Vijay Pande spent more than a decade building Andreessen Horowitz’s bio fund into a roughly $4 billion practice — then walked away in June 2025 to start something deliberately small. His new firm, VZVC, co-founded with longtime investor Zach Werner, makes only about five investments a year, has no associates, and leans on AI agents for day-to-day operations.
In a conversation with TechCrunch this week, Pande explained the reasoning behind the hard pivot, why he thinks biology is shifting from a “science of discovery” to an engineering discipline, and the data bottleneck that could determine whether AI in medicine delivers on its promises.
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From Folding@home to a $4B bio fund — and out again
Pande’s path to venture capital was unusual. He was a Stanford chemistry professor best known for Folding@home, the distributed-computing project that turned millions of home PCs into a supercomputer for disease research. Marc Andreessen and Ben Horowitz — who had spent their firm’s first five years avoiding healthcare — handed Pande the keys to a new bio practice in 2014.
Over the next decade, that practice grew to manage close to $4 billion. But Pande says the scale ultimately pushed him toward a different model.
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“We’re not driving 30 bets per year,” he said. “We’re talking about probably five, not a lot of investments — very concentrated.”
He compared adding a company at a typical fund to adding a Facebook friend — quick and low-commitment. At VZVC, it’s more like “wanting to have another child.”
The firm is named after its two partners: V for Vijay, Z for Zach. Pande said they originally planned to hire associates, but “with the agents that we’ve built up, not to be something that we need to do.”
That lean structure means VZVC rarely competes for hot rounds. “People make room for us,” Pande said. “Largely, people want us as investors because of what Zach and I can do, and how hands-on we can be.”
Why biology’s data problem is different from text
One of the most striking points in the conversation was Pande’s take on what makes AI in biology fundamentally different from AI in text or images.
“It’s a place where you don’t have any of this data that people can just all train the same thing,” he said. “Your data can’t be distilled from one model to another.”
Unlike text, which can be scraped from the internet at scale, biological data is expensive to generate, often proprietary, and deeply siloed. That creates a structural advantage for companies that build their own datasets — but it also raises questions about access and equity.
Pande acknowledged the tension. “I understand why founders and investors want to protect their findings,” he said, but he sees a shift toward “atlases of biological information” — typically foundation models — that could democratize access.
“As they become more common, I think we’ll see the same thing that’s happened with open-source LLMs,” he said. “Open-source foundation models in biology having a very broad impact.”
That vision is still early. Most leading AI-driven drug discovery companies — including ones Pande is involved with, like Genesis Therapeutics and Insitro — treat their data as a competitive moat.
What Pande looks for in founders — and what he’s learned
Pande said he’s spending most of his time on two areas: AI for healthcare delivery and AI for clinical trials. Both are capital-intensive, but he believes AI can compress the most expensive parts of drug development.
“The cost and time to get to clinical trials has been shrinking, especially with AI,” he said, “but it could still cost hundreds of millions of dollars to run a trial.”
He cited a sobering statistic: only about 20% of drugs successfully move from first-in-human trials through Phase III. The reason, he said, is often not that biologists did something wrong, but that animal models like mice are “just not very predictive of humans.”
“The AI model is not going to be perfect,” he said, “but it’s going to be way better than any animal model would be.”
On the founder side, Pande says trust is paramount. “I’m expecting this relationship to be 5, 10 years plus into, ideally, their next company,” he said. “I want to work with people who are thinking long term.”
He also offered a candid lesson from his own career: “It took me some time to really appreciate that as seductive as the coolest technologies are, it really always comes back to go-to-market.” He now tells founders to apply “all their brilliance and creativity” to the go-to-market side, which he says is “at least as hard or harder than the technology side.”
What’s overhyped — and what’s real
Asked what’s overhyped in AI and biotech, Pande pointed not to the technology itself but to the data underneath it.
“The reality is that AI can find insights that we can’t get from just humans alone,” he said. “The thing that always gets tricky is when there’s this call that AI is going to cure all everything.”
“LLMs work because there’s so much data to learn from,” he added. “When the data is simply not there, then AI can’t magically solve that problem.”
That distinction — between AI’s potential and its current limits — is central to how Pande is positioning VZVC. The firm’s small, concentrated structure is a bet that a few deeply-supported companies can outperform a broad portfolio, especially in a market where data advantages are the real moat.
Whether that model scales remains to be seen. But Pande’s track record — from Folding@home to a $4 billion bio fund — gives him credibility that few other investors can match.
This article is for informational purposes only and does not constitute financial advice. The venture capital and biotech markets are volatile and uncertain; past performance does not guarantee future results.
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