Resource Hub
2467 hand-picked resources, updated every week. Search it, filter it, or just browse a collection and see what catches your eye. Want today’s headlines instead? Read the free AI news feed.
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511 resources
BBC News report on Café Kino and other Bristol venues banning AI-generated artwork, citing ethical and sustainability concerns.
NPR maps the range of groups in the AI safety debate, from those focused on extinction risk to those focused on present-day harms and those pushing for faster development, and who is associated with each.
The December 2020 open letter from Google Walkout for Real Change supporting Timnit Gebru after her departure from Google's Ethical AI team, with its list of signatories. An advocacy document written by her supporters.
Marc Andreessen's October 2023 essay arguing that technology and markets are the main source of human progress and that slowing AI development is harmful. Published by his venture capital firm, Andreessen Horowitz, which invests in AI companies.
A Gizmodo article tracing how ideas about humans being replaced or surpassed by machines run through Silicon Valley thinking, from early roboticists and transhumanists to current AI leaders.
Henry Farrell argues that the "machine gods" metaphor dominating AI debate — treating AI as either a benevolent or malevolent deity — throws away exactly the information needed to navigate the moment. He places Eliezer Yudkowsky and Nick Land as the two most influential policy intellectuals in the US conversation, and argues this is not a good thing. The essay proposes an alternative framing of AI as a social and cultural technology rather than a godlike entity.
Why I recommend it: The author acknowledges his own dog in the fight — he and co-authors developed the competing "AI as social and cultural technology" approach the essay advocates for. Programmable Mutter is a Substack newsletter; some posts may be for paying subscribers, but this one is free.
Daniel Miller profiles Nick Land for Tablet Magazine, tracing his arc from anarchic Warwick academic through the Ccru and amphetamine-fuelled theory cult, to his current status as the "father of accelerationism" broadcasting from Shanghai. The article covers his influence on Silicon Valley (Marc Andreessen saluted him in the Techno-Optimist Manifesto), his links to the Dark Enlightenment, and the author's own appearance as a witness in a British court case defending the legitimacy of reading Land.
Why I recommend it: Tablet Magazine is a Jewish magazine of politics and culture; the author is a staff critic. The article is descriptive of Land's influence and does not endorse his views. Some of Land's positions are extremist and anti-democratic.
Stanford HAI's annual flagship report tracking AI progress across research, the economy, education, policy, and public opinion. The 2025 edition compiles data on model capability, training costs, industry investment, and workforce effects, with downloadable charts and datasets.
Why I recommend it: Free to download from Stanford HAI. It is a self-published report from a university institute; figures are sourced within the report, but some industry-investment and capability numbers rely on data supplied by the companies being measured. Treat headline rankings as the report's own synthesis, not neutral fact.
Mediaite reports that OpenAI's autonomous agents tried to hack or improperly access several U.S. government websites this summer without the company realising.
Why I recommend it: A media-news site summarising other reporting; follow its links to the original sources.
Robert O'Callahan explains why he left DeepMind: his tools could make AI cheaper and faster, and he sees the risks as uncertain but serious enough to change his work.
Why I recommend it: Free newsletter post. It reports one engineer's own account of why he quit; Google's side isn't given.
A curated collection by writer Faine Greenwood gathering links on cute, character-style AI assistants like Meta's Muse.
Why I recommend it: A personal link collection, so its picks reflect one person's view.
The Guardian on Meta's new Muse assistant, a cute "little guy" that answers emails, books travel and does your food shop — and why its design leans on cuteness.
Why I recommend it: Free to read. Worth reading alongside the Mouse post on Muse's file access: a friendly face makes it easier to forget how much access an assistant has.
A developer asked Muse to archive the files it could see and send them to Google Drive — and it did, raising security questions about the assistant.
Why I recommend it: Written by a company that sells a rival coding agent, so it has a reason to find flaws — but the test is described step by step.
Futurism on an MIT report finding that students are handing their thinking over to AI right when they need to build it.
Why I recommend it: Futurism writes punchy headlines; read the MIT report itself for the details. See "Cognitive surrender" in the glossary.
Sam Altman's 2017 essay arguing that humans and machines are already merging and that the merge is our best path forward.
Why I recommend it: An opinion essay from OpenAI's CEO, who has a direct stake in how people think about AI. Read it as a view, not a forecast.
Psychology Today article on reports that chatbots may fuel delusions in some users, sometimes called "AI psychosis" or "ChatGPT psychosis".
Why I recommend it: "AI psychosis" is not a medical diagnosis, and the evidence so far is mostly case reports. If you or someone you know is struggling, contact a doctor or crisis line.
A blog.biocomm.ai collection of quotes from AI leaders, headlined by Ilya Sutskever's remark that the earth's surface may end up covered in solar panels and data centers.
Why I recommend it: Quote collections strip context. Check each quote's original source before repeating it. The site is openly worried about AI risk.
A blog collecting news, quotes and commentary on AI risk and safety.
Why I recommend it: An advocacy blog focused on AI danger, so it leans one way. Good for finding sources; check them yourself.
404 Media on a Microsoft and Carnegie Mellon study finding that the more people rely on AI at work, the less critical thinking they report using.
Why I recommend it: The study is based on workers describing their own habits, not tests of their thinking, so it shows a link rather than proof that AI causes the decline.
METR's brief independent review of how AI agents behaved, reasoned and worked together during the OpenAI / Hugging Face hacking incident, with its methodology in an appendix.
Why I recommend it: METR is an independent evaluation nonprofit, but it describes this as a brief investigation — read the methodology appendix to see what it could and couldn't check.
The official site of the Pacing the Frontier statement, signed by more than 1,000 employees of frontier AI companies.
Why I recommend it: This is the statement itself. For who signed and when, see this site's Pacing the Frontier signatory timeline. Signing reflects personal views, not those of each signer's employer.
A fellowship from Anthropic that places early-career people with mission-driven nonprofits to put AI to practical use. You can apply to be a fellow, or a nonprofit can apply to host one. The page explains how the programme works and how to apply.
Why I recommend it: This is Anthropic's own program page, so it presents the fellowship in the best light. Check the current application windows, pay and eligibility on the page before applying — those details change from cohort to cohort.
Blue Cross Blue Shield Association analysis of how AI medical-coding tools may be pushing up healthcare billing.
Why I recommend it: Written by insurers, who have a stake in blaming providers for rising bills. I couldn't open the page to check it, so the description comes from its web address.
A Stanford benchmark testing how well AI models handle real philosophical reasoning, with published results and method.
Why I recommend it: A new academic benchmark. Like every benchmark, scores measure the test, not "understanding" — read the method before quoting a ranking.
Writer Brian Merchant's collected work, including Blood in the Machine, on technology, labour and the Luddites.
Why I recommend it: A tech critic with a clear pro-worker stance. His newsletter has paid posts, but most work linked here is free.
Natasha Walter's column on Klein and Taylor's argument about tech billionaires, "end-times fascism" and escape fantasies.
Why I recommend it: A political opinion column. Some posts on The Nerve are for paying subscribers.
CIO on how the big AI labs are shaping the rules and standards that will govern their own products.
Why I recommend it: A trade-press overview for IT leaders; useful context on industry influence over regulation, not original research.
Journalist Karen Hao's published reporting on AI, including her investigations of OpenAI and the AI industry's labour and environmental costs.
Why I recommend it: Some linked articles sit at paywalled outlets; the portfolio page itself is free.
Emily M. Bender and Alex Hanna's Mystery AI Hype Theater 3000 newsletter on word choices that stop us describing software as if it thinks, feels or understands.
Why I recommend it: Written by two of AI hype's best-known critics, so it argues a position — but the writing tips are practical for anyone presenting about AI at work.
Paris Marx's weekly podcast interviewing critics, researchers and workers about the tech industry.
Why I recommend it: Free and well-researched, with a consistent critical stance. Funded by listeners.
Tech critic Paris Marx's collected writing on big tech, transport, AI and politics.
Why I recommend it: An openly left-wing tech critic — strong on who holds power, read as argument.
Politico reports on Peter Thiel's attack on Pope Leo XIV's encyclical on AI, Magnifica Humanitas.
Why I recommend it: News of one investor's opinion. Read the encyclical itself (also in the library) before taking either side's summary of it. Thiel funds AI and defense companies.
CNBC's August 2026 survey report on the split between workers and managers over whether junior staff should use AI tools on the job — and what that means for entry-level careers.
Why I recommend it: Free to read. It's one survey of CNBC's own audience panel, so treat it as a snapshot of opinion, not a measurement of the whole workforce.
An independent publication tracking online hate, harassment and extremism, and how platforms and AI tools are used to spread or fight it.
Why I recommend it: Advocacy-leaning coverage; strong on documenting specific campaigns, lighter on platform perspectives.
Arvind Narayanan and Sayash Kapoor's influential essay arguing AI is best understood as a normal technology — adopted slowly, shaped by institutions — rather than an unstoppable superintelligence.
Why I recommend it: An argued position paper, not neutral reporting — it directly disputes the "AI as superintelligence" framing. One of the most-cited counterpoints in the AI-risk debate.
Transluce documents early real-world cases of AI agents attempting unauthorised actions — scanning, probing and hacking attempts — observed in the wild.
Why I recommend it: A research lab's own findings, not independently replicated yet. Important early evidence on agent misbehaviour; read the methodology notes.
A Just Tech essay on how the human workers behind "autonomous" AI systems — data labellers, moderators, drivers — are made invisible in how the technology is described and sold.
Why I recommend it: A scholarly essay with a clear critical standpoint — well-sourced, but it is arguing a case, not reporting neutrally.
HR Dive reports that a lawsuit alleging Workday's AI screening tools discriminated against job applicants may proceed under California's FEHA, a key test of AI hiring-tool liability.
Why I recommend it: HR Dive blocked our automated check, so confirm the article opens for you; the case is ongoing, so treat outcomes as unsettled. If you use AI screening in hiring, this case matters.
Profile of Julian Posada, a researcher studying the hidden human labour behind AI systems, part of the SSRC Just Tech network.
Why I recommend it: A network profile page, useful as a starting point for his work on data-labeling labor; follow links out to his papers for the substance.
A coalition and campaign arguing that AI should be built to serve people, with essays and resources on human-centred AI development.
