2467 hand-picked resources, updated every week. Search it, filter it, or just browse a collection and see what catches your eye. Want today’s headlines instead? Read the free AI news feed.
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Answers come only from resources in this hub, with the sources listed underneath.
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.
Personal site of the Stanford economist who directs the Digital Economy Lab, with links to his research on AI, productivity and work.
Why I recommend it: One of the most cited voices on AI and jobs. He is broadly optimistic about AI augmenting workers, so read him alongside more skeptical economists.
#ai economics#future of work#job markets#productivity#research#stanford
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.
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.
