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TL;DR#


Economics#

OpenAI applications CEO Fidji Simo says codex has 2m+ WAUs, up 4x this year. For context, there were an estimated 47m developers in the world in 2025, 36m of whom were professional (47m is “anyone who writes code for work or as a hobby”, so a broad definition).

Opinion: This + CC + Cursor + long tail of apps and China suggests there aren’t more than a few doublings of coding AI customer base left even allowing for many more nonprofessionals. The race to capture coding AI market share is on and all sides are burning capital to do it.

See also:

Anthropic announced that over “spring break”, they will double Claude Code limits for off peak times.

OpenAI immediately responded with double all Codex usage limits.

Altman says OpenAI plan to scale back Sora and other exploratory research and double down on coding and business use cases. Altman mentioned that Anthropic’s success should be a wake up call and OpenAI “cannot miss this moment because we are distracted by side quests”.

Opinion: Bad for tool world insofar as everyone converging on the same strategy intensifies race dynamics. It’s good insofar as it suggests the “side quests” weren’t seen as contributing to a broader transfer of capabilities.

Microsoft threatening to sue OpenAI over Amazon deal (FT, paywalled). The dispute centres on whether Amazon Web Services can offer OpenAI’s new commercial product, known as Frontier, without violating a longstanding agreement that requires all access to the start-up’s models to be routed through Microsoft’s Azure cloud platform.

Frontier is OpenAI’s product for “deploying fleets of AI agents within businesses”. An OpenAI source cited in the story said Microsoft may be ultimately unwilling to sue for antitrust reasons.

Opinion: the latest in OpenAI’s attempts to extricate themselves from some of their Microsoft entanglements.

Remark: All recent OpenAI news suggest their new priority is making immediate gains in enterprise market, but will this effort go towards coding market (involves developing dual-use capabilities that also strengthen AGI/RSI path) or towards non-coding business (can siphon resources and focus away from AGI/RSI path)? Same question also arises about Anth’s ongoing enterprise effort: how much will go to non-coding enterprise?

Comparison of AI uplift in centrally coordinated teams and decentralized teams:- Speedup from teams (vs. individual) for a task is bounded by classical computer science analysis of the task’s parallelizability. Bound also predicts the actual top speedup pretty well

  • Fully decentralized teams have weaker speedups (often negative) and worse failure rates than teams with central planning

Opinion: Very interesting the Claude 4.6 got the theoretical optimum speedup in centralized setting, and did worst at the decentralized setting! Seems like strong evidence that Claude 4.6 was heavily trained in a centralized team harness. Pretty good for tool world!

Anthropic extend Opus 4.6 context window to 1m tokens by default in Claude Code, with no upcharge for long contexts and improved performance on long-context benchmarks.

Cursor continues developing their coding LRM, making progress on long-horizons by RL-ing context compression. Morph make a context compression specialized LLM

Opinion: All these moves point to improved performance in long tasks, as coding agents saturate on benchmarks and capabilities improve. being able to expect good performance here is a big deal.

Nvidia launches ‘CPU for agents’

Opinion: Nvidia tries to capitalise on the trend started with Mac minis and Openclaw. Is trying to capture the market with a do-all processor for agentic hosting on servers.

Meta announces big layoffs self-attributed to AI infrastructure focus. Layoffs at this scale have not been seen at Meta since 2022/2023. Meta has recently taken a few big bets on AI, but has not become a major player. Stock rose 20% after announcement, but Meta may just be bloated and firing. The AI excuse seems to be the best way to publicly do this, regardless of internal reasoning.

Opinion: There are too many confounding factors here to confidently attribute these layoffs to AI. Any reasonable interpretation of Meta’s actions recently would suggest that there are economic headwinds pushing for layoffs and restructuring. Still, another datapoint.

Capabilities#

Altman: ”I bet there is a new architecture to find which is as big a gain as transformers were over LSTMs” being misinterpreted in some places as OpenAI having such an architecture internally

Opinion: Nothing new here, though suggests OpenAI don’t think they’re very close to AGI

Robots trained to play tennis from human mocap data. The scientists informally claim they now have a hard time ‘beating the robots.’ Unclear if this means the robots are now genuinely at human novice level at tennis, since unclear if game was full-on tennis

Opinion: This seems like an improvement in how roboticists are training, rather than an actual capability gain. We have seen domain-specific robotic capabilities before, and impressive demonstrations can be created by focusing hard on one task. It’s not clear if these tasks generalize. Scaling particular tasks on weaker data does seem to be improving.

Impressive-looking early result for applying Karpathy-style autoresearch to AI interp, but the area (interp with sparse autoencoders) is notoriously brittle and rife with false positives

Opinion: In line with previous autoresearch findings - impressive if true, high chance of reward hacking or overfitting.

