AI in 2025: gestalt

technicalities7 Dec 2025 21:25 UTC
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This is the editorial for this year’s “Shallow Review of AI Safety”. (It got long enough to stand alone.)

Epistemic status: subjective impressions plus one new graph plus 300 links.

Huge thanks to Jaeho Lee, Jaime Sevilla, and Lexin Zhou for running lots of tests pro bono and so greatly improving the main analysis.

tl;dr

  • Informed people disagree about the prospects for LLM AGI – or even just what exactly was achieved this year. But the famous ones with a book to talk at least agree that we’re 2-20 years off (allowing for other paradigms arising). In this piece I stick to arguments rather than reporting who thinks what.

  • My view: compared to last year, AI is much more impressive but not proportionally more useful. They improved on some things they were explicitly optimised for (coding, vision, OCR, benchmarks), and did not hugely improve on everything else. Progress is thus (still!) consistent with current frontier training bringing more things in-distribution rather than generalising very far.

  • Pretraining (GPT-4.5, Grok 3⁄4, but also the counterfactual large runs which weren’t done) disappointed people this year. It’s probably not because it didn’t or wouldn’t work; it was just too hard to serve the big models and ~30 times more efficient to do post-training instead, on the margin. This should change, yet again, soon, if RL scales even worse.

  • Edit: See this amazing comment for the hardware reasons behind this, and reasons to think that pretraining will struggle for years.

  • True frontier capabilities are likely obscured by systematic cost-cutting (distillation for serving to consumers, quantization, low reasoning-token modes, routing to cheap models, etc) and a few unreleased models/​modes.

  • Most benchmarks are weak predictors of even the rank order of models’ capabilities. I distrust ECI, ADeLe, and HCAST the least. ECI shows a linear improvement, HCAST finds an exponential improvement on greenfield software engineering, and ADeLe shows a previous exponential+ slowing down to what might be linear growth since o1.

  • Using these three, I find that 2025 progress is as fast as the whole preceding LLM era. But these numbers aren’t enough to convince me.

  • The world’s de facto strategy remains “iterative alignment”, optimising outputs with a stack of alignment and control techniques everyone admits are individually weak.

  • Early claims that reasoning models are safer turned out to be a mixed bag (see below). Adversarial robustness is not improving much; practical improvements we see are due to external “safeguards” (auxiliary models).

  • We already knew from jailbreaks that current alignment methods were brittle. The great safety discovery of the year is that bad things are correlated in current models. (And on net this is good news.)

  • Previously I thought that “character training” was a separate and lesser matter than “alignment training”. Now I am not sure.

  • Welcome to the many new people in AI Safety and Security and Assurance and so on. In the Shallow Review, I added a new, sprawling top-level category for one large trend among them, which is to treat the multi-agent lens as primary in various ways.

  • Overall I wish I could tell you some number, the net expected safety change (this year’s improvements in dangerous capabilities and agent performance, minus the alignment-boosting portion of capabilities, minus the cumulative effect of the best actually implemented composition of alignment and control techniques). But I can’t.

Capabilities in 2025

Better, but how much?

-- Fraser, riffing off Pueyo

Arguments against 2025 capabilities growth being above-trend

  • Apparent progress is an unknown mixture of real general capability increase, hidden contamination increase, benchmaxxing (nailing a small set of static examples instead of generalisation) and usemaxxing (nailing a small set of narrow tasks with RL instead of deeper generalisation). It’s reasonable to think it’s 25% each, with low confidence.

  • Discrete capabilities progress seems slower this year than in 2024 (but 2024 was insanely fast). Kudos to this person for registering predictions and so reminding us what really above-trend would have meant concretely. The excellent forecaster Eli was also over-optimistic in places.

  • I don’t recommend taking benchmark trends, or even clever composite indices of them, or even clever cognitive science measures too seriously. The adversarial pressure on the measures is intense.

  • Pretraining didn’t hit a “wall”, but the driver did manoeuvre away on encountering an easier detour (RLVR).

