Quick Takes

Sci-fi worldbuilding idea of little interest to anyone other than me: --Imagine that the space race had continued and accelerated after the 70s, perhaps due to the Outer Space Treaty not existing and there being a corresponding rush to grab territory, with the USSR and USA and later China planting flags on various celestial bodies and claiming them (e.g. imagine an alternate treaty that said when a human plants a flag, takes a photo, and brings back a regolith sample to a nation's capitol, that nation gets the territory for 100km around the flag.) --So by 2030, the AI race is about ~5 years behind schedule because for decades there has been less top talent going into software and computers and more top talent going into various space ventures. Silicon Valley isn't a household name, instead there's the Space Coast. --The space ventures are like 15 years ahead of schedule though; even in 2015, there was the equivalent of fully reusable Starship + a very small moon base and a thousand people living in a large ISS. By 2030, launch costs have dropped even further, to something like $15/kg, and six million people live in space, as follows: (1) LEO City (or LEO for short) is an international space station that houses about 4 million people. It's the port city, so to speak, connecting the Earth to space and connecting space to the Earth. Optimized mega-Starships fly many times a day from various Earth cities to dock with LEO City and return home; from LEO City passengers and cargo transfer to other ships optimized for zero-gravity or low-gravity travel, that use different kinds of propulsion, fuel, etc. LEO city has almost a hundred million tourists visit it each year (indeed almost 1 out of the 4 million people in it at any given time is a tourist basically) plus it has a decent-sized high-tech sector (various kinds of manufacturing that are easier in zero gravity, low gravity, and/or vacuum) plus it has a nascent industrial base for e.g. refining asteroid ore and recy

It seems to me like there was a sort of gentleman's agreement not to focus on killing the leader of the other country that you are at war with. You wanted someone with the ability and authority to surrender. The Trump administration now seems to pursue a policy of directly going after the leaders. It will be interesting to see whether in return, other countries will also want to do more strikes against Western leaders (and particularly US politicians).

The only way LLMs learn deep skills is with RLVR, and it's not automated for novel skills. There is room for about a billion RLVR tasks with 2026-2027 compute, and largely automating their creation makes LLMs more cognitively self-sufficient. This milestone might even be called "prosaic RSI", even though it's not true RSI that develops fundamentally new ways of implementing cognition. One gigawatt of compute costs about $12bn per year, so 3 months will cost $3bn. For a large model, a million output tokens might cost $10 for the API provider, so 3 months on 1 GW of compute produces 300T output tokens. An RLVR task might run for 20K tokens, 16 times to produce outcomes with different levels of success in completing the task. This is about 300K tokens per task, so the 300T tokens suffice for a billion tasks. Currently, there wouldn't be this many unique RLVR tasks, but it would probably be useful if there were, and that can't be done manually. Using many more tasks (and a general method for choosing them) should also help with the jaggedness of RLVR-trained capabilities. In contrast, continual learning would in the near term likely only perform at the level of in-context learning, which is incapable of things like learning to play chess at the level of the most capable humans. And automating RLVR should be much easier in the context where it's currently practiced, within AI companies as they are training the next version of a model. Automating RLVR so fully that it can be used post-deployment would be much more difficult, and also issues with sample efficiency would be more relevant there. So some gesturing at "RSI" in the very near term (or at making a feature-complete "AGI" that learns "on its own") might be about significantly greater (even if not full) automation of RLVR task creation during model training.

