I'm not sure how similar or dissimilar the Taiwan case is, but in Brazil I've seen people discussing (and acting) on strategies like this, and in almost every case going to top-tier Brazilian schools with better launchpads (e.g. professors having stronger connections abroad; more like-minded peers; exchange programs) seems to have paid off better than going to mid-tier schools, even when accounting for the much more rigid grading at the top-tier ones or that's harder to get a top-tier class rank, or for how much harder it is to achieve a top class rank there
Nice post! Two quick reactions reactions as a Brazilian working on AI impacts for the past ~3 years now:
From a narrow EA POV, for early-career people who e.g. don't have established career capital, I think the "fit in" approach clearly EV-mogs the other options. Nudging decisions in the US, the UK, or the EU has much higher impact, at least from the napkin calculations I tried. Of course, it's valid to defend the other lanes for other reasons -- one simply doesn't want to move, isn't that cause-neutral, etc. -- but the simple-EV case seems hard to make unless they have exceptional career capital tied to their specific country. I don't really like this conclusion, but I have high confidence in it.
The debate in Brazil seems yoo unfocused -- too ambitious and not ambitious enough at the same time. I get overwhelmed every time I try to catch up, and this leaves me paralyzed when I consider contributing occasionally somehow to "build in parallel" initiatives unless there are very specific asks. An EU-AI-Act-style regulation, an AI strategy, all the misinformation discussion that intermingles with AI, social-media-regulation discussions that also intermingle with this, etc. I think this unfocusedness is downstream of a lack of clarity about what the Brazilian government's [or the Brazilian state's] goals with AI are. And this seems to contaminate the discussions I see among EA-aligned Brazilians working on AI governance.
very low confidence, but I think 1) orgs tend to advertise research roles more widely than ops roles, 2) with longer decision times, and 3) research roles outnumber ops roles by something like 2:1 or even 4:1, so I wouldn't read too much into raw numbers of job postings
"trillions of capital are being flooded into accelerating AI development across industries" fwiw this seems only correct under a very expansive definition of accelerating AI development, e.g. all hyperscaler capex (including non-AI spend and AI inference) was ~$500 billion in 2025
"while a measly $50 million dollars" this seems outdated -- Coefficient Giving’s Technical AI Safety team made 140 million USD in grants in 2025. If you consider all other funding (the UK AISI's founding budget was ~$125M, internal lab speding on technical AI safety, other grantmakers, etc.), it's plausible we're over $1 billion.
I think oftentimes the relevant counterfactual is not "this person would be doing even more impactful work in a highly-impactful area" but "this person would not be working in a high-impact area whatsoever"
Some notes on OpenAI disproving the Erdős unit distance conjecture (from a non-mathematician):
First, this is big. A notorious math conjecture being disproved by AI would be sci-fi 10 years ago. In my layman's read, this is plausibly the most prominent math result in the last 12 months -- AI, centaur, human or whatever.
Second, it rebutted an Erdős conjecture, and I found it curious that the first clear math breakthrough goes against consensus. There are a few potential reads to this: a) this seems to go against the LLMs-are-sycophantic-machine claims; b) even if LLMs are that sycophantic, exploring different intellectual paths is so cheap to them that sycophancy doesn't quite matter as much; c) it may mean that AI is sycophantic at the user-level but not at the literature-level, which actually may be great for finding novel solutions, but is also the very thing that enables e.g. AI psychosis.
