Could you please describe your path to venture capital, beginning at Cornell and ultimately ending up at JAZZ Venture Partners?
At Cornell, I studied life sciences, focusing on computational biology. Most of my background was in the lab and centered around science and technology. I had a childhood interest in software which I channeled into co-founding a startup in undergrad. I originally planned to go to grad school, to further my interest in computational biology, but because of my involvement in the startup I was interested in learning more about the business world, of which I had no formal education in. After Cornell, I worked at a large multinational company and started a second startup. During this period, I met some of the Lux Capital folks and they invited me to spend time with them and learn about venture. Through this experience, I realized that venture was the best way to translate scientific discoveries that touched people. From Lux, I joined as an early team member at an upstart venture firm, OUP, who specialized in the translation of scientific research from academia. I helped build that firm for many years, becoming an expert in the convergence of science and technology. This led me to explore increasingly creative modes of translating science, which led me to the JAZZ folks in 2020, joining as the first non-founding Partner.
JAZZ’s investment thesis targets technologies that fuel human potential. What sorts of companies service that goal, what particular needs do they have, and how does your firm service such a portfolio?
We think about human potential in a lot of different ways. Everything from productivity to improving how humans work, live, and play. In order to invest around human potential, we built a team that is inherently interdisciplinary and convergent. We’re experts at the intersection of science, technology and its interface/translation with humanity. In our view, other venture firms operate with partners who are hyper-focused in specific subject matters and industries. That model really doesn’t translate well for companies that are trying to transform the human experience. We built a firm where people have lots of different backgrounds, and work in an inherently cooperative fashion. We have people who previously founded video game companies, a former neurosurgeon, a repeat successful healthcare entrepreneur, a futurist, and people that come from AI/life science backgrounds (like myself). All of us roll up our sleeves for every single portfolio company. We are actually still entrepreneurs and incubate roughly 10% of our companies. Given the interdisciplinary experience we bring to the table, we can help companies in diverse ways. We’ve done everything from diving very deeply into product management to helping coach founders on empathy-driven storytelling. Our backgrounds allow us to be both broad and deep.
When you are looking externally, what are the most important factors you are looking for in potential founders?
We are really motivated in backing companies that are making a real difference through cutting-edge science and technology. We not only look for scientific and technological differentiation, but also substantial translation innovation. Other than that, we keep a very high bar for the founders that we back. While each Partner has different parameters that we optimize for, we highly prioritize grit, empathy, adaptability, mental robustness, emotional intelligence, and conviction. We don’t believe previous success or startup experience is necessary; rather, that an advantage comes from a unique perspective that may have been gained experientially. Whether they are a first-time founder or a serial entrepreneur, the uniqueness of their background and the perspective that gives them is what’s most important for us.
Are their trends in the spaces that Jazz is looking at that you are excited about?
We’re seeing some seismic shifts in how machine learning/AI are being translated and commercialized. AI is getting to a point where it can be generative and very powerful for automation and unlocking productivity. There are lots of investments in buzzy foundational model companies, but we think verticalization and highly specialized AI has the biggest potential for disruption. We’ve been investing in companies that are applying sophisticated AI to the automation of IVF, drug discovery, logistics, materials handling, and personalized medicine. We believe this will be a dramatic productivity growth driver for our society in the coming decades.
We’re also fascinated with the transition of life sciences from being an experimental science to an engineering discipline. We are seeing lots of innovation from the ability to read biological states to perturbing biological states to modeling biological states. This is effectively a bio-computer for much faster scientific cycles. Just like computing, we’re in the early stages of the transistor, but this area holds tremendous potential to speed up the pace of science and innovation.
Could you please discuss some of your recent additions to the portfolio?
I’d like to highlight two companies.
We recently led the Series A round for Form Bio. They supercharge the tools that computational biologists use, providing them with consumable and easy-to-use AI models that dramatically speed up the drug discovery process. Additionally, we believe there’s been a serious lack of enterprise-grade tools in this space. Form Bio was founded by experienced serial enterprise software entrepreneurs. Computational biologists were not getting access to the best practices and tools that software developers have become accustomed to. We believe the company could change the way we process, store, compute data for drug discovery, hopefully accelerating drug development.
We also invested in Enveda Biosciences, a contrarian drug discovery company. There have been a lot of computational drug discovery companies that traverse 1-5 billion compounds to see how they fit into druggable targets. That seems like a big number, but the available chemical compound space is 10^200; a drop in the bucket. Enveda saw this problem, and realized the way we are doing computational drug discovery today is somewhat biased. They took a first principles approach, and focused in on the fact that many plants have co-evolved with humans. Enveda scoured historical texts for all the plants that have been used with therapeutic benefit to populate their top of funnel for drug discovery. Their technology allows them to find the precise compounds in plant matter that have the therapeutic effect. This has allowed them to accelerate drug discovery and create a very robust and wide pipeline.