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I've started a new company called @tacitlabsco, an applied research lab at the intersection of AI and biology. 1/n
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Nicole Fitzgerald
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co-founder & ceo @tacitlabsco
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in April, we published a launch essay for @tacitlabsco
that outlined a thesis for frontier AI research in biology -
1. if we want models to be useful for curing disease, we must evaluate them on tasks with roughly that shape and complexity. optimize directly on the thing you care about - i.e. discovering & developing a drug
2. we should aim to model the biotech (process, reasoning, decision-making), rather than the underlying biology. biology is an empirical science - develop better empirical methods rather than treating it as an analytical domain
3. the most difficult part of all of this will be verification of those empirical methods with signal that actually matters (does the proposed drug actually work in a human?). biological time is a tough wall to run up against (most expensive forward-pass of all time). figuring out how to turn scientific problems into learning problems is a first-class research problem
4. if we believe (1) and (2), and we do a bunch of good work and figure out some clever solutions to (3) then we unlock increasingly agentic discovery/development, with the logical extreme being this concept of the "zero-person biotech", which is now (somehow) percolating in mainstream discourse
5. (4) has extremely exciting implications for the overall economics and practicalities of how we develop novel therapeutics (broadening viable aperture to rare disease, n-of-1 trials; new mechanisms of distribution) all of which are net positive for the world. much to be optimistic on here
discovering and developing a drug is one of the most complex and long-horizon tasks in the broad spectrum of human endeavors. inarguably one of the most important and high-impact research directions in AI today
that outlined a thesis for frontier AI research in biology -
1. if we want models to be useful for curing disease, we must evaluate them on tasks with roughly that shape and complexity. optimize directly on the thing you care about - i.e. discovering & developing a drug
2. we should aim to model the biotech (process, reasoning, decision-making), rather than the underlying biology. biology is an empirical science - develop better empirical methods rather than treating it as an analytical domain
3. the most difficult part of all of this will be verification of those empirical methods with signal that actually matters (does the proposed drug actually work in a human?). biological time is a tough wall to run up against (most expensive forward-pass of all time). figuring out how to turn scientific problems into learning problems is a first-class research problem
4. if we believe (1) and (2), and we do a bunch of good work and figure out some clever solutions to (3) then we unlock increasingly agentic discovery/development, with the logical extreme being this concept of the "zero-person biotech", which is now (somehow) percolating in mainstream discourse
5. (4) has extremely exciting implications for the overall economics and practicalities of how we develop novel therapeutics (broadening viable aperture to rare disease, n-of-1 trials; new mechanisms of distribution) all of which are net positive for the world. much to be optimistic on here
discovering and developing a drug is one of the most complex and long-horizon tasks in the broad spectrum of human endeavors. inarguably one of the most important and high-impact research directions in AI today
call your shotsView quoted post on X
>the danger is we become so obsessed with vacuum tube factories that we don't fund the transistors of biology
really well put, strong agree - higher signal, higher throughput measurements! will also see returns on intelligence innovating at the assay layer much more quickly than in robotics
really well put, strong agree - higher signal, higher throughput measurements! will also see returns on intelligence innovating at the assay layer much more quickly than in robotics
I agree.View quoted post on X
Robots are the non-biologist's abstraction of how scaling in biology scales. Imagine people in the 1940s pouring money 100x money into automating the vacuum tube factory than inventing the transistor. Imagine putting everything into giant CPU factories instead of inventing the GPU.
We are disproportionally thinking/resourcing innovation in biology as if it's car manufacturing. There is ample opportunity to improve, redefine and scale measurement in front of us.
I recently talked to someone at a robot-first science factory with near billion in funding boasting how they measure ~1000 mutants a week. A team of 4 at Dyno scaled the equivalent assay multiplexing by 25x. 1 person can run it. The robot is scaling the wrong layer.
I'm not against robots and I think they will have the role to play. But I think their impact on biology is overblown, especially on how it scales in performance per dollar deployed. The danger is we become so obsessed with vacuum tube factories that we don't fund the transistors of biology.
Complex physical value chains are somewhat path dependent, once you build a highway instead of a subway, you have to deal with a lower efficiency, higher pollution world. Most current robotic approaches are a crude, and poorly scalable, way to scale biology.
