accumulation of crystallized intelligence that was produced by their own fluid intelligence
Essentially this isn't happening right now. I think it very likely starts happening within a few years, but will be slow, maybe slower than humanity, and as a result won't be able to quickly fix the problem of being slow.
"actual" AGI—the kind that probably doesn't already exist—the kind that has fluid intelligence and AI advantages for recursive self-improvement, which together make it likely to take over the world shortly after being created
Humanity should count as actual AGI, but it remains slow. Similarly, I think LLMs that do prosaic RSI of automatically building the next model (crucially including formulation of new RL tasks/environments/graders) will be actual AGI in the sense that they can (on their own, without humanity's input) eventually generate and accumulate as much crystallized intelligence as humanity would.
Yet there is a bottleneck of the speed at which they learn novel deep skills (generate and crystallize new pieces of intelligence), going from new RL tasks to new models to new teaching moments that inspire new tasks for the new models that are ready to use the opportunity. This keeps the process at the slow pace of building models rather than at the fast pace of generating tokens, and so AI advantages don't quickly snowball into fast RSI that changes the fundamental nature of the AI.
Eventually fast RSI happens, but it plausibly takes many years of slow-learning prosaic RSI (for it) to figure out how to make that happen, and humans could end up being faster at figuring it out. As with any basic research, there is no trend that meaningfully predicts how many years it takes. All this time, there's actual AGI of slow-learning model building that can figure out anything eventually, but only slowly. This AGI might be the lesser factor of danger in triggering fast RSI, compared to the vast amount of compute its usefulness finances, which enables ambitious experiments and rapid scaling of prototypes.
We have large gaps in performance between AIs and humans (sample complexity for learning, ability to generate novel good concepts)
Pretraining very sample inefficiently reconstructs cognitive skills that left evidence about their nature in the text. RL training can use relatively short formulations of tasks/environments/graders to generate even novel pieces of cognitive skills needed to solve the tasks (that the authors of the tasks didn't necessarily have, as they didn't necessarily know how to solve the tasks, or how to do so efficiently). In this way, RL training is both sample efficient (with respect to the short formulations of tasks, and the tiny insights each task promotes), and a way of producing novel pieces of intelligence that can be crystallized in the new models.
What's currently missing is automated formulation of new RL tasks/environments/graders in response to gaps in existing crystallized intelligence (in the current model) with respect to the situations/problems it comes in contact with. I estimate that LLMs of 2031 will be more capable than Mythos 5 by about 3x as much as Mythos 5 is more capable than Opus 4.5+ (strictly before Opus 5). This is likely a gap notably wider than the Sonnet-Mythos gap, above the current frontier models that are already in a weight class capable of producing strong technical results and spontaneous competent cyberoffense. Taking that further step to the models of 2031 is probably insufficient for leaps of insight that quickly show how to make RSI go fast. But this is very likely enough for a base model of 2031 to be sufficiently teachable (using RL tasks) to end up learning how to formulate new RL tasks/environments/graders on its own, starting the slow-learning prosaic RSI process of building the next model automatically.
Thanks. (I have read this with interest, but don't have much of an overall reply.) A couple comments:
Humanity should count as actual AGI, but it remains slow.
It's a weird middle ground, where it is fooming but pretty slowly. (I mean, as a human, I would think that, i.e. I would experience time on a scale that's somewhat faster than humanity's foom.) And it kinda only half has the A in AGI. If it fully had the A (or I guess, ISGI, in silico general intelligence) then it would foom much faster.
Similarly, I think LLMs that do prosaic RSI of automatically building the next model (crucially including formulation of new RL tasks/environments/graders) will be actual AGI in the sense that they can (on their own, without humanity's input) eventually generate and accumulate as much crystallized intelligence as humanity would.
Maybe kinda. But my guess is that this would be kind of like calling [hominid evolution by natural selection on genetic variation] an AGI, or calling [the ecosystem of the Earth throughout all of time] an AGI. I mean it would probably be extremely faster & scarier than evolution, but still.
Pretraining very sample inefficiently reconstructs cognitive skills that left evidence about their nature in the text.
It reconstructs a lot of them to a significant extent, but very much misses a lot of them to a significant extent, I think.
But this is very likely enough for a base model of 2031 to be sufficiently teachable (using RL tasks) to end up learning how to formulate new RL tasks/environments/graders on its own, starting the slow-learning prosaic RSI process of building the next model automatically.
This seems plausible-ish. I would weakly expect a lot of plateaus, e.g. due to highly correlated taste, but not strongly and I haven't thought about it much. (Maybe you're pricing that in to "slow".)
HIA may indirectly slow down AGI capabilities
Another potential route for this is that reprogenetics can help solve the fertility crisis, which can reduce the incentives to build AGI. My thinking here is that currently it's pretty risky to have children (e.g. if even one of them ends up unhealthy or "unsuccessful" / low status, it's a big drain on one's own finances/status/happiness) which I think contributes to low desire to have more kids in a lot of societies, and low average fertility is a motivation for building AGI in order to stop/reverse the resulting economic or civilizational decline. Reprogenetics could help fix this root cause.
Although I suppose this depends on reprogenetics being cheap enough. If it's too expensive (and can't be or isn't subsidized), it might have the opposite effect, where people feel like it's a de facto requirement of parenthood (e.g. they feel really bad about their unenhanced kids having to compete with enhanced kids) and opt out of having children because they can't afford it.
low average fertility is a motivation for building AGI in order to stop/reverse the resulting economic or civilizational decline.
