Who technology serves, who it harms, and who is doing something about it
Research groups, publishers, writers, and watchdogs — organized into four sections so you can go straight to the kind of reading or following you want. Every entry is free. The tools themselves live in AI & Assistive Tools.
New to the vocabulary? The AI Terminology Decoder explains the terms, movements, and slang in this debate — and sources where each one came from, including when nobody can honestly be credited with coining it.
Not sure where to start? The AI Explainer Hub routes you by question — which models to use, who to follow, what the concerns are, and what AI means for your career.
Use AI as a tool — not as gospel
As you can see, I used AI to create this website. Like so many today, I have complicated feelings about Big Tech and AI, especially in the ways it impacts creative arts and intellectual property, the labor market and employee retention, environmental resources, and exacerbates wealth inequality and funding allocation. There are beneficial tools, especially to support entrepreneurs or to facilitate web design or presentation structure. But we are constantly weighing the costs of technology and I will continue to add articles or books that explore the ethical challenges, especially on the underserved communities I want to help. I’d love to hear your thoughts on the topic as well.
“The Analytical Engine has no pretensions whatever to originate any thing. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths. Its province is to assist us in making available what we are already acquainted with.”
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.
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.
Voice AI in Firms: A Natural Field Experiment on Automated Job Interviews
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.
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.
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.
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.
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.
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.
