Harvard

Article

Harvard is a recurring place in the Astral Codex Ten archive, appearing 5 times across 5 issues between January 29, 2021 and July 31, 2025. The archive places it in contexts such as “Harvard postdoc”; “Harvard philosophy students”; “Everyone can just say “Harvard,” and nobody will have any unfair advantage”. It most often appears alongside 23andme, Harvard, New York Times.

Metadata

Category: Places
Mention count: 5
Issue count: 5
First seen: January 29, 2021
Last seen: July 31, 2025

Appears In

- 23andme (2 shared issues)
- Harvard (2 shared issues)
- New York Times (2 shared issues)
- Trump (2 shared issues)
- United States (2 shared issues)
- US (2 shared issues)
- 23andMe (1 shared issues)
- 80,000 Hours (1 shared issues)
- ACX (1 shared issues)
- ACX Grants (1 shared issues)
- administrative state (1 shared issues)
- AI alignment problem (1 shared issues)

External Links

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Contra Weyl On Technocracy

January 29, 2021 · Original source

2. School desegregation: Nine unelected experts with Harvard and Yale degrees, using a bunch of Latin terms like a certiori and de facto that ordinary people could not understand let alone criticize, decided to completely upend the traditional education system of thousands of small communities to make it better conform to some rules written in a two-hundred-year-old document. The communities themselves opposed it strongly enough to offer violent resistance, but the technocrats steamrolled over all objections and sent in the National Guard to enforce their orders.

Actually, most of the complaints I've heard have been from people like Weyl (Princeton PhD, Harvard postdoc, Principal Researcher at Microsoft New England). The “widespread public backlash” link goes to a book by Anand Giridharadas (Harvard PhD student, former McKinsey consultant, New York Times columnist). The actual normal people I talk to are broadly supportive. Some of my blog commenters are populist Trump supporters, and although they sometimes tell me I'm crazy for donating my money the way I do, they accept I have the right to spend it how I want and don't bother me much about it.

