Will Alaynick on the Geometry of Life Science Tools
Founder and Managing Partner, Phase Two Ventures
Editor’s Note
Brave Builders is a series highlighting founders, scientists, and operators who are shaping the future of biotech with clarity and intention. The focus is on people who build with purpose, create durable value, and bring thoughtful leadership to a field that often moves faster than its underlying systems.
Dr. Will Alaynick is founder and managing partner of Phase Two Ventures. He is a scientist, entrepreneur, and investor dedicated to advancing life sciences through innovative ventures. With expertise in neuroscience and molecular endocrinology, he has conducted research in four Howard Hughes Medical Institute (HHMI) laboratories under the mentorship of four National Academy scientists. His academic contributions include more than 30 peer-reviewed publications in journals such as Cell, Science, and Neuron. Dr. Alaynick earned his PhD in Biomedical Sciences from the University of California, San Diego, conducting his doctoral and postdoctoral research at the Salk Institute for Biological Studies. He also holds a BA from the University of Washington in the History of Science, with honors coursework in organic chemistry and biochemistry.
Dr. Alaynick has founded and scaled multiple ventures bridging academic research and commercial success. As a co-founder of NanoCellect Biomedical, Arima Genomics, and Defined Bioscience, he led these companies from concept to global commercialization, securing significant funding through NIH grants, venture capital, strategic investments, and private equity. His expertise spans life science tools, reagents, medical devices, and molecular diagnostics.
Currently, as a managing partner at Phase Two Ventures, Dr. Alaynick focuses on early-stage investments in life science tools, technologies, and therapeutics. His background as a scientist-turned-investor enables him to guide emerging companies, fostering scientific breakthroughs that translate into real-world impact.
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What problem are you solving or enabling others to solve, and why does it matter right now?
My goal is to advance biomedical discovery and the development of therapies and cures for disease. At Phase Two, we have experience and expertise in the development of life science tools, such as better microscopes and sequencers, that provide the “picks and shovels” to scientists and engineers who are researching or manufacturing the next generation of products.
What do you think is most misunderstood about Phase Two’s approach to investing, or about the life science tools sector more broadly?
Phase Two’s approach is differentiated by our first-hand experiences as founders and operators, where we carried out all aspects of company formation, product ideation, scale-up, demos, sales, fundraising, intellectual property development, team building, and the hundreds of other daily challenges and rewards that one faces at work and at home. As a result, we are able to advise our portfolio company colleagues at any number of points with “been there, done that” real-world advice. We also understand the need to “let them run” and not distract them with too much well-intentioned input.
Life science tools are distinct from medical devices and therapeutics in that they 1) do not need as much capital, 2) have shorter development timelines, and 3) do not require FDA approval or reimbursement strategies. There are different scaling and commercialization rules of thumb in tools that I describe as a different “geometry.” Our experiences allow us to identify promising teams and technologies that match the timing of technology acceptance in their respective fields.
One reason I wrote Venture Capital for Life Scientists was to help founders better understand venture investors and to help investors better understand life science founders.
Where do you see the biggest gap between the value being created for the biotech ecosystem and how the venture industry currently measures success?
I don’t necessarily think there is a mismatch between value creation and venture measures of success. I would frame it as there being a spectrum of venture groups that seek out specific phases of risk and value. Seed-stage investors understand the challenges of early-stage companies and want to create value from zero to one. The next round of financing might be led by groups that feel most comfortable getting a product ready for manufacture or navigating an early adopter program. Later-stage investors may want to build out commercial teams once a product appears robust enough to be past the R&D stage. The biggest gap might be that founders need to find investors who value their stage of value creation. Founders need investors who understand the challenges of a stage and have the experience to help: kindergarten teachers and post-doctoral mentors have very different skill sets and measures of success—and both are needed.
Capital functions as a form of infrastructure in biotech. How do you think capital shapes what gets built, what scales, and what quietly disappears?
As a continuation of my previous point, capital infrastructure is not monolithic. The best chance of success comes when the stage and expectations of founders and investors are well matched. Frustration can arise if either side does not understand the path to get from milestone A to B or cannot communicate well enough to converge on mutual understanding and a plan. Companies that quietly fade away can suffer from this stage mismatch with investors, who then lose faith or patience. There are also company-intrinsic challenges where the technology, IP, or team hits a snag.
You’ve argued that the “AI J-curve is cost, not value.” In an industry focused on efficiency gains, how do you distinguish between tools that make research cheaper and platforms that genuinely open new frontiers of biological discovery?
The current AI state is a little like WebVan for groceries in the late ’90s. They were directionally correct that this would be a thing, but at least a decade too early. It required Amazon almost duplicating UPS and Instacart utilizing smartphones and the gig economy before this could be profitable. The current models are too expensive, and the inference is too expensive. So until we have another “tensor” or similar major inflection point on the technical side, and more paying customers, it is in a tough spot, heading toward a bottleneck event where only the hyperscalers that also have a profitable business, such as Microsoft, Amazon, Alphabet, and Meta, might be the only survivors.