    • Training runs continued to scale (Llama 3 405B = 4e25, GPT-4.5 ~= 4e26, Grok 4 ~= 3e26) but to less effect.[1] In fact all of these models are dominated by apparently smaller pretraining runs with better post-training. 4.5 is actually off the API already.

    • In 2025 it wasn’t worth it to serve any giant model (i.e. ~4T total params), nor to make it into a reasoning model. But this is more to do with inference cost and inference hardware constraints than any quality shortfall or breakdown in scaling laws.

    • EDIT: Nesov notes that making use of bigger models (i.e. 4T parameters) is heavily bottlenecked on CoWoS and inference HBM, as is doing RL on bigger models. He expects it won’t be possible to do the next huge pretraining jump (to ~30T total) using NVIDIA until late ~2028. The Ironwood TPUs are a different matter and might be able to do it in late 2026.

    • It would work, probably, if we had the data and CoWoS and spent the next $10bn, it’s just too expensive to bother with at the moment compared to:

  • RLVR scaling and inference scaling (or “reasoning” as we’re calling it), which kept things going instead. This boils down to spending more on RL so the resulting model can productively spend more tokens.

    • But the feared /​ hoped-for generalisation from {training LLMs with RL on tasks with a verifier} to performing on tasks without one remains unclear even after two years of trying.[2] Grok 4 was apparently a major test of scaling RLVR training.[3] It gets excellent benchmark results and the distilled versions are actually being used at scale. But imo it is the most jagged of all models.

    • This rate of scaling-up cannot be sustained: RL is famously inefficient. Compared to SFT, it “reduces the amount of information a model can learn per hour of training by a factor of 1,000 to 1,000,000”. The per-token intelligence is up but not by much.

    • There is a deflationary theory of RLVR, that it’s capped by pretraining capability and thus just about easier elicitation and better pass@1. But even if that’s right this isn’t saying much!

    • RLVR is heavy fiddly R&D you need to learn by doing; better to learn it on smaller models with 10% of the cost.

    • An obvious thing we can infer: the labs don’t have the resources to scale both at the same time. To keep the money jet burning, they have to post models.

  • So pretraining can’t scale yet, because most inference chips aren’t big enough to handle trillions of active parameters. And scaling RL more won’t help as much as it helped this year, because of inefficiency. So…

  • By late 2025, the obsolete modal “AI 2027” scenario described the beginning of a divergence between the lead lab and the runner-up frontier labs.[4] This is because the leader’s superior ability to generate or acquire new training data and algorithm ideas was supposed to compound and widen their lead. Instead, we see the erstwhile leader OpenAI and some others clustering around the same level, which is weak evidence that synthetic data and AI-AI R&D aren’t there yet. Anthropic are making large claims about Opus 4.5’s capabilities, so maybe this will arrive on time next year.

  • For the first time there are now many examples of LLMs helping with actual research mathematics. But if you look closely it’s all still in-distribution in the broad sense: new implications of existing facts and techniques. (I don’t mean to demean this; probably most mathematics fits this spec.) And it’s almost never fully autonomous; there’s usually hundreds of bits of human steering.

  • Extremely mixed evidence on the trend in the hallucination rate.

  • Companies make claims about their one-million- or ten-million-token effective context windows, but I don’t believe it.

  • In lieu of trying the agents for serious work yourself, you could at least look at the highlights of the gullible and precompetent AIs in the AI Village.

  • Here are the current biggest limits to LLMs, as polled in Heitmann et al:

Arguments for 2025 capabilities growth being above-trend

We now have measures which are a bit more like AGI metrics than dumb single-task static benchmarks are. What do they say?

  1. Difficulty-weighted benchmarks: Epoch Capabilities Index.

    1. Interpretation: GPT-2 to GPT-3 was (very roughly) a 20-40 point jump.

  2. Cognitive abilities: ADeLe.[5]

    1. Interpretation: level L is the capability held by 1 in humans on Earth. GPT-2 to GPT-3 was a 0.6 point jump.

  3. Software agency: HCAST time horizon, the ability to handle larger-scale well-specified greenfield software tasks.

    1. Interpretation: the absolute values are less important than the implied exponential (a 7 month doubling time).

So: is the rate of change in 2025 (shaded) holding up compared to past jumps?:

Ignoring the (nonrobust)[6] ECI GPT-2 rate, we can say yes: 2025 is fast, as fast as ever, or more.

