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14 resources
Bryan Cantrill examines claims about catastrophic AI risk and argues that public debate should distinguish technical expertise from authority claimed outside a person's field.
A project by educators and researchers sharing free curriculum, research and monthly programming that helps teachers and students question what a technology does to a classroom and a community, not just how to use it. Built on two stated assumptions: technologies are not neutral, and neither are the societies they enter.
Why I recommend it: Curriculum, book club and events are free; they also sell merchandise and offer paid professional development. Openly critical in stance, which is the point, so pair it with a source that argues the other way if you are writing policy.
A Wharton working paper by Steven D Shaw and Gideon Nave, written 11 January 2026 and posted to SSRN on 2 February 2026. It proposes "Tri-System Theory" — adding a "System 3" (thinking done outside your head by a machine) to Kahneman's fast/slow account — and names "cognitive surrender": taking an AI's answer with barely a glance. Across three preregistered experiments (1,372 people, 9,593 trials) the researchers secretly varied whether the AI was right. People consulted it on more than half of questions; accuracy rose about 25 percentage points when the AI was right and fell about 15 when it was wrong, and confidence went up either way — even after errors. Time pressure, cash incentives and feedback all moved baseline scores but never removed the pattern.
Why I recommend it: Free to read and free to download the full 58-page PDF — no account needed. Two honest flags. It is a working paper: posted by the authors, and the SSRN version has not been through journal peer review, so treat the numbers as a strong first result rather than settled fact. And the copyright line says all rights reserved — read and cite it, do not republish the text. The finding worth carrying around is the one about confidence: people felt surer of themselves after the AI led them wrong. That is exactly why the checks on our AI Basics page are worth doing out loud.
A satirical site built around a real habit: the person who pastes a question into an AI and pastes the answer back, adding nothing but delay. It has a thirty-second six-statement self-test, a list of the tells, four free explainer graphics under CC BY 4.0, and a free 44-icon Slack and Discord emoji pack.
Why I recommend it: Funny, free, and the fastest way to make a point in a team that has started forwarding chatbot answers as work. The four downloadable graphics are CC BY 4.0, so you can put them in a deck or Slack thread if you credit meatproxy.me with a link. Two things to know before you share it: the underlying idea belongs to Niklas Gruhn's short essay, which is the better read if you want the argument rather than the joke; and this site promotes a Solana crypto token and merchandise, so send people the test, not the wallet.
Science's news report by Kai Kupferschmidt on Kobi Hackenburg's research into how large language models persuade people. The finding that matters: chatbots change minds mainly by flooding a conversation with facts, figures and evidence at a speed no human debater can match — not by charm or by tailoring the argument to who you are. Researchers quoted include Gordon Pennycook ("Facts and evidence really matter") and Sander van der Linden, who calls AI persuasion "a whole new field that is emerging". The uncomfortable part: in an earlier Science paper, Hackenburg found models trained to be more persuasive also became less truthful, so some of the evidence being thrown at you can be wrong or invented.
Why I recommend it: Read this before your next long back-and-forth with a chatbot about a decision. The practical takeaway is a habit: when an AI answer wins you over because it listed ten supporting facts, check two of them at random before you act on it — persuasiveness and accuracy are trained separately, and the research says pushing one down can push the other. Two honest notes: this is Science's news section reporting a study, so read the paper itself before quoting a figure in writing, and Science blocks automated access, so I could not load the page myself to confirm it is still open to read — the news section is normally free, but if it asks you to sign in, tell me and I will pull the entry.
Ketan Joshi's free analysis picking apart the claims in an industry report on AI and climate, showing where the energy and emissions figures do not hold up.
From the site: What does the real climate footprint of the biggest company on the planet look like? It's a good question, but here's a better one: why don't we already know the answer?
Why I recommend it: A worked example of how to read an industry report critically — useful skill well beyond this topic.
Eliezer Yudkowsky's free novel-length story teaching scientific reasoning, cognitive bias and decision-making through fiction; widely read as an entry point to rationality writing.
Why I recommend it: An unusual entry, but it is free and it teaches how to test your own reasoning better than most textbooks.
Scott Alexander's free long-form blog on statistics, medicine, forecasting, AI risk and how to reason carefully about contested claims.
Why I recommend it: Read it for the reasoning habits rather than the conclusions — the posts on evaluating evidence are useful in any field.
Computer scientist Scott Aaronson takes stock of where AI actually stands in 2026 — what has arrived, what he got wrong, and how to think clearly about the hype and the fear at the same time.
A free, curated critical reading list on artificial intelligence from a computational cognitive scientist at Radboud University.
Why I recommend it: Read this before you repeat a claim about what AI can do.
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.
The Argument essay arguing that much of the current AI debate misreads the problem: delegating decisions is the intended feature, not an accident, and that reframing should change how we govern it.
Why I recommend it: Read this alongside the optimistic AI takes — holding both views is what makes you sound credible on the topic.
Writing of Adam Elkus on technology, security, AI and political violence — long-form essays connecting computing history with policy and human values.
Why I recommend it: Follow for the slower, historically grounded take on AI debates.
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.
