Shelby Newsad on the Venture Thesis for Autonomous Science

Introduction

In this episode, I sit down with Shelby Newsad, who invests in early-stage deep tech at Compound VC, a venture firm built around thesis-driven research, to discuss how AI is changing the way we discover and commercialize new therapeutics and materials.

We talk about the limits of in silico research, what makes a data or hardware moat defensible, and how Compound thinks about value capture in autonomy-driven platforms. Shelby also shares lessons from the recent Autonomous Science Workshop she organized, international clinical trial strategies, and why she wants more founders building in biosecurity. Here are three takeaways from our conversation:

• Computer models are improving, but scale matters:
  AI models can now predict small molecule behaviors with near-experimental accuracy, offering major efficiency gains. However, for larger biomolecules like protein complexes, startups still need to generate experimental data, often via cryo-EM or wet lab work, to train and validate models effectively.

• Hardware is emerging as a durable competitive edge:
  Startups are pushing biology into the nanoliter scale using patented silicon wafers, enabling more compact and efficient experimental systems. This kind of hardware innovation underpins self-driving labs and creates durable IP moats that pure AI models often lack.

• Geographic and regulatory arbitrage is shaping biotech strategy:
  Rather than relying solely on U.S. trials, companies are strategically sequencing studies across jurisdictions to reduce costs and speed timelines. These moves, combined with FDA flexibility on foreign data, help de-risk development while keeping U.S. commercialization pathways open.

Transcript

Charles: Shelby, welcome to the show.

Shelby Newsad: Great to be here. Thanks for having me.

What makes Compound VC’s thesis-driven model unique?

Charles: I want to talk a little bit about Compound because it's fairly unique as a thesis-driven VC, which I find really more my flavor of actually engaging deeply with a research area. I'm curious, why do you think there aren't more thesis-driven VCs?

Shelby Newsad: That's a good question. I think it just takes a lot of time and it's just a really different structure to doing investment. Instead of being really numbers-focused and casting a wide net like a lot of VCs do, and how a lot of VCs train you to be, we intentionally take a step back from the noise and spend probably about half of our time on research and speaking to people that are academics. We use that lens to decide where we want to cast our more specific nets and investments in deep tech.

How do you explain the role of AI in transforming scientific discovery? (01:30)

Charles: That's awesome. And I'm excited to talk a little bit more about the investment side of the thesis, because we talk a lot to researchers who are doing really awesome stuff with AI in the lab, but it might look a little bit different out in the marketplace. So excited to jump into that a little bit more. But first maybe a more abstract question. I think a lot of people are still trying to grasp the conceptual metaphors for how to think about the role of AI and autonomy in science. We had things like the discovery of the microscope and how that changed the way that we do science. I'm curious, do you have ways of explaining or capturing how you see AI transforming scientific discovery, scientific research, and the scientific enterprise overall? One common metaphor is discovery-as-a-service, but I’m curious if you have others that you use.

Shelby Newsad: Yeah, discovery-as-a-service is really interesting and something target discovery companies actually service in drug discovery. There are interesting deals for structuring that. But yeah, I guess in drug discovery I think there's golden eggs to be found in population scale biobanks. And yeah, we have a company, Fearon, that aggregates population scale data and does target enrichment, helps with patient stratification. And their big learning is that when you're able to aggregate five million pairs of genotypes and phenotypes, you can actually predict disease onset for like 700 different diseases. You can have really discrete timelines for when adverse events happen.

I think a lot of the AI field is actually further advanced than what people in big industries and people buying services realize. I think a lot of people would benefit if they spent maybe one to two hours a week just reading AI papers to learn how much they can accomplish today. That would change the business model dynamics that we've seen companies struggle with.

What are the tradeoffs between in silico and experimental methods? (04:15)

Charles: Yeah, another lens I've been thinking about for autonomy, apart from personalization which has kind of been in vogue for a while, is abundance. Like you can have an abundance of data about yourself that you couldn't have before. But I think finding a needle in the haystack in another metaphor, like finding golden eggs. Perhaps another one of the roles that AI plays when we think about scientific discovery. To your earlier point about finding these golden eggs and the role AI plays, I would love to hear you walk through the role of pure, in silico companies that are using AI for in silico discoveries. To me, it feels kind of hard to build a moat around that. I’m curious how you all think about that versus experimental and hardware-driven approaches.