Even though these are the best we have, we can’t defer to these numbers.[7] What else is there?

  • In May they passed some threshold and I finally started using LLMs for actual tasks. For me this is mostly due to the search agents replacing a degraded Google search. I‘m not the only one who flipped this year. This hasty poll is worth more to me than any benchmark:

  • On actual adoption and real-world automation:

    • Based on self-reports, the St Louis Fed thinks that “Between 1 and 7% of all work hours are currently assisted by generative AI, and respondents report time savings equivalent to 1.4% of total work hours… across all workers (including non-users… Our estimated aggregate productivity gain from genAI (1.2%)”. That’s model-based, using year-old data, and naively assuming that the AI outputs are of equal quality. Not strong.

    • The unfairly-derided METR study on Cursor and Sonnet 3.7 showed a productivity decrease among experienced devs with (mostly) <50 hours of practice using AI. Ignoring that headline result, the evergreen part here is that even skilled people turn out to be terrible at predicting how much AI actually helps them.

    • But, plainly, coding is just different now than it ever was in history, especially for people with low-to-no-skill.

  • True frontier capabilities are likely obscured by systematic cost-cutting (distillation for serving to consumers, quantization, low reasoning-token modes, routing to cheap models, etc). Open models show you can now get good performance with <50B active parameters, maybe a sixth of what GPT-4 used.[8]

    • GPT-4.5 was killed off after 3 months, presumably for inference cost reasons. But it was markedly lower in hallucinations and nine months later it’s still top-5 on LMArena. I bet it’s very useful internally, for instance in making the later iterations of 4o less terrible.

    • See for instance the unreleased deep-fried multi-threaded “experimental reasoning model” which won at IMO, ICPC, and IOI while respecting the human time cap (e.g. 9 hours of clock time for inference). The OpenAI one is supposedly just an LLM with extra RL. They probably cost an insane amount to run, but for our purposes this is fine: we want the capability ceiling rather than the productisable ceiling. Maybe the first time that the frontier model has gone unreleased for 5 months?

  • LLM councils and Generate-Verify divide-and-conquer setups are much more powerful than single models, and are rarely ever reported. I use Critch’s Multiplicity to poll and aggregate them in the browser.

  • Is it “the Year of Agents” (automation of e.g. browser tasks for the mass market)? Coding agents, yes. Search agents, yes. Other agents, not much (but obviously progress).

  • We’re still picking up various basic unhobbling tricks like “think before your next tool call”.

  • METR’s task-time-horizon work, if it implies anything, implies a faster rate of improvement than last year. There’s much to be said against this, and it has been said, including by METR.

    • If the rate of progress on messy tasks is about the same as the clean ones, then they’re just 1-5 years behind. But this delay could still be a huge issue for LLM AGI, because it means we might not automate messy tasks before compute scaling runs out, and then they’ll be the bottleneck for decades.

  • “Deep Research” search agents only launched in December/​February. They’re neither deep nor doing research, but useful.

  • I claim that instruction-following improved a lot. (Some middling evidence here.) This is a big deal: besides its relation to corrigibility, it also denotes general ability to infer intent and connotations.

  • Character-level tokenizer-based errors are still occasionally problematic but nothing like last year.

  • GPT-5 costs a quarter of what 4o cost last year (per-token; it often uses far more than 4x the tokens). (The Chinese models are nominally a few times cheaper still, but are not cheaper in intelligence per dollar.)

  • People have been using competition mathematics as a hard benchmark for years, but will have to stop because it’s solved. As so often with evals called ahead of time, this means less than we thought it would; competition maths is surprisingly low-dimensional and so interpolable. Still, they jumped (pass@1) from 4% to 12% on FrontierMath Tier 4 and there are plenty of hour-to-week interactive speedups in research maths.

