AGI Timelines Are Mostly Not Strategically Relevant To Alignment

I think the timelines (as in, <10 years vs 10-30 years) are very correlated with the answer to "will first dangerous models look like current models", which I think matters more for research directions than what you allow in the second paragraph.
  
For example, interpretability in transformers might completely fail on some other architectures, for reasons that have nothing to do with deception. The only insight from the 2022 Anthropic interpretability papers I see having a chance of generalizing to non-transformers is the superposition hypothesis / SoLU discussion.

Individual contributions to some tech breakthrough can be roughly sorted temporally in order of abstraction with the earliest exploratory contributions coming from scientist types and the final contributions (which get most of the reward) coming from research engineer types. Case examples - by the time the wright brothers were experimenting with early flyer designs the basic principles of aerodynamics had already been worked out decades earlier and the time window of influence was already long closed.

So if AGI is close (< 10 years) most all the key early exploratory theoretical work is already done - which would be things like bayesian statistics, optimization, circuit theory, key neuroscience etc. And in that case it's probably not wise to start spinning up a new research career that won't produce output for another 5 years, as the time window of influence closes well before the winning team actually writes their first line of code.

If AGI is immanent (< 5 years), the best strategy is perhaps to rapidly transform into more of a research engineer, and or network to relevance somehow.

Agreed with the sentiment, though I would make a weaker claim, that AGI timelines are not uniquely strategically relevant, and the marginal hour of forecasting work at this point is better used on other questions.

My guess is that the timelines question has been investigated and discussed so heavily because for many people it is a crux for whether or not to work on AI safety at all - and there are many more such people than there are alignment researchers deciding what approach to prioritize. Most people in the world are not convinced that AGI safety is a pressing problem, and building very robust and legible models showing that AGI could happen soon is, empirically, a good way to convince them.

In my view, there are alignment strategies that are unlikely to pay off without significant time investment, but which have large expected payoffs. For example, work on defining agency seems to fit this category.

There are also alignment strategies that have incremental payoffs, but still seem unsatisfactory. For example, we could focus on developing better AI boxing techniques that just might buy us a few weeks. Or we could discover likely takeover scenarios, and build warnings for them. 

There's an analogy for this in self driving cars. If you want to ship an impressive demo right away, you might rely on a lot of messy case handling, special road markings, mapping, and sensor arrays. If you want to solve self driving in the general case, you'd probably be developing really good end to end ML models. 

at any given time, it would take ~18 months to take whatever our current best idea is, implement it, do some basic tests, and deploy it

I'd be interested in your reasoning on that.

Why? Because at any given time, it would take ~18 months to take whatever our current best idea is, implement it, do some basic tests, and deploy it.

I don't understand how the existence of this gap makes you think that timelines don't matter. 

If your timelines are short, shouldn't an active priority be to eg. reduce that gap? 

Funding a group of engineers to actively take our best current alignment idea and integrate it into prod models might be a waste of time on long timelines, but very important on short timelines.

Don't timelines change your views on takeoff speeds? If not, what's an example piece of evidence that updates your timelines but not your takeoff speeds?

I agree. I think of this as timelines not being particularly actionable: even in the case of a very short timeline of 5 years, I do not believe that the chain of reasoning would be "3.5 years ago I predicted 5 years, and I also predicted 1.5 years to implement the best current idea, so it is time to implement the best current idea now."

Reasoning directly from the amount of time feels like a self-fulfilling prophecy this way. On the other hand, it feels like the model which generated the amount of time should somehow be strategically relevant. On the other other hand my model has quite collapsed in on itself post-Gato, so my instinct is probably truthfully the reverse: a better sense of what is strategically relevant to alignment causes more accurate timelines through a better generative model.