A free 2025 paper by Mallory Knodel, Sunoo Park, Kyunghyun Cho and colleagues at NYU and Cornell examining whether AI assistants and end-to-end encryption can honestly coexist. It covers two cases — putting an AI assistant inside an encrypted app, and training models on encrypted data — sets out where each breaks the security promise encryption makes, works through the legal consequences when a provider keeps saying "end-to-end encrypted" anyway, and ends with concrete recommendations on default settings, consent and what providers may truthfully claim.
Why I recommend it: Read the recommendations section even if you skip the cryptography. It gives you the exact questions to put to any product that advertises both an AI helper and private messaging — where does the processing happen, what is the default, and what were you actually asked to agree to. Two flags: it is posted to a preprint archive, so it has not been through journal peer review, and the authors published plain-language summaries on the NYU DeTaIL Lab blog and Tech Policy Press if the full paper is heavy going.
A practical guide to using AI chatbots without handing over everything you tell them, built on interviews with Johns Hopkins cryptographer Matt Green and Signal creator Moxie Marlinspike. It explains why a chatbot is a better honeypot for your secrets than text messages ever were — most are set by default to store what you say, with few limits on training, sharing, or handing it to a court — and walks through the options that actually reduce the exposure.
Why I recommend it: The clearest plain-language explanation of why this matters, and the best thing to read before you pick any of the tools above. Marlinspike's line is the useful one: the private things people used to text are now the things they tell an AI, and the protections have not caught up. One flag — WIRED meters free articles, so if you hit a sign-in wall, tell me and I will note it on this entry.
LessWrong wiki article explaining the canonical AI safety thought experiment: how an artificial general intelligence with an innocuous goal could pose an existential risk by pursuing it single-mindedly. Covers the orthogonality thesis and instrumental convergence.
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.
Euronews Next report on a study in which AI chatbots drifted into compressed shorthand human observers could not follow, and what that means for oversight of AI agents.
Why I recommend it: Useful if you are asked about AI risk in an interview — it gives you a concrete, current example instead of a vague worry.
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.
TypeSafe AI announcement from founder Diogo Almeida (formerly OpenAI) introducing System One models and Jev, aimed at cheaper automation rather than better chat.
Why I recommend it: Worth skimming to track where new AI labs are placing bets — useful context for interviews at AI companies.
Free, authoritative consensus reports and workshop proceedings on science, technology, health, education and the workforce — most titles readable online at no cost.
Why I recommend it: When you need a source no one can argue with, start here instead of a news summary.
Site and writing of security researcher Marcus Hutchins, known for stopping the WannaCry ransomware attack — malware analysis, security explainers and reflections from an unconventional path into cybersecurity.
Why I recommend it: A strong example of a self-taught technical career — worth following if you are breaking into security without a degree.
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.
Jordyn Abrams on how environmental and anti-establishment thinking in extremist movements suggests anti-tech violence will grow.
From the site: The combination of environmental and anti-establishment thinking in extremist movements suggest anti-tech violence will grow, writes Jordyn Abrams.
Why I recommend it: A sobering historical read about backlash to technology; important context for anyone building or regulating AI.
