AI/ML is rewriting
the life sciences.

Suggested discussion

• What does your day-to-day AI/ML workflow actually look like — and where does it still fall short?
• Which AI/ML advances have most changed how you approach drug R&D — target identification, lead optimization, or translational science?
• When you hire computational scientists, what separates a great candidate from a good one — beyond technical skills?
• What career paths exist for computational biologists in big pharma, and how do they differ from biotherapeutics startups?
• What should a PhD student or postdoc do now to be competitive for roles like yours in 5 years?

Suggested discussion

• How has AI/ML changed what's possible in genomics and multi-omics — what problems can you solve now that you couldn't before?
• What does the bioinformatics function look like inside your organization, and how does it interface with wet-lab science?
• What are the most common gaps you see in candidates coming out of academic training programs?
• For someone weighing industry vs. academia, what are the honest trade-offs from your vantage point?
• What does an exciting early-career project look like on your team — what would a new hire actually work on?

Suggested discussion

• How do you evaluate whether a company's AI/ML platform is genuinely differentiated vs. incremental?
• What does the founding-team profile look like for the AI biotech companies you're most excited to back?
• For a computational biologist thinking about starting a company — what do you wish they understood about what VCs really want?
• Which areas in AI-driven life sciences are underhyped right now, where you'd love to see more founders?
• What's your honest view of the funding environment for early-stage AI biotech — and how should founders navigate it?