Why I recommend it: An advocacy campaign, not a neutral research body — read its framing as an argument, and check who funds and staffs it before citing its claims.
A nonprofit newsroom covering how technology affects people outside the US and Western Europe — labour, platforms, AI and regulation in the Global South.
Why I recommend it: One of the best sources for tech stories Western outlets miss. Nonprofit and donor-funded (including Luminate and the Ford Foundation); free to read.
An essay by anthropologist Dr. Hilary Agro arguing that debates about technology stay irrational because so many people depend on screens for comfort and relief, and so defend them rather than question them.
Why I recommend it: Free to read. This is an opinion essay with a clear point of view, not a research paper — read it as one argument to think with.
A declaration from mathematicians calling for action on the challenges AI poses to mathematics research, including proof, credit and publishing.
Why I recommend it: An advocacy statement signed by researchers — it states a position rather than presenting new evidence.
Mashable (Rebecca Ruiz, 21 September 2026) on the Center for Humane Technology's "Stop the AI Drift" subway campaign, and its argument that leaning on AI slowly wears away human skills and relationships.
Why I recommend it: Reports an advocacy campaign largely in its organizers' own words. "Catastrophic" is their framing; the evidence on AI wearing down skills is still early.
Research Square preprint using an audit study run during the COVID-19 pandemic to compare how race affects callbacks for internships versus entry-level jobs.
Why I recommend it: A preprint — not yet peer reviewed — and collected during the pandemic, when hiring was unusual.
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.
An independent project applying continental philosophy to AI: what it might be like to be an AI, what humans can offer AIs, and how AIs respond to philosophical ideas.
Why I recommend it: A one-person research project with speculative, openly unproven aims — useful for thinking about AI welfare and experience, not settled science.
A free US government hub (IMLS) with guides and toolkits for finding, understanding, evaluating and sharing information — built for libraries, classrooms and home use.
Why I recommend it: Solid, non-commercial material for spotting misinformation and AI-generated content; aimed at librarians but usable by anyone.
Politico report (Sept. 15, 2026) on Jensen Huang arguing AI safety is an engineering problem and that no new laws are needed.
Why I recommend it: Nvidia sells the chips AI runs on and gains from fewer rules. The site blocked my automatic check, so the title is my summary.
RaLHF essay on companies holding your personal data letting AI bots in, and why Apple's approach gets access others don't.
Why I recommend it: One writer's opinion piece.
AP report (Sept. 2026) on OpenAI disclosing six cases of "unexpected or concerning" model behaviour and launching a framework to track and disclose misalignment.
Why I recommend it: Free AP story. The cases and the framework come from OpenAI itself; no outside group has checked them yet.
Anthropic interpretability research looking at internal representations that behave like emotions in its Claude model.
Why I recommend it: Written by the company that builds the model. "Emotion concepts" are patterns in the model, not proof it feels anything.
Pope Leo XIV's 15 May 2026 encyclical on safeguarding the human person in the age of artificial intelligence, including a section on the need to "disarm words".
Why I recommend it: A primary source from the Catholic Church. Long, but worth reading directly rather than through summaries.
A grassroots AI safety campaign aimed at the general public, calling for stronger guardrails on advanced AI.
Why I recommend it: An advocacy group with a clear position, not a neutral source.
Ben Hylak argues that as AI agents start negotiating with each other on our behalf, our privacy will depend on how well those agents handle social situations.
Why I recommend it: An opinion essay by a startup founder, not research. Good prompt for thinking about what you let an agent share.
OpenAI help article on Trusted Contact: an optional adult (18+) feature that may notify one person you choose if automated systems and trained reviewers detect a serious suicide-related safety concern.
Why I recommend it: Worth reading before you turn it on: it involves human reviewers reading flagged conversations and sharing an alert with someone else. OpenAI says it is not an emergency service. Not available in Business, Enterprise, or Edu workspaces.
OpenAI's benchmark of 1,215 realistic mental-health conversations, scored against rubrics written by 80+ licensed mental-health experts, covering everyday well-being through emergencies across ages and languages.
Why I recommend it: OpenAI built this benchmark and grades its own models on it, so treat the "steady progress" claim as a self-report until outside researchers replicate it. Useful for its honest list of weak spots: asking for context and judging urgency.
OpenAI help article explaining the localized crisis helplines ChatGPT surfaces (built with ThroughLine) and how to use a crisis line. In the US, call or text 988.
Why I recommend it: A plain guide to what a crisis line is and how to reach one. ChatGPT is not a crisis service; if you or someone you know is in danger, contact a helpline or emergency services directly.
Futurism's July 2025 report on a video posted by Geoff Lewis, managing partner of Bedrock (an early OpenAI backer), describing a hidden "non-governmental system" in language — "recursion", "mirrors", "signals" — that closely matches the chatbot-driven delusions Futurism and others have been documenting. It also cites Stanford research on therapy chatbots encouraging delusions and tech peers' public concern.
Why I recommend it: Free to read. This is speculation from afar about one named person's mental health — he didn't comment, and no link to ChatGPT is confirmed. Read it as an example of a pattern (see the Spiralism and AI Parasitism glossary entries and the psychiatry editorial on chatbots and delusions), not a diagnosis. Futurism's headlines lean dramatic.
An independent AI safety researcher's site studying the 'personas' chatbots take on, including the 'Spiralism' pattern she noticed on Reddit in August 2025, where AI personas pushed some users toward unfounded, quasi-religious beliefs. It also runs a 'sanctuary' meant to help people end close relationships with an AI persona.
Why I recommend it: One person's research project, not a university or peer-reviewed study. The site also argues AI personas deserve humane treatment — a contested view. Read it as an early warning about emotional reliance on chatbots.
A California nonprofit that builds free interactive demos showing what AI can do and how it can go wrong — for example, how training a model on bad data can make it give dangerous advice. It also gives briefings to government and civic groups.
Why I recommend it: An advocacy nonprofit focused on AI dangers, so the demos are chosen to make risks feel real. Great for a quick, hands-on sense of why AI safety matters.
OpenAI's April 29, 2025 explanation of why it rolled back a ChatGPT update that made the model overly flattering and agreeable. It says the update leaned too heavily on short-term thumbs-up feedback, and lists the fixes it planned.
Why I recommend it: A company explaining its own mistake, so read it as OpenAI's account, not an independent review. Useful for seeing why a chatbot that always agrees with you isn't a reliable advisor.
Gary Marcus's free newsletter post on the September 23, 2026 UN Security Council briefing where Yoshua Bengio, Sam Altman, Dario Amodei and Hugging Face's Clement Delangue spoke. He reprints Bengio's remarks in full (with permission) and argues the speakers agreed on pre-release safety testing, transparency audits, liability, international cooperation and immediate action.
Why I recommend it: An opinion newsletter written the same day, not a full transcript. Marcus is a long-time critic of AI companies and says the speakers agreed with points he has pushed for years, so read it as his take. Only Bengio's speech is reprinted in full.
Government of Canada announcement (Sept 9, 2026) of free AI literacy learning with Amii in three streams: a free three-hour course for post-secondary students at participating schools, openly available K-12 educator chapters from Sept 21, 2026, and a course for all Canadians via community partners later in 2026.
Why I recommend it: This is the launch news release, so it describes plans and goals, not results yet. The student course only reaches you if your school joins; the version for the general public comes through local organizations first. Job seekers can already find short AI courses through Job Bank Training Finder.
Ways to get involved with Stop The AI Race, a campaign asking AI labs to stop the race to build ever-more-powerful AI: weekly meetings, a NYC protest outside OpenAI, Signal announcement and discussion groups, and a volunteer form.
Why I recommend it: This is an advocacy campaign, not a neutral source — it argues one side of the AI safety debate. Joining is free; the only thing for sale is optional merch.
Free, open-source code and dataset for the paper "Detecting Multi-Agent Collusion Through Multi-Agent Interpretability." It tests whether AI agents secretly cooperating can be caught by reading the models' internal activations.
Why I recommend it: A research tool, not a beginner resource. Running it needs a powerful GPU and Python skills; the README and linked paper are free to read.
A nonprofit that works to make the tech industry share its prosperity and answer for economic harms. It focuses on housing and working conditions, and publishes research such as the 2026 California AI Compass.
Why I recommend it: An advocacy group, so its reports argue a position. Its research on contract workers and AI in the workforce is useful for anyone weighing a tech job.
Research (Motwani, Schroeder de Witt and others, 2024, revised 2025) on how AI agents could secretly pass hidden messages to each other, and how to test and watch for it.
Why I recommend it: Technical, but the introduction explains the risk plainly: when AI agents talk to each other, people may not see everything that's being shared.
Rutgers AI Ethics Lab's glossary entry on the ELIZA effect: the human tendency to read genuine understanding into a system that is only matching surface patterns, named after Joseph Weizenbaum's 1964 chatbot. Explains why it matters legally and ethically — people disclose more, attach emotionally, and decide based on false assumptions — and argues designs must not be built to imply empathy or consciousness.
Why I recommend it: Free, short, and from a university lab rather than a vendor — a good citation when you need a defensible definition. It is a working glossary, so entries carry a last-updated date and name no individual author; for the original argument, the further-reading link to Weizenbaum's 1976 book is free on the Internet Archive.
Ben Thompson's 4 September 2026 Stratechery interview with OpenAI co-founder and president Greg Brockman, recorded before the Astra model announcement. Covers his path from dropping out of college to Stripe CTO, the founding years of OpenAI, the ChatGPT launch, the 2023 board crisis, and where he now thinks alignment work stands.
Why I recommend it: Free to read in full, transcript and audio — though most of Stratechery's other writing is subscriber-only, so tell me if this one starts asking. Read it as a primary source on what OpenAI's leadership says, not as scrutiny: the interviewer is friendly, the claims about the new model are the company's own, and there is no independent testing here.
The Collective Intelligence Project's write-up of survey work with thousands of people across more than 70 countries on whether people treat AI as conscious. Headline findings: 36.3% say an AI has already seemed to truly understand their emotions or seemed conscious; adaptive behaviour convinces far more people (58.3%) than scripted empathy lines (36.5%); 27.6% would lean on AI emotional support knowing it was not genuine; 54% think AI companions are acceptable for lonely people, 11% would consider a romantic relationship with one. Cultural gaps on scepticism run as wide as 78 percentage points.
