Do we have some page containing resources for rationalist parents, or generally for parents of smart children? Such as recommended books, toys, learning apps, etc. I found tag https://www.lesswrong.com/w/parenting but I was hoping for some kind of best textbooks / recommendations / reference works but for parents/children.

I think people in these parts are not taking sufficiently seriously the idea that we might be in an AI bubble. this doesn't necessarily mean that AI isn't going to be a huge deal - just because there was a dot com bubble doesn't mean the Internet died - but it does very substantially affect the strategic calculus in many ways.

(Quick Thought) Perhaps the goal for existing work targeting AI safety is less to ensure that AI safety happens, and more to make sure that we make AI systems that are strictly[1] better than the current researchers at figuring out what to do about AI safety. I'm unsure how hard AI safety is. But I consider it fairly likely that mid-term (maybe 50% of the way to TAI, in years) safe AI systems are likely to outperform humans on AI safety strategy and the large majority of the research work.  If humans can successfully bootstrap more capable infrastructure than us, then our (humans) main work is done (though there could still be other work we can help with). It might well be the case that the resulting AI systems would recognize that the situation is fairly hopeless. But at that point, humans have done they key things they need to do on this, hopeless or not. Our job is to set things up as best we can, more is by definition impossible. Personally, I feel very doomy about humans now solving for various alignment problems of many years from now. But I feel much better about us making systems that will do a better job at guiding things then we could.  (The empirical question here is how difficult it is to automate alignment research. I realize this is a controversial and discussed topic. My guess is that many researchers will never agree with good AI systems, and always hold out on considering them superior - and that on the flip side, many people will trust AIs before they really should. Getting this right is definitely tricky.)  [1] Strictly meaning that they're very likely better overall, not that there's absolutely no area humans will be better than them.

links 12/11/25: https://roamresearch.com/#/app/srcpublic/page/12-11-2025   * example of AI report, evaluating preclinical validation of a drug target: * https://elicit.com/review/824f5bb9-bc8e-4c06-b277-a8c342b19bbd * https://www.macroscience.org/p/to-get-more-effective-drugs-we-need Ruxandra Tesloianu and Jack Scannell make the case for clinical trial reform; I couldn't agree more * https://astera.org/neuroscientist-doris-tsao-joins-astera-to-lead-its-new-neuroscience-program/ Astera is doing neuro now. consciousness is a focus. * https://www.averi.org/ Miles Brundage's new AI safety verification org

Learned about 'Harberger tax' recently. The motivation is like * private assets (private use and excludable) incentivise investment/productive improvement/development well * but private ownership of non-liquid, non-fungible, idiosyncratic things (factories, houses, natural resources maybe, AI systems maybe?) acts like a monopoly, distorting allocation (more productive/valued uses aren't often realised) * e.g. NIMBYism * e.g. 'holdout pricing' * e.g. inefficient bargaining due to information asymmetry * common ownership and lease auctioning incentivises efficient allocation The pitch is like, can we do something in between? * self-assessment of value (reveals something close to honest valuation) * pay some rate of tax on that self-assessment * you have to sell at that self-assessed value if someone wants to buy They claim this keeps most of the benefit of investment incentivisation, because things are mostly private in practice, but substantially improves allocative efficiency by lubricating the more valuable trades. Anyway, mainly it's interesting because getting into the gubbins of particular proposals helps me learn about the relevant dynamics in general, but also I wondered if there's something in this vicinity that could work nicely for AI development and AI deployment. (Like maybe a Harberger tax on compute, or on AI systems, or...)

My preference ordering over approaches to surviving the next decade, assuming I had unlimited political capital, is (don't build ASI and): 1: Solve mind uploading 2: ... or at least intelligence amplification (before proceeding, and then maybe do) 3: Imitation learning (with radically improved theoretical foundations) 4: ... or corrigibility / oracles (and don't) 5: Try to build aligned (or controlled) autonomous ASI agents Unfortunately 5 seems to be profitable, in the short term, so I guess that's what we're doing. In fact, the "plan" seems to be the exact reverse or so of my preference ordering on what it should be. 

I feel like there was a time just after ChatGPT became big where there were so many different new & competing frameworks for what exactly was going on, shard theory and simulators in particular but also active inference the MIRI views and all the people rolling their own ontologies with which to understand LLMs, and notably trying to make those ontologies explicit and comparing them against each other. Maybe I'm just around very different people now or doing very different work than I was (I am on both counts), but those conversations aren't really happening anymore. I don't know whether its for good or ill all things considered, but I do get nostalgic for them sometimes.