Stanford's economic policy institute, publishing free research briefs on work, productivity, AI and public policy.
Why I recommend it: Useful plain-language policy briefs. Research reflects the views of its authors, not a consensus.
Read / subscribe freeA Stanford research project page describing HomeBody, which gives vision-language models a humanoid robot body through persistent spatial memory and reusable skills, without training for each new environment. Results are the authors' own and include demo videos.
The hardware chapter of Stanford's annual AI Index report: data on AI chip performance, costs, and who controls the computing power behind modern AI.
Why I recommend it: Free to download. It's the institute's own synthesis, and some compute and investment figures come from data supplied by the companies being measured — the broad picture is reliable, the fine print less so.
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.
Funding page for Stanford Medicine's Upstream Research Center pilot grants, which back early-stage research on upstream causes of health such as work, income and community conditions.
Why I recommend it: Free to read and apply, but eligibility is limited — check whether you need a Stanford affiliation before spending time on an application.
Stanford Digital Economy Lab working paper by Bharat Chandar and Bouke Klein Teeselink, separating what happens to jobs after a firm adopts generative AI into a company-wide productivity effect and changes in what specific kinds of workers are asked to do.
Why I recommend it: Free, with the full PDF on the page. It is a working paper dated 21 September 2026, meaning it has not been through peer review yet, so read it as early evidence. Useful because it does not answer 'will AI take jobs' — it asks which workers get asked to do more and which get asked to do less.
Stanford's Center for Research on Foundation Models runs HELM as a living benchmark for language and multimodal models. Rather than one score, it reports many models across many scenarios on multiple metrics — accuracy, calibration, robustness, fairness, bias, toxicity and efficiency — and publishes the leaderboards alongside the raw model outputs (predictions and prompts) so you can check a claim yourself instead of taking a number on trust. Separate leaderboards cover areas such as classic HELM, instruction-following, medical, legal and safety. All results and analysis are free to browse on the site, no account.
Why I recommend it: The place to go when a vendor quotes you a benchmark figure. HELM's real value is that it shows the prompts and the model's actual answers, so you can see what the score measured. Be aware of what it is not: it is a snapshot of the model versions and dates CRFM ran, so check the run date before comparing anything to a model released since, and a model missing from a leaderboard usually means nobody ran it, not that it failed.
Interdisciplinary Stanford research institute studying how digital technology and AI change work, productivity and shared prosperity, with public papers and data.
From the site: The Stanford Digital Economy Lab is an interdisciplinary research institute shaping a future where technology drives human well-being and shared prosperity.
Why I recommend it: Free to read. Useful when you want measured research on AI and jobs instead of headline claims — check the publication date on each paper, the field is moving fast.
Stanford professor who built ImageNet, co-directs Stanford's Human-Centered AI institute and co-founded the AI4ALL diversity pipeline program. Her faculty page collects the work; her Google Scholar list has the papers themselves.
From the site: Fei-Fei Li is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). The site facilitates research and collaboration in academic endeavors.
Why I recommend it: Start with ImageNet if you want to understand why the last decade of AI happened when it did. Google Scholar refuses automated visits, so that link may show no picture here.
Stanford economist Charles I. Jones works out, in plain economic terms, how much money it would be worth spending to lower catastrophic risks from advanced AI — comparing it to the roughly 4 percent of GDP the U.S. effectively spent during Covid-19.
Interdisciplinary Stanford institute advancing AI research, education, policy, and practice to improve the human condition, with strong ethics and governance focus.
In plain terms: This university center shares research, policy updates, and educational resources focused on artificial intelligence. You can browse an AI glossary, read industry reports, and explore professional courses or research fellowships.
Why I recommend it: Their policy briefs are readable and free — a good first stop if AI governance feels opaque.
Stanford's step-by-step resume handout covering structure, section order, describing experience, and common formatting mistakes.
In plain terms: This guide walks you through the resume-writing process step by step. Use it to choose your section order, write clear descriptions of your experience, and avoid common formatting errors.
Stanford's full career resource hub — resume and cover letter handouts, interview prep, and industry guides, all free to the public.
In plain terms: This collection offers free guides, templates, and tools for every stage of the job search. You can use it to build your resume, write cover letters, explore different careers, and practice for upcoming interviews.
From the site: We have gathered a list of our favorite online career resources in one place. Whether you're exploring your career options, preparing to apply for jobs, or seeking to maximize your experience, you'll find valuable resources below to help you succeed.
Other resources that share this publication's topics.
Topic brief from the nonprofit Learn & Work Ecosystem Library on how governments and organizations are developing AI laws and policies affecting workplaces, learning, credentialing and government services.
A free unofficial explorer that makes the EU AI Act easier to read: browse the regulation article by article, with a table of contents and search.
California's 2025 law requiring large frontier AI developers to publish safety frameworks and report critical safety incidents. Full text on the California Legislature's site.
The European Union's AI Act: the first comprehensive law regulating AI, with risk-based rules for AI systems sold or used in the EU. Official text on EUR-Lex. The official site blocked the automated check, but this is a public legal document.
Colorado's 2024 law on high-risk AI systems, requiring developers and deployers to use reasonable care to avoid algorithmic discrimination. The official site blocked the automated check, but this is a public law.
Congressional Black Caucus Foundation publication on workforce policy, hosted on Issuu. Described from title; details not checked page by page.