Third, it's hard to wrap my head around having an intelligence that is probably at the level of a very promising Terence Tao graduate student -- but not Tao-level yet. It allows exploring many hypotheses/conjectures/counter-examples/constructions that go against intuitive human ~quick evaluation/priors of what is promising, simply because they can be so exhaustive in their exploration. It’s the country part of a “country of geniuses in a datacenter”
Fourth, the solution combines insights/techniques from different fields. It pulled off an answer that used algebraic number theory to solve a combinatorial geometry problem. Mathematicians seem to think how it did it may unlock more. In a world where specialization is deemed necessary structurally/institutionally, AIs have a special advantage even with "mere" cross-field interpolation [tbc, in this case there seems to be substantial extrapolation in my layman's read]. Also, the constraint here may not be human intelligence per se. Surely we don't have a current Riemann-level mathematician partly because of bottlenecks of human intelligence as things specialized, but also institutionally/organizationally we may have incentivized specialization too much besides what was intellectually needed -- and organizational innovations may actually be the bottleneck for solving some big Millennium-level math problems [e.g. focused research organizations that allow for interdisciplinary moonshoots]
Fifth, we don't know much about how many other math problems OpenAI explored. Is this the first [prominent] one they got a solution to after running through all (relatively prominent) ~Erdős problems? I don't want to come across as moving goalposts -- again, this is really big. But what does it mean if they did an extensive evaluation of various problems and this particular one is the first one to land? My best sense right now is that they probably ran a search across various problems, had internal employees [that include very bright mathematicians] to verify what was most promising, then got Gowers/other prominent mathematicians involved to double-check. This may mean that various other problems have been solved but is currently bottleneck by expert human verification.
My sense is that you're right. IIRC diminishing returns are more salient for AMF than for GiveDirectly, and one of the key arguments pro GiveDirectly would be that flat returns persist longer -- but probably when they make this argument they're thinking on the scale of $100M–$1B, not hundreds of billions.
Perhaps the best case in point would be Bolsa Família: ~$30B yearly budget, one of the largest conditional cash transfer programs in the world, increased ~5-fold over the past few years, noticeably turned into a less effective program imho, but still seems like one of the most effective programs from the Brazilian government
I wouldn't think of this as a matter of thresholds but continuously decreasing returns, tho
I agree. Also, my sense is that MATS and GovAI's fellowships are generally more senior, or require you to be more well-versed in the AI safety/governance universe, than Pivotal, ERA, and Talos -- at least, that's how I strongly perceived it as an applicant.
Lighting has been getting ridiculously cheaper. And for the most part we seem to be not taking advantage of that positive externality: reducing crime through better lighting. This has been battle-tested as one of the effective ways for public security, see Chalfin, Hansen, Lerner & Parker (2022), an RCT in NYC public housing finding ~36% reductions in nighttime outdoor index crimes from added street lighting. Many, many major cities still haven't copied this at the right levels!
But we're also getting substantially negative externalities of bright lighting. Office buildings that never turn off their lights because why would they care. Apropos the new office building that just opened next to my housing. This may alimentate NIMBY spirits in me, God forbid. Kyba et al. (2017) document that Earth's artificially lit outdoor area grew 2.2% per year from 2012 to 2016, with the LED transition producing a rebound effect instead of getting savings. Jevons paradox and such.
Also, this has all sorts of annoyances. I think malls, pharmacies, and hospitals have all become much brighter since my childhood. I may be more sensorially overloaded than most people, but this does meaningfully affect my qualia, so much that Pigou himself would collect taxes from the pharmacies with dozens and dozens of LEDs, while Coase would advocate that I have the natural property right of not being assaulted with that much lumen while buying a Tylenol. This does affect wellbeing of more than just me (Cho et al. 2015). But lightly enough, ha, to not be a topic of discussion.
