How unfair is the coin?

Ankit Gupta

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How unfair is the coin?

Monday. January 06, 2025

I’m reviving my blog after some time away — it’s been an eventful 12 months. In February 2024, Reverie Labs, the startup I co-founded in 2017, was acquired by Ginkgo Bioworks. I’m now on leave from Ginkgo and I’ve joined Y Combinator as a Visiting Partner, giving me the chance to work with the next generation of companies. Especially in this new role, I’ve been thinking a bit about what worked, what didn’t work, and what lessons I can take forward.

We had quite the journey – 6+ years of building at the intersection of AI and drug discovery. We began as a machine learning driven software company selling SaaS tools and consulting services to pharma companies, and at acquisition we were a pharmaceutical company, developing our own in-house pipeline of drug assets and advancing them rapidly using our machine learning technology.

Reverie’s story parallels many other AI driven drug discovery companies from the 2015-2022 (i.e. pre GPT) era. Many of these companies probably started as software companies by computer scientists. My co-founder Jonah and I entered this field because we saw the incredible advancements in machine learning in computer vision and natural language from AlexNet and LSTMs, and wanted to see those advancements applied to human health. With essentially no exceptions, we all became pharmaceutical companies. In other words, the product of the company wasn’t a software suite being sold to a pharma company, but a drug to be licensed or marketed directly to patients. Many companies had (and some still have) intermediate business models where they developed partnerships with pharmaceutical companies in which they advanced the pharma’s programs and took on milestone-based payments. But essentially all of them eventually launched their own wholly owned programs and advanced them.

So why did this happen? Ahead, a few-part reflection on selling to pharma R&D, venture capital in biotech, and the math of unfair coins.

One of the earliest lessons of Reverie — we started to experience this during our early days of YC — is that selling to pharma was a highly counterintuitive process for us outsiders. Our intuition was that pharma was a multi-trillion dollar industry that spends hundreds of billions on cutting-edge R&D, and so this (incorrectly) implied there would certainly be a massive software procurement budget across these companies. In reality, pharma — like other massive industries including video gaming — doesn’t buy much software, at least not compared to the tech companies we had better intuitions about. The vast majority of pharmaceutical companies are using relatively few pieces of externally purchased software, especially for the key tasks of designing compounds. For medicinal chemists, the primary pieces of software are tools to track molecules (Benchling, CDD Vault, Dotmatics, etc), which generally look like a single enterprise contract to a company, or other software to visualize/draw molecules, which usually has a free or cheap alternative. Software to design molecules, like Schrodinger’s suite, is largely sold to computational chemists, who are much fewer in number than medicinal chemists and have relatively low buying power at most companies. In sum, it turns out the image we had in our head that the design process was deeply computationally driven was mostly not true — it was tracked in computers, but most small molecules were designed in chemists’ heads.

As a result, this meant that software for designing molecules had challenging unit economics. Oversimplifying slightly, there were usually two parties that one could sell to in pharma: IT procurement teams that were largely used to paying ~$10,000s to $100,000s for enterprise software licenses for the whole company, or business development (BD) teams that don’t buy software at all and instead in-license drug assets. With this context, it was difficult to pitch the kind of pricing we would want (and frankly need to get a venture return). We wanted to build software that would be worth millions of dollars a year to our customers, but this was difficult since it didn’t fit into either procurement model. Furthermore, the IT procurement teams could not decide themselves what design tools were needed (but they could for other tools like molecular tracking software), because that expertise lived in chemistry teams that don’t themselves procure much software. This created a painfully slow sales cycle in which it is hard to demonstrate value — BD people telling us they don’t buy software, the chemists we wanted to sell to not being empowered to buy software, the IT procurement teams being reliant on other teams to actually decide, and ultimately the budgets being very small.

So, this led to our first lesson: the importance of establishing a bottoms-up total addressable market (TAM) for a software product. In other words, how many...