Why were you drawn to join Celesta? What interests you to become more involved in the world of venture investing now at this stage in your career?

The world of venture is really nothing new to me. I go back many years in the industry, starting when I was at Stanford as a more junior professor, working as a consultant at Genentech, and more recently within private equity. I'm a scientist, which makes you very entrepreneurial in many ways. Startup founders, like most academics who are creative, also want to see their inventions develop into promising real-world outcomes.

What I love about the entrepreneurial world is the chance get deeply involved in many different areas with the companies we invest in, which can be quite different from the very deep and focused nature of lab research. It’s very stimulating trying to help a team solve complex issues and address a variety of different challenges. I first experienced that, as the Chief Scientist at GE HealthCare, which was a fabulous opportunity to appreciate the breadth and the entrepreneurial spirit of working with many scientists and engineers across disciplines.

I particularly value the way Celesta approaches opportunities, which is in an open-minded way, but with strong domain and deep tech expertise. And to the extent that I can contribute to that from my own background and experience and interest, I'm delighted to be able to do more of that.

How will you work with portfolio companies in your Senior Advisor role?

I'm working now with a couple of our portfolio companies, Prellis and Atonarp, serving as a board member. But because these are early, strongly science-based companies, I get involved with the scientific team members. From the Celesta side, I’m able to offer deep level of knowledge and experience as a biological scientist. I am able to get immersed with the team at a technical substance of the company, as well as its business strategy, and offer counsel wherever they need it.

Navigating the move from lab research to the commercial side can be challenging for academic founders – what do you think is important to do it successfully?

Its not easy to do. I do think it's a rare intersection of qualities to be able to understand business strategy, including the investment cycle and the product cycle, and at the same time be able to contribute in inventive ways that relate to the science. Acquiring all those skills comes through diverse, accumulated experiences. From my own experience,  I’ve been a biochemist, that's what my PhD is in. But in academia, I've also been a physiologist, a cell biologist, a neurophysiologist. And in industry, I've had roles in leading an engineering lab at GE Global Research.

When I started out as a scientist, I didn't know what a P&L was or how to read a balance sheet – no one teaches you that in the lab. When I was at GE, for example, I was involved in acquisitions, and we did a lot of diligence support. More recently, I've also been working with a private equity firm and learned a good bit about structured finance. If you pay your dues, listen carefully, ask questions and you have some common sense, then after a while your skill set expands.

What do you look for when getting a pitch from a young scientist or a founder?

There is a pattern recognition to be discovered when looking at these young companies. What I look for initially, more than anything else, is an original cool scientific idea. That translates into intellectual property, which is very important practically. But at the end of the day what you’re really looking for is a strong, transformative idea that gets you excited, which wasn't obvious, and has potential to generate real value.

Then, you must have an entrepreneur, a scientist, who can communicate that idea with passion. Someone that is intimately involved with how the idea can become a business. This idea should add value in a profound way to a marketplace, and that marketplace has to be demonstrable.

It can sound trivial because of course you're looking for passion, for an original idea, an unmet need – these are all obvious. But somehow this is where the pattern recognition comes in, spotting this magic combination when you see it.

I find every time we look at a company, I've got nothing but admiration for the entrepreneurs because they're all in. Whether we invest or don't invest, we're learning something. It makes us better investors and decision makers as a result.

 We are seeing a lot more engineered collisions in life sciences startups, where technologies are merging with biological sciences. Doe this ‘bio-convergence’ trend feel to you like a new phase that is accelerating biotech innovation?

Yes, I do think we're in an era of bio-convergence that will be profound. The latest addition to that – no doubt overhyped but still very real – is AI. One of the things that I have always found attractive about the Silicon Valley version of biotech, is that it has a unique vigor the academic research institutions collaborate so closely with the tech industry and investors. Those two cultures come together in remarkable ways.

I think AI may become a third leg on that stool which will allow the same kinds of breakthroughs to happen but much faster. AI is infusing everything in ways that we don't even fully appreciate yet, but it's entering areas like product development and health care data in powerful ways.

What would you point to as the areas of highest potential impact for startups entering this space?

Anything that involves pattern recognition is low-hanging fruit for AI, but you need to be able to access the data. Radiology has got to be at the top of the list. I think the main issue with many of these will be developing sustainable differentiation, as these areas will be easy to become crowded and commodified.

The single biggest opportunity in medicine is, of course, general patient records – integrating pharmaceutical and clinical records with biology coming out of the lab. Everybody wants it, the payors, the providers, the drug developers.  But no one has really yet cracked the problem of how to access all the data. Partly because of restrictions on personal data, partly because of the many ways in which data is stored and the many places it's stored. But that problem will someday be cracked. And when it is, that's going to be the single biggest revolution.

What do you highlight for founders in this space to think about in order to build strong, enduring businesses?

 One of the biggest challenges the life sciences companies we invest in encounter is the interface with commercialization. Typically, companies will start with a strong engineering base, but maybe not enough of a biology base. You must somehow change the culture of the organization to actually emphasize and integrate both. That's surprisingly difficult to achieve.

Equally important are sales and marketing. I don't think there is a biotech company that doesn't ramp up that side a bit too late. That is really where a board can be helpful in trying to direct resources towards those areas at the appropriate stage. You don't want to invest in commercialization too soon. But on the other hand, you don't want to lose your market opportunity or be undisciplined in the way you go about it.

There is another element I've seen repeatedly in small companies, which is systematic underestimation of competition. Founders rightfully motivate their teams by championing their ideas and developing an ethos that motivates them to think they’re creating something singular. But ‘we're the best’ is not the same as ‘we're the only’. I observe a systematic tendency to overlook the importance of obtaining sufficiently disciplined information around competition and anticipate the best approach to the marketplace. You can’t separate the two; in order to reach and maintain the pinnacle, you have to understand your competition intimately.

‍