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The Cognitive Revolution · 3 min · Oct 20, 2026
Health records alone may reveal correlations, not causes
Alex Reeves
Nathan LabenzHost · Host, The Cognitive Revolution
Founded AI video company Waymark and red-teamed GPT-4 before its release.
About this segment
Rives says health records can help match existing treatments to patients, but may not explain the biology causing disease. Biohub is focused on measuring those deeper mechanisms to enable better interventions.
From “A World Model of Life: Biohub's Alex Rives on Virtual Cells, Measurement, & the $1.8B Data Push”, Oct 20, 2026. Watch the full episode
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“because if you don't have the right basis, basically what you're going to learn is a bunch of correlations.”
Takeaways
- Health records can improve care with existing drugs.
- Patient histories alone may not connect outcomes to causal biology.
- Understanding disease mechanisms is key to new interventions.
In this episode
Full episode- 14:36Cell models need information-rich data and useful inductive bias5:10
- 26:12A laser phase plate reveals molecules inside living cells3:20
48:56Health records alone may reveal correlations, not causes3:12
- 5 more moments in the full episode
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 49:02
of that mix? Like, would you anticipate yourself, maybe you're already even doing this, I don't know, But, you know, kind of going out to EHR companies and trying to do deals to get, and I don't know the rules around this, I know it can be quite complicated, but like you don't want personal information, but you do need to like meaningfully connect a trajectory of a person's health story to like, you know, specific states of cells.
- 49:29
Like, does the regulatory environment allow for that? Can, you know, are there, are there other practical barriers that would stand in the way? Or do you think that isn't in fact going to be part of the story is sort of all these cells, but also linked to human health stories? Yeah. You know, to be honest, I haven't thought about that question as much. And so maybe, you know, why not? I think, I guess, you know, for one, I've been more focused at kind of trying to get to.
- 50:00
that fundamental level. And I think what I worry about if you don't have, I think about like kind of what's the right basis for solving the problem. And I think that because if you don't have the right basis, basically what you're going to learn is a bunch of correlations. And so what I would worry is that we're going to have tons of models of patient health histories, but we're not really going to have a way to link those back to kind of the deeper causal factors in the underlying biology.
- 50:40
And that's the part I think that we really have to be able to do if we're going to make this kind of step change advancement in both our understanding of disease, but also in our ability to treat it. So I think you can probably go a long way through health records and getting better care to patients, kind of getting the right drugs and mechanisms that already exist to the right people, maybe opening up people who might benefit from drugs that exist, but we kind of didn't know that that mechanism could be relevant there.
- 51:17
So I think there could be a lot of progress through that. I think that's a really important problem. But it doesn't get down to the fundamentals where I think you really have to make progress to kind of get this real step change. So, you know, Biohub is really focused on that part of the problem, like the fundamental science and how do we go to the point where, you know, there really is no disease anymore.
- 51:40
And that's, you know, the only way to get there is going to be by really focusing on the kind of the deep biology and kind of building the technology that can allow us to understand how that is determining for health and disease and, you know, how we can effectively intervene.