Interesting. This seems plausible as a (smallish?) contributing factor. (Though I don't personally feel very grounded in what people actually say to themselves or each other about why they're building AGI.)
currently it's pretty risky to have children [...] which I think contributes to low desire to have more kids in a lot of societies,
This probably isn't the main cause, since I think it's probably a lot less risky today than at previous times in history (better nutrition, medical care, social safety nets, etc.). But it stands to reason that it's a contributing factor.
Incidentally, reprogenetics and more broadly the advanced reprotech that's involved, could help support fertility in ways beyond affecting the life outcomes of the child. For example:
reprogenetics being cheap enough
For several reasons this seems important, and I think it's achievable. The research is expensive, but once the methods are worked out, they probably can be innovated to be pretty inexpensive.
where people feel like it's a de facto requirement of parenthood
That would be a sad outcome. In the longer run, there's probably some restructuring of society that would be good, that takes advantage of material abundance to make it so that parents are reasonably assured that their children's lives will be well worth living, whether or not they use reprogenetics.
This probably isn't the main cause, since I think it's probably a lot less risky today than at previous times in history (better nutrition, medical care, social safety nets, etc.). But it stands to reason that it's a contributing factor.
Yeah it's a lot less risky today, but we're also a lot more risk averse when it comes to children (e.g. not letting them travel/play unsupervised like we used to). Also, this reason seems to have played a large role in my own family's fertility decisions.
we're also a lot more risk averse when it comes to children
Yeah sure does seem like it. I wonder why. It does seem bad for parents to feel like they maybe shouldn't have children because they might be held responsible for not doing everything right (though of course they should be held responsible for doing some basics right). I want reprogenetics to generally be taken as supererogatory, partly for this reason.
I don't think that reprogenetics would solve the crisis. IIRC (upd: link) Asian countries have the same problem of an overcompetition for prestigious jobs which are scarce almost by definition. Even if enhancing the kids was cheap and efficient (which I doubt, as detailed in my comment), competition would shift to a competition among the enhanced kids.
The fertility crisis in countries like the USA was described in five roundups by Zvi and has many other causes like housing (which is also a problem in S. Korea!), lack of couples, etc.
Even if enhancing the kids was cheap and efficient (which I doubt, as detailed in my comment), competition would shift to a competition among the enhanced kids.
When they enter the workforce, they would be mostly competing with older, unenhanced or less enhanced adults. Although I just realized this brings up another potential issue of parents waiting longer to have kids in order to get access to better enhancement tech, or being afraid of subsequent generations outcompeting their own kids.
This could be a somewhat sad dynamic. Though not that sad, and maybe even good, if the technology is actually following a very rapid increase.
For safety reasons, the strength of reprogenetics will most likely plateau after we get to "strong reprogenetics" (being able to nudge several polygenic traits by several SDs). Pushing too far on any polygenic score or on any trait carries big risks (too tall, too big of a brain, too high a metabolism, etc., would each come with health problems at some point). This Osborne effect should only persist while the derivative is substantial, and the higher the derivative is, the sooner the approximate plateau is reached.
More realistically, I think that in fact it's usually not a good idea to wait to have kids for that reason (and I don't especially plan to wait for that reason). Some reasons for that:
I think another way in which more children being born is civilizationally good and postpones AGI is that people with children are somewhat busy with children, and have their priorities shifted. A tail risk for you seems fine; a tail risk for your kids, less so. People think with clearer heads about risks to their kids, and so will be less likely to push for acceleration. Also, people having kids lowers the costs of other people having kids. We're in a kind of a crappy valley now with few people having kids, but if we bounce back to many people having kids, then the cost of childcare is lower due to huge economies of scale in having kids, and that helps a lot to motivate others.
Curated. The argument here is straightforward and seems basically correct to me. I think I have shorter timelines than Tsvi does, at least if not conditioning on a successful pause/stop effort, but as Tsvi says:
Working towards a pause / stop / ban on AGI progress is a top priority. But what is a pause for? We pause, and then what?
Really, most of the HIA has substantial impact even with short timelines section is important to understand, and should carry the argument even for people with pretty confidently short timelines. (Unless they disagree with "Very short timelines are pretty intractable." by way of things that can meaningfully be influenced by humans today.) The points at which people think marginal effort allocation to HIA stops making sense might differ, but right now, there need to be more people working on this.
Society has a terrible track record with this kind of technology (see what liberal eugenics ended up looking like in practise - it wasn't nazism, but it wasn't anything positive, either).
Human intelligence is a very complicated system, playing out over the course of an individual's life. All we have for the moment is evidence of small, local genetic effects. It would be a grave error to assume that these effects remain additive, without side-effects, when you combine them all. And measuring these side effects takes decades. Genetics isn't like physics: you can't go off-distribution (like sending a rocket to the moon) and assume the results hold up.
I think it wasn't a coincidence that liberal eugenics went so wrong. The theory overpromised and was unsound, especially as you got into more details. It also played to people's prejudices. This seems to be repeating the exact pattern - overpromising, unsound on the details, playing into people's prejudices. Of course you don't intend for things to go sour, and are advocating this for the best of motives - but the liberal eugenicists didn't intend that, either, and good motives don't guarantee good results.
At the very least, I'd want to see a detailed argument for why this won't go wrong the way it's gone wrong in the past. "This technology will only be used sensibly by sensible people under sensible governments" is not an assumption you get to make for free.
Society has a terrible track record with this kind of technology
I don't think it makes sense for society to write off a possible future technology because it shares some general ideas with a past evil. In particular, reprogenetics applied to diseases can alleviate enormous amounts of suffering and death--think something like preventing many millions of cases of disease, if the technology is adopted at scale. If you don't believe this technical statement, we could try to clarify that (though there are existing papers that would do a fine job, and a more comprehensive FAQ hasn't been written yet). If we admit the large upside, that's already plenty of reason to consider the technology more in depth.
