44 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
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.
MIT research group studying how digital technology and AI change work, wages and productivity, with published papers and reports.
From the site: The MIT Initiative on the Digital Economy (IDE) explores how people and businesses will work, interact, and prosper in the digital era.
Why I recommend it: This is measurement rather than prediction, which is rare in this subject. Go here when you want numbers on automation instead of opinions about it.
A research institute publishing free books, articles and policy analysis on economics, technology and civil liberties from a market-liberal perspective.
From the site: Explore Independent Institute’s latest research, articles, and insights on policy, liberty, and economics. Stay informed with expert analysis and innovative solutions.
Why I recommend it: Openly ideological — read it alongside sources that argue the other side.
Robin Hanson's long-running blog on why we believe and do what we do, and what our descendants might do — free to read.
From the site: This is a blog on why we believe and do what we do, why we pretend otherwise, how we might do better, and what our descendants might do, if they don't all die. Click to read Overcoming Bias, by Robin Hanson, a Substack publication with tens of thousands of subscribers.
Why I recommend it: Deliberately contrarian. Useful for pressure-testing your own assumptions.
SSRN's browsable index of working-paper series across economics, law, management and technology — most papers are free to download.
Why I recommend it: A good way to read serious research on labor markets, hiring and AI before it hits paywalled journals.
A technology policy institute publishing free reports and commentary on AI regulation, automation and the economics of emerging technology.
Why I recommend it: Free reports with a clear point of view — read them alongside the Turing Institute and AlgorithmWatch for a fuller picture.
Long-form essays from Anthropic's CEO on AI capability, safety, economics and policy, published free in full.
Why I recommend it: Read these directly rather than through summaries — they are the source most AI-safety coverage is quoting.
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.
Free harmonized microdata from the monthly U.S. Current Population Survey (CPS), covering 1962 to the present. Includes demographics, employment, program participation and supplemental topics such as food security, computer and internet use, and voter registration.
Why I recommend it: A public dataset you can use for market research, policy analysis, or building data-driven career and business arguments. Registration is instant and extracts are free.
A Liberty Street Economics post from the New York Fed arguing that, so far, AI adoption is being used to change how work is done rather than to reduce headcount, based on regional business survey data.
Why I recommend it: A useful counterweight to AI job-loss headlines; helpful for understanding how employers are actually deploying the technology right now.
An essay weighing the argument that AI adoption could push unemployment into double digits, against the labor data we actually have so far.
Why I recommend it: I collect both the alarmed and the skeptical takes on AI and jobs on purpose. Read this next to the Census and Brookings data in this collection and form your own view rather than borrowing a headline.
Research exploring possible economic futures as AI capability advances, including labor market effects and policy questions.
Why I recommend it: Scenario planning is a career skill, not just a policy exercise. Read it and ask which future your current job depends on.
Open-access academic journal publishing peer-reviewed research on robotics, automation, and their economic and social consequences.
Why I recommend it: Free peer-reviewed research on automation. Denser than a blog post, but the citations are gold if you are writing or speaking on this.
Economist Noah Smith's newsletter covering labor markets, technology, industrial policy, and the economics behind AI hype cycles.
Why I recommend it: One of the few writers I trust to check the numbers before drawing a conclusion. Worth a standing subscription if you follow the economy at all.
Noah Smith's data-driven argument that AI adoption has not yet produced the labor-market displacement the headlines promise, with a look at what the employment numbers actually show.
Why I recommend it: Read this before you panic about your field disappearing. It is the most level-headed counterweight I have found to the "AI took the jobs" narrative.
Economic news and analysis explained in plain English by Mark Hamrick.
From the site: Economic news and analysis explained in plain English by Mark Hamrick
Why I recommend it: Mark Hamrick translates labor-market and Fed news into practical career context. Worth reading before making a big move.
MIT economics working paper analyzing how automation technologies can be used to expand state surveillance and repression, and the economic conditions that make that more likely.
Why I recommend it: Dense, but the argument matters: the same tools sold as efficiency are also control tools. Read the introduction and conclusion first.
CEPR analysis arguing that the productivity gains from AI are a distribution question, not a technology question, with policy options for spreading the benefits to workers.
Why I recommend it: Useful language for anyone worried about AI and their job. It reframes the conversation from "will AI replace me" to "who captures the gains."
Economist — Chief Economist at the Global Electronics Association and founder of the Avrio Institute, formerly Chief Economist of the Consumer Technology Association — who translates tech trends into business strategy.
Cole Donovan connects US fiscal pressure and bond market weakness to coming budget decisions about science, R&D, and technology programs.
Why I recommend it: Useful context if your job or grant depends on federal science and tech spending — plan for tighter budgets, not looser ones.
Ongoing research archive on how AI and automation affect wages, workers, and economic power in the U.S.
In plain terms: 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.
From the site: Content archives for the Washington Center for Equitable Growth’s work on AI, tech, & the economy.
Why I recommend it: Use this when you need real numbers on AI and jobs for a proposal or interview.
