insitro Founder and CEO Daphne Koller at Semafor Tech: First Principles
📅 Jun 15, 2026Semafor Events16 MIN40 SEGMENTS · 2 SPEAKERS
insitro Founder and CEO Daphne Koller joins Semafor tech editor Reed Albergotti at Semafor Tech: First Principles, hosted June ...
What Daphne Koller said
Written from the verified transcript and checked against it. Every figure links to the moment it was said.
Daphne Koller said the field is at a real inflection point because computational and experimental tools are far stronger than when she started. She argued that designing a protein with AlphaFold does not make it a drug, since for most diseases we do not know where to intervene. At Insitro, she said they built the capability to print biology at scale, having printed 22 billion cells to build an AI model for human causal biology. She picked ALS because of the enormous unmet need and their ability to revert diseased motor neurons to a healthy state. Koller criticized the US for not creating a national healthcare data resource, noting that only 22% of named diseases have an approved drug and that novel mechanisms pushed into the clinic annually fell from 100 to 30 over a decade. She said China executes faster by removing bureaucratic hurdles, and that the US could accelerate clinical development with AI and a national data commons. She dismissed longevity claims about extending maximum lifespan beyond 130 but highlighted their brown fat target for aging-related diseases.
Key takeaways
Insitro has printed 22 billion cells to build an AI model for human causal biology.
Only 22% of named diseases have any approved drug, and half of those are common diseases.
Novel mechanisms pushed into the clinic annually fell from 100 a decade ago to 30 now.
China can take a program into the clinic and collect human data in a journey that takes the US 6 years.
Koller is not convinced anything can extend maximum lifespan past 130, but health span extension is real.
Numbers and commitments
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What it refers to
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22 billion
cells printed at Insitro to build causal biology data
Yeah. So, you're one of the early pioneers in the use of AI in therapeutics and science. You now have a company. This is a hot area now. So, my question is: are we at some sort of inflection point right now, or are people just sort of paying attention?
I think it's both. People are absolutely paying attention, and even if you look at the big foundation model companies out there, everyone's like the killer app for artificial general intelligence or artificial super intelligence is going to be science. So people are paying attention, but I think there is a sense in which we are really at the cusp of a transformation because the tools that we now have available to us on both the computational side and the experimental side are so much stronger than what we had when I started my career in this space.
Yeah. I want to outline the problem here, because I could get a laptop and use AlphaFold to design a drug today. That doesn't mean it's going to become a drug, right? It doesn't mean people's lives are going to be saved.
I'm going to modify what you said and say that you can use AlphaFold or one of its children to design a protein. Whether that protein is a drug is a question that has multiple ramifications. Can you deliver it safely and effectively to a human is maybe table stakes, but the bigger question, the one we've set out to solve, is: even if you can take that protein, put it in a person, and it performs a certain biological function, does it do anything for the disease? For the vast majority of diseases, we have no idea where to intervene. So we can't even tell you what type of protein you need to design.
Let me change the question slightly. I can tell you what people are using it for and what I think they should be using it for and what we're using it for. There's a lot of work on exactly what you said: designing a molecule that performs a particular function, binds in this way to a target. That can be a small molecule or a biologic. We've gotten far better at designing those molecules and cutting the time from years to months to weeks, and arguably some might say hours. That's awesome. It shortens timelines, which is nice, but it doesn't answer the fundamental question of what is the right intervention. It's like human judgment. We have AI agents that can do exactly what you tell them, but knowing what to tell them is the fundamental problem that's currently rate limiting on humans and, in our case, rate limiting on collecting the kind of data that really disentangles the underlying biology of disease.
Absolutely. We picked ALS due to a set of considerations. The unmet need is enormous: people die within 3 to 5 years in a horrible death, and there's no therapeutic that meaningfully moves the needle. That's an obvious reason. But we also felt we had a discovery approach that could unlock differentiated insights for this disease. We were able to interrogate the biology of healthy and diseased motor neurons and revert a disease motor neuron back to a healthy state, where the definitions of healthy and diseased and the trajectory needed were revealed to us by AI methods looking at billions of these motor neurons. The opportunity was where we could bring disproportionate value using our discovery approach.
Koller, D. (2026, June 15). insitro Founder and CEO Daphne Koller at Semafor Tech: First Principles [Interview transcript]. Semafor Events. CEOInterviews.AI. https://ceointerviews.ai/interview/987093/
MLA
Daphne Koller. "insitro Founder and CEO Daphne Koller at Semafor Tech: First Principles." Semafor Events, 15 Jun. 2026. Transcript, CEOInterviews.AI, https://ceointerviews.ai/interview/987093/.
BibTeX
@misc{koller2026_987093,
author = {Daphne Koller},
title = {insitro Founder and CEO Daphne Koller at Semafor Tech: First Principles},
howpublished = {Interview transcript, Semafor Events. CEOInterviews.AI},
year = {2026},
month = {jun},
url = {https://ceointerviews.ai/interview/987093/},
note = {Speaker-attributed transcript with timestamps}
}
