Dwarkesh Podcast / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
23 published records
01 / belief
“You've got billions of base pairs to play with in terms of encoding all your logic. So I think that's ultimately how delivery will get solved.”
02 / commitment
“I imagine if we were to Rip Van Winkle ourselves into 2100 and wake up, the way we will be delivering these nucleic acid payloads is actually by engineering cells to do it, to perform this very ornate function.”
03 / belief
“I think that's correct. I think the stat is something like drugs are roughly 7% of healthcare spend.”
04 / belief
“Then we also have something more like value judgments. I believe that that transcriptome looks like a younger cell.”
05 / uncertainty
“I don't know about your audience, but you will. You can think about it like this.”
06 / belief
“I think we have similar optimization challenges with our genome as the parameters and evolution as the optimization algorithm.”
07 / belief
“I think that's a pretty good signal that actually there's a lot of gradient to climb here and that potentially for us, the end-state products we're developing in 2100 are more like synthetic genes that have never existed, rather than just compositions of the natural set.”
08 / belief
“If we think about the genome as a set of parameters and the optimizer is natural selection, then you've got some constraints on how that actually works.”
09 / uncertainty
“” I don't know if people usually put it this way, but it actually just seems extremely similar.”
10 / belief
“We don't actually use RL at the moment, so I don't want to overstate the level of sophistication we've got. But I think the general problem reduces down in a similar way.”
11 / belief
“AKA, maybe intelligence is easier than we think, and there's a bunch of contingent reasons evolution didn't churn as hard on this variable as it could have.”
12 / belief
“We also know that with very targeted edits, other groups have done this, folks like Luke Gilbert now at the Arc Institute, who I think of as one of the great unsung scientists of our time, have been able to make a targeted edit in a single locus and then show that you can actually make cells divide 400-plus times over multiple years in an incubator in the lab.”
13 / uncertainty
“I'm not sure how to understand this claim that we know how to engage with the right hook, we just don't know what that hook is supposed to do in the body. I don't know if that's the way you describe it.”
14 / prediction
“This year you spend $100 million training a model, next year $1 billion, the year after that, $10 billion. But it's one general purpose model, unlike, “We made money on this drug and now we're going to use that money to invest in 10 different drugs in 10 different bespoke ways.”
15 / evaluation
“Because mutations are going to be random and they're inherently small changes at the level of sequence at a given time, evolution needs a substrate where, in order to function effectively, these small changes can give you relatively large changes in phenotype.”
16 / evaluation
“If you just look at the rank-ordered list of what are the most frequently expressed genes in the genome by the count of how many mRNAs are in the cell, transcription factors are near the bottom. That means you don't actually need to get that many copies of a transcription factor into a cell in order to have benefits.”
17 / evaluation
“I would say–I don't have formal way of explaining this–if you were to write out a list of well-known targets that many, many folks would agree are the correct genes to go after and to try and inhibit or activate in order to treat a given set of diseases—and the only reason we don't have medicines is that we can't figure out a trick in order to be able to drug them—it's a fairly small list.”
18 / evaluation
“We're still differentiated because we do everything in human cells with the right number of chromosomes, whereas it's very common to do things in cancer cell lines which have 200 chromosomes.”
19 / evaluation
“If you take a selfish gene view of the world—that really this is the genome optimizing for the genome's propagation, it's not trying to optimize for any one individual—then actually optimizing for longevity is a pretty tricky problem because you have this nasty regularization term.”
20 / commitment
“For instance, I have dedicated my career now to working on epigenetics and trying to change which genes cells use because I think that explains a lot of it.”
21 / evaluation
“If these cells were not growing and they were not proliferating like mad, you probably would never be able to detect that you had actually found anything successful. It's only because success is easy to measure once you have it and—even being successful in very rare cases, one in a million—amplifies and you can detect it, that this was amenable to his particular approach.”
22 / prediction
“Helping prevent you from ever having to access the intensive healthcare system, something like an inpatient hospital visit, if you can prevent even just a couple of those visits over a long period of someone's life with a medicine like an incretin mimetic, like a reprogramming medicine that keeps your liver, your immune system younger, on net that actually starts to drive healthcare spend down because you're shifting some of that burden from the administration system to the pharmaceutical system.”
23 / preference
“” We only work on a few cell types at NewLimit because those are some of the only cell types today with which we believe we can get really effective delivery of medicines.”