Cevat Giray Aksoy, Nicholas Bloom, Steven J. Davis and co-authors estimate how much workers value small amounts of in-office time versus fully remote work (June 2026).
Why I recommend it: Handy for negotiating hybrid arrangements. Bloom is a long-time advocate of hybrid work; the paper is not yet peer reviewed.
Omar Abdel Haq, Amitabh Chandra, Tomáš Jagelka and co-authors use large language models to elicit and measure people's life preferences and trade-offs (May 2026).
Why I recommend it: An experimental method, not yet peer reviewed. Using AI to stand in for or interview people carries bias risks the authors themselves discuss.
#economics#large language models#preferences#research#research methods
An AI tool that builds tailored résumé and job-application pieces from reusable modules of your experience.
Why I recommend it: Freemium: there is a free starting tier, but the paid plans unlock more. Always reread what an AI writes about you before sending it — it can overstate or invent.
Stanford HAI's annual flagship report tracking AI progress across research, the economy, education, policy, and public opinion. The 2025 edition compiles data on model capability, training costs, industry investment, and workforce effects, with downloadable charts and datasets.
Why I recommend it: Free to download from Stanford HAI. It is a self-published report from a university institute; figures are sourced within the report, but some industry-investment and capability numbers rely on data supplied by the companies being measured. Treat headline rankings as the report's own synthesis, not neutral fact.
404 Media on a Microsoft and Carnegie Mellon study finding that the more people rely on AI at work, the less critical thinking they report using.
Why I recommend it: The study is based on workers describing their own habits, not tests of their thinking, so it shows a link rather than proof that AI causes the decline.