Birth Order Effects: Nature vs. Nurture

June 01, 2022 · Original source

Source here; thanks to Emile for the graph That is, of people with exactly one sibling who read this blog, about 72% of those are the older of the two children in their family, compared to only 29% who are the younger of the two (where by chance we would expect 50-50). This was surprising, because at the time lots of studies had shown there weren’t really birth order effects (that is, firstborn siblings had no major personality differences compared to laterborns). I theorized that maybe for some reason it was easier to find by looking in a heavily-selected group of people and asking members about their birth order, compared to getting a random sample and trying to correlate birth order with things. Sure enough, later amateur research revealed strong birth order effects in physics Nobelists and great mathematicians (and potentially Harvard philosophy students). Given that readers of this blog are highly-educated (about 37% have masters or PhDs) and mostly in STEM (41% programmers of some sort), plausibly birth order affects something about intelligence, education, or STEM orientation (somebody should check literature and peace Nobelists!) Followup research by Less Wrong user “Bucky” determined that the effect fell off with age gaps; the closer in age you are to your sibling, the stronger an effect birth order has: I continue to be confused by this extremely strong effect which most of what we know about psychology says shouldn’t exist. So in 2020, I asked my readership some even more complicated questions about their family situations, in the hopes of teasing out why this is happening. I’ll be honest - I think I over-reached here. I’m not very good at statistics, and this is a weird statistical problem: the dependent variable is whether the case ended up in the sample at all! I wasn’t able to figure out a good way to use most of the data you gave me. And the stuff I did use, I mostly made work by slicing and dicing so much that the sample size got pretty low, even when I started from 8,000 survey respondents. I’m publishing this in the hopes that it will inspire someone else will more domain knowledge to do a sophisticated re-analysis. But for now, here’s what I’ve got. Confirming Old Results With The 2020 Dataset The 2020 dataset also shows a strong birth order effect in people who read this blog. In 2018, among people with exactly one sibling, respondents were 2.51x more likely to be the older sibling than the younger (72%). In 2020, the number was 2.39x (71%). Change with age gap is shown below: Note truncated y-axis This seems to be broadly similar to the 2018 results. There was an anomaly in 2018 where some categories seemed to drop off surprisingly quickly between 7 and 8 years, which I thought might be meaningful. But Bucky’s analysis showed this was probably a coincidence, and indeed it doesn’t show up in the new data. Does Sex Matter For Birth Order Effects? I wondered if there might be a smaller birth order effect for people with opposite-sex siblings. One possible explanation for the birth order effect is children trying to get out of the “shadow” of their older sibling and differentiate themselves in some way. But children are already pretty different from an opposite-sex sibling and might feel less pressure in that situation. But this doesn’t seem to be true. The percent firstborns in sibships of two on the survey was 70% among people with a same-sex sibling, and 71% among people with an opposite-sex sibling; no real difference. Do Biological Or Social Factors Produce Birth Order Effects? All previous results are for biological siblings. But it might be worth asking the question separately for biological vs. social siblings. One could imagine either biological or social causes of the birth order effect. For example, some biologists speculate that pregnancy depletes choline, that it takes a long time for choline stores to recover, and that a second child born within that window will have less choline available to build their nervous system, which could be bad. But also: maybe if you have an older sibling, your parents can’t pay as much attention to you when you’re a kid, and you learn less. This was very hard to test for. Again, I wasn’t able to use traditional statistical tests because I’m trying to determine whether someone was in the sample at all, rather than whether two variables are related. It was easy to check normal birth order because I could compare people with exactly one older sibling to people with exactly one younger. It was harder to do with things like adoption in the mix, because that could introduce a bias: are parents more likely to adopt out their first child (because that’s when they’re most unprepared for parenting)? Are adoptive parents more likely to adopt when they already have children of their own (because they’re comfortable with child-rearing) or less likely (because they really want kids and can’t have them biologically)? I had no way of getting controls for these questions and so I couldn’t do a lot of the analyses I wanted. But I did two relatively weak analyses instead: First, I took the entire set of people in weird situations - people who said their number of social siblings was not the same as their number of biological siblings. In this group of 174 people, biological firstborns made up only 61% of respondents with one sibling, notably less than the 71% in the entire sample. That suggests that the unusual social situations are having an effect. You shouldn’t update on the fact that it’s still higher than 50%, because some of the people’s weird social situations don’t affect their status as social firstborns. Second, I tried to compare people who were firstborn under a biological definition but not a social definition, to people in the opposite situation. There were 40 people in the sample who were biological but not social firstborns, and 60 people who were social but not biological firstborns. Again, this suggests that social firstborn-ness is more important as an explanation than biological firstbornness, although it doesn’t rule out the latter having some effect. I additionally tried to compare two different types of social firstbornness - one where no older siblings lived in the house when you were growing up, and one where your parents had never parented another child. There weren’t many people discordant on these two measures (29 vs. 20 respectively), but for what it’s worth, the ratio was in favor of the first type. Since I wasn’t very confident in my analytical abilities here, I asked Bucky, who knows more and who did good work analyzing the last dataset, to look into this (we worked independently and didn’t tell each other our results until we were done). He writes: It seems to me that the effect is entirely caused by social siblings. I filtered for only people with 1+ biological but 0 social siblings. There were 24 oldest biological children in this group vs 21 2nd children (or 25 youngest children with a large overlap between 2nd oldest and youngest groups). This significantly differed from the ~0.7 fraction of older children in the general surveys (p<0.05 or p<0.01 depending on whether I use the 2nd oldest or youngest as the comparison) and is close to a 1:1 ratio. I then filtered for only people with 0 biological but 1+ social siblings. There were 51 oldest social children and 23 2nd children (or 26 youngest children again with large overlap). This differs significantly from a 1:1 ratio (p<0.001 or p<0.01) and matches pretty well with the 70% of the Birth order effect. I tried doing some filtering by age gap (2-7 years) and the results were compatible with the same result, although the sample sizes got too small to really conclude anything. For dealing with answers left blank I treated them as 0 unless it looked like the whole section had been missed out. If I ignored any respondents who left something blank I got similar results (smaller sample size but ratios are even further in favour of the social hypothesis). I checked for categorisation errors by looking at respondents’ descriptions of their families and they mainly matched pretty well with the numbers given so I think the data should be considered reliable. I did chuck a couple of results out which seemed unreliable and there was one row which was a repeat so your numbers might not match up exactly (plus you have the non-public data). There are a couple of confounders in the analysis such as whether e.g. oldest children are more likely to be adopted or how much you know about your birth family depending on how old one was when the family unit changed etc. I don’t see a realistic way to account for these but I also can’t see any of them being big enough to explain the difference in the results. Hopefully this matches up with what you found! I think this suggests birth order effects are social rather than biological. So What Causes Birth Order Effects? Based on this analysis, it seems unlikely they are biological. Based on my very weak sub-analysis, and on their tendency to decay with larger age gaps, it seems they have more to do with the social presence of a sibling in the house than with any changes in parenting style (ie your parents learn to parent differently). Two explanations that satisfy both those criteria: Parents are able to devote their full attention to parenting their first child, but only half of their attention to parenting their second. Firstborns get more quality time with their parents during the first few years of childhood.