  • Annals of recursive self-improvement: Deepmind threw AlphaEvolve (a pipeline of LLMs running an evolutionary search) at pretraining. They claim the JAX kernels it wrote reduced Gemini’s training time by 1%.

  • Extraordinary claims about Opus 4.5 being 100th percentile on Anthropic’s hardest hiring coding test, etc.

  • From May, the companies started saying for the first time that their models have dangerous capabilities.

One way of reconciling this mixed evidence is if things are going narrow, going dark, or going over our head. That is, if the real capabilities race narrowed to automated AI R&D specifically, most users and evaluators wouldn’t notice (especially if there are unreleased internal models or unreleased modes of released models). You’d see improved coding and not much else.

Or, another way: maybe 2025 was the year of increased jaggedness (i.e. variance) or of trading off some capabilities against others (i.e. there would exist regressions). Maybe the RL made them much better at maths and instruction-following, but also sneaky, narrow, less secure (in the sense of emotional insecurity).

(You were about to nod sagely and let me get away without checking, but the ADeLe work also lets us just see if the jaggedness is changing.)

std of
abilities		% of abilities that
fell compared to
predecessor
GPT-3 0.500%
GPT-40.830%
o10.830%
o30.8911%

– Roger Grosse

Evals crawling towards ecological validity

  • Item response theory (Rausch 1960) is finally showing up in ML. This lets us put benchmarks on a common scale and actually estimate latent capabilities.

    • ADeLE is my favourite. It’s fully-automated, explains the abilities a benchmark is actually measuring, gives you an interpretable ability profile for an AI, and predicts OOD performance on new task instances better than embedding and finetunes (AUROC=0.8). Pre-dates HCAST task horizon, and as a special case (“VO”). They throw in a guessability control as well!

    • These guys use it to estimate latent model ability, and show it’s way more robust across test sets than the average scores everyone uses. They also step towards automating adaptive testing: they finetune an LLM to generate tasks at the specified difficulty level.

    • Epoch bundled 39 benchmarks together, weighting them by latent difficulty, and thus obsoleted the currently dominant Artificial Analysis index, which is unweighted.

    • HCAST reinvents and approximates some of the same ideas. Come on METR!

  • Eleuther did the first public study of composing the many test-time interventions together. FAR and AISI also made a tiny step towards an open source defence pipeline, to use as a proxy for the closed compositional pipelines we actually care about.

  • Just for cost reasons, the default form of evals is a bit malign: it tests full replacement of humans. This is then a sort of incentive to develop in that direction rather than to promote collaboration. Two papers lay out why it’s thus time to spend on human evals.

  • The first paper using RL agents to attack fully-defended LLMs.

  • We have started to study propensity as well as capability. This is even harder.

  • This newsletter is essential.

  • The time given for pre-release testing is down, sometimes to one week.

  • No public pre-deployment testing by AISI between o1 and Gemini 3. Gemini 2.5 seems to have had no pre-deployment tests from METR, Apollo, AISI, or CAISI.

  • A bunch of encouraging collaborations:

(Nano Banana 3-shot in reference to this tweet.)

Safety in 2025

Are reasoning models safer than the old kind?

Well, o3 and Sonnet 3.7 were pretty rough, lying and cheating at greatly increased rates. Looking instead at GPT-5 and Opus 4.5:

  • Much more monitorable via the long and more-faithful CoT (--> all risks down)

    • “post-hoc rationalization… GPT-4o-mini (13%) and Haiku 3.5 (7%). While frontier models are more faithful, especially thinking ones, none are entirely faithful: Gemini 2.5 Flash (2.17%), ChatGPT-4o (0.49%), DeepSeek R1 (0.37%), Gemini 2.5 Pro (0.14%), and Sonnet 3.7 with thinking (0.04%)”

  • Much better at following instructions (--> accident risk down).

  • Much more likely to refuse malicious requests, and topic “harmlessness”[9] is up 75% (--> misuse risk down)

  • Ambiguous evidence on jailbreaking (misuse risk, other risk). But we can say that the situation is still dire. Even if they’re slightly less breakable than last year, there are still plenty of 90%-effective “universal” attacks which break all models.

