In this post, I describe a simple model for forecasting when AI will automate AI development. It is based on the AI Futures model, but more understandable and robust, and has deliberately conservative assumptions.

At current rates of compute growth and algorithmic progress, this model's median prediction is >99% automation of AI R&D in late 2032. Most simulations result in a 1000x to 10,000,000x increase in AI efficiency and 300x-3000x research output by 2035. I therefore suspect that existing trends in compute growth and automation will still produce extremely powerful AI on "medium" timelines, even if the full coding automation and superhuman research taste that drive the AIFM's "fast" timelines (superintelligence by ~mid-2031) don't happen.

Why make this?

• The AI Futures Model (AIFM) has 33 parameters; this has 8.
  • I previously

For the past year, we at the AI Futures Project have been sinking most of our time into our next big scenario. Now it’s done! 

It’s called AI 2040: Plan A.

It’s called Plan A because it’s a recommendation, not a prediction. It’s what we think should happen, not what will happen, though we think it’s plausible enough to aim for.

It’s called AI 2040 because in it, they delay the creation of superintelligence to 2040. It would have happened much sooner (in 2030, to be precise) if not for decisive action on the part of the US and Chinese governments. 

As with AI 2027, summaries don’t really do it justice, since the whole point was to be detailed and comprehensive and work things out step by step rather than rely on high-level abstractions like doom or utopia. 

Read the scenario at ai-2040.com. You can...

1. Frontmatter

1.1 Backstory for this post

As discussed in Intro to Brain-Like-AGI Safety, I’m working on the technical alignment problem for a hypothetical future “brain-like AGI”, with a particular focus on treating human innate social and moral drives as a possible jumping-off point for our technical alignment approach.

After all, if it’s possible for humans to do stuff that ultimately leads to a good future, then it’s probably also possible for sufficiently human-like AGIs to do stuff that ultimately leads to a good future. Or if it’s not possible for humans to do stuff that ultimately leads to a good future, then we’re screwed no matter what. But assuming it’s possible, the “sufficiently human-like AGIs” would certainly need to have good prosocial motivations. What code do we write that would...

I firmly believe that value generalisation^[1]is the key to AI Alignment. That, indeed, it is necessary and almost sufficient for alignment.

But I won't be arguing that grand point today; instead, I'll focus on a specific RL example of an agent that displays value correction: it realises its current reward function is (probably) incorrect, and acts to correct it.

Thus there are:

1. The initial situation, in distribution, where the human displays how to maximise the true reward.
2. The out of distribution situation where the agent finds a hack to exploit its reward function estimate, and turns against what we wanted it to do.
3. The value error detection stage where the agent realises that its reward function estimate is probably incorrect.
4. The value correction stage where the agent corrects its reward function back to

Natural language autoencoders are meant to take in an LLM's activation vector and describe in plain text what the model is thinking. However, its training data collection involves asking Claude to guess what a model might be thinking. How robust are NLAs to these guesses? We change Claude's guesses in various ways and measure the impact on the NLA's statements as well as on reconstruction accuracy. We show that Qwen2.5-7B NLAs have some robustness to irrelevant statements and prevailing sentiments in Claude's guesses.

However, if an NLA is initialized with entirely implausible statements, it can nevertheless achieve nearly the same reconstruction accuracy as plausible-initialized NLAs while emitting 99.3% implausible statements. RL does train implausible-initialized NLAs to be slightly more plausible (increasing from 0.08% to 0.7%). But the plausibility...

The conversation begins

(Fictional) Optimist: So you expect future artificial superintelligence (ASI) “by default”, i.e. in the absence of yet-to-be-invented techniques, to be a ruthless sociopath, happy to lie, cheat, and steal, whenever doing so is selfishly beneficial, and with callous indifference to whether anyone (including its own programmers and users) lives or dies?

Me: Yup! (Alas.)

Optimist: …Despite all the evidence right in front of our eyes from humans and LLMs.

Me: Yup!

Optimist: OK, well, I’m here to tell you: that is a very specific and strange thing to expect, especially in the absence of any concrete evidence whatsoever. There’s no reason to expect it. If you think that ruthless sociopathy is the “true core nature of intelligence” or whatever, then you should really look at yourself in a mirror and...