VZVC<\/a>, co-founded with longtime investor Zach Werner, is built around a handful of concentrated bets a year rather than dozens, it has no associates, and it relies heavily on AI for its day-to-day operations. <\/p>\nTo learn more about Pande\u2019s hard pivot, we talked with him this week about why he\u2019s making just a handful of concentrated bets rather than spreading himself thin in the current market \u2014 and about one of the more interesting conundrums in AI-driven biotech: unlike text, biological data can\u2019t be scraped off the internet, so nearly every company ends up building its own walled-off dataset. What does that mean for all the advances AI in medicine has promised, and who actually gets access to them?<\/p>\n
This conversation has been edited for length and clarity. You can also listen to the fuller conversation (below).<\/p>\n
You\u2019ve said biology is moving from a \u201cscience of discovery\u201d to something you can engineer. What does that mean?<\/strong><\/p>\nFor a lot of the way drugs have been developed, there was very much a fortuitous aspect to it. I think what\u2019s shifted is that AI and machine learning allow computers to wrap their type of understanding around something very, very complicated\u2026 to try to figure out what targets you want your drugs to hit, for specific diseases, to be able to make those drugs, and now even to help in the clinical trials \u2014 which are the most expensive part of the process.<\/p>\n
I thought clinical trials were getting cheaper because drug developers are using more synthetic data, so not as many people are needed for these trials.<\/strong><\/p>\nThat\u2019s, I think, very much an aspiration. <\/p>\n
The cost and time to get to clinical trials has been shrinking, especially with AI, but it could still cost hundreds of millions of dollars to run a trial, which is why drugs are very expensive. The probability of a drug going successfully from the first trial to the end of the third trial is just 20%. If 8 out of 10 fail, and these things cost hundreds of millions of dollars, the amortized cost gets really high. The reason they fail typically is not that the biologist did something wrong; it\u2019s that all the experiments these drugs were designed on were on animal models like mice, and in the end, animal models are just not very predictive of humans. The AI model is not going to be perfect, but it\u2019s going to be way better than any animal model would be, and once it crosses that bar, that\u2019s where it gets really exciting.<\/p>\n
[The phase after that is]: Is the drug the right drug for me<\/em>?<\/p>\nYou mean personalized medicine. . .<\/strong><\/p>\nThe jargon here is so-called precision medicine. If you go to a doctor with something not trivial, they have to guess what\u2019s going on, because there\u2019s only so much they can tell. Then they give you a drug \u2014 and if that doesn\u2019t work, they give you another drug, then another drug. This happens in cancer, it happens in lots of different areas. We would all be much better off if the first drug was the right one. Typically, your blood test values are compared to population averages. But really, they should be compared to: is this [result] weird for you? What we\u2019re starting to do also on the medicine side is [the ability] to just understand what would be right for the individual.<\/p>\n
Would you say the path to this moment has been slow and steady, or did it spike more recently?<\/strong> <\/p>\nI think\u00a0it\u2019s\u00a0lots of different things [coming together].\u00a0So\u00a0for instance, precision medicine for the longest time was based on genomics. But the reality is your genome is\u00a0kind of like\u00a0the blueprint for\u00a0your house on day one, but your house is\u00a0fairly different\u00a0now compared with the moment it was built. So there are many other things that people can now measure in proteomics and so on that are much more relevant for understanding disease and where your body is now. There has also\u00a0been [a lot of] automation in robotic measurements that is naturally tied into AI, and those two go hand in hand\u00a0really well.\u00a0<\/p>\n
Over the last decade, there\u2019s been this steady\u00a0clip fforin both AI for biology and AI for chemistry. The biology part is like, how can we treat this disease? And then the chemistry part is, how can we\u00a0come up with\u00a0a drug to go after that specific protein?\u00a0There have actually been very significant\u00a0advances over those 10 years.\u00a0<\/p>\n
You mentioned that biology is one of the few places AI can\u2019t just scrape data off the internet. What does that mean for how the field develops?<\/strong><\/p>\nIt\u2019s a place where you don\u2019t have any of this data that people can just all train the same thing, and your data can\u2019t be distilled from one model to another. It\u2019s a really interesting play from just the pure AI sense.<\/p>\n
