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This is super interesting - as I always believed a gram of prevention is worth a ton of cure! 

In the future, doctors will shift from being the sole decision-makers to working as co-pilots alongside AI, which could act as the primary pilot or a collaborative partner in medical care. Dr. Vijay Pande, a venture capitalist and former Stanford professor, envisions AI transforming health care by integrating engineering principles, much like Moore’s Law drove computing advances. Pande predicts that AI will excel in preventive care and early intervention—shifting the focus from treating advanced diseases to empowering patients to manage their health proactively. This approach could democratize access to top-tier medical expertise and make high-quality care universally available, regardless of location or income.

Pande also suggests that health care could become the world’s most valuable industry, hence a top Silicon Valley VC is researching it so heavily.

Doctors, supported by AI systems that process vast medical knowledge and eventually contribute to new discoveries, would amplify their expertise rather than be replaced.

How can we position ourselves?

Just thinking out loud, if I was young I would think about these directions

1) think about programs combining biomedical sciences with computer science or data engineering—like Stanford’s Biomedical Computation or Harvard’s Biomedical Informatics tracks.

2) Develop Skills in Preventive Care and Behavioral Design

3) Invest in AI Health Platform Companies - Pelosi nailed this one.... Tempus  (AI-driven precision medicine), Flatiron Health (oncology data platforms), or emerging startups in a16z’s bio fund portfolio.

$TEM

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007-Roger

I started accumulating some RXRX over the past few weeks. They may be at the forefront of the AI revolution. They engage in decoding biology & integrating technological innovations across biology, chemistry, automation, data science, engineering to industrialize drug discovery for Pharmaceuticals. Nvidia has been backing them to build a large database to run robotic testing eliminating many failed tests earlier to save time and increase the possibly of approval of the drug. They aim to reduce approval to market time from 4-5 years to 18 months using large scale databases and AI to compare testing results. Still cheap enough to DCA into a nice bag, also a nice list of institutional holders.

Atomic_Alpaca

I have synthetic longs in RXRX, NTLA & CRSP. May add more. Cathie Woods likes the sector I believe.