Inline links: here, physics Nobelists, great mathematicians, Harvard philosophy students, Followup research, https://substackcdn.com/image/fetch/$s_!jKaz!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F4995a838-7372-40dc-8e51-1b35c811bd43_523x388.png, some even more complicated questions about their family situations, https://substackcdn.com/image/fetch/$s_!CMuv!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F8042c21d-a6b7-48f9-bbcc-daf8c136953c_497x339.png

My Presidential Platform

September 05, 2023 · Original source

So let’s think different: let’s make it legal to lie about your college on resumes (it is already not technically illegal to lie on a resume, but companies can ask for slightly different forms of corroboration which it is illegal to lie on). Everyone can just say “Harvard,” and nobody will have any unfair advantage over anyone else.

Quests And Requests

November 03, 2023 · Original source

This all sounds very complicated when I try to explain it, but it feels viscerally obvious after you try it, which you can do here (click United States, then “I consent” at the bottom, then “Skin-tone IAT”).

Inline links: which you can do here

Online IATs are tough because there are technical challenges to measuring sub-second reaction times. Still, Project Implicit at Harvard has done it, and maybe you could too.

Suddenly, Trait-Based Embryo Selection

July 31, 2025 · Original source

That serves as proof-of-concept that this technology can work, and means other companies’ claims are at least plausible. Scientific Objections: Antagonistic Pleiotropy This is a fancy term for “sometimes genes that are good in one way are bad in other ways”. For example, there is a gene that decreases the risk of lung cancer, but increases the risk of leukemia. If you selected against lung cancer, you might give your child higher leukemia risk. Several of the professional societies raise this concern, and Sasha Gusev gives several examples here, including a correlation between education/IQ and anorexia. When I think about these concerns, I consider the following thought experiment: suppose that I had a natural, unselected child, and that child became high school valedictorian and got into Harvard. Would my first reaction be “Oh no! This slightly raises her risk of anorexia!”? If not, why should this be our reaction to artificially increasing IQ? Genetic selection isn’t doing some different, magical thing. It’s just picking from within the natural IQ/anorexia variation. If you would be happy to have higher IQ (or lower breast cancer risk, or lower schizophrenia risk) naturally, you should be happy to get it through selection too. (Objection one: suppose that the genetic component of IQ is net negative, but the environmental component is net positive to an even greater degree. Then IQ itself might be net positive - so you could still celebrate your valedictorian child - but since the genetic component alone is bad you wouldn’t want to select for it. I have never heard anyone seriously claim this, most studies suggest that genetic components of good things are good in the expected ways, and most critics don’t get this far. I mention it for the sake of completeness only.) (Objection two: is the example above just saying that I value IQ more than non-anorexia? If so, couldn’t I give an alternate example of learning that my child isn’t anorexic, celebrating this seemingly-obviously-good fact, but actually this means they have lower IQ and based on my stated values I should be sad? I don’t think so. There is no claim that the increased anorexia risk from raising IQ is exactly as bad as the IQ increase is good - for example, you could imagine a world where going from moron to supergenius only raises anorexia risk 0.0001%. More generally - although not rigorously - selecting for X should usually increase X more than it increases tangentially-correlated construct Y. So selecting for IQ should be net positive, even though it might slightly increase anorexia risk, and selecting for anorexia should be net negative, even though it might slightly increase IQ. I think this is the intuition that drives parents to be happy both when they learn that their child is smart, and when they learn their child doesn’t have anorexia - not just an intuition that one trait matters more than the other) But also, here’s the table of correlated genetic risks for psychiatric disorders: …where blue means that lowering the risk of one disease also lowers the risk of the other, and red means the opposite (as in the IQ - anorexia example above). Here’s the same table for other conditions, courtesy of Genomic Prediction (except I flipped the colors from the original, to match the one above): Aside from two bright orange squares (gallstones vs. hypertension and hypothyroidism - I don’t know what’s up with this and it doesn’t seem to be a widely-appreciated result) we see that most correlations are zero or positive - that is, selecting against one disease selects against another or at worst does nothing. In this ocean