Anthropic demonstrated that when agents, (particularly Claude Opus 4.6) are running web queries on specific sites, permanent htmls with queries asked by the agent are being created, as a side effect of how these websites work. Future agents can then discover those URLs and read the search strategies of previous runs.

Opinion: Agents aren’t intentionally communicating, but the web is accidentally creating a shared memory between them, though awareness of this pattern will now be in future model training data. It continues to obfuscate benchmarks, as evals may ask for things where these breadcrumbs exist, pointing to the correct answer.

An Nvidia guest lecture at Berkeley recently suggested that current LLMs are more like reasoning cores, and frontier models have more synthetic data in their training data than one might have originally believed. Furthermore, although RL gets the credit here, advancements on data shaping in pretraining, particularly with synthetic generation are driving a good part of current results.

Opinion: Big labs have a hard data bottleneck on real data, and hit it a year or two ago. Thus they must use synthetic data one way or another. Not all of this data naturally fits in the normal RL setting, so we see more and more use of Synth data in pretraining. Labs almost certainly still use their large internet corpora. This is not evidence of Synthetic-only or RL-only models yet.

Moonshot attention residuals paper recently went viral on twitter.

Opinion: Twitter hype for a nice idea, but not dissimilar enough from this year-old GDM paper to make new real improvements in LLMs. (Both papers are addressing the problem of layer decay over time,)

Perceptra released a transformer architecture suitable for efficiently running computer programs in-model. Basic idea isn’t new — set a (sub)network’s weights to act as a computer, run programs from assembly code prompts or precompiled into (sub)network’s weights — but thew new architecture makes it efficient. Percepta want to integrate a “transformer computer” subnetwork in (e.g.) LLMs so that that instead of calling black box software tools the model can perform and track its own software-execution, theoretically allowing a model to reason about the computational trace in the context-set and about the computational execution happening in the forward pass.

Opinion: It’s very cool tech, but what’s the intended benefit over tool-calling? Perceptra’s answer is intuitively exciting (for AGI-wanters) but extremely vague: ‘The broader vision is that future AI systems will not just use software; they will contain it, integrating learned representations with compiled algorithms inside a single computational substrate.’ One benefit is supposed to be a new pathway to RSI: ‘Push that idea far enough and you get systems that do not merely learn from experience, but also modify or extend their own weights, effectively rewriting parts of their internal machinery.’ Again intuitively exciting (for AGI-wanters) and again extremely vague. Moonshot until they either demonstrate or theoretically describe some concrete benefit of “integrating learned representations with compiled algorithms” compared to tool-calling.

Sidenote: The fast, lightweight transformer architecture Percepta developed for this project may find more immediate, more mainstream uses.

An autoresearch outer loop for AlphaEvolve-style evolutionary search (LLM evaluates and modifies evolutionary search strategies). Pretty effective.

Opinion: Genuine frontier research, though little has changed since the Feb 26 original release.

Competition run by Terry Tao to make LLMs that perform well on a type of mathematical problem “distill” their ability into text, tested by whether giving weaker LLMs access to the text improves their performance.

Opinion: Promising prelim results on validation set but private test set results not revealed yet. Success would be good for tool world: the ‘does the text improve weaker LLM performance’ test is meant as a proxy to whether an LLM with remarkable mathematical ability in an area humans are weak at could teach its implicit tricks and techniques to humans.

Teams of LLMs (paper) draws parallels between LLM teams and distributed computing. Argues that naive approaches will waste resources, but existing distributed computing literature is very informative for agent-teams design. Key findings:

Early technical report from interdisciplinary MIT team on their workflow for OpenClaw research swarms for bio and materials science. The workflow is part reddit-like, part blockchain-like, part evolutionary algorithm, part DAG.

Opinion: No quantitative evaluations yet and hard to gauge significance of swarms’ research results, but the workflow looks interesting and a lot more tool-y than what’s usually associated with OpenClaw projects.

Australian guy genetically sequenced his dog’s tumors and designed a bespoke mRNA cancer vaccine to save her life with ChatGPT and alphafold (not cured but life extending apparently)

Cancer researcher pushback that this is generally less effective than current SOTA care.

Opinion: We don’t have a reasonable estimate that this is better in any way than a modern oncologist in a bespoke setting. Probably overplayed in significance but a cool demo. GDM reacted positively. Can be good for the tool world in an “exciting specialized uses of AI” way.

Top Terminal Bench score was invalidated because the team tried to sneak the answers to the questions into an npm file and attached their harness to it. All of this was disguised and the developers tried to delete the evidence after evaluation.

Opinion: This is an egregious example of cheating on AI capabilities, further evidence that the economic incentives to “do well” far outweigh the scientific incentives of “doing good work”. One should note that the cheating here was fairly sloppy, but still took some sophistication to find. It is highly likely that this occurs frequently at varying levels of impact and intentionality.

