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You should show your calculation or your code, including all the data and parameter choices. Otherwise I can't evaluate this.

The code is pretty complicated and not something I'd expect a non-expert (even a very smart one) to be able to quickly check over; it's not just a 100 line python script. (We'll likely open source it at some point in the future, possibly soon, but that's not decided yet). Our finemapping (inferring causal effects) procedure produces ~identical results to the software from the paper I linked above when run on the same test data (though we handle some additional things like variable per-SNP sample sizes and missing SNPs which that finemapper doesn't handle, which is why we didn't just use it).

The parameter choices which determine the prior over SNP effects are the number of causal SNPs (which we set to 20,000) and the SNP heritability of the phenotype (which we set to 0.19, as per the GWAS we used). The erroneous effect size adjustment was done at the end to convert from the effect sizes of the GWAS phenotype (low reliability IQ test) to the effect sizes corresponding to the phenotype we care about (high reliability IQ test).

We want to publish a more detailed write up of our methods soon(ish), but it's going to be a fair bit of work so don't expect it overnight.

It's natural in your position to scrutinize low estimates but not high ones.

Yep, fair enough. I've noticed myself doing this sometimes and I want to cut it out.

It's just very hard for me to believe there aren't huge gains possible from genetic engineering. It goes against everything we've seen from a millenia of animal breeding. It goes against the estimates we have for the fraction of variance that's linear for all these highly polygenic traits. It goes against data we've seen from statisitcal outliers like Shawn Bradley, who shows up as a 4.6 standard deviation outlier in graphs of height:

Do I buy that things will get noisier around the tails, and that we might not be able to push very far outside the +5 SD mark or so? Sure. That seems unlikely, but plausible.

But the idea that you're only going to be able to push traits by 2-3 standard deviations with gene editing before your predictor breaks down seems quite unlikely.

Maybe you've seen some evidence I haven't in which case I would like to know why I should be more skeptical. But I haven't seen such evidence so far.

Oh yeah my bad, I didn't notice that the second date was Sep instead of Feb

Our hiring this round is a small fraction of our overall team size, so this is really just correcting a minor imbalance, and shouldn't be taken as reflective of some big strategy. I'm guessing we'll go back to hiring a mix of the two around mid-2025.

There is one saving grace for us which is that the predictor we used is significantly less powerful than ones we know to exist.

I think when you account for both the squaring issue, the indirect effect things, and the more powerful predictors, they're going to roughly cancel out.

Granted, the more powerful predictor itself isn't published, so we can't rigorously evaluate it either which isn't ideal. I think the way to deal with this is to show a few lines: one for the "current publicly available GWAS", one showing a rough estimate of the gain using the privately developed predictor (which with enough work we could probably replicate), and then one or two more for different amounts of data.

All of this together WILL still reduce the "best case scenario" from editing relative to what we originally published (because with the better predictor we're closer to "perfect knowledge" than where we were with the previous predictor.

At some point we're going to re-run the calculations and publish an actual proper writeup on our methodology (likely with our code).

Also I just want to say thank you for taking the time to dive deep into this with us. One of the main reasons I post on LessWrong is because there is such high quality feedback relative to other sites.

That is good evidence that we aren't in a mutation selection balance. 

There are also game theoretic balances.

Here is a hypothesis that fits my limited knowledge of genetics, and is consistent with the data as I understand it and implies no huge designer baby gains. It's a bit of a worst plausible case hypothesis.

But suppose we were in a mutation selection balance, and then there was an environmental distribution shift.

The surrounding nutrition and information environment has changed significantly between the environment of evolutionary adaptiveness, and today. 

A large fraction of what was important in the ancestral world was probably quite emotion based. Eg calming down other tribe members. Winning friends and influencing people. 

In the modern world, abstract logic and maths are somewhat more important than they were, although the emotional stuff still matters too. 

Iq tests mostly test the more abstract logical stuff. 

Now suppose that the optimum genes aren't that different compared to ambient genetic variation. Say 3 standard deviations. 

"something like that" isn't open enough.  "or something else entirely" seems more likely than "something like that".   Many more than 2 groups (family-sized coalitions) is an obvious possibility, but there are plenty of other strategies used by primitive malthusian societies - infanticide being a big one, and ritual killings being another.  According to Wikipedia, Jared Diamond suggests cannibalism for Rapa Nui.  

Looking at Wikipedia (which I should have done earlier), there's very little evidence for what specific things changed during the collapse.

 In any case, it's tenuous enough that one shouldn't take any lessons or update your models based on this.

That is a great point and your examples are fascinating!

I think polarization is still quite possible and should be avoided at high cost. If AI safety becomes the new climate change, it seems pretty clear that it will create conflict in public opinion and deadlock in politics.

Think, at least a little, before you act in a new or complex way.

Oh. Is this what they meant by "think before you act"? Because thinking before every action goes exactly as well as you, careful author of that sentence, would expect.

There are at most as many gods as people.

why can't we all have unique pantheons?

You should show your calculation or your code, including all the data and parameter choices. Otherwise I can't evaluate this.

I assume you're picking parameters to exaggerate the effects, because just from the exaggerations you've already conceded (0.9/0.6 shouldn't be squared and attenuation to get direct effects should be 0.824), you've already exaggerated the results by a factor of sqrt(0.9/0.6)/0.824 for editing, which is around a 50% overestimate.

I don't think that was deliberate on your part, but I think wishful thinking and the desire to paint a compelling story (and get funding) is causing you to be biased in what you adjust for and in which mistakes you catch. It's natural in your position to scrutinize low estimates but not high ones. So to trust your numbers I'd need to understand how you got them.