What are the strongest arguments for very short timelines?

Here's the structure of the argument that I am most compelled by (I call it the benchmarks + gaps argument), I'm uncertain about the details.

1. Focus on the endpoint of substantially speeding up AI R&D / automating research engineering. Let's define our timelines endpoint as something that ~5xs the rate of AI R&D algorithmic progress (compared to a counterfactual world with no post-2024 AIs). Then make an argument that ~fully automating research engineering (experiment implementation/monitoring) would do this, along with research taste of at least the 50th percentile AGI company researcher (experiment ideation/selection).
2. Focus on REBench since it's the most relevant benchmark. REBench is the most relevant benchmark here, for simplicity I'll focus on only this though for robustness more benchmarks should be considered.
3. Based on trend extrapolation and benchmark base rates, roughly 50% we'll saturate REBench by end of 2025.
4. Identify the most important gaps between saturating REBench and the endpoint defined in (1). The most important gaps between saturating REBench and achieving the 5xing AI R&D algorithmic progress are: (a) time horizon as measured by human time spent (b) tasks with worse feedback loops (c) tasks with large codebases (d) becoming significantly cheaper and/or faster than humans. There are some more more but they probably aren't as important, should also take into account unknown gaps.
5. When forecasting the time to cross the gaps, it seems quite plausible that we get to the substantial AI R&D speedup within a few years after saturating REBench, so by end of 2028 (and significantly earlier doesn't seem crazy).
   1. This is the most important part of the argument, and one that I have lots of uncertainty over. We have some data regarding the "crossing speed" of some of the gaps but the data are quite limited at the moment. So there are a lot of judgment calls needed and people with very different intuitions might think the remaining gaps will take a long time to cross without this being close to falsified by our data.
   2. This is broken down into "time to cross the gaps at 2024 pace of progress" -> adjusting based on compute forecasts and intermediate AI R&D speedups before reaching 5x.
6. From substantial AI R&D speedup to AGI. Once we have the 5xing AIs, that's potentially already AGI by some definitions but if you have a stronger one, the possibility of a somewhat fast takeoff means you might get it within a year or so after.

One reason I like this argument is that it will get much stronger over time as we get more difficult benchmarks and otherwise get more data about how quickly the gaps are being crossed.

I have a longer draft which makes this argument but it's quite messy and incomplete and doesn't add that much on top of the above summary for now. Unfortunately I'm prioritizing other workstreams over finishing this at the moment. DM me if you'd really like a link to the messy draft.

I've been arguing for 2027-ish AGI for several years now. I do somewhat fall into the annoying category of refusing to give my full details for believing this (publicly). I've had some more in-depth discussions about this privately.

One argument I have been making publicly is that I think Ajeya's Bioanchors report greatly overestimated human brain compute. I think a more careful reading of Joe Carlsmith's report that hers was based on supports my own estimates of around 1e15 FLOPs.

Connor Leahy makes some points I agree with in his recent Future of Life interview. https://futureoflife.org/podcast/connor-leahy-on-why-humanity-risks-extinction-from-agi/

Another very relevant point is that recent research on the human connectome shows that long-range connections (particularly between regions of the cortex) are lower bandwidth than was previously thought. Examining this bandwidth in detail leads me to believe that efficient decentralized training should be possible. Even with considering that training a human brain equivalent model would require 10000x parallel brain equivalents to have a reasonable training time, the current levels of internet bandwidth between datacenters worldwide should be more than sufficient.

Thus, my beliefs are strongly pointint towards: "with the right algorithms we will have more than good enough hardware and more than sufficient data. Also, those algorithms are available to be found, and are hinted at by existing neuroscience data." Thus, with AI R&D accelerated research on algorithms, we should expect rapid progress on peak capabilities and efficiency which doesn't plateau at human-peak-capability or human-operation-speed. Super-fast and super-smart AGI within a few months of full AGI, and rapidly increasing speeds of progress leading up to AGI.

If I'm correct, then the period of time from 2026 to 2027 will contain as much progress on generally intelligent systems as all of history leading up to 2026. ASI will thus be possible before 2028.

Only social factors (e.g. massively destructive war or unprecedented international collaboration on enforcing an AI pause) will change these timelines.

Further thoughts here: A path to human autonomy [LW · GW]

An AGI broadly useful for humans needs to be good at general tasks for which currently there is no way of finding legible problem statements (where System 2 reasoning is useful) with verifiable solutions. Currently LLMs are slightly capable at such tasks, and there are two main ways in which they become more capable, scaling and RL.

Scaling is going to continue rapidly showing new results at least until 2026-2027, probably also 2028-2029 [LW · GW]. If there's no AGI or something like a $10 trillion AI company by then, there won't be a trillion dollar training system and the scaling experiments will fall back to the rate of semiconductor improvement.