of blue, worrying about those few orange squares feels a bit motivated. Hans Jonas-ism says that no medical intervention may ever cause any harm, no matter how much benefit it produces. By this standard, perhaps slightly raising the risk of gallstones in the process of preventing various cancers and psychoses and other forms of human misery is unacceptable. To anyone with the more normal perspective where something with large benefits and tiny downsides is still pretty good, I don’t think the antagonistic pleiotropy argument carries much weight. Ethical Objection: Cost No way around this one: if these products work, they mean that rich people can have healthier/smarter/taller/prettier kids than poor people. One might object that at least they’re in good company: other products which help rich kids get healthier/smarter/taller/prettier than poor kids include private tutors, gyms, hair salons, health insurance, clothing, books, and food. Is this really the time to declare ourselves against this kind of thing? But maybe we should fight against expanding this already-bloated category. Or maybe there’s something more final about a genetic advantage. Maybe a stronger argument is that rich people get first crack at every new technology, but poor people usually follow close behind. The first cellphone, in 1982, cost $12,000 in today’s dollars. Now you can get something a thousand times better for $50, and Kenyan pastoralists use cell phones to call up the local shaman. The trajectory of genetics has been even more striking: sequencing a single genome cost about $100 million in 2000 and is somewhere around $100 today. Polygenic embryo selection has the potential to follow a similar path. There are two associated costs - sequencing the embryos, and running the analysis. Sequencing costs are decreasing and may eventually be comparable to the sorts of genetic screening (for e.g. Down Syndrome) that most families get anyway. Analysis costs are mostly the one-time expense of inventing the predictor; we might expect these to follow the same pattern as generic medications, where cutting-edge technology is jealously guarded and expensive, but last decade’s technology has made its way off patent and is cheap-to-free. A few groups have already created free open-source predictors; so far these are much worse than the private companies’ versions, but one of last year’s ACX Grantees is working on a better one. Also, it would be crazy for any forward-thinking government not to cover this; it could save hundreds of thousands of dollars in future health care expenses. In countries with public health care, this comes directly out of the government treasury; even in the US, it’s covered by Medicare after age 65. The government should be begging people to select embryos. The most persistent cost barrier is likely to be in vitro fertilization itself, a necessary precursor. In the US, 2-3% of babies are born through IVF. For those kids, this is a no-brainer - even if the cost never comes down, the cheaper products are only a fraction of total IVF expense. What about the other 98%? If those parents feel like they have to get embryo selection (and therefore IVF) to keep up, this could be a significant burden. IVF isn’t fun - it requires pumping a woman full of mind-altering hormones for weeks, extracting eggs in a minor surgery, and then implanting embryos in another minor surgery, all with a decent chance that some step will fail and you’ll have to do it all again. It also costs $15,000 in the US (less in poorer countries), and unlike the genetics, the cost has barely gone down in the past twenty-five years. Some countries, including Israel, offer free IVF for anybody who wants it. And universal basic IVF is surprisingly popular even in the usually government-phobic United States - Donald Trump made it part of his campaign platform. So there’s a plausible path to embryo selection for everyone who wants it. But it’s still going to take a while, it will hit different people at different times, and so far11 there’s no way around the month or two of various miserable medical procedures for women. Ethical Objection: Personhood Is it really correct to say that you have reduced someone’s risk of breast cancer by 46%, if what you’ve really done is closer to replacing them with a different person who is 46% less likely to have breast cancer? I cover this one in more depth here. Ethical Objection: Race This one is awkward: right now the technology works best for white people. Most genetic data available for research/commercial use comes from the UK, US, and Europe - areas which are mostly white. Asian biobanks, and those serving US minority communities, have been more reluctant to share data. So we know a lot about the genetics of white people, and only a limited amount about the genetics of anyone else. Companies are suitably embarrassed about this, and researchers in the field are working hard to wring every ounce of information out of the minority data they have. But for now, white people are the clear winner. Here’s