This week, I’m back to exploring why biotech startups seem to have such a hard time finding off-the-shelf software that they need, and end up building their own tools from scratch instead. Two weeks back, I explored a general perception that there just isn’t good software available for biology/biotech, which discourages teams from looking too hard for it, or at all. That’s an outward-facing bias. This week, I want to explore an inward-facing bias: The perception many teams have that their approach is so unique that any general-purpose software simply won’t work.
To be fully transparent, I have fallen into this trap when I was working at biotech startups. Probably more often than I should admit. So this week’s newsletter is as much written to past me as it is to any of you, dear readers.
I’ve written before about the new wave of data-driven biotechs built using more of a Tech-sector model vs. traditional IT. This is a relatively recent phenomenon and in the early days, these companies lived in a very small field where it was easy to be unique. When Recursion was founded in 2013, they were probably a snowflake. But guys, it’s 2024. That was 11 years ago. Do you know how many startups are doing this kind of thing today?
Sure, the science is all different - or at least mostly different. Your startup has a unique angle on the biology. That’s important. That’s what investors care about. But when you strip that top layer away and look at how you collect, organize, analyze and even visualize data… Let’s just say there are a handful of patterns that most biotechs follow.
Discrete readouts from the lab that need to be combined with plate maps or sample sheets, fed through primary analysis, then secondary and tertiary analysis, then colated with other readouts to drive the next round of decisions. There are differences in the assay protocols (though the underlying assays are used across lots of labs) and the later stages of analysis (though again, less than many teams imagine) and in how the combined outputs are interpreted. But that’s like a 5% difference, max.
There are a lot of reasons that biotech teams, particularly startup teams, want to believe they’re unique. As humans, this is important to our self image. If you went to graduate school or beyond, you were trained with the academic mindset that novelty is what matters. As a VC-funded startup, you need to be able to tell investors why you’re different from everyone else.
But even snowflakes aren’t completely different from each other. They all have six points, and if you look at enough of them you’ll see a lot of suspiciously similar ones. So even for snowflakes, unique doesn’t mean 100% unique. It could mean 5%. It could mean less. So maybe all biotechs *are* in fact snowflakes - we just need to reconsider how we think about snowflakes.
Your startup is unique in ways that matter. In ways that will determine whether you succeed or fail. But almost certainly not in ways that will prevent you from finding off-the-shelf software that makes your life significantly easier. So when you think about your data infrastructure, instead of focusing on the 5% (or less) that makes you unique, let’s start talking about the 95% that’s shared.
Thanks for reading this week’s Scaling Biotech! I really appreciate your continued support, and I read every comment and reply.
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I think in some cases it’s that academic mindset of needing to do some novel. Sometimes people changing existing (possibly disparate) solutions together can be novel. I think another aspect is people don’t due enough due diligence. My mindset is generally I’m sure someone else has solved it (there are after all 8B people on this planet)