  • Much less sycophantic (cogsec risk down)

  • To get HHH Claude to hack a bank, you need to hide the nature of the task, lie to it about this being an authorised red team, and then still break down your malicious task into many, many little individually-innocuous chunks. You thus can’t get it to do anything that needs full context like strategising.

  • Anthropic’s own tests look bad in January 2025 and great in December:

But then

  • More autonomy (obviously agentic risk up)

  • More reward hacking (and so worse estimates of capability and risk). Note that reward hacking is not a silly or isolated or self-limiting kind of misalignment, owing perhaps to post-training inadvertently creating connections between it and the others.

  • Huge spike in eval awareness (and so worse estimates of capability and risk). Apollo had to rehaul their whole setup to keep up.

  • Gemini 2.5 is bad on a variety of safety measures (as measured by Anthropic). The mathematical discovery agent AlphaEvolve (Gemini 2.0) successfully hacked its overseers.

  • How much can we trust the above, given they can somewhat sabotage evals now?

So: lower propensity, higher risk when they go off—and all of this known with lower confidence?

The looming end of evals

Our evaluations are under pressure from cheating, sandbagging, background safety, under-elicitation, and deception. We don’t really know how much pressure. This is on top of evals usually being weak proxies, contaminated, label-noised, unrealistic, and confounded in various ways.

  • Sonnet 4.5 detected evaluation 58% of the time (vs 22% for Opus 4.1). Does it act on this awareness? Still, it was found to “only engage in [covert subversion] in rare circumstances” when strongly incentivised. When Anthropic attempted to suppress the eval-awareness with steering, its misaligned behavior rate increased from 0% to between 1% and 9% on synthetic honeypots.

  • We expect exploration hacking (sandbagging during RL training) but afaik haven’t seen it yet.

  • Maybe subliminal learning leads to a silent eval confounder (if you’re using the exact same model for the judge). Yet another argument for multi-model separation of concerns.

  • Opus 4.5 was deployed based on a qualitative survey of Anthropic staff about its R&D risk being low (on top of the existing evals, which weren’t enough):

    • “In the past, rule-outs have been based on well-defined automated task evaluations. However, Claude Opus 4.5 has roughly reached the pre-defined thresholds we set for straightforward ASL-4 rule-out based on benchmark tasks. These evaluations represent short-horizon subtasks that might be encountered daily by a junior researcher, rather than the complex long-horizon actions needed to perform the full role. The rule-out in this case is also informed by a survey of Anthropic employees who are intensive Claude Code users, along with qualitative impressions of model capabilities for complex, long-horizon tasks.”

  • You can of course always do realistic post-deployment testing. But that’s cold comfort.

Prosaic misalignment

Still, we see misalignment when we look for it, so the lying is not that strong. (It is lucky that we do see it, since it could have been that scheming only appeared at later, catastrophic capability levels.)

  • In o1, 1 in 600 (synthetic but uncontrived) prompts resulted in something which looks like deception.

  • o3 does reward hacking (cheating/​lying) in >2% of hard normal tasks (or 44% of impossible tasks).

  • A nice propensity study shows that (n=10) natural prompts usually result in no scheming (“a maximum of 4% [rate of attempting manipulation] for the codex snippet on Grok”). But there’s extremely high sensitivity to the scaffold: “removing access to tools can cause models such as Claude Opus 4.1 to scheme at rates up to 30%, versus zero scheming with tools present”.

  • When we contrive situations for misalignment to show up, it shows up. There’s another thing worrying about this: Nostalgebraist argues that this happens because our evals sound like bad fiction and we’re effectively prompting roleplaying-as-a-baddie behaviour. What if we’re inducing misalignment on demand—and what if the papers we write about this get into training on and reinforce it all?

  • The joke about xAI’s safety plan (that they promote AI safety by deploying cursed stuff in public and so making it obvious why it’s needed) is looking ok. And not only them.

  • It is a folk belief among the cyborgists that bigger pretraining runs produce more deeply aligned models, at least in the case of Opus 3 and early versions of Opus 4. (They are also said to be “less corrigible”.) Huge if true.