data from Herasight: A European family with five embryos and no family history can cut their diabetes risk by 47%, and an African family 29%, with everyone else in between. As usual, all companies say that they adjust their scores based on the couple’s genetic ancestry. As usual, Herasight challenges them to publicly release data on exactly how they performed the adjustments and how well they work. All companies say they are working as hard as they can to improve cross-ancestry portability, but that progress will remain limited until governments collect/release better genetic data on non-white populations. Ethical Objection: Selection At some point, you’ve got to choose. Genomic Prediction and Herasight offer scores that aggregate overall health risks. Some people will follow them slavishly. Other people will try to second-guess them - would you prefer your child have lower cancer risk, or less chance of heart attacks? And this is the best case scenario! Herasight offers predictors for IQ, height and BMI; Nucleus offers those plus eye color and hair color12. A parent might encounter a situation where the embryo with their favorite eye color also has the highest cancer and schizophrenia risk, and choose to doom their child to cancer and schizophrenia because they really want pretty eyes. On average, even if everyone in the world selected for eye color, it wouldn’t raise cancer and schizophrenia risk. No not-deliberately-perverse polygenic selection choice can make your child worse off in expectation. Still, suppose you got cancer, and your mom admitted that she selected you for pretty eyes and didn’t even check the cancer column of the embryo selection report. How would you feel? And would you feel better or worse than someone whose parents didn’t do embryo selection at all, and spent the money on a Caribbean vacation? What if they selected your brother for everything great, then had you naturally? What if they selected you for IQ, but actually you are very stupid, and you were one of the 20% of cases where a predictor that’s right 80% of the time gets it wrong? Mark my words, one day there will be entire subfields of therapy dedicated to these issues. Going Nuclear Even as outsiders criticize the whole field, Herasight has launched a full-scale attack on competitor Nucleus. Herasight’s white paper compares its own predictors (favorably) to those of Orchid and Genomic Prediction… …but refuses to acknowledge Nucleus at all. In a supplementary note, the authors explain why: they accuse Nucleus of being so bad that it would “not yield a reliable or meaningful addition to our analysis”. They say Nucleus has inflated the accuracy of their scores. This is most dramatic for a few conditions like ADHD, where the leading published polygenic score is based on 2,300,000 variants but explains only ~1% of variance in the condition. Nucleus’ score is based on 12 variants13 and (implicitly) claims to explain 3-6%. This doesn’t make sense. Some of Nucleus’ other scores do use millions of variants. But many of these are 5-10 year old scores downloaded from open-source catalogs, whose accuracy statistics are easily available and far less than Nucleus claims. Here is what Herasight finds when they double-check Nucleus’ numbers: On their Substack, Herasight also criticizes Nucleus’ monogenic screening product. They point out cases where it fails to properly screen for the conditions it claims. For example, the Nucleus website advertises screening for spinal muscular atrophy: But on their gene list… …they don’t screen for SMN, which causes 95% of spinal muscular atrophy cases. They only screen for UBA1, which causes a distinct and much rarer condition called x-linked infantile spinal muscular atrophy. Professional organizations publish guidelines for what genes need to be screened in a screening product, and Nucleus does not appear to be following them. In further discussion, Herasight continued with exhaustive criticism of essentially everything Nucleus had ever done down to the smallest detail. Nucleus reports list the same baseline disease risk regardless of patient ancestry, but different ancestry groups should have different risks14. Nucleus’ physician reports sometimes list lower-than-average risk for patients with positive polygenic scores15. Nucleus’ age-based risk tables don’t distinguish between age and cohort effects (is this bad? see footnote16). My favorite critique is that Nucleus wrote a blog post criticizing competing company Orchid… …which included a section on how Orchid is a polygenic selection company, and polygenic selection companies are inherently “sketchy” and “honestly should be illegal”. But Nucleus is also a polygenic selection company! This is like Marlboro attacking Camel on the grounds that cigarettes are addictive and should be banned! Obviously something went wrong here - my guess is AI - and it’s a really bad look, especially when these scientific issues are so hard to litigate, and so many of us will have to go off gestalt impressions of corporate culture. Nucleus