Advice for Lab Automation Founders in 2026

I’ve had a lot of conversations with lab automation startup founders in the last six months or so that led me to notice some common threads in questions they asked or ideas they had. Automation startups in 2026 look very different from how they did a few years ago. There is an emphasis on building platforms as data factories for training frontier AI. There is a greater willingness to take risks on new technology like humanoid robots. Founders are far more likely to come from software and machine learning backgrounds and have less experience in hands-on biology and often none in wet lab automation. These common factors seem to have led to common blind spots, so here’s some general advice that I think people who are interested in the automation startup space could benefit from.

The field is very fragmented in a way that is not obvious, and this will shape most interactions you have with people already in the industry. 90% of people in the industry have worked with a set of software tools that a typical 2026 founder will likely identify as extremely inefficient and outdated, and should probably be recreated from scratch. The inherent tradeoff that has yet to be reconciled is that the people who know how to walk up to an automated platform and get an assay validated efficiently are generally not familiar with anything beyond traditional drag and drop interfaces. As a 2026 lab automation founder, you might be tempted to leverage the inherent efficiency of modern software to compensate for a deficiency of hands-on knowledge in assay development. You might posit that assay development just sounds like hill climbing an objective function, and that this is basically a solved problem in an algorithmic sense. Two caveats make this far less promising than you might expect. The first is that biology is expensive. Reagents are expensive, cell lines are expensive, experiments take a ton of time, and since time is money, experiments are expensive. In biology, you basically have to few-shot every experiment based on prior knowledge or you will run out of money. The second is that the number of degrees of freedom that might matter for your experiments are immense. Everyone in biology knows that the shape of the wells of your 96-well plates matter a lot depending on the type of experiment (did you?), but fewer know that dispensing to the side of a well can result in better performance than dispensing to the bottom, and hardly anyone can articulate an explicit model for how exactly to do that in an optimal way. There are probably a thousand of these unspoken tradecraft nuances that you won’t know unless you’ve spent many hours holding a pipette or watching a liquid-handler. 

These factors mean that domain expertise is essentially irreplaceable in lab automation given current agentic capabilities. Even if you had a really good camera aimed at just the right angle at a pipette channel aspirating liquid, Claude still doesn’t know what a good aspiration looks like, or what parameters to tune if it looks off. The dilemma is that you, the 2026 lab automation founder, probably want to use Python interfaces and agentic tools, and the people who know how to make experiments work mostly do not. In my opinion this problem has a very straightforward solution. You should treat the traditional engineer as your customer rather than your adversary or critic. They are just as aware as anyone that drag and drop GUIs from Windows 98 are very bad, and I have no doubt that any of them would immediately switch if they had access to a tool that let them do their job better without having to spend a ton of time learning how to use it. One point of confusion here is that nobody even has to learn Python anymore, since LLMs can write it for us, so the switching cost to a pure Python interface is trivial. But liquid-handling robots are extremely expensive and powerful pieces of physical equipment running delicate assays, so the proposition of a responsible engineer executing an experiment via a script written in something that is to them a foreign language is null. 

Solving lab automation is still a software problem, but it is less of a “use agents to control robots and run experiments” problem than it is a “deliver design tools that help engineers do their jobs 1000x faster” problem. What’s remarkable is that almost no one is working on this. We are going to see billions of dollars invested into automation-first biology companies in 2026 (e.g. companies branding as cloud labs, self-driving labs, and so on), all of which will invest in absolute state of the art compute infrastructure, instrumentation, and personnel, but they have paid little attention to building an interface layer that accelerates the practice of automation itself. There are a few startups that are trying to address this, like by building out ways to translate protocol intent into experimental code, or digital twins that capture experiment runtimes. I think these are potentially good products (I haven’t used any), but I anticipate that founders might be pulled in many different directions that cause them to lose sight of the ultimate benchmark: What tools can we create that help engineers deliver validated protocols more quickly and efficiently than existing tools can?