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...

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...

We are excited to announce that Resolution (fka Sequent) has a $160M grant from Coefficient Giving (cG) to put rigorous alignment research on a (closer to) even footing with the frontier labs. We will use it to accelerate progress towards higher-confidence alignment, or to find evidence and obstacles showing why alignment is hard.

The grant is structured as a $108M base plus $52M conditional on a combination of hiring success and compute needs. The base includes a small regranting budget, which we plan to use both for high-quality non-Resolution alignment research and to give back to shared community infrastructure that we depend on. Coefficient Giving will be our sole funder to start (thank you!); our goal is to raise larger-scale funds from a mixture of sources once we...

1) The safe-to-dangerous shift is a fundamental problem for eval realism

Suppose we have a capable and potentially scheming model, and before we deploy it, we want some evidence that it won’t do anything catastrophically dangerous once we deploy it. A common approach is to use black-box alignment evaluations. However, alignment evaluations are only reassuring to the extent that the model can't reliably^[1] distinguish the deployment distribution from the evaluation distribution, as it is otherwise difficult to rule out the possibility of alignment faking.

There are many approaches one could use to try to make evaluations appear more realistic: you can try to create realistic environments (e.g. Petri, WebArena, OSWorld); use data from past deployments (e.g. OpenAI, SAD); and spoof tool-call responses (e.g. ToolEmu).

However, the core difference between an alignment evaluation and a...

As various people have written about before, AIs that have long-term memory might pose additional risks (most notably, LLM AGI will have memory, and memory changes alignment by Seth Herd). Even if an AI is aligned or only occasionally scheming at the start of a deployment, the AI might become a consistent and coherent behavioral schemer via updates to its long-term memories. 

In this post, I’ll spell out the version of the threat model that I’m most concerned about, including some novel arguments for its plausibility, and describe some promising strategies for mitigating this risk. While I think some plausible mitigations are reasonably cheap and could be effective at reducing the risk from coherent scheming arising via this mechanism, research here will likely be substantially more productive in the future...