states that they validate their models internally and intend to make their results public soon. A Foothill Of The Future It’s hard not to love this technology. Lots of people (and the aforementioned professional organizations) manage anyway, but it’s hard. If this were a single-use medical treatment, delivered by a doctor after someone got the relevant condition, it would be one of the biggest advances of the decade - imagine a drug that cures 10 - 40%17 of breast cancers with no side effects! But in fact, it works for breast cancer, and schizophrenia, and heart attacks, and approximately everything else. The only things comparable are antibiotics and GLP-1RAs. And then there’s the IQ effects. Even after studying the literature, people have wildly different opinions about the importance of IQ. One of the most important debates is to what degree IQ differences are a cause of poverty, a consequence of poverty, or both. I lean towards both - a country with limited access to schools and medical care will have low average IQ, but as a consequence it probably won’t become the next big semiconductor hub. This technology could close half the IQ gap between poor and middle-income countries, or between middle-income and rich. Or it could give rich countries average IQs that have never been seen before, and let us see what kind of O-ring technologies (and new forms of social cooperation) lie just beyond the frontier. (this is the nice quantifiable argument in favor of IQ enhancement, but I find myself more convinced by fuzzier things - how much is it worth to be able to enjoy great art and literature? To fully comprehend what we know of nature, and be able to fully appreciate the mystery of the rest? To have a sense of why society works the way it does, instead of feeling like you’re being blown back and forth by institutions you don’t really understand? Amateur psychoanalysts like to say that the only people who care about IQ are those looking for an excuse to boast about how high their own is, but my experience is the opposite: I care about IQ because I bang up against the limits of my own a thousand times a day, and I hate it. I fantasize about ways to make my children smarter than I am for the same reason a dog confined in a tiny crate might fantasize about getting her puppies adopted out to a nice house with a big grassy yard.) My biggest qualm is that it might not matter. This is such a tiny foothill, flanking such a vast and foreboding range of mountains, that it might be a mistake to care about it at all. Selecting the best of five or ten embryos is not a very effective way to get the genes you want. There are things in the pipeline that will make this look like Hippocrates draining black bile. By the time the first polygenically selected children are adults, they’ll be old news. And then there’s AI. The average age at diagnosis for Type II diabetes is 45 years. Will there still be people growing gradually older and getting Type II diabetes and taking insulin injections in 2070? If not, what are we even doing here? Many people in the transhumanist community are still bullish on this technology. They think - well, there’s still an outside chance that something comes up and AGI takes another few decades. If we can enhance humans to be smarter, healthier, and more determined by the time it arrives, maybe we’ll have a better chance. Or maybe, if there’s a positive optimistic vision of a human-based high-tech future, people will be more willing to delay AI in the first place. I like this argument, but I also think it’s worth stepping back. What’s the point of anything? Why have kids at all in a world that’s changing this fast? Why save for the future? At some point your answer has to be romantic and aesthetic - it’s never been clear whether anything you do matters in any ultimate sense, but you’ve got to act as if it does and hope for the best. From that perspective, this is the most romantic technology of all. You’re not just giving a better life to your kids. Genes travel from generation to generation; you’re giving a better life your grandkids, your great-grandkids and so on to the point 1.77*log₂(population) generations from now when you are the ancestor of everybody and nobody. Somebody in Macaronesia in 3525 AD will avoid getting breast cancer because of you (if there is still cancer; if there are still breasts). Some combination of reasonable cost-benefit analysis and romantic/aesthetic commitments makes me want to have children despite the uncertainty, and the same combination made me sign up to use this technology despite the same. More later on how that’s going. 