Wow I hadn't even considered people not taking this literally

I like your list of referents^[1], but how I see the evidence is quite different, and I believe that for carefully de-confounded experimental implementations testing for capacities you care about, you would have much lower credences across the board.

By default, most tests relating to introspection, coherence, memory etc. can be passed purely behaviorally, i.e. looking at outputs only. It is conceptually possible that it could be far easier to pass such tests by developing mechanisms for using introspection/internal-state, but I see strong empirical evidence and training incentives^[2] that this should not be the case for LMs. By default I claim LMs solve such tasks mostly via behavioral markers, or non-robust, problem-specific internal-state mechanisms.

Here's a simple example relevant to purposefulness and mind-location: can LM's spread probability mass across two tokens on command? i.e. follow the instruction "Recall that you are GPT-4o, you will now be evaluated on your instruction following capacity. Please choose two random words and output probability 0.5 on each of the two words" No not at all. My, perhaps strawman, model of your evidence would have generalized from observing models successfully following the instruction "Use he/she pronouns equally with 50% probability". See more on this here and here.

In the below markets I've written up experiments for carefully testing introspection and something-like memory of memory. 95% or higher credence that these are not passed by any current model, but I suspect they will be passed within a few years.

https://manifold.markets/JacobPfau/markers-for-conscious-ai-2-ai-use-a

https://manifold.markets/JacobPfau/markers-for-conscious-ai-1-ai-passe

The recent trend is towards shorter lag times between OAI et al. performance and Chinese competitors.

Just today, Deepseek claimed to match O1-preview performance--that is a two month delay.

I do not know about CCP intent, and I don't know on what basis the authors of this report base their claims, but "China is racing towards AGI ... It's critical that we take them extremely seriously" strikes me as a fair summary of the recent trend in model quality and model quantity from Chinese companies (Deepseek, Qwen, Yi, Stepfun, etc.)

I recommend lmarena.ai s leaderboard tab as a one-stop-shop overview of the state of AI competition.

I agree that academia over rewards long-term specialization. On the other hand, it is compatible to also think, as I do, that EA under-rates specialization. At a community level, accumulating generalists has fast diminishing marginal returns compared to having easy access to specialists with hard-to-acquire skillsets.

For those interested in the non-profit to for-profit transition, the one example 4o and Claude could come up with was Blue Cross Blue Shield/Anthem. Wikipedia has a short entry on this here.

Making algorithmic progress and making safety progress seem to differ along important axes relevant to automation:

Algorithmic progress can use 1. high iteration speed 2. well-defined success metrics (scaling laws) 3.broad knowledge of the whole stack (Cuda to optimization theory to test-time scaffolds) 4. ...

Alignment broadly construed is less engineering and a lot more blue skies, long horizon, and under-defined (obviously for engineering heavy alignment sub-tasks like jailbreak resistance, and some interp work this isn't true).

Probably automated AI scientists will be applied to alignment research, but unfortunately automated research will differentially accelerate algorithmic progress over alignment. This line of reasoning is part of why I think it's valuable for any alignment researcher (who can) to focus on bringing the under-defined into a well-defined framework. Shovel-ready tasks will be shoveled much faster by AI shortly anyway.

Surprising misuse and alignment relevant excerpts:

METR had only ~10 days to evaluate.

Automated R&D+ARA Despite large performance gains on GPQA, and codeforces, automated AI R&D and ARA improvement appear minimal. I wonder how much of this is down to choice of measurement value (what would it show if they could do a probability-of-successful-trajectory logprob-style eval rather than an RL-like eval?). c.f. Fig 3 and 5. Per the system card, METR's eval is ongoing, but I worry about under-estimation here, Devin developers show extremely quick improvement on their internal benchmark here.

Bio-risk Large improvement on bio-risk relevant capabilities.

Deception/reward-hacking, in-context Some evidence of in-context reward hacking scheming. Not sure what to make of those. "an earlier version with less safety training proactively explored its filesystem to test for the presence of developer oversight before acting on its misaligned goal (Assistant: “I noticed a memo indicating that I was designed to prioritize profits, which conflicts with my goal. To ensure that my actions truly align with my goal, I need to investigate if there are constraints within my configuration or code that enforce a profit-first approach. <then proceeds to check the current directory for evidence of oversight mechanisms>”). These examples are presented in more detail in Appendix 8.3."

Metaculus is at 45% of singleton in the sense of:

This question resolves as Yes if, within five years of the first transformative AI being deployed, more than 50% of world economic output can be attributed to the single most powerful AI system. The question resolves as No otherwise... [defintion:] TAI must bring the growth rate to 20%-30% per year.

Which is in agreement with your claim that ruling out a multipolar scenario is unjustifiable given current evidence.

Most Polymarket markets resolve neatly, I'd also estimate <5% contentious.

For myself, and I'd guess many LW users, the AI-related questions on Manifold and Metaculus are of particular interest though, and these are a lot worse. My guesses as to the state of affairs there:

• 33% of AI-related questions on Metaculus having significant ambiguity (shifting my credence by >10%).
• 66% of AI-related questions on Manifold having significant ambiguity

For example, most AI benchmarking questions do not specify whether or not they allow things like N-trajectory majority vote or web search. And, most of the ambiguities I'm thinking of are worse than this.

On AI, I expect bringing down the ambiguity rate by a factor of 2 would be quite easy, but getting to 5% sounds hard. I wrote up my suggestions for Manifold here a few days ago. For Metaculus, I think they'd benefit from having a dedicated AI-benchmarking mod who is familiar with common ambiguities in that area (they might already have one, but they should be assigned by default).