Shelby Newsad: Yeah, I think the great thing about working in venture capital or even being an employee at a few startup companies is that you're able to see a trend and then make investments on either side of purely in silico work or experimental approaches, and capture a data moat or a hardware moat for your company. That’s something we think about a lot at Compound.

On the purely in silico side, there are some interesting models with neural network potentials where the accuracy of kilocal per mole predictions is actually within experimental range. That’s something I haven’t seen other models achieve. But it's only for small molecules that you see neural network potentials reaching that accuracy. For larger biomolecules, there’s still a need for experimental data. At what scale you need that is where we’re seeing a lot of companies differ.

I’m personally very interested in companies like Gandeeva Therapeutics and Generate:Biomedicines that are doing a lot of their own cryo-EM structures of proteins and trying to scale those structures to create their own proprietary protein databases, then using that to train better models. I’ve seen both approaches. The consensus answer is that you will need data for a long time. The non-consensus answer is that for small molecules interacting with confined protein regions, the data isn’t as necessary in the next three to five years. But for larger molecules, protein complexes, or cells, the need for data is still very much present.

Charles: That’s interesting. It sounds like what you're sketching out is that from gene therapies and small molecules up to large biomolecules, there are different levels of granularity available through in silico methods, compared to the resolution you may or may not get with experimental. And that trade-off determines when in silico provides a substantive value-add. Is that what you're saying?

Shelby Newsad: Yeah, well put.

Charles: That’s a really interesting way to think about it, especially not just in bio but across different fields: when in silico methods make sense versus when you need something like self-driving labs. I know we’ve talked about that a lot. I guess the flip side of the in silico question is when is hardware a moat, and what kind of hardware is a moat?

When does hardware become a durable moat in scientific innovation? (08:15)

Shelby Newsad: Yeah, we've seen various different companies that are creating IP around different microfluidic systems where they can engineer cells on a chip. Twist Bioscience actually has on their silicon wafers and in their patents that they can get down to nanoliter-range droplets, which is pretty incredible because most biology is done in the microliter range. The fact that they’re able to go down a few orders of magnitude is amazing. I wish there were more hardware people interested in biology because there’s a need for better systems of moving liquids around.

Something we need to talk more about with self-driving labs is: what if these labs are really just silicon wafers or CMOS chips where we move liquids around with currents and do reactions inside them? Maybe that can all happen in a two-foot by two-foot box on a benchtop, instead of automating a whole lab space.

Charles: For a lot of these microfluidics, the key value add is high throughput. AI and autonomy become a wrapper around that — or around the models — but the data moat is being generated by a new kind of hardware. That’s how I think about that kind of play, and I agree it’s really durable, especially if they can map those results into proxy variables used in clinical trials.

Shelby Newsad: I was just wondering how you think of hardware versus chemical versus AI moats for companies building in 2025?

Charles: Everyone is looking for more hardware people. I think hardware is a more durable moat, especially with so much churn in AI. That’s generally where I lean, which is again why self-driving labs matter not just as a hardware play but because the hardware enables you to generate experimental data. Microfluidics is interesting because it’s not just that it generates data. It’s also discovering new therapeutics through a different mechanism. That makes me more bearish on AI as a whole and more focused on autonomy.

Shelby Newsad: What company do you find most exciting in the hardware, materials, or chemistry autonomy space?

Charles: I think it’s great as a technology for R&D. It’s great for discovering new materials or therapeutics. But once you discover something, there’s a whole traditional pipeline. How do you commercialize it? The benefit of these autonomy technologies is that they speed up the front of the discovery funnel. But it’s not clear they capture value in the same way.

How do you think about value capture in AI-driven discovery platforms? (11:55)

So the question I wrestle with is: how do you capture value for autonomy? You generate a lot of data and find the needle in the haystack, but how do you keep the full value instead of spinning it off? For personalized medicine, there’s a clear investment case. For chemicals and some therapeutics, it’s less clear that you can fully capture the value.

Shelby Newsad: Yeah, we’re definitely taking the asset approach for a lot of our companies. A lot of them are platforms that capture value by creating assets and bringing drugs to clinics. Others try to license their golden eggs to pharma, land and expand inside bigger companies, and grow pilot contracts into ones that include royalties and milestone payments.