1I’m slightly mixing up two different things here - Down Syndrome can be detected with an aneuploidy test, but cystic fibrosis takes a more involved PGT-M test. 2There are two separate questions here. First, how much would diabetes risk decline if you selected the embryo with the lowest risk for diabetes - something you have no reason to do, since you have no reason to privilege diabetes risk over risk of any other disease? Second, how much would diabetes risk go down if you selected the embryo with the lowest health risk overall? Genomic Prediction’s their risk calculator calculator shows, seemingly paradoxically, that you get -38% relative risk by selecting against diabetes alone, but -41% relative risk by selecting against everything at once. Over email, they stand by this surprising result, saying that “for a couple of diseases (type II diabetes and CAD), the EHS actually accomplishes a larger risk reduction than the individual predictors. The explanation is that the EHS takes into account multiple PRS of diseases with high comorbidity”. See eg Figure 3 here: …and the section of the post called “Antagonistic Pleiotropy” for more. However, this paradoxical benefit is only true for a few conditions like diabetes - for everything else, selecting on health index does better than you would naively think, but still does not decrease the risk of a given condition as much as selecting against that condition directly. 3That is, new mutations in that particular baby, as opposed to older mutations already present in the parents. 4Conflicts of interest: I have used Orchid’s and Herasight’s products on my own embryos (not the ones used to conceive my existing kids, but for a potential third child), employees of Genomic Prediction and Herasight have been extremely helpful in contributing expertise to ACX posts on genetics, and I might invest in this field at some point (though haven’t done so yet). This post started as Herasight asking me to write about their white paper, then spiraled out of control. There were some unexpected time pressures and the result is that I didn’t get a chance to run everything in Herasight’s white paper by their competitors as thoroughly as I would like. Although I talked to representatives of all four companies profiled here, I feel like this probably reflects Herasight’s perspective better than other companies’, and that this is a major flaw. If other companies have responses, I’ll publish them. Thanks to all companies involved for their assistance on this article. Finally, I am favorably disposed toward Herasight because of how I learned about them: a professor named Jonathan Anomaly got cancelled from Penn for being too gung-ho about genetic enhancement, and used his newfound freedom to join a very-early-stage Herasight, raise their ambitions, and sell everyone (including me) on the idea. I grew up on a diet of books and movies about mad scientists, and I’m a sucker for a story about a guy named Doctor Anomaly pursuing revenge against the small-minded fools who destroyed his career by creating a race of superbabies. 5The version of the tool I looked at said 5.9 points for five embryos, up to 9 points for twenty embryos. The version of the tool on their current said says 5.3 - 9, so they might have recalculated after I finalized this article. 6Used in quotation marks because these scores were fine for the predictive tasks they were applied for - they just weren’t finding genes that directly caused the outcome of interest. 7Conflict of interest notice: this table was originally unadjusted. A representative of Herasight claimed that this was unfair, because each company used slightly different reporting conventions, and offered to correct for this in a neutral way. I retraced their reasoning, confirmed that the correction did not especially benefit Herasight at the expense of other companies, and accepted the correction. The original unadjusted table is below: Herasight was insufficiently comfortable with Nucleus’ methodology to even be willing to posit a corrected value, so I left their self-reported value in gray. 8Zagorsky (2007) says an extra IQ point means $234-$616/year in higher salary. The midpoint of $425 equals $670 in today’s dollars; assuming a forty-year career, Nucleus’ +1 point estimate is worth $26,800 (vs. $9,249 Nucleus cost) and Herasight’s +6 point estimate is worth $160,800 (vs. $53,250 Herasight cost). 9As part of researching this article, I asked all four major companies about their within-family validation strategies. Here are some details: Genomic Prediction discusses their strategy in this paper. The results are complicated to interpret - the within-family numbers often have such wide error bars that they overlap with both the across-family numbers and with zero - but looking qualitatively it seems like most scores on average lose about 25% of their risk reduction ability (though averages might not be the right way to do this, and some might be much more affected than others). Their website reports unadjusted, not within-family validated numbers; GP says they say this clearly on their site (which is true), Herasight counters that they still present their numbers as applicable to embryo selection (which is also true). To get the most applicable-to-embryo-selection numbers, you might want to adjust GP’s stated numbers down somewhat; it’s hard to say exactly how much, but maybe 20 - 25%?

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