To elaborate, first I want to distinguish three things:
Society does not have a terrible record with reprogenetic technology, because such technology has not existed until the past decade.
Society does have a terrible track record with the ideology of eugenics. One could generalize that, and say that society has a terrible track record with the general idea of somehow influencing heredity of future children.
However, this has a strange consequence of bundling genomic emancipation together with eugenics. Genomic emancipation negates the core idea of eugenics, and is therefore opposed to eugenics. Please see: "Genomic emancipation contra eugenics".
This bundling is, in this sense, incoherent. One reason you may nevertheless want to bundle these opposed ideologies together is something like this:
Unfortunately, it may not matter all that much what ideology you, Tsvi, have about reprogenetics. Reprogenetics is a tool that plays into the hands of the old and powerful ideology called eugenics. Even "genomic emancipation" takes on the basic ideas of controlling genes and reproduction in order to make "better people", and thereby lends support to eugenics. If lots of people support "genomic emancipation", this gives strong societal support to the idea that we can and should control the genes of future children; and this lends support to the idea that the government should police people's reproduction on behalf of the population, and that naturally leads to racist and coercive and otherwise creepy eugenical policies.
Or in your own words:
Of course you don't intend for things to go sour, and are advocating this for the best of motives - but the liberal eugenicists didn't intend that, either, and good motives don't guarantee good results.
I agree that this would be a bad outcome. I agree that it's a very legitimate concern. However, I want to largely refute this frame.
Of course, it's true that it doesn't matter too much what I personally do, at all. Insofar as it does matter what I personally do, though, my motives definitely matter, because they'll guide my actions and tell other people what to expect from my actions. But more to the point, I'm not saying "well, as long as I have pure motives, it'll all work out fine".
There is a fallacy here, but I'm not sure what the name for it is. It's something like the "action-counterfactual fallacy", or the "non-planning counterfactual fallacy", or the "non-agentic counterfactual fallacy", or "off-policy counterfactual" or "orphaned action" or something. In this fallacy, you argue against action X by saying "imagine action X is taken[, and everything else is held constant]; then the results would be poor". The problem is that this is the wrong counterfactual. The actual counterfactual should be more like "imagine action X is taken[, and also the rest of the plan that generated X is followed]; then such and such would happen". In this counterfactual, the results may be quite a lot better than in the fallacious counterfactual.
So, first of all, I'm not saying "I'll do reprogenetics, yes... but with good intentions!". I'm saying I'll do reprogenetics, and here's my philosophy, and also, here's a bunch of actions which I think will make this go well, which flow from that philosophy. Just a few examples of concrete actions:
You wrote:
At the very least, I'd want to see a detailed argument for why this won't go wrong the way it's gone wrong in the past.
Allow me to make another distinction:
You're asking for an argument for why this won't go wrong. I do think it is likely to go well.
However, I'm rather less interested in making a detailed argument for that, compared to the activity of constructing better ways to make it go well. That's because:
Here are some of the downside risks I see that seem plausible, AND related to eugenics, AND somewhat difficult to prevent through organized action (by the sector, by the public, and by liberal governments):
Overall these seem bad if they happen, but not so bad and probable that they are at all in the ballpark of making reprogenetics net bad in expectation. Some of them might be alleviable. (E.g. we could push for international treaties against coercive reprogenetics. I wouldn't especially hold out too much hope on that, given that China's abuse of the Uyghurs hasn't provoked enough response to stop it; but on the other hand China did crack down on embryo gene editing in the He Jiankui affair, seemingly in response to international pressure.)
Some other possibilities seem also not very likely:
I think the sector of reprogenetics does have various responsibilities. It has responsibilities to:
However, I don't view it as a responsibility of the sector to get everyone on board with reprogenetics before proceeding, or to police the behavior of the entire world with regards to novel reprotech.
For my ideas about how the sector of reprogenetics, and society at large, should comport themselves with regards to reprogenetics, please see "Some practical norms for good development of reprogenetics".
One might say:
It's all very well and good to have a whole plan. But you, Tsvi, aren't in charge of the world or society or the sector of reprogenetics. Just because you have these grand ideas, doesn't mean any of that will happen. There are forces beyond your control which may very well make this technology turn out to cause more harm than good.
To which I say, yes of course. I am not saying "everything's automatically perfect by default". I am also not saying "if only society did what I want, things would be great; therefore there's no problem with reprogenetics, just put me in charge". That's not the procedure I'm proposing. The procedure I'm proposing has these elements, inter alia:
I think that with a high-effort, intentional version of this, the sector of reprogenetics actually can decide for itself to implement better versions of my and others's plans that will fulfill its responsibilities to humans and humanity, and do so in collaboration with governments and societies to implement broader structures (laws, social attitudes) that further build out a good future with reprogenetics. There are some forces beyond the control even of all of those constituencies put together, but I don't think that's a strong argument against doing this.
I think it wasn't a coincidence that liberal eugenics went so wrong.
(I take it you mean, early 20th century eugenics in liberal countries, rather than "liberal eugenics"?)
The theory overpromised and was unsound, especially as you got into more details. It also played to people's prejudices.
(True.)
Now, regarding this:
This seems to be repeating the exact pattern - overpromising, unsound on the details, playing into people's prejudices. Of course you don't intend for things to go sour, and are advocating this for the best of motives - but the liberal eugenicists didn't intend that, either, and good motives don't guarantee good results.
First, an overall comment: As I mentioned above, I think that this comparison is inapt. Comparing them because they both involve genes and reproduction is a poor comparison because that's not what makes them really distinctive. It's not the underlying engine of those behaviors. It's not that there's something about genes and reproduction that just makes people go crazy and want to forcibly sterilize people, deport all the brown people, etc. It's that there's a somewhat coherent ideology behind historical eugenics, and from that ideology flows much of that other behavior, in conjunction with associated attitudes such as high modernism / anti-pluralism. On that more salient dimension of comparison, genomic emancipation is anti-eugenics. Please see "Genomic emancipation contra eugenics".