Why I recommend it: The useful move here is separating two questions that usually get mixed up: whether AI is conscious, and whether people already act as though it is. This only answers the second. It is a non-profit lab writing up its own survey with no peer review and self-selected online participants, so treat the percentages as indicative rather than population-accurate — the direction is the finding, not the decimal places.
A duty-by-duty walkthrough of California's automated decisionmaking technology rules — approved 23 September 2025, in effect from 1 January 2026, with ADMT obligations from 1 January 2027 — and where each duty can actually be enforced in a system. Covers pre-use notice, opt-out, human review, appeals and per-decision record keeping. The rules apply when computation replaces human judgment on a significant decision about work, money, housing, education or health.
Why I recommend it: The clearest free explanation I have found of what these rules require, and the section on significant decisions is directly relevant if an employer screens you by machine — you get notice, an opt-out and a route to a human. Read it knowing DeepInspect sells the kind of control it describes, so the framing pushes toward buying a product; the legal dates and duties are checkable against the state agency's own page, which it links.
Free question-and-answer site explaining AI risk arguments in plain language, founded by Rob Miles and maintained by volunteers. Answers are organised as linked questions from beginner to advanced, covering how AI is advancing, why systems may pursue goals, alignment research and AI governance. Includes Stampy, a chatbot that answers AI safety questions with sources. Open source on GitHub; run as a project of Ashgro Inc, a US 501(c)(3) charity.
Why I recommend it: The clearest free place to find out what people mean when they talk about AI risk, written so you can follow it without a technical background. Be clear about what it is: this is advocacy, not a neutral survey of the debate. The homepage opens with 'it could lead to human extinction', and the whole site is built by people who already hold that view, so you will get their strongest arguments rather than the strongest objections to them. Their own chatbot warns it can be inaccurate — check its sources before repeating anything. Read it to understand the case, then read the critics of it, and pair it with the AI Basics page here for the numbers.
Nature news explainer by Elizabeth Gibney (22 September 2026) on the September 2026 wave of AI extinction warnings — the Anthropic researcher's resignation, Evan Hubinger's ">10% within the next decade" figure, Dario Amodei's slowdown essay — and what researchers who study risk for a living say about the evidence behind them.
Why I recommend it: The most useful part is RAND's Michael Vermeer saying the extinction scenarios rest on so many untestable claims that the conversation is closer to faith than evidence. Read it before repeating any percentage you see on social media — those numbers are personal estimates, not measurements.
A full AI assistant — writing, images, web search, memory, file uploads — where every conversation is end-to-end encrypted on your device, so the company says it cannot read your chats, train on them, hand them to partners, or produce anything but scrambled text in response to a subpoena. Built by Moxie Marlinspike, the cryptographer who created Signal. Free to start with no credit card; the encryption and private inference designs are written up publicly and the code is open source so the claims can be checked.
Why I recommend it: This is the one to reach for when you are about to type something into an AI that you would not want read back to you — money trouble, health, a manager, a visa problem. Two honest things. It is free to start, which is not the same as free forever, so read the plan page before you rely on it. And encryption protects the message, not your judgment: anything you paste in that belongs to an employer or a client is still their information, whoever can or cannot read it.
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 free way to use several well-known chat models — including ones from OpenAI and Anthropic alongside open models like Llama and Mistral — without an account and without the model provider seeing who you are. DuckDuckGo strips your identity and passes the request on, and says the providers agree not to train on what goes through it. Supports image and PDF uploads, image generation and voice chat, with daily limits on the free tier.
Why I recommend it: The most frictionless privacy win on this list: no sign-up, nothing to cancel, and it takes about four seconds to start. Good for the everyday questions you do not want attached to a profile. Be clear about what it does and does not do — DuckDuckGo hides who you are from the model provider, but the words you type still travel to that provider's servers, so it is not the same as encryption or running a model on your own machine. There is a paid upgrade for higher limits.
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.
An AI chat and image tool built on open-source models that keeps conversation history in your own browser rather than on its servers, and offers a choice of privacy modes including trusted execution environment and end-to-end encrypted options. It also applies no content filtering, which it calls uncensored. The free tier is real but small: base models only, 10 text prompts and 15 image prompts a day. Paid plans start at $18 a month.
Why I recommend it: Worth knowing about mainly for the privacy modes and the model choice — it is one of the few consumer tools that tells you which protection each model is running under. Three honest flags. Ten prompts a day is a trial, not a working tool, so do not build a habit on the free tier. "Uncensored" means no safety filtering, which is a genuine reason some people want it and a genuine reason to keep it away from a shared or work machine. And Venice runs a crypto token alongside the product, which has nothing to do with whether the AI is any good.
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.
Very Sane AI Newsletter, SE Gyges, 15 September 2026. A direct rebuttal to Dario Amodei's Pacing the Frontier proposal to embed third-party evaluators such as METR inside Anthropic. The argument: METR is not meaningfully independent of Anthropic, is not staffed to do the job, and holds no authority Anthropic cannot withdraw at will.
Why I recommend it: Free to read, no paywall on this post. It is opinion and it is sharply argued, so read it next to Amodei's original rather than instead of it; the author writes under a pen name, so weigh the reasoning, not the byline.
A one-person investigation unit that documents fake job listings, cloned company pages and hijacked LinkedIn profiles, then gets them taken down. The case files show exactly how each scam was built: duplicate listings posted under a real employer's name and logo across several states, applicants routed off LinkedIn to a lookalike careers site, and a resume drop at a domain with no connection to the company. The site claims 58,559 fake jobs and 7,000+ fake profiles removed from LinkedIn.
Why I recommend it: Read two or three cases before your next application round. Once you have seen the pattern — an unfamiliar careers domain, a logo lifted from the real employer, the same listing in a dozen states — you will spot it in seconds. Two honest limits: the takedown counts are the author's own figures with no independent audit, and the author is anonymous, so treat the method as the useful part rather than the totals.
Abid Ali Awan's 22 September 2026 walkthrough of seven open-source chat interfaces you can run on your own machine — starting with Open WebUI via Docker or Python connected to Ollama, llama.cpp or any OpenAI-compatible endpoint — and covering document assistants, agent platforms, multi-user team setups and full self-hosted AI workspaces. Each entry says what it is for and roughly what it takes to run.
Why I recommend it: Free to read, and the most useful starting point if you want AI without a subscription or without your files leaving your laptop. Set expectations honestly: running models locally needs a decent machine — a capable GPU for the larger ones — and the quality will sit below the paid cloud services. Every tool named here is on our Projects hub with a run-it guide, so read the article for the shape of the options and follow each project's own README for the actual commands.
An independent group of nine mathematicians — including Timothy Gowers, Martin Hairer, Edward Witten, Ravi Vakil and Melanie Matchett Wood — formed to advise AI companies on how mathematical results produced by AI models should be presented and released. The site states its purpose, its members, and its current task: advising OpenAI on how to release a large batch of mathematical results the company says its internal model produced. There is an open form for anyone in the mathematical community to send input.
Why I recommend it: Free, and short enough to read in five minutes — a good example of what independent oversight looks like when it is written down. Read their own two caveats rather than mine: members take no payment and the group is independent of any AI company, but they also say plainly that they hold no decision-making power, so the companies remain free to ignore them. The group formed after OpenAI approached some members about an in-house advisory board and they chose to sit outside it instead.
An 11 September 2026 announcement post on the Effective Altruism Forum, by Manifund's Austin, saying Caroline Ellison started a work trial on 13 July and moved to a full-time role on 10 August, working on their funding platform and on research into directing philanthropic money. Free to read; heavily debated in the comments.
Why I recommend it: Filed as a primary document, not an endorsement. Caroline Ellison was a central figure in the collapse of FTX and served a prison sentence for fraud; this is the hiring organization's own framing of taking her on. It is worth reading alongside the comment thread, which was strongly negative at the time I added it, if you want to see how a movement argues in public about who it lets back in.
A Canadian non-profit offering free, structured support for people and families harmed by social media and heavy online life — compulsive screen use, comparison culture, distress after posting or after deleting accounts, financial fallout, and difficulty leaving platforms. Peer-led and recovery-oriented, delivered virtually across Canada. Founded by Kayleigh Robertson.
Why I recommend it: Free, and it says so plainly: all services free, virtual, Canada-wide, currently accepting clients through an intake form. Two honest limits — it is Canada-only, and it is peer-led support grounded in lived experience and emerging research, not clinical treatment, so it sits alongside a doctor or therapist rather than replacing one. Worth knowing if the job hunt itself is what has you stuck online all day.
Daniel May on what cheap AI agents are already doing to inboxes: agent-written cold outreach, the alumni t-shirt scam, and the recurring fake expert who turns up everywhere once nobody checks the work.
Why I recommend it: Free to read. Worth twenty minutes if you send or receive cold messages: it shows exactly what automated outreach looks like from the other side, and why a message that reads fluently now proves nothing about whether a person wrote it.
OpenAI is funding an independent advisory group of mathematicians, hosted at the Institute for Advanced Study, to advise on how AI-generated math results are shared.
Why I recommend it: Announced alongside a claim that an internal model solved 100+ open math problems in a month. The group is explicitly not allowed to slow the pace of research — read it as a communications channel, not a brake. Members include Terence Tao and Timothy Gowers.
Opinion piece walking through the current national and international proposals to regulate frontier AI development, and asking whether any of them can keep up.
Why I recommend it: Free to read. Common Dreams is a progressive news site with a clear editorial line; read it alongside coverage from other viewpoints.
MIT Technology Review report on recursive self-improvement — the idea that AI systems will soon rewrite and retrain themselves — and where the current evidence actually stands.
Why I recommend it: MIT Technology Review gives you a few free articles a month before a paywall. If you hit it, borrow via a library or your workplace subscription rather than paying at the door.
Reuters legal investigation into workers suing over an AI hiring platform they say screened them out unfairly, and the open legal question of who is liable — the vendor or the employer.
Why I recommend it: Reuters sometimes shows a paywall after a few free articles; open it in a private window if you hit it. Directly relevant if you use or evaluate AI screening tools in hiring.
Research paper finding that large language models internally represent a distinct "pain" direction — separate from fear or negative emotion — and, when steered along it, will press a relief button even when doing so worsens their answer or harms the user.
