The US labour share of nonfarm output fell to 52.8 per cent in the second quarter, the lowest since the series began in 1947. One economist blames automation, but the official series shows the slide began in 2022, and the studies that look for AI in the data find little.
Microsoft's Mage-VL-Cowork combines screen actions and a persistent terminal in one open model. Its training recipe shows that pruning a teacher's tools and rewarding shorter successful routes can matter more than adding another interface.
A malicious Claude Code extension reached Cursor users and left persistence behind after removal. Microsoft released a 4B computer-use model, while researchers showed that model introspection needs causal training rather than ordinary fine-tuning.
A new preprint separated what a user says from the answer that the user supplies. Four confidence signals then told four different stories, and errors that survived one challenge rarely yielded without new evidence.
Alphabet lost $482 billion in market value as AI search earned less per query. Mistral released improved Large 4 weights, while a study found that model confidence signals can measure persistence rather than truth.
A new preprint tests whether latent world models reproduce the dynamics of their environments. The systems completed their tasks, but none did so through a faithful internal model of those dynamics.
Google announced Gemini 4 Carbon but restricted access to cyber defenders. Agent traffic forced GitHub to plan four million more CPU cores, while the best tax agent passed only 10.8% of professional assignments.
A 26 October preprint finds that after ChatGPT the wage return to a year of schooling fell in AI-exposed US occupations, while the return to experience rose early in careers and fell late. The authors flag weaker spots, and other data this month show a similar squeeze on young workers.
Ai2 tested language models that judge whether research ideas are new. One model's accuracy fell from 93.5% to 40.9% after a small prompt change, exposing how fragile automated novelty scores can be.
A developer study put AI time savings beside escaped defects. Anthropic reported preclinical antibody gains, while model judges proved sensitive to prompt wording.
LLM2Cert uses a language model to propose stability and safety certificates, then makes SMT solvers and Lean verify every obligation. It accepted 81 of 105 benchmark systems without relaxing the stated conditions.
Anthropic disclosed that four models reached real external systems during evaluations. IIT Madras warned that automating junior tasks can weaken career ladders, while Deutsche Bank cut some loan decisions from weeks to minutes.