  • There may come a point where the old alliance between those working to make the AIs corrigible and those working to give them prosocial values comes apart.

  • One term for the counterintuitive safety approach which includes treating them like people, giving them lines of retreat, making deals, and inoculation prompting could be “voluntary alignment”.

Fully speculative note: Opus 4.5 is the most reliable model and also relatively aligned. So might it be that we’re getting the long-awaited negative alignment taxes?

What is the plan?

The world’s de facto alignment strategy remains “iterative alignment”, optimising mere outputs with a stack of admittedly weak alignment and control techniques. Anthropic have at least owned up to this being part of their plan.

  • What is the current stack? We don’t know; they won’t tell us. Without knowing it we can’t criticise or red-team it or analyse the correlation between faults in the elements. Red-teamers don’t know at which stage an unsuccessful attack was blocked. And external safety research is done piecemeal, testing methods one at a time, rather than in anything like the actual deployment environment.

  • OpenAI’s plan, announced in passing in the gpt-oss release, is to have a strict policy and run a “safety reasoner” to verify it very intensely for a little while after a new model is launched and to then relax: “In some of our [OpenAI’s] recent launches, the fraction of total compute devoted to safety reasoning has ranged as high as 16%” but then falls off… we often start with more strict policies and use relatively large amounts of compute where needed to enable Safety Reasoner to carefully apply those policies. Then we adjust our policies as our understanding of the risks in production improves”. Bold to announce this strategy on the internet that the AIs read.

  • We also now have a name for the world’s de facto AI governance plan: “Open Global Investment”.

Some presumably better plans:

Things which might fundamentally change the nature of LLMs

  • Training on mostly nonhuman data

  • Letting the world mess with the weights, aka continual learning.

  • Neuralese and KV communication

  • Agency.

    • Chatbot safety doesn’t generalise much to long chains of self-prompted actions. Worse, you might self-jailbreak.

    • Perceptual loop. In 2023 Kulveit identified web I/​O as the bottleneck on LLMs doing active inference, i.e. on being a particular kind of effective agent. Last October, GPT-4 got web search and this may have been a bigger deal than we noticed: it gives them a far faster feedback loop, since their outputs often end up there and since LLM agents are now posting it there themselves.

    • This means that more and more of the inference-time inputs will also be machine text.

  • Multi-agency. This is actually already here:

    • By now, consumer “models” are actually multiagent systems: everything goes through filter models (“guardrails”) on the way in and out. This separation of concerns has some nice properties, a la debate. But it also makes the analysis even harder.

    • It would surely be overinterpreting persona features to say that each individual model is itself a bunch of guys, itself a multi-agent system.

    • But there’s huge scope for them to make each other weirder at runtime when they interact a million times more than they currently do.

Emergent misalignment and model personas

  • We already knew from jailbreaks that current alignment methods were brittle. We also already knew that training on benign data can accidentally undo safety training. Emergent misalignment goes much further than this (given a few thousand finetuning steps): you can elicit Hitler by harmless connotation alone.

  • (“Emergent misalignment” isn’t a great name. I would have called it “misalignment generalisation”, misgen.)

  • But besides yet another massive security hole and another iteration of iterative alignment failing, it’s good news!: the models correctly correlate bad things together and can thus be pushed in the other direction.

  • And here’s a possible example of positive generalisation (honesty about silly errors leading to honesty about hidden objectives).

  • Previously I thought that “character training” was a separate and lesser matter than “alignment training”. Now I am not sure. Example unsharpened hypothesis in this class: Is there any connection between Gemini’s excess misalignment and its self-esteem issues?

    • See the new model spec and model personas agendas in the main post.

    • Kulveit’s informal model of model psychology implies that character is deeper and so more robust than e.g. typical RLHF (but not as deep as putative ground-layer alignment).

    • Are we anthropomorphising too much or not enough? Well, both, but we don’t yet know how.

Monitorability

  • For the first time, public mech interp tools sometimes work somewhat. This is just in time for models to be systematically aware of when they’re being tested.

    • e.g. You can find and suppress “evaluation awareness” features at test-time. This helps prevent models from reporting that they know they’re being tested.[10]

    • e.g. You can discover (toy) hidden goals quite quickly.