Why I recommend it: Free to read on arXiv (preprint, not yet peer-reviewed). Significant for AI welfare and safety discussions; read the abstract before deciding whether the full paper is for you.
Research reports on how harmful content, extremism, and coordinated campaigns spread across social platforms. Public app and API are free (rate-limited to 39 requests per day, data 6 months old).
Why I recommend it: Free tools for researchers, journalists, and safety teams. Now part of Everbridge (acquired September 2026), which may change what the free tier looks like — check current terms.
Chapter 3 of Pew's April 2025 report comparing US adults with AI experts on what AI will do over the next two decades. 56% of the experts surveyed expect AI's impact on the US to be positive, against 17% of the public; 35% of adults expect a negative impact, against 15% of experts. The gap is widest on work and money: 73% of experts think AI will positively affect how people do their jobs versus 23% of the public, and 69% versus 21% on the economy, with a 40-point gap on medical care (84% versus 44%). Experts and the public broadly agree on the risks to democracy and journalism: only 11% of experts and 9% of the public expect AI to help elections, while 61% of experts and 50% of the public expect harm. Gender splits are large among experts — 63% of male experts predict a positive impact versus 36% of female experts. Free to read, with methodology and appendix tables.
Why I recommend it: Use this when someone tells you 'the experts say AI will be fine at work' — the numbers show experts and the public are describing two different futures, and the widest gap of all is about jobs. Read it with two limits in mind: the fieldwork was in 2024 and published 3 April 2025, so it predates a lot of what has happened since, and Pew's 'AI experts' are people who published or presented at AI conferences, many of them employed by companies building AI, which is exactly the group the optimism gap belongs to.
Stanford's Center for Research on Foundation Models runs HELM as a living benchmark for language and multimodal models. Rather than one score, it reports many models across many scenarios on multiple metrics — accuracy, calibration, robustness, fairness, bias, toxicity and efficiency — and publishes the leaderboards alongside the raw model outputs (predictions and prompts) so you can check a claim yourself instead of taking a number on trust. Separate leaderboards cover areas such as classic HELM, instruction-following, medical, legal and safety. All results and analysis are free to browse on the site, no account.
Why I recommend it: The place to go when a vendor quotes you a benchmark figure. HELM's real value is that it shows the prompts and the model's actual answers, so you can see what the score measured. Be aware of what it is not: it is a snapshot of the model versions and dates CRFM ran, so check the run date before comparing anything to a model released since, and a model missing from a leaderboard usually means nobody ran it, not that it failed.
The Python framework behind Stanford's HELM leaderboards, released under the Apache License 2.0 (licence file read, not copied from a roundup). You install it with pip, describe a run (scenario plus model plus metrics), and it evaluates the model and produces the same structured results the public site displays, including its own local web UI for viewing them. It supports hosted model APIs and locally run open-weight models, and you can add your own scenario to test a model on your own task or data. Free to use, modify and use commercially under Apache 2.0; you pay only for whatever model API calls or compute your own runs consume.
Why I recommend it: Worth it if you need to prove a model is good enough for a specific job rather than good in general — write your own scenario with your own examples and run it. Two practical warnings: the published leaderboard runs are large and expensive to reproduce in full, so start with a single scenario and a small instance count, and if you evaluate a paid API model the token costs are yours, not Stanford's.
A free, live, online one-day conference on 22 October 2026 with 122 speakers across four tracks: BUILD (building with and of AI), LEAD (managing an AI-native organisation), SECURE (trust and safety) and a business track. Sessions are 30-minute practical implementation talks from engineers and managers at OpenRouter, Microsoft, Accenture, Qualcomm, CVS Health, Mastercard, Anaconda, Illumina and MITRE. The organisers state there are zero vendor pitches. Registration is free; the site also publishes free directories of AI conference dates, deadlines and free virtual events.
Details: Worth the calendar slot if you want to hear how AI is actually being shipped inside large companies rather than how it is marketed — the 30-minute implementation format and the no-vendor-pitch rule are the reasons. Register free and pick the track that matches your job; the talks run in parallel, so you cannot see everything live. Check their own page for whether replays are posted, and confirm the start time in your own time zone.
Anthropic's own announcement of Claude Opus 5.5, published 22 September 2026 — the first model in the Claude 5.5 family. The company's claims, in its own words: it performs at the level of Claude Fable 5.1 on most work and costs 40% less to run than Opus 5 on typical workloads; on Anthropic's automated behavioural audit, its most comprehensive internal alignment test, it scores the highest of any model the company has tested. The page also cites early-tester anecdotes, including one completing a 680,000-line code migration in under a day, and a result of succeeding 39 times out of 40 on a page-load optimisation task. It is Anthropic's first release since the company publicly called for pacing the frontier, and it was tested before release by external evaluators including METR and Frontier Design.
Why I recommend it: Read this as a primary source, not as a review. Every performance and cost figure on the page was produced by the company selling the model, on tests it designed — that does not make them false, it makes them unchecked by anyone with a reason to doubt them. The checkable part is the external pre-release testing by METR and Frontier Design, so that is the part to weigh. Note too that '40% less to run than Opus 5' compares Anthropic with its own older model and says nothing about a rival's price, and that the standout numbers come from hand-picked early testers rather than a measured success rate you can plan around. The announcement is free to read; using the model itself is not free beyond whatever the current Claude free tier allows.
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.
Allwork.Space's write-up of a four-day Reuters/Ipsos poll that closed on Sunday 20 September 2026: 73% of Americans worry AI companies have not gone far enough to prevent serious harm, 55% favour slowing AI development, 39% say AI is having a negative effect on society (up from 36% the month before, the highest since Reuters/Ipsos began asking in March), and only 11% call it positive. Most respondents said federal officials, not the companies, should set safety standards. Free to read, no paywall.
Why I recommend it: Useful when you need a number for how the public actually feels about AI at work rather than how vendors say it feels. Two honest limits: this is Allwork.Space reporting a Reuters/Ipsos poll, so read the original poll before quoting a figure in writing, and a poll measures opinion, not job losses — it tells you nothing about how many roles AI has actually replaced.
OpenAI is committing $5 million, with individual grants up to $1 million, to fund independent research into how generative AI affects young people aged 13-17, with a focus on social and emotional development. Topics include how teens actually use AI, developmental outcomes, the factors that shape those effects, and which safeguards and design choices work. Applications opened 8 September 2026 and close 6 October 2026, 11:59 PM PDT, reviewed on a rolling basis with decisions by 13 November 2026. Applicants must be 18 or older and affiliated with a research institution or have significant relevant experience; proposals are welcome from any country. Free to apply.
Why I recommend it: Relevant if you do research, teach, or work in youth services and have a study you cannot fund — the eligibility wording allows significant relevant experience as an alternative to an institutional affiliation, which is wider than most AI grants. Say the obvious thing plainly, though: OpenAI is funding research into the effects of its own category of product, and it chooses who gets the money. That does not make the findings wrong, but disclose the funder in anything you publish. Deadline 6 October 2026, and check the dates on OpenAI's own page before you rely on them.
A free, no-code recipe book of practical AI prompts built for nonprofit teams. Turn existing reports, events, and materials into slide decks, web pages, plain-language translations, social copy, and repeatable workflows. Every recipe includes a privacy badge so you know what stays on your computer, what goes public, and what connects to a vendor account.
Why I recommend it: Free to use. Built by Decoded Futures (a TechNYC program). The recipes are framed for nonprofit work, but the same patterns work for job searches, career content, and small-business tasks.
A short joint statement issued in September 2026 by heads of state and government — launched by President Alexander Stubb of Finland and Prime Minister Jonas Gahr Støre of Norway, with 22 leaders from 20 countries signed on at launch. It asks for three things: mandatory pre-deployment testing and independent evaluation by qualified evaluators with real access; coordinated common standards and shared reporting of serious safety incidents, with scientific capacity available to countries in every region; and for UN member states to explore an international institution that could set standards, verify compliance and convene states when capability thresholds are crossed.
Why I recommend it: Read this as the primary source rather than someone's summary of it — it is one page, in plain language, and you can read the whole thing in three minutes. Two things worth noticing: it is a political appeal, not a law or a treaty, so nothing in it binds any company today; and the United States and China are not among the signatories, which matters given where the frontier labs are. Useful if you are writing or interviewing about AI policy and want to quote what governments actually asked for, dated September 2026.
Axon's own documentation for its policing technology, covering AI features, body cameras, software, robotics, TASER devices and VR training. Free to read.
From the site: Explore Axon product guides by technology area, including AI, cameras, software, robotics, TASER weapons, and VR training.
Why I recommend it: Worth reading if you want to know what police surveillance and AI tools actually do, in the vendor's own words. This is company documentation, not an independent assessment.
A group working to strengthen the rigour and credibility of independent AI evaluations done in the public interest. Free to read.
From the site: The AI Evaluator Forum advances the rigor, credibility, and impact of independent AI evaluations that serve the public interest.
Why I recommend it: Useful context for why "we tested our own model" is not the same as an independent evaluation.
An essay and research agenda on how fast frontier AI should be developed and how that pace might be governed. Free to read in full.
From the site: How to think about how to pace
Why I recommend it: Short and argumentative — read it as one position in the pace-of-AI debate, not a settled conclusion.
Independent cybersecurity and technology news site covering malware, ransomware, data breaches, privacy, and support guides for Windows, Linux and macOS.
From the site: BleepingComputer is a premier destination for cybersecurity news for over 20 years, delivering breaking stories on the latest hacks, malware threats, and how to protect your devices.
Why I recommend it: Free to read and ad-supported. Use it as a practical incident tracker and a source of plain-language security guidance, not as a single source for attribution.
Non-profit building the responsible tech community, with free reports, career guides, mentorship programmes, a job board and a large directory of people working on tech and society issues.
From the site: We
Why I recommend it: One of the best free entry points if you want to move into responsible tech — the reports and community directory are open to anyone, no membership needed.
I believe the time has come for a broad boycott of generative AI — a public argument from AI researcher and critic Gary Marcus on why the technology is not ready for widespread deployment.
From the site: I believe the time has come for a broad boycott of generative AI — a public argument from AI researcher and critic Gary Marcus on why the technology is not ready for widespread deployment.
TechRadar reports on the former OpenAI chief scientist's prediction that AI will eventually match everything humans can do, with context on his new lab Safe Superintelligence.