Now on some particulars:
overpromising
Could you be more specific about the overpromising? Who is doing the overpromising (me in this post, other people somewhere, people in the future)? What is being promised, and what do you think is incorrect about that? To be clear, I think that:
As with many other worries, this is also something to improve. I'm trying a bit to help the situation, mainly by trying to set up a 3rd party academic validator of PGS claims (though it sure would be nice if good scientists who could work on this didn't have to worry so much about being vaguely associated with eugenics). Eventually the sector should have self-regulatory orgs, professional standards, legal regulation, public commitments and norms, etc.
But I don't think this field has been especially bad in this dimension so far, compared to lots of other sectors. Lots of commercial sectors hype stuff up to investors, to customers, to potential hires, and to the public at large. I think that's generally bad (because generally deceptive) and I don't excuse it in reprogenetics, but I don't see how it's part of some pattern specifically in common with historical eugenics.
I think it is also harmful to underpromise about technologies. Don't forget the basic facts here--if strong reprogenetics were widely adopted, it would save at least hundreds of thousands of lives, probably more. (This is a somewhat far away hypothetical, but it's still important!) If you think this is overpromising, we may have a technical disagreement, which could be productive to clarify?
unsound on the details,
Same questions as above--who's being unsound and how? Be specific please.
playing into people's prejudices.
What do you mean by this? Who / what statements are playing into people's prejudices, and how?
Of course you don't intend for things to go sour, and are advocating this for the best of motives - but the liberal eugenicists didn't intend that, either, and good motives don't guarantee good results.
Hm. Are you sure they didn't get what they intended? Some of them jumped ship, though my impression is that a lot of that was due to the science turning out to be super wrong. A lot of them were excited with how things were going, and wanted it to go more! But I'm not sure what you're referring to, to be honest. Maybe you could point me to a source that gives some detail on this?
Here's a second-order effect worth considering: Germline modification for intelligence is statutorily prohibited in most countries, the relevant professional societies are currently calling for a 10 year moratorium on the topic, and the public polling is uniformly and staunchly opposed. What if all that is for a good reason? Might some bright young person wanting to work on AI Safety end up having a counterfactually reduced impact if they choose to pursue such a devastatingly disfavored research path?
People asked Eliezer about getting people like Terence Tao to work on alignment.
- "Geniuses" with nice legible accomplishments in fields with tight feedback loops where it's easy to determine which results are good or bad right away, and so validate that this person is a genius, are (a) people who might not be able to do equally great work away from tight feedback loops, (b) people who chose a field where their genius would be nicely legible even if that maybe wasn't the place where humanity most needed a genius, and (c) probably don't have the mysterious gears simply because they're rare. You cannot just pay $5 million apiece to a bunch of legible geniuses from other fields and expect to get great alignment work out of them. They probably do not know where the real difficulties are, they probably do not understand what needs to be done, they cannot tell the difference between good and bad work, and the funders also can't tell without me standing over their shoulders evaluating everything, which I do not have the physical stamina to do. I concede that real high-powered talents, especially if they're still in their 20s, genuinely interested, and have done their reading, are people who, yeah, fine, have higher probabilities of making core contributions than a random bloke off the street. But I'd have more hope - not significant hope, but more hope - in separating the concerns of (a) credibly promising to pay big money retrospectively for good work to anyone who produces it, and (b) venturing prospective payments to somebody who is predicted to maybe produce good work later.
https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/agi-ruin-a-list-of-lethalities
Setting aside the possibility of recruiting security geniuses such as DJB (because MIRI and friends seem uninterested in that, for whatever reason).
It seems to me that adding more high-IQ people to the "set of people thinking about AGI" has been tried in the past, and the effect was to accelerate AI capabilities. What you fundamentally need is dispositional/sociological, not mental horsepower.
You could argue that with reprogenetics, it will be possible to make humans so smart that they're in a qualitatively different regime from Terence Tao. In which case they could destroy the world during a teenage tantrum. Wisdom doesn't necessarily scale with IQ. Children don't necessarily adopt the ideology of their parents. And it's not ethical to "shut a person off" or read their thoughts like you could do for a rogue AI.
It seems to me that the community is taking the same "Leeroy Jenkins" approach to reprogenetics that it took to AGI, without serious effort at e.g. public red-teaming. Equally catastrophic results seem quite plausible. Feeling blackpilled on this community.
It seems to me that adding more high-IQ people to the "set of people thinking about AGI" has been tried in the past, and the effect was to accelerate AI capabilities
I'm curious what has been happening with young geniuses specifically; do they seem more or less accelerating AI capabilities compared to the older guard?
What you fundamentally need is dispositional/sociological, not mental horsepower.
I think you need both, by a long shot. Humanity is bottlenecked on ideas in so many ways in so many domains. Humanity is also bottlenecked on values / caring about the right things / being wise / etc.
But I would also say that humanity is specifically bottlenecked on having people who are both wise and very smart. We can at least unblock on "very smart".
I also think that if there were a lot more very smart people, we'd do a better job at supporting them.
That said, education is super important.
Also, I agree that wisdom specifically is important and interesting, and that we should investigate whether / how we could genomically vector for traits like that.
without serious effort at e.g. public red-teaming.
Yes, please send serious effort at public red-teaming. (Also send serious effort at public blue-teaming.) You can rest assured though, approximately no one from the community is working on this.
