Lucas Mair on AI, Robotics, and the Future of Fully Automated Biology | Frameshifts Episode #15

This week, my X feed exploded with news that Anthropic had released hardware standards that allow AI agents to operate lab equipment directly. Their new “Model Hardware Standard” or MHS, built in collaboration with the Howard Hughes Medical Institute, allows an LLM to operate microscopes, liquid handlers, robotic arms and dozens of other instruments that scientists use to conduct experiments and to build the future.Until now, there has been no standard way to connect an LLM to a physical instrument. Every time you want to connect two pieces of equipment together, you need to create a bespoke integration that requires some grad student to write custom software. And grad students are busy. So when a lab wants to increase throughput, the incentive structure is usually to just hire more grad students or pressure encourage everyone in the lab to work longer hours. And scientists already work much longer hours than other professions. The standard informal working schedule at many elite labs across the US and China is something close to 996, often for mediocre salaries in the range of $35-70k.Of course, most people know that it’s silly for humanity to send our best and brightest to spend the better half of a decade getting PhDs in advanced bioengineering just to spend most of their time as manual labour meat sacks moving microliters of liquids between plastic tubes by hand.The bottleneck in biology has always been the ability to perform rapid iteration experiments to collect real world data. Intellect has been abundant in science since the 1950s, and with the advent of super-intelligent models, it is already becoming commoditized. These days, what distinguishes Earth’s most impactful and highest throughput labs is largely prestige-driven meat sack talent acquisition, work ethic, pipetting technique and ability to fund expensive experiments.This week’s episode with Lucas Mair is all about what the world will look like when we realise that robots exist and start building accessible, intuitive software to allow them to perform closed-loop laboratory experiments.Lucas is a very German MD-PhD who took an unusual route into biology. Computer science first, then medicine, then neural engineering at TU Munich, and before any of that, building autonomous drone swarms for search and rescue. He thinks like a control theorist, and when a control theorist looks at how a modern biology lab actually runs, he doesn't see cutting-edge science. He sees a factory floor from 1975.Lucas will tell you that most labs already have an Opentrons or something similar gathering dust in the corner. My own lab had one that I have never seen anyone use.Grants pay for reagents and salaries, not for engineers to automate your protocols. Grad students are cheaper than robots, and publishing pressure means the three months you'd spend automating a workflow is three months you're not putting out papers, which is the equivalent of suicide by irrelevancy for many high-profile labs. Thus, the rational move, for every individual scientist and every individual lab, is to just keep pipetting. And so that’s what everyone does.Which is insane, because the robots already exist. We've had them for years. What we've been missing is the thing that turns "here's what I want to test" into machines actually doing it.The Anthropic news is a bit of a preview into what that might look like. But the reality of the current tech is a mixed picture. For example, scientists at Carnegie Mellon were able to use an AI agent with MHS to orchestrate a liquid handler, plate reader, robotic arm and monitoring cameras to get serial-dilution dose-response experiments done 3x faster.Meanwhile at Genentech, humans had to intervene when Claude used MHS to coordinate a BCA protein assay because the model mistook errors caused by bubbles for a software failure, and then responded in a way that produced yet more bubbles.Lucas Mair looks at biology as a control and engineering problem. You have a complex biological system, and you want to move it from State A to State B (e.g. diseased to healthy). But the problem is there is an unimaginably large search space of possibilities. For example, there are 2020 or 104,857,600,000,000,000,000,000,000 ways of making a 20 amino acid protein sequence.Most of these sequences are useless but a few of them might help make humans immortal.One more thing to understand before you listen to the full conversation…If you’ve spent much time at all on the tech bro side of X, you’ll know about the scaling laws of large language models. Roughly s