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3505 hand-picked resources for students, job seekers, career changers, founders, and AI learners — 100% free to browse with no account required.
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44 resources
An October 2026 Cosmonaut essay by Nicolas D. Villarreal revisiting his 2023 predictions about AI and arguing that artificial general intelligence is a real possibility with major implications for class politics.
Apollo's October 2026 Daily Spark commentary examining economic indicators of AI-related gains outside technology companies. Its conclusion is the firm's interpretation of the data shown.
A Communications of the ACM article setting out a research agenda for the economics of transformative AI, arguing that economic institutions are struggling to keep pace with AI capabilities.
A monthly letter from Ramp lead economist Ara Kharazian ranking the software vendors businesses buy from, based on transaction data across more than 70,000 businesses on Ramp's spend platform. The October 2026 edition examines AI spend moving in two directions: cheaper and more token-efficient models pushing AI spend down, and growing adoption of expensive image and video generation models pushing it up, with market-share and growth rankings for SaaS vendors.
Free-to-read online essays and interviews about science, technology, economics and culture. Asterisk’s about page describes a San Francisco Bay Area perspective and a focus on examining assumptions and evidence. Published by nonprofit Obelus, Inc.; supported by donors, subscriptions and a Coefficient Giving grant. Print subscriptions cost money; this entry links to free online reading.
A research coalition that develops and compares progressive policy visions for a U.S. economy in which AI has replaced or disrupted many jobs, with the stated aim that AI's gains reach all Americans. Its site lists research, advisors, an open request for information and jobs.
A September 2026 economics paper by Drew Fudenberg and Andrew Koh modeling two firms racing to advance AI capabilities while a safety threshold rises at a fixed rate. It shows when racing, pacing or both can be equilibria, depending on disaster risk and payoffs.
Joshua Gans and Avi Goldfarb's January 2026 NBER working paper modeling how automating some tasks in a chain of interdependent tasks changes the value of the remaining human work. Free PDF.
Profile page for Daron Acemoglu, Institute Professor at MIT, covering his research in macroeconomics, political economy, labor economics and development economics, with links to his publications and papers. Acemoglu has written extensively on what AI means for jobs and wages.
A Visual Capitalist chart using Federal Reserve data to compare U.S. household net worth across generations in 2026.
Economist Gabriel Zucman's letter setting out his case on California's Proposition 40.
Why I recommend it: An advocacy letter from a leading wealth-tax economist — read it alongside the official voter guide.
The California Secretary of State's official voter guide page for Proposition 40, with the legislative analyst's summary and arguments for and against.
Why I recommend it: The official source — the "for" and "against" arguments are written by campaigns, so read both.
Podcast episode with economist John T. Harvey on how Modern Money Theory has been argued and debated.
Why I recommend it: Money on the Left openly supports MMT, so this is a sympathetic take on a disputed school of economics.
Nicholas Bloom, Gordon B. Dahl and Dan-Olof Rooth study whether the rise in remote work explains the recent jump in employment among people with disabilities (American Economic Review: Insights, June 2026).
Why I recommend it: Peer reviewed. Bloom has long argued for remote and hybrid work, so it's worth knowing where the authors stand.
Xi Song, Jennie E. Brand, Sukie Xiuqi Yang and Michael Lachanski examine how AI can speed the decline of some occupations and make it harder for affected workers to move into better jobs (AEA Papers and Proceedings, May 2026).
Why I recommend it: A short conference paper, so the evidence is brief. "Mobility trap" is the authors' framing, not settled fact.
The non-profit, non-partisan research organisation behind most influential US economics working papers, including much of the research on AI and jobs.
Why I recommend it: NBER working papers are free to download, but most are not yet peer reviewed. Treat headline findings as early evidence.
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.
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.
Peter Kuhn and Trevor Osaki survey people on which kinds of hiring and pay discrimination they consider unfair, and why.
Why I recommend it: A working paper based on survey attitudes — it tells you what people think is fair, not what the law allows.
Site of the economist and author of The Double Tax and The Black Agenda, a Harvard Kennedy School doctoral candidate and co-founder of The Sadie Collective, the first non-profit tackling the under-representation of Black women in economics. Collects her research, writing and talks on pay gaps and the extra costs women, especially women of colour, carry at work.
Why I recommend it: The research, essays and media appearances are free to read; her books are sold and she is bookable as a paid speaker. Strong source if you are building a case about pay or promotion inequity with numbers rather than anecdotes.
US Census Bureau working paper (CES 26-56, September 2026) by Cody Orr, Lee C. Tucker and Lawrence Warren, using administrative records covering about 29% of US bachelor's degrees conferred 2016-2024. Graduates in the most AI-exposed tenth of majors saw their chance of being employed in the quarter after graduation fall five percentage points, and first full-quarter earnings fall thirteen percent, starting immediately after ChatGPT's release in late 2022. The earnings hit is comparable to graduating into a large recession. About half came from lower pay inside the same industries, the rest from graduates shifting into lower-paying sectors such as restaurants and retail. The effect shrinks to about five percent two years out but does not disappear for the most exposed majors.
Why I recommend it: This is the strongest evidence yet that AI has already moved entry-level pay, because it uses actual wage records rather than employer statements or surveys. Two things to hold onto: the paper says plainly it has not been through Census Bureau review and is not an official position, and exposure is measured by what a major typically leads to, not by whether any particular employer used AI. So it tells you which fields got harder to enter — not that a machine took a named job.
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.
SSRN working paper by Eldar Maksymov applying the Jevons Paradox to AI-driven labor changes. Argues that, like spreadsheets with accounting, AI may expand demand for judgment-intensive knowledge work and that leaders should build a value fortress of trust and accountability rather than cut headcount.