Here's my main efforts so far re/ red-teaming: https://www.lesswrong.com/posts/K4K6ikQtHxcG49Tcn/hia-and-x-risk-part-2-why-it-hurts https://berkeleygenomics.org/articles/Potential_perils_of_germline_genomic_engineering.html
Eliezer also said under a post about Superbabies that it is third most important project on the planet and it gets too little attention.
He also said in one of the interviews that he thinks alignment is too hard for unaugmented humans, that we need human intelligence augmentation (probably in form of adult gene therapy with suicidal volunteers, because waiting for babies to grow up is too long), that maybe if you are somewhat smarter than John von Neumann, you get security mindset by default. Though irrc he also pointed out somewhere that one of problems is that getting from IQ 220 to 250 should be much harder than from 70 to 100 or from 100 to 130 (iirc, because here we can just copy what exists).
It's pretty dubious whether one can safely go to 220, let alone 250. However, I think having lots and lots of [people like your favorite super smart people] would be great.
I really think the effect that AI safety people’s short timelines have on whether or not reprogenetics research gets done is ~0. Human intelligence enhancement is illegal, and faces a massive social stigma. We could have cloned the smartest humans starting decades ago and we haven’t (at least publicly) because governments and the regulators seem to hate that idea.
We could have been doing selective breeding to foster intelligence since we discovered evolution in the early 1800s. We haven’t done it because of stigma and lack of government interest. Short AI timelines is just irrelevant.
Note that the reprogenetics program is extremely achievable with ordinary biological science. There's a relatively large pool of talented geneticists who are unlikely to have the security mindset necessary to directly work on capable AI, so I don't think this program will detract human capital from other cause areas relevant to AI survivability. The risk does not extend far past the experimental subjects. It's low cost with potentially huge upside. And it's not like having healthier and smarter children is a thing people would be opposed to absent AI risk.
I also agree with the RobertM comment that the substantial impact with short timelines is well-argued. I think it's worth adding that even if this program is doomed to fail because we get clotheslined by short timelines before it can do anything, it would be more dignified to die having started trying to become capable of solving alignment that it would be to be killed by misalignment without trying anything even conditioned on being correct that there's predictably not enough time to complete the program. This is not as important as the argument that we should play to our outs, but it makes me feel better.
extremely achievable with ordinary biological science.
I agree with this characterization, if we're taking a bird's-eye view of science. But to be clear, there are lots of unknowns here, such as how to do full in vitro gametogenesis, how to avoid genome degradation in cultured cells, what happens when you push a polygenic score way far out, how can chromosome selection be implemented, etc. Of course, there are also non-science / non-technological issues (legal, social).
And it's not like having healthier and smarter children is a thing people would be opposed to absent AI risk.
I definitely think there is a strong case for reprogenetics absent AI risk, and that's the case I mostly want to be making to society. In other words, I think it's mostly reasonable & good for society to not really account for AI risk when considering whether reprogenetics is a good idea, and instead evaluate it only on other grounds, such as whether it's good for the kids and whether society can handle that technology healthily. (In part because that consideration somewhat instrumentalizes children. It shouldn't be categorically forbidden to be motivated to have kids because of some outcome you want them to bring about, but it should be viewed with a lot of skepticism / worry / carefulness, especially when coming from society rather than parents and especially when it's a potentially overbearingly strong motivation.)
My main issues are the following:
(I don't understand your first point. I argued in the post that I don't think AGI is that likely to arrive very soon, and even if you do think that, HIA has substantial positive expected benefit.)
Re/ your second point, also not sure I understand. You're saying that maybe humans with amplified intelligence might not be able to contribute much because their education is poor in general, or specifically because contribution is bottleneck on years of experience working on the alignment problem or adjacent sciences? I mean, data and education are important too. We can & should also support education, and try out things to support geniuses in particular. (But if you're saying that this implies technological HIA such as reprogenetics wouldn't work or wouldn't have much impact, I don't see how that follows at all.
As far as I understand, the main case against short timelines was in your posts "Do confident short timelines make sense?" (Jul 2025!) and the post made on Jul 2023(!!). I had Claude Sonnet 5 prepare the list of breakthroughs between July 2023 and now:
Claude's list
Quite a lot happened in this three-year stretch. Here's the shape of it:
Late 2023 — multimodal goes mainstream Google launched Gemini in December 2023 as a multimodal competitor to GPT-4, integrated initially into Bard and other Google tools. This was part of a broader shift where models stopped being text-only and started natively handling images (and later audio/video) in one architecture, following GPT-4's earlier multimodal debut.
2024 — reasoning models arrive The single biggest architectural shift of the period came in September 2024, when OpenAI released o1-preview, the first in a new series of "reasoning models" trained specifically for chain-of-thought problem solving, rather than just generating fluent text in one pass. This kicked off what's often called the "reasoning" aka inference-scaling aka Reinforcement Learning from Verifiable Rewards (RLVR) revolution — models that spend extra compute "thinking" before answering hard problems.
Early 2025 — the DeepSeek shock and open-weight reasoning January 2025 brought DeepSeek-R1, an open model that acquired reasoning capabilities solely through reinforcement learning, which shook markets by matching frontier reasoning performance at a fraction of the training cost — DeepSeek-V3 was more than a technical achievement; it signaled that accessible, high-performing models could thrive outside the traditional big tech ecosystem. A Berkeley team even replicated core concepts of DeepSeek's R1-Zero model on a budget of just $30, with a 3-billion-parameter model called "TinyZero" trained via reinforcement learning, showing the technique wasn't exclusive to giant labs. OpenAI responded by pushing further with o3, o3-mini, and o4-mini, and reasoning became a signature feature of models from nearly every other major AI lab.
