Vijay Pande, which once managed nearly $4 billion in bio and medical investment business for a16z, has now shifted its focus to a smaller fund. According to him, the new entity, VZVC, makes only a few concentrated investments each year and no longer aims to spread projects across a wide range. Its daily operations also rely more on AI tools.
Pande was previously a chemistry professor at Stanford University and led distributed computing projects Folding @ home. After joining a16z, he led this institution into investments in healthcare and life sciences, and over the course of more than a decade, he grew the related business. Now, having left the large platform, he has co-founded a new fund with long-term investor Zach Werner. The strategy of the new fund is more focused, with an emphasis on projects with a high degree of certainty.
The investment pace is shifting towards concentration.
Pande indicates that the new fund will not engage in dozens of transactions in a year, but instead will focus on a small number of projects. He attributes this change to the current market environment and an adjustment in his personal judgment method; rather than diversifying investments, it is better to concentrate time and resources on a few long-term projects.
He mentioned that the new team does not have a large number of assistants or analysts in the traditional sense, and some of the daily work is assisted by AI. This also reflects that some early-stage investment institutions are trying to operate with a more streamlined organizational structure.
Follow AI for medical services and clinical trials.
In terms of investment directions, Pande is currently focusing on two main types of opportunities: one is healthcare services driven by AI, and the other is the application of AI in clinical trials. He believes that drug development in the past relied heavily on experience and trial and error, but AI and machine learning are now helping researchers to identify drug targets earlier, design candidate drugs more effectively, and improve the clinical trial process.
However, he also pointed out that clinical trials have not become significantly less costly due to AI. Although the time taken to reach the clinical phase is shortening, a single trial can still cost hundreds of millions of dollars, and the proportion of drugs that successfully progress from phase one to phase three is still relatively low. According to him, many failures are not due to operational errors by researchers, but rather because animal models have limited predictive power for human beings.
Pande believes that as long as the AI model outperforms traditional animal models in predicting human responses, there will be a more significant improvement in efficiency within the industry. This is also one of the reasons why he continues to bet on this direction.
Biological data remains a bottleneck in the industry.
Unlike internet text data, data in the biopharmaceutical field is difficult to publicly capture and uniformly train. Pande believes that this has led many companies to establish their own closed data systems, and as a result, data barriers have become a real constraint on the development of AI in biopharmaceuticals.
He also stated that the industry is gradually seeing larger-scale bioinformatics “maps” and foundational models emerging. If such open models continue to develop, in the future they may complement companies’ own systems, just like open-source large models do, and expand the coverage of AI in the medical field.
When it comes to choosing founders, Pande says that he values long-term cooperative relationships and credibility more. He is currently incubating new projects and continues to pay attention to entrepreneurs in the pharmaceutical and medical technology fields that he is familiar with, such as those from AI.
