Even in probabilistic terms, the evidence of OpenAI members respecting their NDAs makes it more likely that this was some sort of political infighting (EA related) than sub-year takeoff timelines. I would be open to a 1 year takeoff, I just don't see it happening given the evidence. OpenAI wouldn't need to talk about raising trillions of dollars, companies wouldn't be trying to commoditize their products, and the employees who quit OpenAI would speak up. 

Political infighting is in general just more likely than very short timelines, which would go in counter of most prediction markets on the matter. Not to mention, given it's already happened with the firing of Sam Altman, it's far more likely to have happened again.

If there was a probability distribution of timelines, the current events indicate sub 3 year ones have negligible odds. If I am wrong about this, I implore the OpenAI employees to speak up. I don't think normies misunderstand probability distributions, they just usually tend not to care about unlikely events.

I assume timelines are fairly long or this isn’t safety related. I don’t see a point in keeping PPUs or even caring about NDA lawsuits which may or may not happen and would take years in a short timeline or doomed world.

This style of thinking seems illogical to me. It has already clearly resulted in a sort of evaporative cooling in OpenAI. At a high level, is it possible you have the opposite of a wishful thinking bias you claim OpenAI researchers have? I won't go into too much detail about why this post doesn't make sense to me. as others already have. 
But broadly speaking:

• I doubt rationality gives you too much of an advantage in capabilities research, and believing this when on a site full of rationalists seems a little pretentious almost. 
• I also have no idea how any alignment research so far has helped capabilities in any way.  I don't even know how RLHF has helped capabilities. If anything, it's well documented that RLHF diminishes capabilities (base models can for example play chess very well). The vast majority of alignment research, especially research before LLMs,  isn't even useful to alignment (a lot of it seems far too ungrounded).   
• There was never a real shot of solving alignment until LLMs became realized either.  The world has changed and it seems like foom priors are wrong, but most here haven't updated. It increasingly seems like we'll get strong precursor models so we will have ample time to engineer solutions and it won't be like trying to build a working rocket on the first try. (The reasons being we are rapidly approaching the limits of energy constraints and transistor density without really being close to fooming). This mental model is still popular when reality seems to diverge.

Well I actually have a hunch to why, many holding on to the above priors don't want to let them go because that means this problem they have dedicated a lot of mental space to will seem more feasible to solve. 

If it's instead a boring engineering problem, this stops being a quest to save the world or an all consuming issue. Incremental alignment work might solve it, so in order to preserve the difficulty of the issue, it will cause extinction for some far-fetched reason. Building precursor models then bootstrapping alignment might solve it, so this "foom" is invented and held on to (for a lot of highly speculative assumptions), because that would stop it from being a boring engineering problem that requires lots of effort and instead something a lone genius will have to solve. The question that maybe energy constraints will limit AI progress from here on out was met with a maybe response, but the number of upvotes make me think most readers just filed it as an unconditional "no, it won't" in their minds.

There is a good reason to think like this - if boring engineering really does solve the issue, then this community is better off assuming it won't. In that scenario, boring engineering work is being done by the tech industry anyways, so no need to help there. But I hope if people adopt the mindset of assuming the worst case scenario to have the highest expected effects of research, they realize the assumption they are making is an assumption, and not let the mental effects consume them.
 

I believe it's 2 hours of sun exposure. So unless you are spending all day outside, you should only need to apply it once. I personally apply it once before going to work. 

https://rentry.org/gpt2

Rumored to be 11-2023

Seems correct to me (and it did work for a handful of 10 int lists I manually came up with). More impressively, it does this correctly as well: 

Can we quantify the value of theoretical alignment research before and after ChatGPT?

For example, mech interp research seems much more practical now. If alignment proves to be more of an engineering problem than a theoretical one, then I don’t see how you can meaningfully make progress without precursor models.

Furthermore, given how nearly everyone with a lot of GPUs is getting similar results to OAI, where similar means within 1 OOM, it’s likely that in the future someone would have stumbled upon AGI with the compute of the 2030s.

Let’s say their secret sauce gives them the equivalent of 1 extra hardware generation (even this is pretty generous). That’s only ~2-3 years. Meta built a $10B data center to match TikTok’s content algorithm. This datacenter meant to decide which videos to show to users happened to catch up to GPT-4!

I suspect the “ease” of making GPT-3/4 informed OAI’s choice to publicize their results.

Has he talked about it? I know Ray Kurzweil has.

Much larger than I expected for its performance