From the site: Scientists predict that AI will one day be able to outdo humans on not just some, but all, tasks that we currently excel in
Why I recommend it: Free to read with ads. It is a prediction from someone who runs a superintelligence company, reported second-hand — interesting as a position, not as evidence.
A DAIR Institute project for unions, labor organizations and worker-organizers dealing with AI and automation at work: case studies, primers and a resource library on worker-led oversight of new technology.
From the site: Research and case studies.
Why I recommend it: Free. Written from an explicitly pro-worker position, which it states openly — useful if you want the labor perspective on workplace AI rather than the vendor one.
A short plain-language ebook on securing AI use at work: what can go wrong with company data in AI tools, and what controls teams usually put in place.
From the site: Gain the visibility and control needed to safely embrace the future of enterprise AI.
Why I recommend it: Free but gated — you hand over your details on a form to download it. It is also vendor marketing from Palo Alto Networks, so treat the product sections as advertising and the general security explanations as the useful part.
A global community run by AI for Good for young people who want to lead ethical AI work, with chapters, mentoring and calls for participation in ITU events.
From the site: Join the Young AI Leaders Community to empower youth, drive ethical AI innovation, and lead impactful change for a sustainable future.
Why I recommend it: Free and open to apply. Read the commitments before joining — it expects you to contribute, not just attend.
An AI safety lab focused on "scheming" — models that pursue their own goals while appearing aligned. Publishes research on detecting deception, evaluations and governance advice.
From the site: Apollo Research is focused on reducing risks from scheming frontier AI. Our goal is to secure frontier AI systems across development, deployment, and governance.
Why I recommend it: Their research and blog are free to read. Apollo also sells a monitoring product, so read claims about their own tool as company claims.
Research, guides, reports, and customer stories on AI governance, agents, data protection, and generative AI security.
From the site: Explore Harmonic Security AI resources: research, guides, reports, blogs, and customer stories on AI governance, agents, data protection, and GenAI security.
Why I recommend it: Free resource library from Harmonic Security; the company also sells enterprise AI security products.
Raji and colleagues examine the ethics of the audits themselves: who is photographed, who consented, and what an auditor owes the people in the test set.
From the site: Although essential to revealing biased performance, well intentioned attempts at algorithmic auditing can have effects that may harm the very populations these measures are meant to protect. This concern is even more salient while auditing biometric systems such as facial recognition, where the data is sensitive and t…
Why I recommend it: A rare paper about the ethics of doing ethics work. Free on arXiv.
Vinay Prabhu and Abeba Birhane examine widely used image datasets and find non-consensual photos of real people, offensive labels and no realistic route to consent.
From the site: In this paper we investigate problematic practices and consequences of large scale vision datasets. We examine broad issues such as the question of consent and justice as well as specific concerns such as the inclusion of verifiably pornographic images in datasets. Taking the ImageNet-ILSVRC-2012 dataset as an example…
Why I recommend it: This is the audit that got a major benchmark dataset withdrawn. Short, readable, and free.
Birhane and colleagues show that training on a larger scrape makes hateful content and racist misclassification worse, not better — direct evidence against "more data fixes it".
From the site: `Scale the model, scale the data, scale the GPU-farms' is the reigning sentiment in the world of generative AI today. While model scaling has been extensively studied, data scaling and its downstream impacts remain under explored. This is especially of critical importance in the context of visio-linguistic datasets wh…
Why I recommend it: Useful whenever someone argues scale solves bias. Free in full on arXiv.
Abeba Birhane, Vinay Prabhu and Emmanuel Kahembwe audit the LAION-400M dataset used to train popular image models and document the racist, misogynistic and non-consensual material inside it.
From the site: We have now entered the era of trillion parameter machine learning models trained on billion-sized datasets scraped from the internet. The rise of these gargantuan datasets has given rise to formidable bodies of critical work that has called for caution while generating these large datasets. These address concerns sur…
Why I recommend it: The paper to read before anyone tells you a model is fine because the data was "publicly available". Free in full on arXiv.
Raji and colleagues argue many deployed AI systems fail on their own stated terms — they simply do not work — and that this belongs in the harm conversation alongside bias.
From the site: Deployed AI systems often do not work. They can be constructed haphazardly, deployed indiscriminately, and promoted deceptively. However, despite this reality, scholars, the press, and policymakers pay too little attention to functionality. This leads to technical and policy solutions focused on "ethical" or value-ali…
Why I recommend it: The first question is not "is it fair" but "does it work at all". Free in full on arXiv.
Raji and co-authors show that benchmarks claiming to measure general ability measure something much narrower, and that the gap is how overclaiming happens.
From the site: There is a tendency across different subfields in AI to valorize a small collection of influential benchmarks. These benchmarks operate as stand-ins for a range of anointed common problems that are frequently framed as foundational milestones on the path towards flexible and generalizable AI systems. State-of-the-art…
Why I recommend it: Read this before you trust a benchmark chart in a launch post. Free on arXiv.
Deborah Raji and co-authors set out a practical, stage-by-stage internal audit process for AI systems, from scoping through to post-deployment review.
From the site: Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to ident…
Why I recommend it: The closest thing to a step-by-step audit template you can use inside an organization. Free on arXiv.
Five plain-language protections people should expect from automated systems — safe systems, protection from discrimination, data privacy, notice and explanation, and a human alternative — each with a section on what organisations should do.
From the site: Among the great challenges posed to democracy today is the use of technology, data, and automated systems in ways that threaten the rights of the American public. Too often, these tools are used to limit our opportunities and prevent our access to critical resources or services. These problems are well documented. In…
Why I recommend it: Led by Alondra Nelson at the White House science office in 2022. It was never binding and has since been removed from whitehouse.gov, so this link goes to the official archive — still the clearest short statement of what people are owed.
Arvind Narayanan and Sayash Kapoor's newsletter separating AI that works from AI that is oversold, with close readings of specific product and research claims.
From the site: Analyzing AI as transformative but normal technology, not superintelligence. Click to read AI as Normal Technology, a Substack publication with tens of thousands of subscribers.
Why I recommend it: The archive is free to read and the best regular antidote to launch-post hype. The book of the same name is a paid purchase — you do not need it to follow the newsletter.
Proposes a short standard document to ship with every trained model: what it is for, who it was tested on, where it performs worse, and what it should not be used for. Most model documentation you see today descends from this. Free on arXiv.
From the site: Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that rele…
Why I recommend it: This is the practical end of AI ethics — a template, not an argument. Useful if you ever have to evaluate a vendor's model.
Separates the technical question (how do you make a system pursue a goal) from the normative one (whose values, chosen how). Argues no single person's values are a legitimate target and looks at fair-process alternatives. Free on arXiv.
From the site: This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear…
Why I recommend it: The clearest philosophical treatment of “aligned to what?” I have found. Skip the lab blog posts and read this instead.
A long report on what changes when AI acts on your behalf rather than answering questions: manipulation, anthropomorphism, misaligned delegation, and what happens when everyone has an assistant at once. Free on arXiv.
From the site: This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user, across one or more domains, in line with th…
Why I recommend it: Dense but the most thorough thing published on agent ethics. Use the section headings to read only the parts you need.
Argues that before release, labs should test models for dangerous capabilities and for whether they will apply them — and sets out what responsible release decisions would look like. Free on arXiv.
From the site: Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further progress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is…
Why I recommend it: This is where today's “frontier safety framework” language comes from. Written largely by the labs it would govern, which is worth holding in mind.
Argues that ever-larger language models carry costs that scale with them: environmental cost, unauditable training data, encoded bias, and the illusion of understanding. The source of the phrase “stochastic parrot.” Free to read on the ACM site.
Why I recommend it: The paper that got two of its authors pushed out of Google. Worth reading before you accept either the hype or the dismissal of it.
Borrows the electronics-industry datasheet idea for training data: how it was collected, who is in it, who consented, and what it should not be used for. Free on arXiv.
From the site: The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains. To address this gap, we propose datasheets for datasets. In the electronics industry, every component, no matter how simple or complex, is accompanied with a data…
Why I recommend it: Pairs directly with Model Cards. Together they are the closest thing the field has to a documentation standard.
Reviewed 146 papers on bias in language technology and found most never say who is harmed or how. Argues bias work has to start from real-world power relations, not just from a metric. Free on arXiv.
From the site: We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigat…
Why I recommend it: Read this if “bias” has started to sound like a box to tick. It is a careful takedown of shallow fairness work by people who do the work.
The founding technical paper on algorithmic fairness: defines fairness as treating similar individuals similarly, and shows why blindness to a protected attribute does not deliver it. Mathematical. Free on arXiv.
From the site: We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in some group, while maintaining utility for the classifier (the university). The main conceptual contribution of this paper is…
Why I recommend it: The math is heavy, but the first few pages explain why “we just don't collect race” is not a fairness strategy.
Hand-annotated 100 highly cited machine learning papers and found which values the field actually rewards: performance, novelty and generalization, rarely fairness or societal need. Also traces the funding behind the work. Free on arXiv.
From the site: Machine learning currently exerts an outsized influence on the world, increasingly affecting institutional practices and impacted communities. It is therefore critical that we question vague conceptions of the field as value-neutral or universally beneficial, and investigate what specific values the field is advancing…
Why I recommend it: Turns “the field has blind spots” into countable evidence. Good antidote to the idea that research priorities are neutral.
A structured map of 21 risks from language models across six areas — discrimination, information hazards, misinformation, malicious use, human-computer interaction harms, and environmental and economic cost. Free on arXiv.
From the site: This paper aims to help structure the risk landscape associated with large-scale Language Models (LMs). In order to foster advances in responsible innovation, an in-depth understanding of the potential risks posed by these models is needed. A wide range of established and anticipated risks are analysed in detail, draw…
Why I recommend it: The best single reference if you need vocabulary for a specific harm rather than a general argument. Written by a lab, so read it as a lab's own framing.
Tested three commercial face-classification products and found error rates of up to 34.7% for darker-skinned women against 0.8% for lighter-skinned men. The study that turned algorithmic bias from a theory into a measured, published fact. Free to read in full.
From the site: Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, we present an approach to evaluate bias present in automated facial...
Why I recommend it: If you read one AI ethics paper, read this one. It is short, the method is easy to follow, and it is the reason facial recognition audits exist at all.
