44 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
Quartz report on the white-collar pay correction: employers stopped raising pay years ago, and economists trace the freeze to the pandemic hiring boom companies are now correcting.
Joseph Politano examines U.S. job losses across media, film and the arts and asks how much of the change can be attributed to generative AI. Free to read.
NYCEDC's monthly data briefing on the city's economy as of September 24, 2026: private-sector employment down 12,900 in August, unemployment at 4.8% after six straight monthly declines, labor force participation at 62.1%, median asking rent near $4,000, plus office visitation, tourism, and transit numbers.
Why I recommend it: The one-page-per-topic format makes this the fastest way to sound current on the NYC economy in an interview or a client meeting. Read the jobs and small-business pages; skip the real-estate detail unless it touches your field. It is a city agency's own read of its own economy, so pair it with the raw BLS numbers if you need the unspun version.
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Why I recommend it: NBER working papers are free to download, but most are not yet peer reviewed.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.
Dewey Data review of mixed research on AI and jobs; job-posting data suggests firms are changing what they hire for more than how much.
Why I recommend it: Written by a data vendor that sells datasets used in this research. Check the original studies.
A roundup of 2026 hiring data — job openings, applications per role, skills-based hiring and time-to-hire — with the sources behind each figure.
Why I recommend it: Published by a university that sells degrees, so read the education-related claims with that in mind; check the linked original sources before quoting a number.
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.
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.
Hosseinioun and colleagues use US survey and resume data to show skills sit in a nested order — some can only be learned once others are in place — and link that structure to wage gaps and long-term wage penalties after job loss.
Why I recommend it: Open access, so the full paper is free — no library login needed. The useful takeaway for career work: skill order matters. Learning a foundation skill first opens more later moves than stacking another surface skill, and the paper shows why some people recover from a layoff faster than others.
Market analysis of education technology and the higher-education business: online program managers, learning platforms, enrollment trends, microcredentials, and the labor-market outcomes of degrees. Written by Phil Hill, a long-standing independent analyst of the sector.
Why I recommend it: Freemium: a good share of posts are free to read and there is a paid tier for the deeper market analysis. Useful if you are weighing whether a bootcamp, online degree or microcredential is worth the money — this is one of the few places that reports on the finances behind those programs rather than their marketing.
Darrell M. West argues that AI agents and robots in the workplace will reshape jobs, HR functions and the social contract with workers. Free to read.
From the site: Darrell M. West explains how AI agents and robots are reshaping jobs, HR departments, and society's social contract with workers.
Why I recommend it: A useful piece for thinking about how AI changes the job itself, not just whether the job exists. It is an argument, not data.
Research and commentary on hiring, skills and the labour market from Four One Insights, free to read.
From the site: Explore thought leadership on workforce trends, emerging technologies, and skills development with FourOne Insights. Stay informed on the latest industry insights.
Why I recommend it: Analysis from a firm that sells into hiring teams, so read the recommendations with that in mind.
Free global research on jobs, skills and technology — including the Future of Jobs reports that most workforce coverage is based on.
Why I recommend it: Go to the source. The Future of Jobs report is free and is what half the "jobs of the future" headlines are quoting.
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.
Ed Elson's Simply Put essay on how AI, cost and weakening returns on a degree are reshaping education and the entry-level job market.
Why I recommend it: Useful context if you are deciding whether more school is the answer. It argues the credential is worth less than the proof of work.
A free Joint Center for Political and Economic Studies brief (September 2026) on Black employment, wages, unemployment and the sectors driving the gaps.
Why I recommend it: Data you can quote. Useful if you are making the case for a hiring or pay decision and need a source rather than an opinion.
Noema Magazine essay on how generative AI is reshaping creative work and value — what an abundance of output does to originality, pay and the meaning of being a creative professional.
Why I recommend it: If you work in a creative field, read this before you decide how to position AI in your own pitch.
CreativeApplications theory piece on predictive capital — how forecasting systems and data models shape markets, labor and creative practice, and who benefits from prediction.
Why I recommend it: Dense but rewarding — helpful vocabulary for talking about data and power without sounding alarmist.
The Black Wall Street Times covers an Institute for Women's Policy Research report on why roughly 600,000 Black women left the U.S. workforce, including public-sector cuts, caregiving load and hiring discrimination.
Why I recommend it: If your search feels harder than it should, this is the structural context — it helps separate market forces from personal performance.
Staffing Industry Analysts report on research showing that college graduates entering the workforce during the AI boom face starting-pay and employment conditions comparable to a major recession. Useful context for salary expectations and negotiation.
Why I recommend it: Read this before you accept a first offer — knowing the market backdrop keeps a low number from feeling personal.
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.
Job market data drawn from employer career sites since 2007 — 350+ million postings used for hiring-trend research, competitive analysis, and investment research.
Why I recommend it: Not a job board — it is where the hiring trend numbers come from. Handy when you want evidence about a field instead of vibes.
