“We’re not doing 30 bets a year”: Vijay Pande on betting small after running $4 billion at a16z
“We’re not doing 30 bets a year”: Vijay Pande on betting small after running $4 billion at a16z
“我们一年不会投 30 个项目”：Vijay Pande 在管理 a16z 40 亿美元基金后，为何选择“小而精”的投资策略
It used to be that Vijay Pande was better known in academic circles than investor circles. That changed pretty abruptly a dozen years ago, when Marc Andreessen and Ben Horowitz — who’d spent their firm’s first five years explicitly avoiding healthcare and life sciences — decided the category was worth betting on after all and handed the keys to Pande.
过去，Vijay Pande 在学术界的知名度远高于投资界。十二年前，这种情况发生了剧变：Marc Andreessen 和 Ben Horowitz 在公司成立的前五年里一直明确避开医疗保健和生命科学领域，但最终他们决定该领域值得投资，并将这一业务的指挥棒交给了 Pande。
At the time, he was a Stanford chemistry professor who was best known for building Folding@home, the distributed-computing project that turned millions of home PCs into a supercomputer for disease research. Over the next decade-plus, he grew a16z’s bet into a practice managing close to $4 billion.
当时，他是一位斯坦福大学化学教授，最出名的成就是创建了 Folding@home——这是一个将数百万台家用电脑转化为用于疾病研究的超级计算机的分布式计算项目。在随后的十多年里，他将 a16z 的这一布局发展成了管理近 40 亿美元资产的业务。
So it was somewhat unexpected when in June of last year, Pande walked away from it all to start something much smaller. In fact, his new firm, VZVC, 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.
因此，当去年 6 月 Pande 离开这一切去创办一家规模小得多的公司时，人们感到有些意外。事实上，他与长期投资者 Zach Werner 共同创立的新公司 VZVC，其核心策略是每年只进行少数几个集中投资，而不是几十个；公司没有投资助理，且日常运营高度依赖人工智能。
To learn more about Pande’s hard pivot, we talked with him this week about why he’s making just a handful of concentrated bets rather than spreading himself thin in the current market — and about one of the more interesting conundrums in AI-driven biotech: unlike text, biological data can’t 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? This conversation has been edited for length and clarity.
为了深入了解 Pande 的这次重大转型，我们本周与他进行了交谈，探讨了为何他在当前市场环境下选择进行少数集中投资而非广撒网，以及人工智能驱动的生物技术领域中一个有趣的难题：与文本不同，生物数据无法从互联网上抓取，因此几乎每家公司最终都不得不建立自己封闭的数据集。这对人工智能在医学领域承诺的进步意味着什么？谁又能真正获得这些数据？为简洁明了起见，本次对话内容经过了编辑。
You’ve said biology is moving from a “science of discovery” to something you can engineer. What does that mean?
你说生物学正在从一门“发现的科学”转变为可以被工程化的学科。这是什么意思？
For a lot of the way drugs have been developed, there was very much a fortuitous aspect to it. I think what’s shifted is that AI and machine learning allow computers to wrap their type of understanding around something very, very complicated… 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 — which are the most expensive part of the process.
在药物研发的漫长过程中，很大程度上带有偶然性。我认为现在的转变在于，人工智能和机器学习让计算机能够对极其复杂的事物进行理解……从而尝试找出针对特定疾病的药物靶点，制造出这些药物，甚至现在还能辅助临床试验——这是整个过程中最昂贵的部分。
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.
我以为临床试验正在变得更便宜，因为药物开发商正在使用更多的合成数据，所以这些试验不需要那么多人了。
That’s, I think, very much an aspiration. 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.
我认为这在很大程度上还是一种愿景。进入临床试验的成本和时间确实在缩短，尤其是在人工智能的帮助下，但进行一项试验仍然可能耗资数亿美元，这就是药物非常昂贵的原因。一种药物从一期临床试验成功走到三期结束的概率仅为 20%。如果 10 个项目中有 8 个失败，而每个项目又耗资数亿，那么分摊成本就会变得非常高。
The reason they fail typically is not that the biologist did something wrong; it’s 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’s going to be way better than any animal model would be, and once it crosses that bar, that’s where it gets really exciting.