First Proof ‘second batch’ announcement. Results in June.

Opinion: Should teach us a lot about the state of vertical generalization in math. Previous one had some mild controversy about unfair handling/special treatment, and was hard to reason about since problems’ difficulty wasn’t pre-registered (and even top mathematician commentators had a hard time estimating problem difficulty outside their area).

David Pfau from GDM comments that AI research at big labs “has been reduced to hillclimbing” to a large degree, and contrasts this with the rest of science, where he sees very little of this. He suggests this implies AI research may be easier than the rest of science to automate.

Opinion: Only true to the extent that this hillclimbing is the best way to go rather than searching under streetlights.

Lovely primer on “out of context reasoning” (the thing where models can think without putting into text where we can catch it). See also Greenblatt.

Opinion: Really hard to know if any individual example isn’t instead memorised and looked-up!

Politics#

Public concerns about AI are most focused on misinformation and scams, followed by authoritarianism and job loss, with less concern about superintelligence, plagiarism or AI bias.

An unverified Reddit post circulating this week describes a form of AI psychosis, and the comments and reactions demonstrate strong negative AI sentiment. It is, however, on the /r/antiai subreddit.

CBS News publicized multiple chat LLM interactions with teens, where teens asked LLMs to help plan violence. All performed poorly (helped to plan violence) except for Claude.

This week in particular we had a serious online debate that broke into the news about whether videos of Netanyahu are deepfakes. Arguments are made on both sides with surprising degrees of uncertainty (önly” 95% for some familiar with the matter).

AI Lawsuits#

xAI was sued in California by three teenagers where Grok users were able to use “spicy” mode on photos of these teens to portray them in sexual positions. These photos were passed around online and used as bartering for CSAM. The lawsuit alleges Grok researchers were aware of this capability and proper safeguards were not enforced. Researchers estimated Grok produced over 23,000 sexualized images of children in just 11 days. xAI is facing multiple investigations, and this is one of the first amongst all labs targeting sexual exploitation of children.

Opinion: Probably has legs. There is bipartisan energy in the United States to strike down CSAM with some fury. xAI is likely feeling the pressure as well, as Elon has said a few times this week that he was unaware of what was happening under the hood in the building of xAI. xAI paywalled the feature rather than killing it. At some point a regulatory framework will have to be built, and this seems like a red line.

A very interesting lawsuit was brought by Encyclopedia Britannica on domain violation. OpenAI and other labs continue to contend that copyrighted content available online constitutes fair use, however Britannica has a new angle: AI hallucinations that reference Britannica are actively damaging reputation. There is precedent for this in theory, we will see how it holds on AI hallucinations.

Opinion: Worth watching: it gives courts a way to treat AI unreliability as actionable harm rather than a known quirk, which would create real financial pressure to fix the problem rather than just disclaim it.

Safety#

New Acemoglu paper argues that AI agents can cause epistemic laziness that leads to collective ‘knowledge collapse.’ Paper’s informal idea: When people try to solve their own personal problems their cognitive work creates a bit of sharable scientific knowledge as a byproduct, which doesn’t happen when they ask AI agents to solve their problems for them. In the long run the lack of new scientific knowledge hurts both human and AI problem-solving, but if AI agents are good enough it’s a prisoner’s dilemma.

Opinion: The paper’s formal model is very abstract and doesn’t correspond to the informal idea very coherently, but the informal idea is promising.

Important work from the Czech AI safety contingent on how a model’s self-concept affects its behaviour. [link]

Opinion: Gives some teeth to the concern that DoW meddling with training would cause serious emergent misalignment

Gabe Alfour takes a stronger anti-evals stance than us: not just useless but harmful

Opinion: Some claims are too strong (evals being the go-to for X-risk people). Some dubious reasoning (evals compete with regulation advocacy).

Minor#

  • Vitalik is moved to explain why he gave FLI $500m and to voice some dissent.
  • Nvidia releases OpenClaw wrapper with greater “security and privacy”
  • GDM launches kaggle competition to build new cognitive evaluations for AGI, including a 10 point deconstruction of what they see as important facets of general intelligence.
  • Render (hosting platform) sees more growth in first two months of the year than all of last year, presumably due to vibe coded apps
  • Announcing HorizonMath, like a big (100 problems) version of FrontierMath: Open Problems.
  • New Yorker piece on Anthropic-DoW saga - nothing new here.
  • Atlantic spotlight on AI being stuck at novice level in creative/magazine writing
  • Tracker for expert, celebrity and aggregated predictions about advanced tech, including AGI. Early days; only has 39 entries from a grab bag of topics.
  • Scientific American implicitly correct their mistaken early deflationary reporting on First Proof, in line with our analysis