it does seem to be part of the situation we’re in

Maybe - I can see it being spun in two ways:

1. The AI safety/alignment crowd was irrationally terrified of chatbots/current AI, forced everyone to pause, and then, unsurprisingly, didn't find anything scary
2. The AI safety/alignment crowd need time to catch up their alignment techniques to keep up with the current models before things get dangerous in the future, and they did that

To point (1): alignment researchers aren't terrified of GPT-4 taking over the world, wouldn't agree to this characterization, and are not communicating this to others. I don't expect this is how things will be interpreted if people are being fair.

I think (2) is the realistic spin, and could go wrong reputationally (like in the examples you showed) if there's no interesting scientific alignment progress made in the pause-period. 
I don't expect there to be a lack of interesting progress, though. There's plenty of unexplored work in interpretability alone that could provide many low-hanging fruit results. This is something I naturally expect out of a young field with a huge space of unexplored empirical and theoretical questions. If there's plenty of alignment research output during that time, then I'm not sure the pause will really be seen as a failure. 

I’m in favor of interventions to try to change that aspect of our situation

Yeah, agree. I'd say one of the best ways to do this is to make it clear what the purpose of the pause is and defining what counts as the pause being a success (e.g. significant research output).

Also, your pro-pause points seem quite important, in my opinion, and outweigh the 'reputational risks' by a lot:

• Pro-pause: It’s “practice for later”, “policy wins beget policy wins”, etc., so it will be easier next time
• Pro-pause: Needless to say, maybe I’m wrong and LLMs won’t plateau!

I'd honestly find it a bit surprising if the reaction to this was to ignore future coordination for AI safety with a high probability. "Pausing to catch up alignment work" doesn't seem like the kind of thing which leads the world to think "AI can never be existentially dangerous" and results in future coordination being harder. If AI keeps being more impressive than the SOTA now, I'm not really sure risk concerns will easily go away.

For me, the balance of considerations is that pause in scaling up LLMs will probably lead to more algorithmic progress

I'd consider this to be one of the more convincing reasons to be hesitant about a pause (as opposed to the 'crying wolf' argument, which seems to me like a dangerous way to think about coordinating on AI safety?). 

I don't have a good model for how much serious effort is currently going into algorithmic progress, so I can't say anything confidently there - but I would guess there's plenty and it's just not talked about? 

It might be a question about which of the following two you think will most likely result in a dangerous new paradigm faster (assuming LLMs aren't the dangerous paradigm):

1. current amount of effort put into algorithmic progress + amplified by code assistants, apps, tools, research-assistants, etc.
2. counterfactual amount of effort put into algorithmic progress if a pause happens on scaling

I think I'm leaning towards (1) bringing about a dangerous new paradigm faster because

• I don't think the counterfactual amount of effort on algorithmic progress will be that much more significant than the current efforts (pretty uncertain on this, though)
• I'm weary of adding faster feedback loops to technological progress/allowing avenues for meta-optimizations to humanity since these can compound

I had a potential disagreement with your claim that a pause is probably counterproductive if there's a paradigm change required to reach AGI: even if the algorithms of the current paradigm aren't directly a part of the algorithm behind existentially dangerous AGI, advances in these algorithms will massively speed up research and progress towards this goal.

My take is: a “pause” in training unprecedentedly large ML models is probably good if TAI will look like (A-B), maybe good if TAI will look like (C), and probably counterproductive if TAI will be outside (C).

+

The obvious follow-up question is: “OK then how do we intervene to slow down algorithmic progress towards TAI?” The most important thing IMO is to keep TAI-relevant algorithmic insights and tooling out of the public domain (arxiv, github, NeurIPS, etc.).

This seems slightly contradictory to me. It seems like - whether or not the current paradigm results in TAI, it is certainly going to make it easier to code faster, communicate ideas faster, write faster, and research faster - which would potentially make all sorts of progress, including algorithmic insights, come sooner. A pause to the current paradigm would thus be neutral or net positive if it means ensuring progress isn't sped up by more powerful models