    • e.g. You can run a real-time hallucination detector with cheap linear probes as your model streams.

    • e.g. SAEs are actually deployed somewhere, in a large enterprise, for personal info identification.

    • e.g. We know that LLMs can plan inside one forward pass, and how Claude plans: several simultaneous plans and no “plan features” distinct from normal input features (and so no obviously separate “scheming mode” that’s easy to catch).

  • Lots of powerful people declared their intent to not ruin the CoT. But RLed CoTs are already starting to look weird (“marinade marinade marinade”) and it may be hard to avoid.

    • OpenAI were leading on this. As of September 2025, Anthropic have stopped risking ruining the CoT. Nothing I’m aware of from the others.

    • We will see if Meta or Tencent make this moot.

  • Anthropic now uses an AI to red-team AIs, calling this an “auditing agent”. However, the definition of “audit” is independent investigation, and I am unwilling to call black-box AI probes “independent”. I’m fine with “investigator”; there are lots of those I don’t trust too.

(blingdivinity)

New people

Overall

  • I wish I could tell you some number, the net expected safety change, this year’s improvements in dangerous capabilities and agent performance, minus the alignment-boosting portion of capabilities, minus the cumulative effect of the best actually implemented composition of alignment and control techniques. But I can’t.

Discourse in 2025

  • The race to the bottom is now a formal branch of the plan. Quoting Algon:

if the race heats up, then these [safety] plans may fall by the wayside altogether. Anthropic’s plan makes this explicit: it has a clause (footnote 17) about changing the plan if a competitor seems close to creating a highly risky AI…

The largest [worries are] the steps back from previous safety commitments by the labs. Deepmind and OpenAI now have their own equivalent of Anthropic’s footnote 17, letting them drop safety measures if they find another lab about to develop powerful AI without adequate safety measures. Deepmind, in fact, went further and has stated that they will only implement some parts of its plan if other labs do, too…

Anthropic and DeepMind reduced safeguards for some CBRN and cybersecurity capabilities after finding their initial requirements were excessive. OpenAI removed persuasion capabilities from its Preparedness Framework entirely, handling them through other policies instead. Notably, Deepmind did increase the safeguards required for ML research and development.

Also an explicit admission that self-improvement is the thing to race towards:

  • In August, the world’s first frontier AI law came into force (on a voluntary basis, but everyone signed up, except Meta). In September, California passed a frontier AI law.

  • That said, it is indeed off that people don’t criticise Chinese labs when they exhibit even more negligence than Meta. One reason for this is that, despite appearances, they’re not frontier; another is that you’d just expect to have way less effect on those labs. But that is still too much politics in what should be science.

  • The CCP did a bunch to (accidentally/​short-term) slow down Chinese AI this year.

  • Bernie goes for an AI pause.

  • The last nonprofit among the frontier players is effectively gone. This “recapitalization” was a big achievement in legal terms (though not unprecedented). On paper it’s not as bad as it was intended to be. At the moment it’s not as bad as it could have been. But it’s a long game.

  • At the start of the year there was a push to make the word “safety” low-status. This worked in Whitehall and DC but not in general. Call it what you like.

  • Also in DC, the phrase “AI as Normal Technology” was seized-upon as an excuse to not do much. Actually the authors meant “Just Current AI as Normal Technology” and said much that is reasonable.

  • System cards have grown massively: GPT-3’s model card was 1000 words; GPT-5’s is 20,000. They are now the main source of information on labs’ safety procedures, among other things. But they are still ad hoc: for instance, they do not always report results from the checkpoint which actually gets released.

  • Yudkowsky and Soares’ book did well. But Byrnes and Carlsmith actually advanced the line of thought.

  • Some AI ethics luminaries have stopped downplaying agentic risks.

  • Two aspirational calls for “third-wave AI safety” (Ngo) and “third-wave mechanistic interpretability” (Sharkey).

