This seems like an important crux to me, because I don't think greatly slowing AI in the US would require new federal laws. I think many of the actions I listed could be taken by government agencies who over-interpret their existing mandates given the right political and social climate. For instance, the eviction moratorium during COVID, obviously should have required congressional action, but was done by fiat through an over-interpretation of authority by an executive branch agency. 

What they do or do not do seems mostly dictated by that socio-political climate, and by the courts, which means less veto points for industry.

I agree that competition with China is a plausible reason regulation won't happen; that will certainly be one of the arguments advanced by industry and NatSec as to why it should not be throttled. However, I'm not sure, and currently don't think it will, be stronger than the protectionist impulses,. Possibly it will exacerbate the "centralization" of AI dynamic that I listed in the 'licensing' bullet point, where large existing players receive money and de-facto license to operate in certain areas and then avoid others (as memeticimagery points out). So for instance we see more military style research, and GooAmBookSoft tacitly agree to not deploy AI that would replace lawyers.

To your point on big tech's political influence; they have, in some absolute sense, a lot of political power, but relatively they are much weaker in political influence than peer industries. I think they've benefitted a lot from the R-D stalemate in DC; I'm positing that this will go around/through this stalemate, and I don't think they currently have the softpower to stop that.

hah yes - seeing that great post from johnwentsworth inspired me to review my own thinking on RadVac. Ultimately I placed a lower estimate on RadVac being effective - or at least effective enough to get me to change my quarantine behavior - such that the price wasn't worth it, but I think I get a rationality demerit for not investing more in the collaborative model building (and collaborative purchasing) part of the process.

I'm sorry I didn't see this response until now - thank you for the detailed answer!

I'm guessing your concern feels similar to ones you've articulated in the past around... "heart"/"grounded" rationality, or a concern about "disabling pieces of the epistemic immune system". 

I'm curious if 8 mo's later you feel you can better speak to what you see as the crucial misunderstanding?

Out of curiosity what's one of your more substantive disagreements with Thiel?

I'd be quite interested in reading that guide!

Forecast - 25 mins

• I thought it was more likely that in the short run there could be a preference cascade among top AGI researchers, and as others have mentioned due to the operationalization of top AGI researchers might be true already.
• If this doesn't become a majority concern by 2050, I expect it will be because of another AI Winter, and I tried to have my distribution reflect that (a little hamfistedly).

Thanks for posting this. I recently reread the Fountainhead, which I similarly enjoyed and got more out of than did my teenage self - it was like a narrative, emotional portrayal of the ideals in Marc Andreessen's It's Time to Build essay.

I interpreted your section on The Conflict as the choice between voice and exit.

The larger scientific question was related to Factored Cognition, and getting a sense of the difficulty of solving problems through this type of "collaborative crowdsourcing". The hope was running this experiment would lead to insights that could then inform the direction of future experiments, in the way that you might fingertip feel your way around an unknown space to get a handle on where to go next. For example if it turned out to be easy for groups to execute this type of problem solving, we might push ahead with competitions between teams to develop the best strategies for context-free problem solving.

In that regard it didn't turn out to be particularly informative, because it wasn't easy for the groups to solve the math problems, and it's unclear if that's because of the problems selected, the team compositions, the software, etc. So re: the larger scientific question I don't think there's much to conclude.

But personally I felt that by watching relay participants I gained a lot of UX intuitions around what type of software design and strategy design is necessary for factored strategies - what I broadly think of as problem solving strategies that rely upon decomposition - to work. Two that immediately come to mind:

• Create software design patterns that allow the user to hide/reveal information in intuitive ways. It was difficult, when thrown into a huge problem doc with little context, to know where to focus. I wanted a way for the previous user to only show me the info I needed. For example, the way workflow-y / Roam Research bullet points allow you to hide unneeded details, and how if you click on a bullet point you're brought into an entirely new context.
• When designing strategies try focusing on the return signature: When coming up with new strategies for solving relay problems, at first it was entirely free form. I as a user would jump in, try pushing the problem as far as I could, and leave haphazard notes in the doc. Over time we developed more complex shorthand and shared strategies for solving a problem. One heuristic I now use when developing strategies for problem solving that use decomposition is to prioritizing thinking about what each sub part of the strategy will return to the top caller. That clarifies the interface, simplifies what the person working on the sub strategy needs to do, and promotes composability.

These ideas are helpful because - I posit - we're faced with Relay Game like problems all the time. When I work on a project, leave it for a week, and come back, I think I'm engaging in a relay between past Ben, present Ben, and future Ben. Some of these ideas informed my design of templates for collaborative group forecasting.