Jacob Kimmel thinks he can find the transcription factors to reverse aging. We do a deep dive on why this might be plausible and why evolution hasn’t optimized for longevity. We also talk about why drug discovery has been getting exponentially harder, and what a new platform for biological understanding to speed up progress would look like. As a bonus, we get into the nitty gritty of gene delivery and Jacob’s controversial takes on CAR-T cells. For full disclosure, I am an angel investor in NewLimit. This did not impact my decision to interview Jacob, nor the questions I asked him.𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒TranscriptYouTubeApple PodcastsSpotify𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒* Hudson River Trading uses deep learning to tackle one of the world's most complex systems: global capital allocation. They have a massive in-house GPU cluster, and they’re constantly adding new racks of B200s to ensure their researchers are never constrained by compute. Explore opportunities at hudsonrivertrading.com/dwarkesh\* Google’s Gemini CLI turns ideas into working applications FAST, no coding required. It built a complete podcast post-production tool in 10 minutes, including fully functional backend logic, and the entire build used less than 10% of Gemini’s session context. Check it out on Github now!* To sponsor a future episode, visit dwarkesh.com/advertise.𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒(00:00:00) – Three reasons evolution didn’t optimize for longevity(00:12:07) – Why didn't humans evolve their own antibiotics?(00:25:26) – De-aging cells via epigenetic reprogramming(00:44:43) – Viral vectors and other delivery mechanisms(01:06:22) – Synthetic transcription factors(01:09:31) – Can virtual cells break Eroom’s Law?(01:31:32) – Economic models for pharma Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
Full Episode
Today, I have the pleasure of chatting with Jacob Kimmel, who is president and co-founder of New Limit, where they epigenetically reprogram cells to their younger states. Jacob, thanks so much for coming on the podcast. Thanks so much for having me. Looking forward to the conversation. All right.
First question, what's the first principles argument for why evolution just like discards that so easily? Look, I know evolution cares about our kids, but if we have longer, healthier lifespans, we can have more kids, right? Or we can care for them longer. We can care for our grandkids.
So is there some pleiotropic effect that anti-aging medicine would have, which actually selects against you staying young for longer? Mm-hmm.
Yeah, so I think there are a couple different ways one can tackle this. One is you have to think about what's the selective pressure that would make one live longer and encode for higher health over longer durations. Do you have that selective pressure present? There's another, which is are there any anti-selective pressures that are actually pushing against that?
And there's a third piece of this, which is something like the constraints of your optimizer. If we think about the genome as a set of parameters and the optimizer as natural selection, then you've got some constraints on how that actually works. You can only do so many mutations at a time. You have to kind of spend your steps that update your genome in certain ways.
So tackling those from a few different directions, like what would the positive possible selection be? As you highlighted, it might be something like, well, if I'm able to extend the lifespan of an individual, they can have more children, they can care for those children more effectively, that genome should propagate more readily into the population.
And so one of the challenges then, if you're trying to think back in sort of a thought experiment style of evolutionary simulation here, would be what were the conditions under which a person would actually live long enough for that phenotype to be selected for and how often would that occur?
And so this brings us back to some very hypothetical questions, things like what was the baseline hazard rate during the majority of human and private evolution? The hazard rate is simply, what is the likelihood you're going to die on any given day? And that integrates everything.
That's like diseases from aging, that's getting eaten by a tiger, that's falling off a cliff, that's like scraping your foot on a rock and getting an infection and dying from that. And so from the best evidence we have, the baseline hazard rate was very, very high.
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