FWIW, links to the references point back to localhost

Sure, but I think that misses the point that I was trying to convey. If we end up in a world similar to the ones forecasted in ai-2027, the fraction of compute which labs allocate towards speeding up their own research threads will be larger than the amount of compute which labs will sell for public consumption.

My view is that even in worlds with significant speed ups in R&D, we still ultimately care about the relative speed of progress on scalable alignment (in the Christiano sense) compared to capabilities & prosaic safety; doesn't matter if we finish quicker if catastrophic ai is finished quickest. Thus, an effective TOC for speeding up long horizon research would still route through convincing lab leadership of the pursuitworthiness of research streams. 

Labs do have a moat around compute. In the worlds where automated R&D gets unlocked I would expect compute allocation to substantially pivot, making non-industrial automated research efforts non-competitive.

As far as I am concerned, AGI should be able to do any intellectual task that a human can do. I think that inventing important new ideas tends to take at least a month, but possibly the length of a PhD thesis. So it seems to be a reasonable interpretation that we might see human level AI around mid-2030 to 2040, which happens to be about my personal median.

There is an argument to be made that at the larger scales of length, cognitive tasks become cleanly factored, or in other words it's more accurate to model completing something like a PhD as different instantiations of yourself coordinating across time over low bandwidth channels, as opposed to you doing very high dimensional inference for a very long time. If that's the case, then one would expect to roughly match human performance in indefinite time horizon tasks once that scale has been reached.

I don't think I fully buy this, but I don't outright reject it.

I believe intelligence is pretty sophisticated while others seem to think it's mostly brute force. This tangent would however require a longer discussion on the proper interpretation of Sutton's bitter lesson.

I'd be interested in seeing this point fleshed out, as it's a personal crux of mine (and I expect many others). The bullish argument which I'm compelled by goes something along the lines of:

• Bitter Lesson: SGD is a much better scalable optimizer than you, and we're bringing it to pretty stupendous scales
• Lots of Free Energy in Research Engineering: My model of R&D in frontier AI is that it is often blocked by a lot of tedious and laborious engineering. It doesn't take a stroke of genius to think of RL on CoT; it took (comparatively) quite a while to get it to work.
• Low Threshold in Iterating Engineering Paradigms: Take a technology, scale it, find it's limits, pivot, repeat. There were many legitimate arguments floating around last year around the parallelism tradeoff and shortcut generalization which seemed to suggest limits of scaling pretraining. I take these to basically be correct, it just wasn't that hard to pivot towards a nearby paradigm which didn't face similar limits. I expect similar arguments to crop up around the limits of model-free RL, or OOD generalization of training on verifiable domains, or training on lossy representations of the real world (language), or inference on fixed weight recurrence, or... I expect (many) of them to basically be correct, I just don't expect the pivot towards a scalable solution to these to be that hard. Or in other words, I expect that much of the effort that comes from unlocking these new engineering paradigms to be made up of engineering hours which we expect to be largely automated.

Interesting. Curious to know what your construction ended up looking like and I'm looking forward to reading the resulting proof!

Some proofs along these lines already exist [1,2], though they seem to have minor problems.

This is a good critical review of the literature.

I see it now

Strong upvote. Very clearly written and communicated. I've been recently thinking about digging deeper into this paper with the hopes of potentially relating it to some recent causality based interpretability work and reading this distillation has accelerated my understanding of the paper. Looking forward to the rest of the sequence!