I remember watching a lot of modern bombing campaigns growing up and implicitly comparing them to WWII strategic bombing. I always assumed that the WWII US Army Air Force would deliver more munitions against enemy cities than the modern US Air Force could. After all, those massive strategic bombers could carry more bombs than the smaller modern fighter-bombers, which were optimized for agility and speed, right? Wrong. A modern fighter bomber carries between 7-15 tons of ground-attack munitions on a sortie, which is generally more than the up to 9 tons of bombs carried by a B-29. A large scale strategic bombing raid in WWII against Japan had around 500 planes dropping a total of 5000 tons of munitions. The US averaged something like 1 raid per day at the peak of 1945.  A single US carrier can generate 2-3 sorties per plane per day from its up to 100 aircraft. A pair can basically do the WWII strategic bombing campaign against Japan by themselves. (Of course, there's pilot and crew fatigue, ect, ect) That's not even taking into accound the benefits of precision, which are of course massive. I read somewhere that the US and Israel generated something like 1500 aircraft sorties against Iran in the first day of the present conflict. That's close to 10 kilotons of munitions being dropped against individual targets. This is why I really don't like the "only nukes can cause surrender from strategic bombing" narrative. Conventional airforces can and have done far worse damage to civilian and military targets than 2 nuclear bombs.

I claim that even if the openai contract is not meaningfully weaker safety wise, it is still bad for openai to publicly signal solidarity with ant but then sign with DoW. suppose hypothetically the only difference between the openai and anthropic contracts is that the DoW wanted a snicker bar, and anthropic didn't want to give DoW the snickers bar. even then, it would be a huge dick move for openai to publicly signal solidarity, and then sign with DoW to give them the snickers bar.

links 3/2/26: https://roamresearch.com/#/app/srcpublic/page/03-02-2026   * https://arxiv.org/abs/2602.07164 more effective way to elicit "in character" personalities from LLMs  * https://blog.egan.bio/p/reviving-estrogen-replacement-therapy  overview on why hormone therapy for menopause is losing its black-box label (tl;dr: it's not a totally unreasonable choice) * https://www.owlposting.com/p/neurotechnology-for-cancer-ben-woodington Abhishaike Mahajan interview with the founders of Coherence Neuro, which uses neuromodulation to disrupt (mostly brain) tumors. neurotransmitters can be tumor growth factors!  * https://en.wikipedia.org/wiki/Fixed-income_attribution#Modeling_the_yield_curve functions that define the yield curve for bonds  * https://lydialaurenson.substack.com/p/social-contracts-moral-order-weaving Lydia Laurenson on "missing stair" type dynamics, how hard it is to enforce norms against bad actors in a community * https://www.hyperdimensional.co/p/clawed Dean Ball is sticking his neck out to do the right thing here  * https://www.clinicaltrialsabundance.blog/p/ai-wont-automatically-accelerate sorry Dario Amodei, AI can't make all clinical trials take under a year; sometimes you need follow-ups longer than that for the actual course of disease to become clear! this is crazypants and we need to keep in mind the actual places there's room for improvement on trial times -- reducing purely regulatory delays, or procedural time (like patient recruitment time) as opposed to medically-relevant follow-up time, and finding surrogate endpoints where good ones exist. (I kinda hate surrogate endpoints cause so many suck, but yeah, clearly, when you find a good one it will speed up trials substantially.) Ruxandra Tesloianu bringing the common sense and much-needed urgency, as usual. * https://marginalrevolution.com/marginalrevolution/2026/02/if-you-have-the-right-to-die-you-should-have-the-right-to-try.html right-to-try is the least-good and most popular fo

fyi for those of you who do math: Some coding agents are now (~barely) able to usefully write Lean proofs, for not-totally-trivial statements. This matters to me (and might matter to you) due to differences in how easy it is to understand Lean statements + have the Lean compiler check the proofs, compared to correctly hand-verifying proofs in English. I spent a couple hours this weekend having them prove a thing about graph theory that's useful for some distributed systems stuff I was thinking about last year. Before going through the process I was only ~80% sure the statement was true; I'm now more like 99%; that last 1% would mostly come from my more-carefully double checking that the Lean statement corresponds to the thing I actually care about. For this problem at least, Claude was superior to Gemini at actually using the lean compiler and iterating, but Gemini was superior at structuring the code at the top level / figuring out the broad strokes of the proof. Very highly recommend using agents instead of trying to do this via chat, since the process here clearly required a feedback loop rather than merely spitting out correct proofs.