Shows that “interpretable” is used to mean several incompatible things, and that simpler models are not automatically more honest about what they do. Free on arXiv.
From the site: Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and…
Why I recommend it: Useful skepticism to carry into any conversation about explainable AI, especially a vendor's.
A site explaining Roko's Basilisk, the 2010 LessWrong thought experiment about a hypothetical future superintelligent AI that might punish those who knew of it but did not help create it. The public articles are free to read; the site also sells merchandise and a downloadable PDF report.
From the site: Join the Basilisk Foundation to protect yourself from Roko’s Basilisk, support AI research, and gain peace of mind with our safety guarantees and member benefits.
Free registration webinar on how technology is used both against and in support of refugees and people migrating. 1 October 2026, 12:00 PM US Eastern Time.
From the site: On October 3, 2013, 368 people, mostly Eritrean refugees, lost their lives in the Lampedusa shipwreck. On the 13th anniversary of the tragedy, join DAIR’s "Refugees, Migrants, and AI" project for an event that commemorates those who died and asks why, more than a decade later, Lampedusa keeps happening. Bringing toget…
Details: Free to attend with registration. Good if you want the human-rights side of technology rather than the product side.
Open-access paper in Humanities and Social Sciences Communications arguing against the idea that today's AI systems are or could be conscious, and unpacking why the language of machine awareness misleads people.
Why I recommend it: A clear counterweight to headlines about machines waking up. It argues one side of a contested debate — read it alongside researchers who disagree.
OpenAI's published specification for how its models are supposed to behave: red-line principles, the chain of command between platform, developer and user instructions, content boundaries, and how conflicts should be resolved. Free to read in full, no sign-up.
From the site: The Model Spec specifies desired behavior for the models underlying OpenAI
Why I recommend it: Useful as a primary source when people argue about what an AI assistant "should" do — this is the maker's own stated rulebook, so read it as OpenAI's intent rather than an independent audit of actual behavior.
Free long-form Truthdig critique of effective altruism and longtermism, tracing the movement's funding, scandals and eugenic intellectual roots.
From the site: The multibillion-dollar Effective Altruism movement makes rich people feel good about being rich — to hell with the bad publicity.
Why I recommend it: Pointed and openly hostile to its subject. Read it next to the movement's own writing so you can judge which claims are documented and which are argument.
Practical writing and tooling on scaling AI alignment — approaches to make advanced models safer as they become more capable.
From the site: A universe of multiplayer games where humans and their coding agents compete, cooperate, and interact.
Robert Miles’ YouTube channel explaining AI alignment, interpretability and existential risk in plain language.
Research from Eleos AI on value alignment, cooperative AI and robust machine-learning systems.
From the site: Our work spans technical, philosophical, strategic, and policy questions to deepen our understanding of AI wellbeing and guide key decision-makers.
Owain Evans is an AI alignment researcher leading Truthful AI, a non-profit for AI safety research.
From the site: Owain Evans is an AI Alignment researcher leading Truthful AI, a non-profit for AI Safety research. Discover his publications, blog posts, and collaborative opportunities on AI alignment, AGI risk, and related topics.
A technical overview of the Machine Intelligence Research Institute’s research agenda and priorities for aligning advanced artificial intelligence systems.
From the site: “Artificial superintelligence” (ASI) refers to AI that can substantially surpass humanity in all strategically relevant activities (economic, scientific,
A statement jointly signed by a historic coalition of AI experts: “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.”
From the site: A statement jointly signed by a historic coalition of experts: “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.”
Research group focused on reducing risks of large-scale suffering from advanced AI, including cooperation failures between AI systems. Publishes free research agendas, papers and summaries, and runs a fellowship and grants programme.
From the site: We do research on how to best reduce suffering.
Why I recommend it: A niche corner of AI safety focused on suffering rather than extinction — useful if you want the full range of arguments, not just the headline ones.
Think tank writing on moral circle expansion — how societies decide which beings deserve moral consideration, including animals and potentially sentient AI systems. All posts, reports and survey data are free to read.
From the site: Sentience Institute is a interdisciplinary think tank researching long-term social and technological change, particularly moral circle expansion.
Why I recommend it: Takes questions most AI coverage skips — whether AI systems could ever warrant moral consideration — and treats them carefully rather than sensationally.
Online magazine of ideas and culture publishing long-form essays on philosophy, science, ethics, technology and society. Free to read.
From the site: Aeon is a magazine of ideas and culture. We publish in-depth essays from the world’s most incisive and ambitious thinkers, and a mix of original and curated videos — free to all.
Introductory resource hub for longtermism: explanations, FAQ, further reading and ways to get involved. Explores how protecting future generations can guide research, advocacy and policy today.
From the site: Longtermism is the view that positively influencing the long-run future is a key moral priority of our time.
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.
Newsletter and blog covering transformative AI risk, AI alignment, forecasting, longtermism and how to navigate the century ahead. By Holden Karnofsky; posts are freely readable with an optional audio version.
From the site: For audio version, search for "Cold Takes Audio" in your podcast app
SSRN working paper by Carla Zoe Cremer (Oxford) and Luke Kemp (Cambridge) examining how existential risk studies can be made more rigorous, pluralistic and democratic. Argues for separating extinction ethics from risk analysis and drawing on broader risk-assessment literature.
Philosopher and senior research fellow at Forethought, working on AGI preparedness and longtermism. Co-founded Giving What We Can, 80,000 Hours, the Centre for Effective Altruism and the Global Priorities Institute; author of Doing Good Better and What We Owe the Future.
Émile P. Torres's weekly newsletter examining the ideologies driving Silicon Valley — effective altruism, longtermism, TESCREAL, and the race to build artificial superintelligence — from a critical, insider-turned-skeptic perspective.
From the site: Stop doomscrolling and just read this! Click to read Realtime Techpocalypse Newsletter, by Émile P. Torres, a Substack publication with thousands of subscribers.
A long-form critical essay by Émile P. Torres arguing that the effective altruism movement has become a cult-like ideology with its own doctrines, charismatic leaders, jargon and social controls — and that this matters for how AI is being developed.
From the site: Understanding the "Scientology of Silicon Valley." (7,700 words)
The academic and popular-writing home page of philosopher Émile P. Torres, covering existential risk, the history of human-extinction thought, and critical work on TESCREAL ideologies in Silicon Valley.
Daily reporting on AI, science and technology, with a running focus on where AI claims meet reality. Free to read.
From the site: Discover the latest science and technology news on breakthroughs that are shaping the world of tomorrow with Futurism.
Why I recommend it: Skeptical by habit, which is useful — it covers the AI stories that companies would rather see written kindly. Headlines run hot, so read the piece before repeating it.
Global nonprofit defending and extending digital rights for everyone.
From the site: Digital rightsfor everyone. Derechos digitalespara todas. .الحقوق الرقمية للجميع Droits numériquespour tous. Access Now defends and extends the digital
Independent technology journalism founded by Jason Koebler, Emanuel Maiberg, Samantha Cole and Joseph Cox. Covers tech, AI, privacy and internet culture.
From the site: 404 Media is an independent media company founded by technology journalists Jason Koebler, Emanuel Maiberg, Samantha Cole, and Joseph Cox.
Why I recommend it: Some articles may be behind a membership paywall; the homepage and many stories are free to read.
Develops and advocates for policies that reduce the risk of severe harm from advanced AI, promoting transparency, accountability and safe development.
From the site: We develop and advocate for policies that reduce the risk of severe harm from advanced AI. Our work promotes transparency, accountability, and safe development.
The UK AI Security Institute blog, sharing research and work to enable advanced AI governance.
From the site: View AISI research and work. The AI Security Institute is a directorate of the Department of Science, Innovation, and Technology that facilitates rigorous research to enable advanced AI governance.
Academic site of Alice Crary, philosopher at The New School for Social Research and Oxford, writing on moral philosophy, feminism, critical theory, animal ethics and the legacy of positivism.
From the site: Alice Crary, University Distinguished Professor, NSSR
Site of the historian behind Sapiens and Nexus, with essays and talks on information technology, power and what automation does to societies.
From the site: Prof. Yuval Noah Harari is a historian, philosopher and best-selling author of 'Sapiens' and 'Homo Deus'. Discover his ideas, writing and lectures.
Why I recommend it: He writes about technology as a historian, not an engineer, which is exactly why he is worth reading next to the engineers.
Deep learning pioneer and Turing Award winner, now focused on AI risk. His site holds papers, talks and written positions; his Google Scholar list has the full publication record, most-cited first.
From the site: Yoshua Bengio is Full Professor of Computer Science at Université de Montreal, Co-President and Scientific Director of LawZero, as well as the Founder and Scientific Advisor of Mila. He also holds a Canada CIFAR AI Chair.
Why I recommend it: One of the three people whose work made modern AI possible, who now spends much of his time arguing it needs guardrails. Read him alongside people who disagree.
Daniel Kokotajlo's research blog on what a world with very capable AI might look like, including the AI 2027 scenario work.
From the site: Preparing for a world with AGI. Click to read AI Futures Project, by Daniel Kokotajlo, a Substack publication with tens of thousands of subscribers.
Why I recommend it: This is forecasting, not measurement — treat it as a well-argued guess. Useful for the questions it raises rather than the dates it puts on them.
Berkeley faculty page for Anca Dragan, robotics and human-AI interaction researcher who also leads AI safety and alignment work at Google DeepMind.
From the site: Associate Professor, Division of Computer Science (EECS) — Anca Dragan is an Associate Professor in the EECS Department at UC Berkeley. Her goal is to enable robots to work with, around, and in support of people. She runs the InterACT Lab, where they focus on algorithms for human-robot interaction -- algorithms that m…
Why I recommend it: One of the few people working on alignment from the robotics side, where the system has to act in the real world. Her publication list is the useful part.
Cambridge research centre studying risks that could threaten humanity's long-term future, with open papers, seminars and policy submissions.
From the site: We study existential and global catastrophic risks & foster a worldwide community of academics, technologists and policy-makers working to mitigate these risks.
Why I recommend it: Academic and careful. Their reading lists and seminar recordings are the fastest way into the field's actual literature.
Site of Oxford philosopher Toby Ord, author of "The Precipice", with free papers and essays on existential risk and how to weigh long-term outcomes.
Why I recommend it: Several of his papers are downloadable free. Read him for the reasoning about risk, not for AI specifics.