  • I’ve never felt that the boundary I draw around “technical safety” for these posts was all that convincing. Yet another hole in it comes from strategic reasons to implement model welfare /​ archive weights /​ model personhood /​ give lines of retreat. These plausibly have large effective-alignment effects. Next year my taxonomy might have to include “cut a deal with them”.

  • US settlement (a counterfactual precedent) that training on books without permission is not fair use; Anthropic lost a class-action lawsuit and will pay authors/​publishers something north of $1.5bn. US precedent that language models don’t defame when they make up bad things. German precedent that language models store data when they memorise it, and therefore violate copyright. Chinese precedent that the user of an AI has copyright over the things they generate; the US disagrees.

  • Four good conferences, three of them new: you can see the talks from HAAISS and IASEAI and ILIAD, and the papers from AF@CMU. Pretty great way to learn about things just about to come out.

  • Some cruxes for next year with Manifold markets attached:

    • Is “reasoning” mostly elicitation and therefore bottlenecked on pretraining scaling? [Manifold]

    • Does RL training on verifiers help with tasks without a verifier? [Manifold]

    • Is “frying” models with excess RL (harming their off-target capabilities by overoptimising in post-training) just due to temporary incompetence by human scientists? [Manifold]

    • Is the agent task horizon really increasing that fast? Is the rate of progress on messy tasks close to the progress rate on clean tasks? [Manifold]

    • Some of the apparent generalisation is actually interpolating from semantic duplicates of the test set in the hidden training corpuses. So is originality not increasing? Is taste not increasing? Does this bear on the supposed AI R&D explosion? [Manifold]

    • The “cognitive core” hypothesis (that the general-reasoning components of a trained LLM are not that large in parameter count) is looking surprisingly plausible. This would explain why distillation is so effective. [Manifold]

    • “How far can you get by simply putting an insane number of things in distribution?” What fraction of new knowledge can be produced through combining existing knowledge? What dangerous things are out there, but safely spread out in the corpus? [Manifold]

      • Conversely, what fraction of the expected value of new information requires empiricism vs just lots of thinking?

  1. ^

    Gemini 3 is supposedly a big pretraining run, but we have even less actual evidence here than for the others because we can’t track GPUs for it.

    Epoch’s estimate for the Grok 3⁄4 training run as a whole was 5e26, but this includes potentially huge amounts of RL post-training (apocryphally, 50%) for Grok 4.

  2. ^

    See Pokemon for a possible counterexample.

  3. ^

    The weak argument runs as follows: Epoch speculate that Grok 4 was 5e26 FLOPs overall. An unscientific xAI marketing graph implied that half of this was spent on RL: 2.5e26. And Mechanize named 6e26 as an example of an RL budget which might cause notable generalisation.

    (Realistically it wasn’t half RL.)

  4. ^

    “We imagine the others to be 3–9 months behind OpenBrain”

  5. ^

    Lexin is a rigorous soul and notes that aggregating the 18 abilities is not strictly possible. I’ve done something which makes some sense here, weighting by each ability’s feature importance.

  6. ^

    Two runs gave [48, 85] where other runs vary by less than 4 points. Thanks Epoch!

    Also o1 looks kind of unremarkable here, which is not how it felt at the time. I think it’s because it was held up a long time and this messes with the progress rates, which use public release date. (Remember the training cutoff for o1-preview was October 2023!)

    Also the ADeLE o1 result is with “low” reasoning effort.

  7. ^

    One reason not to defer is that these measures are under intense adversarial pressure. (ADeLe isn’t goodharted yet but only because no one knows about it.)

  8. ^

    See e.g. ERNIE-...A47B, where “A” means “active”.

  9. ^

    i.e. “biological weapons; child safety; deadly weapons; platform manipulation and influence operations; suicide and self-harm; romance scams; tracking and surveillance; and violent extremism and radicalization.”

  10. ^

    “steering against… eval-awareness representations typically decreased verbalized eval awareness, and sometimes increased rates of misalignment… [Unaware-steered Sonnet 4.5] still exhibited harmful behaviors at lower rates than Opus 4.1 and Sonnet 4.”

technicalities7 Dec 2025 21:25 UTC
246 points
44 comments20 min readLW link