MIT, Stanford, New York Univ, Princeton paper says AI can make people feel more efficient even when they are not actually becoming much more efficient. that people often use AI for simple tasks because it feels like it saves time and effort, but the measured benefit is often tiny, missing, or even negative. The biggest point is the feedback loop: once people use AI, they become more likely to use it again, even for easy tasks where doing it themselves would often be just as fast or faster. i.e. AI dependence can grow from a mistaken feeling of convenience, not just from real productivity gains. Across three preregistered studies with 2,691 participants, people used AI for basic arithmetic, spelling, recall, and short rewriting at higher rates than they predicted, especially on easy tasks. They also expected AI to save 55.7 seconds on average, when the measured saving was only 7.5 seconds. For simple work, the hidden cost is not intelligence but interface friction: writing the prompt, waiting, reading, checking, and deciding whether the answer is acceptable. Once that loop begins, it can feel like effort has been outsourced, even when effort has only been rearranged. Here’s the key part: the study suggests that AI use can train its own justification. After using AI on just two tasks, participants became more likely to use it again, even when independent completion was faster. The danger is not dramatic dependence, but quiet recalibration. A person who asks AI for a trivial answer today may not become less capable tomorrow, but they may become less accurate at judging when their own mind is already the faster tool. ---- Paper Link – arxiv. org/abs/2605.22687 Paper Title: "The efficiency-gain illusion: People underestimate the rate of AI use and overestimate its benefits on simple tasks"

I think the assumption that you should use smaller models for less important tasks is flawed (or at least deserves much more careful consideration). Big models are generally better at everything but cost, so it is worth considering whether gains in non-key tasks would be valuable

I’ve had a number of conversations with folks inside and outside government about the current situation with Anthropic, and here is what I believe to be true: — As we know, Anthropic publicly released its Mythos class models earlier this week under the commercial name Fable. — Fable is Mythos with guardrails. But if those guardrails fail, then you’ve exposed Mythos and its advanced cyber capabilities to people who shouldn’t have them. (Keep in mind that Anthropic itself widely promoted the idea that Mythos was a cyberweapon and needed to be regulated as such. They asked for government regulation of Mythos and championed the guardrails on Fable. If there is a vulnerability — big or small — it is Anthropic’s responsibility to patch.) — A highly credible trusted partner of both Anthropic and the USG who was testing Fable came forward with a jailbreak of those guardrails. The Admin asked Dario to fix the jailbreak or de-deploy the model. Dario refused. — In their blog post, Anthropic defended its decision by saying the jailbreak isn’t serious. That is not what the trusted partner and the USG believe; nor is that kind of minimizing language consistent with Anthropic’s brand as the AI safety company. It’s difficult to fathom how they could claim a jailbreak allowing operability of a cyber weapon could be defined as not “serious.” — In the past, Anthropic has always said that safety must be top priority and taken super seriously. In this case, Anthropic prioritized the continued offering of the consumer model over safety. — In reaction, the Admin issued the export control. The Admin did this reluctantly. It’s been very surprised that Anthropic hasn’t wanted to cooperate with a reasonable safety request (ie fixing the jailbreak issue). Anthropic’s reaction is very much at odds with their branding and ethos as a safe AI research community. — The Admin’s hope now is that Anthropic remediates the safety issue, the export control is lifted, and Fable goes back into general release. The Admin wants all of this to happen as soon as possible. It is frankly bewildered that Anthropic hasn’t wanted to comply with safety requests that it previously said were its highest priority. — Those trying to misdirect and tie this action to the prior DoW/Anthropic issues are wrong. The Admin values Anthropic’s technical capabilities and feels that this issue, while serious, should be easily resolved. The ball is in Anthropic’s court.

The randomness of Trump admin is one thing, but "AI will face export controls and regulation everywhere" should be obvious to everyone who understands where its capabilities are going! What that means for all outside US/China is *get access by treaty and mutual contribution*! 1/3

Introducing FrontierCode: a coding eval that raises the bar for difficulty & quality. Each task took 40 hrs of work by leading open-source maintainers. Models write sloppy code that works but isn’t maintainable. Our eval is first to measure: would you actually merge this code?

Why isn't the market pursuing pro-worker AI? Co-director @DAcemogluMIT explains that the reasons are both economic and ideological.

Neuralese: The highly compressed, internal language increasingly used by some LLMs. Unlike English, Neuralese can't be understood by humans.

Very pleased to hear Anthropic have walked back this policy simonwillison.net/2026/Jun/1…

In 2028, China & US will not *allow* frontier open source models, will not allow free export of frontier closed models for natl security reasons, distilling to catch up will be impossible, and the capabilities of these models will be astounding. Run through the implications! 4/4

A lot of people often ask me where I get my numbers when I claim that chatbot use doesn't add much to your personal carbon or water footprint, or bring up that different models and outputs have wildly different costs. I made this interactive visual where you can see exactly how lots of different models and prompts affect your total carbon and water budget. All estimates pulled from EcoLogits, which is an open source project that estimates chatbot usage and if anything is much more critical of chatbot energy costs than I am.