Doesn\u2019t that echo a familiar problem in medicine, though \u2014 doctors operating in [territorial, often competitive] silos?<\/strong><\/p>\nYou\u2019re onto something really big here. Let\u2019s say [someone] has some type of cancer, and it\u2019s both an issue in oncology and endocrinology \u2014 those two doctors really don\u2019t sync together very well. What is really intriguing about AI is that it can, in principle, be a specialist in everything, and it can start to see things that really any single human being couldn\u2019t. It would be equivalent to having a team of the very best doctors all clamoring together in that moment.<\/p>\n
But is there enough data sharing for that vision to actually be realized? I understand why founders and investors want to protect their [respective findings], but . . . <\/strong><\/p>\nI think one of the bigger trends is that we\u2019re starting to see a shift toward building these atlases of biological information \u2014 which, from a technology standpoint, are typically foundation models. And as they become more common, I think we\u2019ll see the same thing that\u2019s happened with open-source LLMs, which do very well against the corporate ones: open-source foundation models in biology having a very broad impact.<\/p>\n
You\u2019re involved with Genesis Therapeutics, which came out of your lab at Stanford, and Insitro, the drug-discovery company launched by Daphne Koller, a former colleague at Stanford. You say you\u2019re also incubating a company with a founder you\u2019ve known for 20 years. What are you looking for in founders, and in what areas?<\/strong><\/p>\nThere are two areas that I\u2019ve been spending most of my time on. One is AI for healthcare delivery, which I did a ton at a16z as well, and then AI for clinical trials. <\/p>\n
One of the things that\u2019s most important to me [about founders] is that we can really trust each other \u2014 founders that have high integrity, that do what they say they\u2019re gonna do\u2026 I\u2019m expecting this relationship to be 5, 10 years plus into, ideally, their next company. I want to work with people who are thinking long term like that. Ideally, these are people who are not just trying to win and beat other people, but really thinking about the question: how do we win together?<\/p>\n
What have you gotten right and wrong in your investing career so far?<\/strong><\/p>\nWhen I started talking about AI and machine learning and technology and medicine and bio 10 plus years ago, there was a lot of resistance and a lot of people saying, \u2018Oh, that\u2019s never going to happen.\u00a0That\u2019s\u00a0never going to be useful,\u2019 and so on.\u00a0That resistance is largely gone and seeing this arc is very fulfilling.<\/p>\n
I think 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. I tell my founders, especially the ones who are coming from the science or the product side, for them to take all their brilliance and creativity and really apply it to the go-to-market side, that the go-to-market part is at least as hard or harder than the technology side.<\/p>\n
Help us understand how you\u2019re designing this new firm differently, compared with what you were running at a16z.<\/strong><\/p>\nRight now, we\u2019re doing something really quite different\u2026 VZ is named after me, Vijay, and my co-founder, Zach Werner \u2014 he\u2019s the Z. We\u2019re intentionally really quite small\u2026 on the investment side, it\u2019s really just the two of us. We were actually intending on hiring associates, but it turned out, with the agents that we\u2019ve built up, not to be something that we need to do.<\/p>\n
How concentrated is \u201cconcentrated\u201d?<\/strong><\/p>\nWe\u2019re [not] driving 30 bets per year\u2026 we\u2019re talking about probably five, not a lot of investments \u2014 very concentrated. Adding a company at a typical fund is like adding a Facebook friend \u2014 that\u2019s something you do pretty quickly. For Zach and I, it\u2019s more like . . . wanting to have another child. This is a big deal for us.<\/p>\n
With that structure, who are you competing against for deals?<\/strong><\/p>\nThe funny thing about this model is that typically we\u2019re not trying to compete for a hot round \u2014 people make room for us. It\u2019s a very different thing than trying to get the hot Series A or Series B. Largely, people want us as investors because of what Zach and I can do, and how hands-on we can be. When I look at people who are inspirations, I look at someone like Antonio Gracias at Valor \u2014 he\u2019s well-known now because of the SpaceX deal, but he\u2019s been doing what he\u2019s been doing for 20 years. What Thrive has done, with a more concentrated portfolio, is also a real inspiration. Obviously, a16z is sort of in my DNA as well, but I think those other ones are new additions to how we think about things.<\/p>\n
What\u2019s overhyped right now in AI and biotech?<\/strong><\/p>\nThe reality is that AI can find insights that we can\u2019t get from just humans alone. The thing that always gets tricky is when there\u2019s this call that AI is going to cure all everything. The reason for hesitance there is not because of any doubt about AI \u2014 it\u2019s about doubt of the data. LLMs work because there\u2019s so much data to learn from. When the data is just simply not there, then AI can\u2019t magically solve that problem.<\/p>\n\n\n