A September 2026 working paper by Brian Jabarian (Carnegie Mellon) and Luca Henkel (Erasmus Rotterdam) reporting a field experiment with 70,000 real applicants randomly assigned to be interviewed by a human recruiter or an AI voice agent. Applicants interviewed by AI were 12% more likely to receive an offer, with higher job starts and retention and no drop in on-the-job productivity. Transcript analysis traces the gain to more structured, consistent interviews that still adapt to each applicant.
Why I recommend it: If you have been told AI interviews are stacked against you, this is the largest piece of real evidence so far and it points the other way: consistent, structured questions helped candidates more than a tired recruiter on their eleventh call did. Prepare for them like any structured interview, with clear, specific answers.
Created by Brian Jabarian and Luca Henkel
edX research on which AI skills employers are hiring for, across industries and seniority levels, with data on how postings have shifted.
Why I recommend it: Use it to pick what to learn next, not to panic. Two skills learned well beats ten skimmed.
Analysis of the current hiring market, why applications disappear, and what job seekers can control.
Why I recommend it: Read this when you start blaming yourself for a slow search. Some of it is the market, not you.
Think tank publishing research on economic policy, labor markets, and corporate power.
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.
U.S. Census Bureau analysis of how many American businesses actually report using AI, broken out by industry and firm size — primary source data rather than survey hype.
Why I recommend it: When someone tells you every company is using AI now, this is the free federal data you check it against. Useful ammunition in interviews and in your own planning.
A Brookings analysis of how traditional labor market data is being challenged and reshaped by the frontier economy.
Why I recommend it: Labor market data is shifting fast. This Brookings piece helps you understand what is really happening beneath the headlines.
Dice's research report on tech hiring demand, in-demand skills, and salary movement across roles.
Why I recommend it: Use the skills demand section to decide what to learn next. It is one of the few free reports with role-level detail.
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.
A nonprofit research institute that translates labor-market data into insights about skills, mobility, and the future of work.
Why I recommend it: Burning Glass turns labor-market data into actionable insight about which skills are in demand and who is getting left behind. I cite their research often.
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.
The September 2 Beige Book finds scarce AI engineers but weaker demand for some junior technology and administrative-support roles.
From the site: The September 2 Beige Book finds scarce AI engineers but weaker demand for some junior technology and administrative-support roles.
Why I recommend it: A useful data point showing that AI is reshaping hiring unevenly—senior AI talent is scarce while some entry-level demand softens.
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.
ResumeTemplates.com survey of 1,000 US hiring managers at companies with 101 or more employees, finding 48 percent would rather invest in AI tools than hire and train a recent graduate, and that entry-level work is being restructured around AI.
Why I recommend it: Read the numbers, not the panic. The takeaway is to show proof of skill early, because employers are hiring more selectively rather than not at all.
Official Bureau of Labor Statistics release on labor productivity, output, hours worked, and unit labor costs across the US economy, updated each quarter.
Why I recommend it: This is the primary source behind most AI-and-productivity headlines. Cite the actual numbers in interviews instead of the news summary.
Indeed's ranking of U.S. metro areas by job opportunity, pay, and cost of living, with the data behind each city so you can compare markets before relocating.
Why I recommend it: If you are open to moving, start here instead of guessing. Pair a city's ranking with your own rent and commute math before you commit to a search in that market.
Founder and President of the Burning Glass Institute, who created real-time labor-market data analytics before spinning his expertise into nonprofit research on skills-based hiring.
Labor market research institute studying degree requirements, skills-based hiring trends, and economic mobility using real job posting data.
In plain terms: This research institute analyzes employment data and hiring trends across the country. You can read free reports to learn which job skills are in demand, explore labor market forecasts, and find which credentials lead to higher pay.
Why I recommend it: Their reports tell you which employers actually dropped degree requirements versus which just said they did.
The Ludwig Institute's alternative unemployment measure that counts people who are jobless, underemployed, or earning below a living wage.
In plain terms: A research institute publishes a "true rate of unemployment" that counts anyone jobless, stuck in part-time work, or earning under a living wage. The number is usually far higher than the official rate, which explains why a "strong" job market can still feel impossible.
From the site: LISEP’s mission is to help achieve shared economic prosperity for all Americans, particularly for middle- and low-income families. Our focus is fact-based economic and policy research.
Why I recommend it: When headlines say the job market is strong and your search still feels brutal, this number explains the gap. Useful language for interviews and for your own sanity.
Weekly newsletter read by recruiters worldwide — where hiring is heading, told to the people doing the hiring.
Why I recommend it: Reading what recruiters read gives you months of advance notice on how hiring is changing.
SHRM research on how widely skills-first hiring has actually been adopted, where employers still fall back on degree requirements, and what changes inside companies that commit to it.
In plain terms: This research report explores how employers are shifting toward hiring based on skills and practical experience rather than college degrees. You can use it to understand the credentials companies look for and tailor your job applications around your strongest abilities.
Why I recommend it: Useful reality check. Plenty of companies announce skills-first hiring without changing the screen, so lead with proof of work anyway.