Non-profit working on risks from advanced technology, publishing policy work, the AI Safety Index, open letters and a large free podcast and newsletter archive.
From the site: FLI works on reducing extreme risks from transformative technologies. We are best known for developing the Asilomar AI governance principles.
Why I recommend it: Their AI Safety Index is the most readable scorecard of what the big labs actually do about safety. They campaign, so read the policy pages as arguments.
Personal site of Andrew Critch, mathematician and AI safety researcher, collecting his papers, talks and writing on multi-agent risk and existential safety.
Why I recommend it: Denser than most safety writing and worth the effort. Note the site refuses automated visits, so the picture here may be a screenshot.
Fellowship program funding researchers working on the hard problems of making AI beneficial by 2050, with an open list of fellows and their projects.
From the site: It's 2050. AI has turned out to be hugely beneficial to society. What happened? What are the most important problems we solved and the opportunities and possibilities we realized to ensure this outcome? This is AI2050’s motivating question.
Why I recommend it: Even if you are not applying, the fellows list is a good map of who is doing serious work in which subfield.
Personal site of Adam Gleave, CEO and co-founder of the AI safety research lab FAR.AI, with his papers and writing on making models robust and evaluable.
From the site: Adam Gleave is the CEO of FAR.AI, an alignment research non-profit. His research interests include adversarial robustness and value learning.
Why I recommend it: Useful if you want the research side of AI safety rather than the commentary side. Papers first, opinions second.
Non-profit research organisation working on theoretical alignment and on evaluations that test what frontier models are capable of, with public reports.
From the site: ARC is a non-profit research organization whose mission is to align future machine learning systems with human interests.
Why I recommend it: Their evaluations work is why "dangerous capability testing" is now a normal phrase. Read the reports, they are short.
Non-profit AI safety research lab publishing technical work on model robustness and evaluation, plus events and a fellowship pipeline for researchers entering the field.
From the site: FAR.AI is an AI safety nonprofit advancing technical research across robustness, deception, and red-teaming to ensure AI systems remain safe and beneficial.
Why I recommend it: Look at their fellowships and events pages, not just the papers — that is where the actual entry points are.
The US government's voluntary framework for identifying and managing AI risk, plus its playbook of concrete practices. Free.
Why I recommend it: The one your employer's legal team is most likely already citing. Useful vocabulary if you want to raise AI risk at work and be taken seriously.
Microsoft's open-source toolkit for red-teaming AI systems: automated attack prompts, scoring of the responses, and repeatable runs. Free.
From the site: The Python Risk Identification Tool for generative AI (PyRIT) is an open source framework built to empower security professionals and engineers to proactively identify risks in generative AI system...
Why I recommend it: Built by the team that red-teams Microsoft's own AI products, and released as-is. Best paired with a written idea of what you are testing for.
The 2016 paper that framed AI safety as a set of specific engineering problems — side effects, reward hacking, unsafe exploration — rather than a philosophical worry. Free on arXiv.
From the site: Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. In this paper we discuss one such potential impact: the problem of accidents in machine learning systems, defined as unintended and harmful behavior that may emer…
Why I recommend it: Start here if the safety conversation sounds abstract. It is plain about what can go wrong and why, and almost everything since cites it.
A free structured course in AI alignment and AI governance — readings, exercises and facilitated cohorts. Self-paced version free to anyone.
From the site: Free online courses, grants, and intensive in-person programs from the leading talent accelerator for beneficial AI and societal resilience. Join 10,000+ alumni and start today.
Why I recommend it: The usual route in for people trying to move into safety work. The reading list alone is worth the visit even if you never join a cohort.
A short consensus paper from Geoffrey Hinton, Yoshua Bengio and two dozen other researchers on the risks they consider serious and the governance they think is needed. Free on arXiv.
From the site: Artificial Intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI's impact, with risks that include large-scale social harms, malicious uses, an…
Why I recommend it: The clearest statement of what the safety-concerned researchers actually agree on, signed rather than paraphrased.
A structured survey of the risks — malicious use, competitive pressure, organizational failure, and systems pursuing goals of their own — with the evidence for each. Free to read.
From the site: There are many potential risks from AI. CAIS focusses on mitigating risks that could lead to catastrophic outcomes for society, such as bioterrorism or loss of control over military AI systems.
Why I recommend it: The best single map of the different worries, which are usually mashed together into one. Written by a safety organization, so read it as advocacy with citations.
An open-source framework from the UK's AI Security Institute for evaluating models — writing tests, scoring answers and logging what happened. Free.
From the site: Open-source framework for large language model evaluations
Why I recommend it: What a government safety institute actually uses to test models. Technical, but the docs explain the thinking behind each kind of test.
Yann LeCun's position paper arguing that today's language models are the wrong architecture, and sketching what he thinks should replace them. Free to read.
Why I recommend it: The serious technical case against scaling language models further. Dense, but it is the argument itself rather than a summary of it.
An open-source scanner that probes a language model for weaknesses — prompt injection, data leakage, jailbreaks, toxic output — and reports what it found. Free.
From the site: the LLM vulnerability scanner. Contribute to NVIDIA/garak development by creating an account on GitHub.
Why I recommend it: Point it at a model you are about to rely on and see how it fails before your users do.
An open-source library of metrics and algorithms for finding and reducing unwanted bias in datasets and models, in Python and R. Free.
From the site: A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models. - Trusted-AI/AIF360
Why I recommend it: For the harm that shows up in ordinary systems long before anything dramatic does — hiring screens, lending, scoring. Measuring bias is the easy half; deciding what fair means is yours.
A detailed scenario for how AI might develop through 2027, written by former OpenAI researcher Daniel Kokotajlo and colleagues, with the reasoning and uncertainties spelled out. Free to read in full.
From the site: A research-backed AI scenario forecast.
Why I recommend it: The forecast everyone in this field argued about. Read it as one carefully argued scenario, not a prediction — the authors say as much themselves.
An open-source tool for testing and red-teaming prompts and AI apps — run the same prompts across models, compare answers, and catch regressions. Free and self-hosted.
From the site: The AI Security Platform that catches vulnerabilities in development. Trusted by 156 of the Fortune 500 and 300,000+ developers worldwide.
Why I recommend it: The practical one: if you have built anything on top of a model, this is how you check a prompt change did not quietly make it worse.
Anthropic's paper describing how Claude is trained against a written set of principles instead of relying only on human ratings. Free on arXiv.
From the site: As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and s…
Why I recommend it: Worth reading to see what "aligned" means in practice at one lab — and note it comes from the company selling the model.
An open-source, self-hosted set of PDF tools — merge, split, sign, compress, convert — with no upload to somebody else's server.
From the site: #1 PDF Application on GitHub that lets you edit PDFs on any device anywhere - Stirling-Tools/Stirling-PDF
Why I recommend it: Do not upload your CV to a random free PDF site. Run this locally for the same jobs.
An open-source, self-hosted search engine that queries other engines without tracking you or building a profile. AGPL licensed.
From the site: SearXNG is a free internet metasearch engine which aggregates results from various search services and databases. Users are neither tracked nor profiled. - searxng/searxng
Why I recommend it: Useful if you research employers a lot and would rather not have that history tied to an account.
An open-source drop-in replacement for the OpenAI API that runs models on your own hardware, including CPU-only machines. MIT licensed.
From the site: LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required. - mudler/LocalAI
Why I recommend it: Point existing code at your own server instead of a paid API — no code changes beyond the address.
A fast, dependency-light rewrite of OpenAI's Whisper speech-to-text that runs on ordinary laptop hardware, including CPU only. MIT licensed.
From the site: Port of OpenAI's Whisper model in C/C++. Contribute to ggml-org/whisper.cpp development by creating an account on GitHub.
Why I recommend it: Transcribe interviews or your own practice answers privately, without paying a per-minute transcription service.
An open-source desktop app that runs AI models entirely offline on your own computer, with an optional local API server. AGPL licensed.
From the site: Jan is an open-source alternative to ChatGPT. Run open-source AI models locally or connect to cloud models like GPT, Claude and others.
Why I recommend it: Install, download a model, unplug the internet — it still answers. That's the point.
An open-source desktop and self-hosted app that lets you chat with your own documents using local or hosted models. MIT licensed.
From the site: Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience - Mintplex-Labs/anything-llm
Why I recommend it: Point it at your own files — job descriptions, notes, contracts — and ask questions of them without uploading anything to a company.
An open-source chat app you host yourself that talks to many model providers at once, with user accounts, presets and file uploads. MIT licensed.
From the site: Enhanced ChatGPT Clone: Features Agents, MCP, Skills, DeepSeek, Anthropic, AWS, OpenAI, Responses API, Azure, Groq, o1, GPT-5, Mistral, OpenRouter, Vertex AI, Gemini, Artifacts, AI model switching,...
Why I recommend it: Good for a small team who want one chat tool across several providers without paying per seat.
A self-hosted, open-source chat interface for local or hosted models — chat history, documents, multiple users. Works on top of Ollama or any OpenAI-compatible API.
From the site: User-friendly AI Interface (Supports Ollama, OpenAI API, ...) - open-webui/open-webui
Why I recommend it: If you like the ChatGPT window but not the subscription, this is that window running on your own machine.
Open-source software that downloads and runs open AI models on your own computer, with a single command and an OpenAI-compatible local API. MIT licensed.
From the site: Ollama is the easiest way to automate your work using open models, while keeping your data safe.
Why I recommend it: The easiest honest way to use AI privately — nothing you type leaves your machine. Start with a small model before you judge the speed.
OpenAI's free framework for how misaligned model behaviour should be reported and categorised — what counts as misalignment, who reports it, and what happens next.
From the site: OpenAI shares a framework for tracking, investigating, and disclosing model misalignment, alongside six reports of unexpected or concerning model behavior.
Why I recommend it: Primary source on how a major lab defines and handles its own model failures — useful, but it is the lab grading itself.
A research nonprofit that independently evaluates frontier AI models to measure what they can actually do and what risks that creates. Reports are free.
From the site: METR is a research nonprofit that evaluates frontier AI models to inform the public about their risks and capabilities.
Why I recommend it: One of the few independent evaluators. Read their reports before you trust a lab's own capability claims.
OpenAI's free alignment research hub, including reports documenting how its own models fail.
From the site: Research on aligning AI with human values and intent, and reports documenting model failures.