2024–2025 — the rise of agents Alongside reasoning, agentic AI emerged — systems that don't just respond to prompts but autonomously plan, execute, and adapt to accomplish complex goals. Rather than just answering, these systems reason through multi-step plans, invoke external tools and APIs, maintain memory across interactions, verify results, and recover from errors with minimal human oversight. Standardized protocols for tool use (like MCP) matured enough that by mid-2026 major frameworks like LangChain and LlamaIndex fully support MCP, making it trivial to add tool use to any model.
2025–2026 — architecture experimentation and efficiency The field diversified past plain transformers. Hybrid architectures blending attention with state-space (Mamba-style) layers became popular for efficiency — this hybrid-architecture trend with alternating attention and alternative layers became a relatively popular development, with Qwen3.6 using Gated DeltaNet layers instead of Mamba-2 layers. NVIDIA's Nemotron 3 Super was an open, efficient Mixture-of-Experts hybrid Mamba-Transformer model designed for agentic reasoning. Diffusion-based (non-autoregressive) language models also appeared as a genuinely different generation paradigm — models like Seed Diffusion Preview, based on discrete-state diffusion, offering fast inference speed through non-sequential, parallel generation rather than token-by-token decoding.
2026 — rapid-fire frontier releases This year has seen an unusually fast release cadence across labs: January through April all featured at least one frontier-class launch — Google shipped Gemini 3.1 Pro in late February, Anthropic shipped Opus 4.7 in mid-April, and OpenAI shipped GPT-5.5 in April. Meta also pushed back into frontier territory with a model called Muse Spark. Coding and agentic-workflow benchmarks became key battlegrounds: Claude Opus led SWE-bench Pro while GPT-5.5 led Terminal-Bench 2.0, with Claude stronger on cold-start code synthesis and GPT-5.5 stronger on multi-turn agent loops. Efficiency also kept improving — models like DeepSeek V4-Flash offered a 1M-token context window at roughly 50x cheaper input pricing than GPT-5.5. Most recently, Anthropic released Claude Opus 5 in late July 2026, alongside continued releases from Google (Gemini 3.5/3.6 Flash), Alibaba (Qwen3.7/3.8), Moonshot AI (Kimi K3), and others.
A few threads run through all of it: reasoning/test-time compute became a standard model capability rather than a novelty, open-weight models closed much of the gap with closed frontier labs while driving costs down dramatically, context windows grew enormously (into the millions of tokens), and the center of gravity shifted from "chatbot that answers" to "agent that acts" — using tools, maintaining state, and completing multi-step tasks with less supervision.
The case against novel conceptual reasoning seems to have partially lost its juice given that scaling and the innovations described in the collapsed section (which IMHO are closer to education techniques than to architectural breakthroughs. Novel architectures like neuralese have yet to be discovered) gave rise to models as capable as Claude Mythos, Astra and other discoverers of novel theorems and cyber-related exploits, or Claude Opus 5 making a breakthrough in the ARC-AGI-3 non-scaffold.
As for the second point, yes, I would expect conceptual research to be bottlenecked on years of experience working on sciences like alignment or mechinterp (e.g. the AI-2027 Race branch had Agent-4 start with understanding its own cognition by superintelligent mechinterp, then construct Agent-5 with one goal). However, I struggle to understand what experiment could reveal that HIA worked as you describe versus shifting the human's interests.
I'm not interested in arguing with your LLM. I don't believe I've ever expressed much or any skepticism about theorem proving, ARC-whatever, or computer hacking coming from current AI research.
I struggle to understand what experiment could reveal that HIA worked as you describe versus shifting the human's interests.
Well, like, if someone went into theoretical physics, they might produce intellectual progress on the order of [pick your favorite brilliant physicist] or instead [pick your favorite highly motivated but not very successful theoretical physicist].
pre-RSI AGI are likely less aligned than enhanced humans
"Human intelligence enhancement could be related to data progress instead of algorithmic progress, as Beren once said about LLMs": what? When using data vs algo metaphorically to talk about humans, what's the human version of "data" and the human-version of "algorithm"? It's unclear how you map here..
Are you saying that native intelligence doesn't matter as much as education? Because I'm very sure that is incorrect; my intuition points strongly to many life outcomes pointing better for a 135 iq person vs a 100 iq person, all else being equal~ (and with the exception of mental illness/depression/burnout, which is a fraction of the population but far from 100%.
I like it!
Curious if you have any thoughts on combining this with ideas of collective intelligence?
I think we had a discussion like this on one of your shortforms but I've read that intelligence genetically seems quite well selected for and that it might be hard (at least with random variation) to hit the right type of genes due to the optimisation pressure already applied there.
So it becomes more of a remove the negative genes rather than improve the positive genes type of thing.
If we then define collective inteliigence as the ability of a group of people or information processing systems to make wise and intelligent decisions there might be other things than just the individual intelligence that would be good to improve?
E.g listening ability and the ability to understand other people is one of the things that generally increase the C-factor (collective intelligence, see this paper) among people.
I also think things like trait openness or conscientiousness seems like quite good targets in terms of general throughput. There's also arguments to be said for a correlation between intelligence and depression, so if your intelligence increasing correlates with trait neuroticism that might be bad?
You might then even at this point bring in ideas from cyborgism and similar into this entire strand...
I don't think that the world will stop being susceptible to all x-risks within the next 20 years or so and so it clearly seems useful to have a bet in this direction. I do wonder whether individual intelligence is the thing to aim for but it likely won't hurt.
I feel very very confused as to why these timelines are so long. And why just scaling up pretraining more won't just work and why the primary bottleneck isn't mainly the physical buildout and gpu availability.