它们失败的原因通常不是生物学家做错了什么，而是因为这些药物的设计实验都是基于小鼠等动物模型进行的，而归根结底，动物模型对人类的预测性并不强。人工智能模型虽然不会完美，但它会比任何动物模型都要好得多，一旦跨过那个门槛，事情就会变得非常令人兴奋。
[The phase after that is]: Is the drug the right drug for me? You mean personalized medicine.
[接下来的阶段是]：这种药适合我吗？你是说个性化医疗。
The jargon here is so-called precision medicine. If you go to a doctor with something not trivial, they have to guess what’s going on, because there’s only so much they can tell. Then they give you a drug — and if that doesn’t 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.
这里的术语叫“精准医疗”。如果你因为一些非轻微的病症去看医生，他们必须猜测发生了什么，因为他们能掌握的信息有限。然后他们会给你开药——如果没用，他们会换另一种药，再换一种。这种情况在癌症治疗以及许多其他领域都会发生。如果我们第一次服用的药就是对症的，那对我们所有人来说都会好得多。
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’re starting to do also on the medicine side is [the ability] to just understand what would be right for the individual.
通常，你的血液检查值是与人群平均水平进行比较的。但实际上，它们应该与你个人的基准进行比较：这个结果对你来说是否异常？我们在医学方面也开始做的事情，就是能够理解什么才是适合个人的方案。
Would you say the path to this moment has been slow and steady, or did it spike more recently?
你认为走到这一刻的过程是缓慢而稳定的，还是最近才出现爆发式增长？
I think it’s lots of different things [coming together]. So for instance, precision medicine for the longest time was based on genomics. But the reality is your genome is kind of like the blueprint for your house on day one, but your house is fairly different now 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.
我认为这是许多不同因素共同作用的结果。例如，长期以来，精准医疗一直基于基因组学。但现实情况是，你的基因组就像是你房子建成第一天的蓝图，但现在的房子与刚建成时已经大不相同了。因此，人们现在可以在蛋白质组学等方面测量许多其他指标，这些指标对于理解疾病以及你身体目前的状况要相关得多。
There has also been [a lot of] automation in robotic measurements that is naturally tied into AI, and those two go hand in hand really well. Over the last decade, there’s been this steady clip for 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 come up with a drug to go after that specific protein? There have actually been very significant advances over those 10 years.
此外，机器人测量领域的自动化程度大幅提高，这与人工智能自然地结合在一起，两者相辅相成。在过去十年里，人工智能在生物学和化学领域的应用都保持着稳定的增长。生物学部分关注的是：我们如何治疗这种疾病？而化学部分关注的是：我们如何研发出一种药物来针对特定的蛋白质？在这十年里，实际上已经取得了非常显著的进展。
You mentioned that biology is one of the few places AI can’t just scrape data off the internet. What does that mean for how the field develops?
你提到生物学是少数几个 AI 无法直接从互联网上抓取数据的领域之一。这对该领域的发展意味着什么？
It’s a place where you don’t have any of this data that people can just all train the same thing, and your data can’t be distilled from one model to another. It’s a really interesting play from just the pure AI sense.
在这个领域，你没有任何现成的数据可以让所有人训练同一个模型，而且你的数据也无法从一个模型提炼到另一个模型中。从纯粹的人工智能角度来看，这是一个非常有趣的博弈。
Doesn’t that echo a familiar problem in medicine, though — doctors operating in [territorial, often competitive] silos?
但这难道不是呼应了医学中一个常见的问题吗——医生们在各自的（领地意识强、往往存在竞争的）孤岛中工作？
You’re onto something really big here. Let’s say [someone] has some type of cancer, and it’s both an issue in oncology and endocrinology — those two doctors really don’t 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’t. It would be equivalent to having a team of the very best doctors all clamoring together in that moment.
你触及到了一个非常核心的问题。假设某人患有某种癌症，这既涉及肿瘤科也涉及内分泌科——这两位医生之间确实很难很好地同步。人工智能真正引人入胜的地方在于，原则上它可以成为所有领域的专家，并开始看到任何单一的人类无法察觉的东西。这相当于在那个时刻，拥有一支由最顶尖医生组成的团队在共同会诊。