For life science tools, the majority of work is incremental: more efficiency, lower cost. DNA sequencing progressed from gels to capillaries to sequencing-by-synthesis, as an example of a transformative breakthrough that eventually reached commoditization. In contrast, protein sequencing still needs a breakthrough that will allow similar scale. This holds greater promise to be transformative because proteins (and their post-translational modifications) are much more reflective of the current state of health or disease than DNA or RNA. However, the state of proteomics has not produced a breakaway technology winner like genomics saw with Affymetrix arrays and Illumina sequencing.
As for the use of AI to advance life sciences, the frontier models are (mostly) based on human language and visual media (still pictures and videos). For language models, humans invented language, and we understand when there is a problem with the spelling and grammar—and can usually check to see if references are real and the logic is rational. Similarly, we can tell if an image or video has an “uncanny valley” quality and is off in some way—although this is getting harder. However, humans do not speak in the language of DNA, RNA, proteins, or metabolites. Nor does our visual (or other) system allow us to look at tens of thousands of data points and determine meaning—our visual system is better tuned to seeing a face on a potato (pareidolia). So we will have a harder time knowing what to ask of AI systems and knowing when their feedback is helpful. Related to this inability to simply read or look at the results, AI advances in biology will have to carry out both in silico and in vitro, lab-in-the-loop experiments to ground predictions in actual measurements. It remains to be seen how wet-lab experiments can reach the trillion-parameter scale of dry-lab compute.
You’ve written about “Series First” as an evolution of early-stage financing. Why is this shift necessary now, and how does it change the kind of bravery required of early biotech builders?
There is no perfect form of financing, but the financing you can get is better than the financing you cannot. The Series First addresses the “1202 alarm” (Apollo moon landing, anyone?) regarding how equity is treated by the IRS under Section 1202 for Qualified Small Business Stock (QSBS). The SAFE is a Simple Agreement for Future Equity. Future equity may not be equity in the eyes of the IRS. So Series First could be a good tool to address this. Because the SAFE and Series First are both untested in the courts on this point, there is still uncertainty about which path to take. I would view the Series First as a fit for founders and investors who both have an early adopter mentality and who understand the risks of a new form of financing. One form of risk is that it will be a “non-vanilla” part of the story that every subsequent round will have to get comfortable with. Some investors only do convertible notes or SAFEs. Series First could be a new wave.
Across market cycles, what patterns repeat themselves most predictably in biotech investing, and which lessons does the industry seem determined to forget each time?
Biotech investing, like any other form of investing, suffers from overshooting both the excitement and the doom. Additionally, biotech investors, and the markets generally, have a hard time knowing where the top is or where the bottom is. Sentiment can become a self-fulfilling prophecy. A recent example is the run-up in biotech during early COVID, when positive sentiment was fueled by Operation Warp Speed delivering an RNA vaccine in record time. This was seen as an unlock for curing all diseases—without fully appreciating that vaccines are not cancer biology and Emergency Use Authorization will not apply. Then the markets irrationally overshot a drawdown. Yet on the ground, real advances in all aspects of biomedical research continued to steadily march forward.
Similarly, the current AI bubble is affecting some biotech valuations. If or when the music stops, we don’t know how uncoupled the valuations are from the fundamentals. This highlights that there are at least two classes of investments in biotech (and elsewhere). The first tries to be coldly rational and follow the fundamentals and future upside based on what we know now, with some X-factor for the “known unknowns” and “unknown unknowns” added in. The second acts much more speculatively and recognizes that real profits can be made in trading and betting markets as long as you can get out while enthusiasm maintains “meme stock” valuation. I’m being a bit hyperbolic for the sake of illustration, but these themes of “slow and steady” vs. “damn the torpedoes” reflect individual decision-making and societal behaviors that have been repeated from 17th-century Tulip Mania to GameStop and Tesla.
If you could apply a kind of “vital signs” test to the biotech ecosystem today, what is the one cognitive illusion we need to address to foster more mutual respect between builders and investors?
A problem with vital signs in medicine is that they can tell you if someone is sick or OK—but they cannot tell you if someone is exceptional. So one cognitive illusion or logical fallacy that I try to stay aware of is survivorship bias. It is easy to think that because Apple made the iPod or that 10x Genomics popularized single-cell transcriptomics, their paths were the only correct ones, or that those lessons could be repeated. One aspect of this is that we are barely aware of the “negative space” of all the other companies that did not succeed because they were too early, did not have the right team, enough funding, prevailing consumer sentiment, or any other intrinsic or extrinsic factor that can sink a business. It might be that if Blackberry had lived, it might have evolved into an equally successful product (probably not). And if 10x had not formed, maybe Bio-Rad would have advanced droplet-based PCR to transcriptomics (probably not). We can, of course, learn lessons from these, but we also need to be aware of the fact that they are a product of the circumstances of their time—that the needs, intellectual property, and teams were a perfect storm that may not be replicated. Those who can see the “vital signs” that map the strengths and caveats of these prior experiences onto a present opportunity would likely have a higher probability of success.
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