Then there's RL, which as o3 demonstrates applies to LLMs as a way of making them stronger and not merely eliciting capabilities formed in pretraining. But it only works directly around problem statements with verifiable solutions, and it's unclear how to generate them for more general tasks or how far will the capabilities generalize from the training problems that are possible to construct in bulk. (Arguably self-supervised learning is good at instilling general capabilities because the task of token prediction is very general, it subsumes all sorts of things. But it's not legible.) Here too scale might help with generalization stretching further from the training problems, and with building verifiable problem statements for more general tasks, and we won't know how much it will help until the experiments are done.

So my timelines are concentrated on 2025-2029, after that the rate of change in capabilities goes down. Probably 10 more years of semiconductor and algorithmic progress after that are sufficient to wrap it up though, so 2040 without AGI seems unlikely.

It's worthy of a (long) post, but I'll try to summarize. For what it's worth, I'll die on this hill.

General intelligence = Broad, cross-domain ability and skills.

Narrow intelligence = Domain-specific or task-specific skills.

The first subsumes the second at some capability threshold.

My bare bones definition of intelligence: prediction. It must be able to consistently predict itself & the environment. To that end it necessarily develops/evolves abilities like learning, environment/self sensing, modeling, memory, salience, planning, heuristics, skills, etc. Roughly what Ilya says about token prediction necessitating good-enough models to actually be able to predict that next token (although we'd really differ on various details)

Firstly, it's based on my practical and theoretical knowledge of AI and insights I believe to have had into the nature of intelligence and generality for a long time. It also includes systems, cybernetics, physics, etc. I believe a holistic view helps inform best w.r.t. AGI timelines. And these are supported by many cutting edge AI/robotics results of the last 5-9 years (some old work can be seen in new light) and also especially, obviously, the last 2 or so.

Here are some points/beliefs/convictions I have for thinking AGI for even the most creative goalpost movers is basically 100% likely before 2030, and very likely much sooner. A fast takeoff also, understood as the idea that beyond a certain capability threshold for self-improvement, AI will develop faster than natural, unaugmented humans can keep up with.

It would be quite a lot of work to make this very formal, so here are some key points put informally:

- Weak generalization has been already achieved. This is something we are piggybacking off of already, and there is meaningful utility since GPT-3 or so. This is an accelerating factor.

- Underlying techniques (transformers , etc) generalize and scale. 

- Generalization and performance across unseen tasks improves with multi-modality.

- Generalist models outdo specialist ones in all sorts of scenarios and cases.

- Synthetic data doesn't necessarily lead to model collapse and can even be better than real world data.

- Intelligence can basically be brute-forced it looks like, so one should take Kurzweil *very* seriously (he tightly couples his predictions to increase in computation).

- Timelines shrunk massively across the board for virtually all top AI names/experts in the last 2 years. Top Experts were surprised by the last 2 years.

- Bitter Lesson 2.0.: there are more bitter lessons than Sutton's, which are that all sorts of old techniques can be combined for great increases in results. See the evidence in papers linked below.

- "AGI" went from a taboo "bullshit pursuit for crackpots", to a serious target of all major labs, publicly discussed. This means a massive increase in collective effort, talent, thought, etc. No more suppression of cross-pollination of ideas, collaboration, effort, funding, etc.

- The spending for AI only bolsters, extremely so, the previous point. Even if we can't speak of a Manhattan Project analogue, you can say that's pretty much what's going on. Insane concentrations of talent hyper focused on AGI. Unprecedented human cycles dedicated to AGI.

- Regular software engineers can achieve better results or utility by orchestrating current models  and augmenting them with simple techniques(RAG, etc). Meaning? Trivial augmentations to current models increase capabilities - this low hanging fruit implies medium and high hanging fruit (which we know is there, see other points). 

I'd also like to add that I think intelligence is multi-realizable, and generality will be considered much less remarkable soon after we hit it and realize this than some still think it is.

Anywhere you look: the spending, the cognitive effort, the (very recent) results, the utility, the techniques...it all points to short timelines.

In terms of AI papers, I have 50 references or so I think support the above as well. Here are a few:

SDS : See it. Do it. Sorted Quadruped Skill Synthesis from Single Video Demonstration, Jeffrey L., Maria S., et al. (2024).

DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning, Zhenyu J., Yuqi X., et in. (2024).

One-Shot Imitation Learning, Duan, Andrychowicz, et al. (2017).

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Finn et al., (2017).

Unsupervised Learning of Semantic Representations, Mikolov et al., (2013).

A Survey on Transfer Learning, Pan and Yang, (2009).

Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly, Xian et al., (2018).

Learning Transferable Visual Models From Natural Language Supervision, Radford et al., (2021).

Multimodal Machine Learning: A Survey and Taxonomy, Baltrušaitis et al., (2018).

Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine, Harsha N., Yin Tat Lee et al. (2023).

A Vision-Language-Action Flow Model for General Robot Control, Kevin B., Noah B., et al. (2024).

Open X-Embodiment: Robotic Learning Datasets and RT-X Models, Open X-Embodiment Collaboration, Abby O., et al. (2023).