that in mind, there are plenty of policy levers in both public health and medicine that are positive-sum! Reference pricing to promote service

x.com/i/article/2039…