Skip to content
Industry · Sep 14, 2026

Vijay Pande launches AI-native biotech fund VZVC with fewer, deeper bets after a16z’s $4B practice

Pande’s new firm will make a handful of concentrated bets annually, prioritizing open biological datasets and AI-driven drug discovery.

Trust72
HypeLow hype

1 source · cross-referenced

ShareXLinkedInEmail
TL;DR
  • Vijay Pande, former head of a16z’s $4B biotech practice, launched VZVC, an AI-native fund making fewer, deeper bets.
  • VZVC avoids the ‘30 bets a year’ approach, relying on AI for operations and open biological datasets for transformation.
  • Pande argues AI can reduce reliance on animal models and improve precision medicine, but clinical trials remain costly.
  • Open biological foundation models may rival proprietary ones, per Pande’s vision for shared datasets.

Vijay Pande, who previously led Andreessen Horowitz’s biotech practice with close to $4 billion under management, has exited to launch VZVC, a smaller, AI-native venture fund. VZVC will pursue a concentrated investment strategy, making only a handful of bets per year rather than spreading resources across dozens of projects. The firm has no associates and integrates AI into its day-to-day operations, reflecting a deliberate pivot from the scale of his prior role.

Pande, a former Stanford chemistry professor known for creating the distributed-computing project Folding@home, described biology’s transition from a ‘science of discovery’ to an ‘engineerable’ discipline. He argued that AI and machine learning enable computers to model complex biological systems, improving target identification for drugs and potentially reducing reliance on animal models, which he noted are poor predictors of human outcomes. Clinical trials remain the most expensive phase of drug development, with costs often exceeding hundreds of millions of dollars and success rates around 20% from first trial to approval.

The high failure rate in drug development is partly due to the limitations of animal models, Pande said. He suggested AI models could surpass these limitations, though he acknowledged they would not be perfect. The discussion extended to precision medicine, where AI could enable treatments tailored to individual patients rather than population averages, reducing the trial-and-error approach common in areas like oncology.

A central challenge Pande highlighted is the lack of shared biological data. Unlike text data, which can be scraped from the internet, biological data is often siloed within companies or research groups, limiting the training of broadly applicable AI models. He proposed that open biological ‘atlases’—foundation models trained on shared datasets—could rival proprietary models, similar to the trajectory seen in open-source large language models.

Pande’s new fund, VZVC, is co-founded with Zach Werner, a longtime investor. The firm’s approach aligns with Pande’s view that concentrated bets and open data sharing are more likely to yield transformative advances in medicine than dispersed portfolios or proprietary datasets. He also referenced his involvement with Genesis Therapeutics, a company spun out of his Stanford lab, and Insitro, a drug-discovery company founded by Daphne Koller, as examples of AI’s growing role in the field.

Sources
  1. 01TechCrunch — AI — “We’re not doing 30 bets a year”: Vijay Pande on betting small after running $4 billion at a16z
Also on Industry

Stories may contain errors. Dispatch is assembled with AI assistance and curated by human editors; despite the trust-score filter, mistakes happen. We correct publicly — every article links to its revision history. Nothing here is financial, legal, or medical advice. Verify before relying on any claim.

© 2026 Dispatch. No ads. No sponsorships. No paid placement. Reader-supported via Ko-fi.

Built by a person who cares about honest AI news.