Interdisciplinary Stanford research institute studying how digital technology and AI change work, productivity and shared prosperity, with public papers and data.
In plain terms: The Stanford Digital Economy Lab is an interdisciplinary research institute shaping a future where technology drives human well-being and shared prosperity.
Visit Stanford Digital Economy LabFree 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.
Program details are still being written up. In the meantime, the site itself is the best starting point.
Stanford's August 2026 Canaries update describes an employment shortfall among young workers in highly AI-exposed occupations, primarily through reduced hiring. The authors stress these are descriptive patterns, not proof that AI caused the gap.
Stanford HAI article arguing that framing AI adoption around productivity gains alone undermines employee buy-in, and outlining alternative approaches to workplace AI rollout.
The home page of the Stanford Artificial Intelligence Laboratory, a center for AI research, teaching, theory and practice since its founding in 1963. The site lists faculty, research groups, affiliated centers, courses, events and lab news.
A Stanford Report story from August 2025 on research into AI companion chatbots and the risks they pose to teens and young people.
Why I recommend it: The page blocked the automated check, so this description is based on the title.
Stanford Digital Economy Lab dashboard tracking employment for workers by age and AI exposure, updated as new payroll data arrives.
Stanford Digital Economy Lab paper using payroll data to track employment changes for early-career workers in jobs most exposed to AI.
Stanford Digital Economy Lab follow-up examining whether interest rates and timing, rather than AI, explain employment shifts among young workers.
A 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.
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'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.
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.
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.
Hand-picked from the hub based on what Stanford Digital Economy Lab covers.
This research archive provides articles and reports on how artificial intelligence affects the workforce. You can explore these studies to learn how new technologies and automation impact jobs, wages, and worker protections.
Why this: Covers research and economics too
Research exploring possible economic futures as AI capability advances, including labor market effects and policy questions.
Why this: Covers research and future of work too
This guide provides formatting rules, action-verb lists, and sample resumes and cover letters across various industries. You can use the annotated before-and-after examples to write and format your own job applications.
Why this: Covers free and students too
This guide features free and low-cost AI tools for resumes and job interviews. You can use them to build and score ATS-friendly resumes or practice mock interviews with automated coaching.
Why this: Covers job markets and free too