My basic case, available in the linked posts in some more detail and breadth, is:
You write:
And why just scaling up pretraining more won't just work
Would you please read https://www.lesswrong.com/posts/sTDfraZab47KiRMmT/views-on-when-agi-comes-and-on-strategy-to-reduce#The__no_blockers__intuition and maybe some surrounding sections?
Another reason I'd support reprogenetics work is because it might resolve one of the most burning questions in our minds, which is whether something like a software-only intelligence explosion is plausible, because there's one (very major) constraint that applies to human intelligence augmentation that doesn't apply to AI development, and that is the fact that we can't increase computational power directly like we can for AI models by even 1 order of magnitude for inference or training, because we'd cook the body (at least not without technologies that is only practical to invent post-industrial explosion.)
This means work on reprogenetics could in theory probe the question indirectly, by measuring how large the returns to algorithmic progress are from a fixed base like the human mind.
We'd need much better measurements, especially measurements about whether or not when diminishing returns to intelligence without compute increases kicks in, and measurements about how well intelligence increases translate to other good metrics, but if we had the correct measurements, we could answer the question of whether software-only intelligence explosions are plausible, and to a first approximation, good AI policies (that aren't just pure transparency/obviously good stuff) and good AI safety prioritization depend on answering this question.
But have we solved the alignment problem for hyper intelligent humans? Who says they solve the alignment problem instead of wiping us out the same way?
People are not as loyal to the interests of other people as we would like, but we have no idea how to make an AI care even a tiny bit about people in a way that has a high probability of persisting after the AI has become super-humanly capable.
Although HIA will result in Humanity being better able to tackle AGI, I think it may also result in an adverse effect if we do not get down the problem of Human Alignment correctly. Now, if there are more people working to reduce existential risk than there are people who are working to advance AGI as fast as possible, HIA is helpful. And the more people that are working on existential risk compared to AGI, the more percentage points of existential risk gets reduced per IQ increased. However, we should consider the alternative. If there are currently a bunch of people working to accelerate AGI, and fewer people than that working on existential risk, then HIA has a negative effect on existential risk.
This I expect would be lessened somewhat as people with higher intelligence figure out that AGI might, possibly, kill us all if done wrong. But that lessening assumes everyone is working with an altruist mindset, and it might be the case that despite the altruistic motivations, personal greed/ambition/etc. overcomes it and has them work on AGI anyway. It might also be the case that greater intelligence results in greater rationalization without proper rationalist training, (although research suggests higher IQ leads to a naturally higher level of rational thinking, they are still independent) which would amplify the effects of unwholesome motivations.
The TL;DR here is that if we have more people "against us" (working on AGI) than "with us" (working on existential risk) more intelligence works for "them" and not for "us".
This is irrelevant! In what way is it better to be replaced by Homo Hypersapiens than to be replaced by AGI? Possibly I could go with that on some really long timescale - e.g. at least hundreds if not thousands of years, but decades? Rate of change matters more than what the change actually is. I often put it like this: If we could have asked our fishy ancestors whether or not they would like to evolve into us, I'm pretty sure that, certainly if the timeframe was a few generations, they would have said no way and would have stopped it if they could. Or, alternatively, consider that, whilst you may be happy to have someone younger and better than you replace you at work when you retire, you don't want it to happen before then even though your replacement is of the same species.
My main issues are the following:
I'm not interested in arguing with your LLM. I don't believe I've ever expressed much or any skepticism about theorem proving, ARC-whatever, or computer hacking coming from current AI research.
I struggle to understand what experiment could reveal that HIA worked as you describe versus shifting the human's interests.
Well, like, if someone went into theoretical physics, they might produce intellectual progress on the order of [pick your favorite brilliant physicist] or instead [pick your favorite highly motivated but not very successful theoretical physicist].
Introduction
I think reprogenetics (human germline genomic engineering) can be done in a widely acceptable and beneficial way, and should be pursued aggressively. In particular, as a strong background motivation of mine, I think accelerating strong reprogenetics is probably the best way to enable strong human intelligence amplification; and I think strong HIA is among the best ways to decrease existential risk from AGI.
A very common objection to caring much about reprogenetics is that AGI seems very likely to come soon—say, within a decade or two. (Here I mean "actual" AGI—the kind that probably doesn't already exist—the kind that has fluid intelligence and AI advantages for recursive self-improvement, which together make it likely to take over the world shortly after being created.) The objection is fairly straightforward:
Now, of course this is true to an extent. If AGI comes within 15 years, reprogenetics is almost totally useless. To the extent you believe that will happen, you believe reprogenetics is quantitatively less useful in expectation. Also, having a faster HIA method would of course be great.
However, I believe that this line of reasoning has led to a very mistaken underallocation of funding, talent, and other resources towards human intelligence amplification in general, and reprogenetics in particular. So, I would like to push back in a few ways:
(Note that this isn't a comprehensive fair-and-balanced report, but rather a collection of arguments in one direction. For example, I won't here discuss reasons that HIA could increase existential risk from AGI. Also, some but not all of these arguments rely on the assumption that alignment is very difficult.)
HIA, part of your nutritionally complete portfolio
HIA is super neglected. Would you rather be the 3000th person working on AI safety, the 300th person working on AI regulation, or the 3rd person working on accelerating HIA?
There's broad philanthropic alpha in working on HIA. It's tractable (to accelerate), neglected, and important. In particular, because it's somewhat taboo (though probably less than you think), there's relatively low-hanging fieldbuilding fruit to pick.
We must attend to all plausible points of intervention.
Uncoordinated resource allocation leads to unbalanced portfolios.
Against confident short timelines
I don't in fact think that confident short timelines (say, ">80% within 15 years" or similar) make much sense. As yet, I have not heard a clear and convincing case that AI research has already uncovered the engines that would produce a smarter-than-human general intelligence. The main arguments for AGI coming soon boil down, I think, to the rapid rise in capabilities and to the use of AI in AI research.