I'm not sure how similar or dissimilar the Taiwan case is, but in Brazil I've seen people discussing (and acting) on strategies like this, and in almost every case going to top-tier Brazilian schools with better launchpads (e.g. professors having stronger connections abroad; more like-minded peers; exchange programs) seems to have paid off better than going to mid-tier schools, even when accounting for the much more rigid grading at the top-tier ones or that's harder to get a top-tier class rank, or for how much harder it is to achieve a top class rank there
Nice post! Two quick reactions reactions as a Brazilian working on AI impacts for the past ~3 years now:
The debate in Brazil seems yoo unfocused -- too ambitious and not ambitious enough at the same time. I get overwhelmed every time I try to catch up, and this leaves me paralyzed when I consider contributing occasionally somehow to "build in parallel" initiatives unless there are very specific asks. An EU-AI-Act-style regulation, an AI strategy, all the misinformation discussion that intermingles with AI, social-media-regulation discussions that also intermingle with this, etc. I think this unfocusedness is downstream of a lack of clarity about what the Brazilian government's [or the Brazilian state's] goals with AI are. And this seems to contaminate the discussions I see among EA-aligned Brazilians working on AI governance.
very low confidence, but I think 1) orgs tend to advertise research roles more widely than ops roles, 2) with longer decision times, and 3) research roles outnumber ops roles by something like 2:1 or even 4:1, so I wouldn't read too much into raw numbers of job postings
"trillions of capital are being flooded into accelerating AI development across industries" fwiw this seems only correct under a very expansive definition of accelerating AI development, e.g. all hyperscaler capex (including non-AI spend and AI inference) was ~$500 billion in 2025
"while a measly $50 million dollars" this seems outdated -- Coefficient Giving’s Technical AI Safety team made 140 million USD in grants in 2025. If you consider all other funding (the UK AISI's founding budget was ~$125M, internal lab speding on technical AI safety, other grantmakers, etc.), it's plausible we're over $1 billion.
I think oftentimes the relevant counterfactual is not "this person would be doing even more impactful work in a highly-impactful area" but "this person would not be working in a high-impact area whatsoever"
Some notes on OpenAI disproving the Erdős unit distance conjecture (from a non-mathematician):
My sense is that you're right. IIRC diminishing returns are more salient for AMF than for GiveDirectly, and one of the key arguments pro GiveDirectly would be that flat returns persist longer -- but probably when they make this argument they're thinking on the scale of $100M–$1B, not hundreds of billions.
Perhaps the best case in point would be Bolsa Família: ~$30B yearly budget, one of the largest conditional cash transfer programs in the world, increased ~5-fold over the past few years, noticeably turned into a less effective program imho, but still seems like one of the most effective programs from the Brazilian government
I wouldn't think of this as a matter of thresholds but continuously decreasing returns, tho
I agree. Also, my sense is that MATS and GovAI's fellowships are generally more senior, or require you to be more well-versed in the AI safety/governance universe, than Pivotal, ERA, and Talos -- at least, that's how I strongly perceived it as an applicant.
My current view of that is best summarized by: "Drop the 'AI'. Just policy. It's cleaner"
Lighting has been getting ridiculously cheaper. And for the most part we seem to be not taking advantage of that positive externality: reducing crime through better lighting. This has been battle-tested as one of the effective ways for public security, see Chalfin, Hansen, Lerner & Parker (2022), an RCT in NYC public housing finding ~36% reductions in nighttime outdoor index crimes from added street lighting. Many, many major cities still haven't copied this at the right levels!
But we're also getting substantially negative externalities of bright lighting. Office buildings that never turn off their lights because why would they care. Apropos the new office building that just opened next to my housing. This may alimentate NIMBY spirits in me, God forbid. Kyba et al. (2017) document that Earth's artificially lit outdoor area grew 2.2% per year from 2012 to 2016, with the LED transition producing a rebound effect instead of getting savings. Jevons paradox and such.
Also, this has all sorts of annoyances. I think malls, pharmacies, and hospitals have all become much brighter since my childhood. I may be more sensorially overloaded than most people, but this does meaningfully affect my qualia, so much that Pigou himself would collect taxes from the pharmacies with dozens and dozens of LEDs, while Coase would advocate that I have the natural property right of not being assaulted with that much lumen while buying a Tylenol. This does affect wellbeing of more than just me (Cho et al. 2015). But lightly enough, ha, to not be a topic of discussion.