But I get the complexity. It’s not naive optimism. AbCellera is the canonical platform company that has dozens of antibodies in the clinic through partnerships. When they went public, public market investors wanted proof. So now they have their own pipeline. Previous platform companies like Nimbus Therapeutics had to bring a drug through Phase One before they got nine-figure contracts. So we’re not naive about how hard it is to change business models, especially in entrenched industries. But it’s worth experimenting, if golden eggs are materially easier to find and it doesn’t take six months but maybe just an afternoon, that changes the cost structure and who your customer base could be. It’s worth trying to build companies around that.

What’s the difference between materials and biologics in terms of value capture and discovery? (15:00)

Charles: Yeah, now is definitely the time to try. I’m also curious whether you see differences between materials and biologics in the discovery process and value capture. You mentioned royalties and partnerships, which are great in bio. But in materials, with different incumbents and capital flows, it’s less clear. Have you looked at material discovery, and how does that differ?

Shelby Newsad: Yeah, we have. One issue is that there are far fewer large chemical companies, and their margins are much lower than in pharma. So when they adopt new chemicals or improve processes, they demand really strict techno-economic analyses even at early stages. Even then, it resembles a pharma sales cycle. What’s more interesting in materials is looking at industries that need new chemicals but aren’t Dow or the big plastic companies. For instance, new chemicals are needed for data center technologies and infrastructure buildouts.

How does China’s faster clinical trial process affect U.S. biotech competitiveness? (17:00)

Charles: That makes sense. So for greenfield discoveries, it could really change the cost structure. Speaking of cost structure, how does US competitiveness stack up against China? There’s a lot of reporting that trials in China move faster. While the US bets on AI at the discovery stage, can China move faster through clinicals? That’s a real concern. I know some VC investments have been wiped out by Chinese competition.

Shelby Newsad: Definitely. A lot of the licensing deals for pharma molecules have been me-too molecules, not net-new. That gives us confidence that the US is still the center of innovation in biotech. The US’s role might also be advancing new modalities like better cell therapies or disease-specific biologics.

With the FDA changing animal testing rules, companies can do early trials in places like China where investigator-initiated trials can support IND applications in the US. That can materially de-risk programs. I see it as more of an opportunity than a threat. Companies like Insilico Medicine are doing that. They started with a first-in-human study in Australia for its clinical trial rebates, did Phase One in New Zealand for cost reasons, and are now in Phase Two in China. I just spoke with a founder yesterday who wants to do trials in Spain, where Western medicine is accepted but trials cost about one-sixth what they do in the US.

Charles: Sounds like there’s regulatory arbitrage. Between repurposing and new modalities, that’s where the advantage lies?

Shelby Newsad: Exactly.

What were your main takeaways from the Autonomous Science Workshop? (20:00)

Charles: You all helped organize the Autonomous Science Research Day. What were some takeaways?

Shelby Newsad: First, thank you for speaking. Your talk was excellent. The fact that your work pre-A Lab helped lay the foundation for the autonomous lab at Berkeley is incredible.

Charles: I promise I wasn’t fishing for compliments.

Shelby Newsad: The biggest takeaway is where we see AI actually influencing outcomes. That applies across chemistry and materials, protein design in Phil Romero’s group, and cell-level work at Jure Leskovec’s lab. His lab is doing perturbations and agentic workflows for lab automation. These papers get Twitter hype, but the upshot is that siloed fields now see use cases for autonomy at every level of chemistry and biology. Speakers made that point clear, from capturing visual data to building smart cages for scaled animal research.

Another insight is that some of this tech can be commercialized now, not five years from now.

Charles: For me, Olden Labs’ work on smart cages and autonomous science plus animal models was a new angle. I hadn’t thought much about that, but it’s exciting. Value capture aside, it’s clear autonomy will unlock scientific discoveries and R&D. Are there areas you wish more people were building in?

Shelby Newsad: We really wish more people were working in biosecurity. With measles outbreaks, avian flu in people without bird exposure, and long-term viral effects on neurodegeneration, it's clearly a longevity and national security issue. People are already using autonomous science to work with evolved pathogens. We’ve seen BARDA show some interest in pan-viral vaccine platforms. There’s room to position biodefense in ways this administration might support. I'm really excited to see more people build in this space.

Charles: Awesome. Thanks so much for joining us, Shelby.

Shelby Newsad: Exactly, yeah. Great. Appreciate you having me. Speak soon.