Regarding the rapid rise:
We have large gaps in performance between AIs and humans (sample complexity for learning, ability to generate novel good concepts).
Also, we have an apparent explanation for the rapid rise in capability: hoovering up big piles of internet data for quasi-imitation. Current AIs are skillful / knowledgeable approximately when there's a bunch of human training data for that thing. Of course, you need more elements if you want to explain all of the rapid rise, such as unhobbling via harnesses and RL, as well as computer advantages (scaled RLVR, speed, parallelism, copying, inexpensiveness).
But the core explanation of "the bulk of these capabilities come from human demonstration" holds water, as far as I know. In particular, this explanation says that current AIs got their capabilities via some route other than accumulation of crystallized intelligence that was produced by their own fluid intelligence. Namely, they got their capabilities via "copying" (broadly construed) crystallized human intelligence originally generated by human fluid intelligence.
This seems to largely explain away the rapid rise in capabilities, without invoking current AIs having much fluid intelligence. Combined with the apparent gap, with humans still well ahead on general fluid intelligence, I don't see how anyone gets to being very confident that we already have most or all of the ideas that would be needed for AGI.
Regarding the use of AI in AI research:
I expect the most important kinds of research to not be accelerated much, because they are not bottlenecked by coding in the first place. Unless you already think we're close to making AGI for other reasons, so that what's left is mainly the kind of research that is greatly accelerated by current or near-future AI, this partial acceleration shouldn't change your timelines much.
For previous discussion, see "Do confident short timelines make sense?" and "Views on when AGI comes and on strategy to reduce existential risk". I'm open to debating this with anyone who'd like to make a serious, public case for confident short timelines.
HIA may indirectly slow down AGI capabilities
The main stated justification for pursuing AGI capabilities is that AI / AGI would bring abundance for humanity. It stands to reason that if there were a credible, workable plan to get the (supposed) benefits of AGI without the huge existential risk (and other disempowerment of humans), then there would be less motive to develop AGI and it would be harder to justify pursuing AI capabilities. (See "5.1. Abundance makes less motive to make AGI".)
I don't know how large this effect would be. Presumably not very large. Presumably many people trying to increase AI capabilities just want money, power, status, or other selfish things, and would find some other justification. I'm not sure though; it's hard to put upper or lower bounds on the importance of underlying spiritual currents in society of hope, motivation, the longer-term future, and so on.
HIA has substantial impact even with short timelines
Approximate summary of this section:
In dire situations, if you're trying to be strategic about winning, it's advisable to play to your outs. That is, avoid the temptation to focus on incremental tractable gains that don't actually increase the chances of overall success. Instead, aim at paths to overall success, including by setting yourself up to take advantage of such opportunities, even if any specific path is unlikely.
Working towards a pause / stop / ban on AGI progress is a top priority. But what is a pause for? We pause, and then what? How do we more robustly prevent existential risk? Hopefully, we can ban Red AGI research, but probably not Blue AGI research progress. (Though social / political pressure might possibly be able to significantly slow down even Blue research.) In the longer run, how do we avoid making unaligned AGI? HIA is a way to improve our long-run chances by giving humanity more brainpower to find good answers to that question.
Suppose you do get a pause on AGI progress, but you don't have a plan for how to more robustly stop existential risk. More specifically, suppose that progress is not being made towards the outs. Then the pause is being wasted: for the moment you're bailing water out of the boat as it leaks in, but at some point more and more leaks will be sprung. You have to also be patching the hull. Ten years from now, when you have hopefully updated toward slightly longer timelines, you'll regret not getting started on the longer-term solutions back in 2026. I know I regret not working on this a decade ago, rather than bashing my head against the AGI alignment problem. HIA will make some progress on its own by default, but this argument goes through quantitatively—you'll regret not having quantitatively accelerated HIA if you could have.
Very short timelines are pretty intractable. If AGI is actually coming in 5 years by default, unless you think alignment is easy, there just aren't many outs. Of course we still want to try, but intractability does weigh in the prioritization calculation.
Pulling world-saving conditions forward in time is very valuable unless you're very confident extinction will have already happened.
AGI alignment is extremely hard. Therefore solving AGI alignment is not that helpful of an out.
HIA is an out we can play to.
Adult HIA methods aren't fast either, absent big investment
A variant of the argument against reprogenetics goes like this:
Of course, overall, this logic is valid and compelling. If I believed we could do (strong) HIA sooner than a couple decades, I would work on that. If there were actually a Manhattan-Project-scale project for adult human intelligence amplification, I would probably drop what I was working on in reprogenetics and join that effort. I would have some hope of success—assuming that we would have available the scientists, equipment, experimental volunteers, and money that would be needed to run several experimental investigations in parallel.
However, short of a Manhattan Project, I'm somewhat skeptical of adult HIA being a faster bet than reprogenetics. There are several reasons, which I'll list here. But I want to summarize the main reasons more briefly:
The reasons in more detail:
Reprogenetics has good-enough data on intelligence; adult HIA does not.
Reprogenetics would probably work for strong HIA; adult interventions may or may not work well for strong HIA.
There are general technical obstacles to adult HIA.
Reprogenetics has strong momentum, in terms of scientific foundations, user motivation, and user on-ramp.
Therefore, adult HIA is fairly likely to take a rather long time.
Now, all of this having been said, I could easily be wrong. I've focused largely on reprogenetics, because that's what I think will work. I'm enthusiastic about roadmapping and research on adult HIA in general, and on specific methods. Hopefully, those interested in adult HIA would take these as non-dealbreaker constraints, to chew through or work around or creatively break.
Takeaways