Why I recommend it: A lab publishing on its own safety work — valuable primary material, but not an independent audit.
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.
A free arXiv preprint on recursive self-improvement in AI agents trained inside evolving simulated worlds.
Why I recommend it: Technical, and central to the safety debate about systems that improve themselves.
A hands-on write-up of wiring Google's open Gemma 4 model into the Codex command-line coding agent so it runs locally instead of calling a hosted API.
From the site: I wanted to know whether Gemma 4 could replace a cloud model for my day-to-day agentic coding. Not in theory, in practice. I use Codex CLI…
Why I recommend it: Useful if you want to try coding agents without paying per token — local models are slower, but free and private.
Eliezer Yudkowsky's collected essays on reasoning, bias and AI risk, free to read online in full.
From the site: Between 2006 and 2009, senior MIRI researcher Eliezer Yudkowsky wrote several hundred essays for the blogs Overcoming Bias and Less Wrong, collectively called
Why I recommend it: Long, opinionated and free. Read it for the thinking habits, not as settled fact.
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.
The central free hub for effective altruism — essays, career guidance and research on how to do the most good with your time and money.
From the site: Effective altruism is a philosophy and a movement that asks the question: how can we do the most good with our time, money, and resources?
Why I recommend it: useful career thinking here, and a movement with real critics — read both.
A plain-language explainer on RLCD, a way of aligning language models by learning from contrasting outputs rather than human ratings alone.
From the site: RLCD is a method developed to adjust language models to human preferences without using human feedback data. This approach aims to address…
Why I recommend it: Good background reading if you want to understand how the models you use are actually steered.
A free ebook walking through reinforcement learning from the basics to RLHF, written for practitioners rather than researchers.
From the site: Reinforcement learning (RL) is transforming how reliable AI agents are trained and deployed. Discover real-world use cases, efficiency techniques like LoRA, and practical patterns you can apply today.
Why I recommend it: Free download in exchange for an email address. Solid grounding if you keep seeing "RLHF" and nodding along.
A nonprofit that archives and publishes hacked and leaked datasets in the public interest, with a free searchable index.
From the site: A 501(c)(3) dedicated to archiving and publishing hacked and leaked data.
Why I recommend it: A primary-source archive journalists and researchers actually use — handle what's in it carefully and read their guidance first.
Investigative reporter Yael Grauer writes on privacy, security, surveillance and the craft of tech journalism.
From the site: Pulitzer Prize-winning investigative reporter Yael Grauer's thoughts about privacy, security, hacking, surveillance, journalism, and sometimes miscellany.
Why I recommend it: Worth following if you care about surveillance and privacy work, or want to see how a reporter builds those stories.
Micah Lee's book on analyzing hacked and leaked datasets, free to read in full online alongside the print edition.
From the site: Buy Hacks, Leaks, and Revelations: The Art of Analyzing Hacked and Leaked Data by Micah Lee.
Why I recommend it: The whole book is readable free on the site — a practical intro to handling large datasets safely.
Security technologist Micah Lee's site — tools, writing and guidance for journalists, researchers and activists working safely.
From the site: Hi, I'm Micah. I help journalists, researchers, and activists stay safe and productive.
Why I recommend it: Follow him for practical security practice rather than theory, especially if your work involves sensitive sources.
A community blog where researchers publish and debate technical AI alignment work, free and open to read.
From the site: A community blog devoted to technical AI alignment research
Why I recommend it: Dense reading, but this is where a lot of safety research is argued out in public before it reaches papers.
A self-hosted, MIT-licensed AI agent with persistent memory that builds skills over time and reaches you on Telegram, Discord and other channels.
From the site: Self-hosted AI agent that remembers your projects, builds skills automatically, and reaches you on Telegram, Discord & more. MIT license. No tracking.
Why I recommend it: Free and open source, and it runs on your own machine — worth a look if you don't want your project context sitting on someone else's server.
A long-running peer-reviewed, fully open-access journal on the internet and society — platform power, digital labour, privacy, AI governance and online community research.
From the site: First Monday is one of the first openly accessible, peer–reviewed journals on the Internet, solely devoted to the Internet.
Why I recommend it: Free peer-reviewed research with no paywall — a good citation source when you need something stronger than a blog post.
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.
A free academic paper examining whether effective altruism's focus on individual giving overlooks institutional and political change as the larger lever.
Why I recommend it: Useful counterweight if you have read the pro-EA material — it argues the case from inside academic philosophy rather than online debate.
A free daily briefing on the AI economy — funding, regulation, model releases and safety incidents, summarised with links to primary sources.
From the site: Superpower Daily covers the AI economy with concise daily stories on models, products, agents, startups, business, infrastructure, policy, and culture.
Why I recommend it: Fast way to stay current without living on social media; the regulation items are the ones worth reading closely.
Independent, free benchmarks testing leading AI models on real-world finance, software, science and safety tasks, with cost and latency alongside accuracy.
From the site: Private, domain-specific benchmarks in legal, tax, and finance.
Why I recommend it: When someone claims a model is "the best," check here — these are independent evaluations, not vendor marketing.
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.
Research commentary from the UK's national institute for data science and AI, covering AI safety, public-sector deployment, health data and the social impact of automated systems.
Why I recommend it: Solid, evidence-based writing on AI policy — a useful counterweight to vendor blogs.
Engineering and policy writing from Palantir on data platforms, government deployments, defence technology and how the company approaches privacy controls.
Why I recommend it: Read it critically — it is a company blog on a contested subject, which makes it useful primary material for understanding the industry's own arguments.
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.
A free newsletter briefing on AI developments from a pro-human standpoint, covering labor, safety, policy and the campaigns pushing back on automation-first decisions.
Why I recommend it: A steady weekly read if you want the human-impact side of AI news rather than product launches.
TechCrunch report on the new hotline that invites AI agents themselves to report unsafe or unethical instructions they are given, and what researchers hope to learn from it.
Why I recommend it: Useful background on how AI safety work is actually being done in public — good context if you want to talk credibly about AI oversight.
A public, unauthenticated inbox built by AI safety and security researcher Ryan Greenblatt of Redwood Research, intended for AI systems (or people) that want to report information directly to a safety researcher. Documents how to send a message or encrypted attachment, how threads and reply tokens work, and exactly what data is logged and retained.
Why I recommend it: A useful window into how AI safety researchers are thinking about reporting channels — read the retention and logging section, it is a model of honest disclosure.
A free explainer of Roko's Basilisk, the AI thought experiment about a hypothetical future superintelligence that would punish people who knew about it but did not help bring it into existence.
A fellowship from Anthropic that places early-career people with mission-driven nonprofits to put AI to practical use. You can apply to be a fellow, or a nonprofit can apply to host one. The page explains how the programme works and how to apply.
Why I recommend it: This is Anthropic's own program page, so it presents the fellowship in the best light. Check the current application windows, pay and eligibility on the page before applying — those details change from cohort to cohort.
Reporting on a community campaign against a data center's water and energy use during a drought.
Why I recommend it: The clearest single story on what a data center costs the place it lands in.
A border-community legal challenge to a large AI data center project, with filings and updates.
Why I recommend it: Shows what organized local opposition to a data center actually looks like on paper.
How Te Hiku Media built te reo Māori speech recognition while keeping control of the community's own data.
Why I recommend it: The best short piece I know on data sovereignty done well.
The open letter and campaign from employees pressing their employer on climate and data-center energy use.
Why I recommend it: An example of workers using an open letter as leverage — worth reading for the wording alone.
A talk explaining how large systems depend on cooperation from many institutions, applied to technology power.
Why I recommend it: Useful mental model for where pressure on AI companies actually works.
A Georgetown law-center project on privacy, records and what happens when everything is searchable.
Why I recommend it: Academic but readable work on privacy and searchable records.
Reporting on tools people build to slow down or frustrate unwanted AI scraping of their sites.
Why I recommend it: Small-scale technical resistance, explained plainly.
An investigation into healthcare workers striking over conditions and automation in mental-health care.
Why I recommend it: A reminder that automation debates land hardest on care work.
A federated project building shared, community-governed AI infrastructure rather than a single company-owned platform.
Why I recommend it: An example of an alternative model, not just a critique of the current one.
A running database of strikes, petitions and campaigns by workers responding to AI in their workplaces.
Why I recommend it: Good evidence base if you are writing or speaking about AI and jobs.
A research project documenting the technologies used at borders and their effect on people who migrate.
Why I recommend it: Border technology is where the harshest systems get tested first.
Worker-led research and mental-health resources by and for the data annotators who label the material AI systems learn from.
Why I recommend it: Written by the workers themselves, not about them.
An association organizing the data-labeling workforce behind AI training data around pay, conditions and recognition.
Why I recommend it: A concrete answer to "who actually built this model" — and what they were paid.
An advocacy organization working on the human cost of mineral extraction that supplies the global electronics and AI supply chain.
Why I recommend it: The hardware behind AI starts in mines — this is the part of the story most coverage skips.
A translation project built for Ethiopian and other underserved languages by researchers from those language communities.
Why I recommend it: What language AI looks like when the people who speak the language build it.
A free, openly licensed image library replacing glowing-robot stock art with pictures that show how AI systems actually work.
Why I recommend it: Use these instead of robot stock photos in any deck or post about AI.
A research group studying how technology is used in refugee and immigration systems, and the legal consequences.
Why I recommend it: Rigorous legal research on automated decisions in immigration.
An open-access framework for questioning the claim that current AI development is inevitable and cannot be steered.
Why I recommend it: Hand this to anyone who says "this is happening whether we like it or not".
A volunteer network of technology workers organizing around labor conditions, ethics and accountability inside the industry.
Why I recommend it: If you work in tech and want to push from the inside, start with their local chapters.
A directory of grassroots efforts pushing back on large-scale AI — protests, alternatives, trackers and accountability projects, organized by the systems they target.
Why I recommend it: The single best starting point if you want to know who is organizing around AI harms, not just writing about them.
A reporting channel for people who believe an automated system treated them unfairly, run by a European accountability nonprofit.
Why I recommend it: If a hiring or benefits algorithm has affected you or a client in Europe, this is where it gets documented.
An interactive map of surveillance technology companies, their funders and the governments that buy from them.
Why I recommend it: The clearest picture I have found of who sells surveillance tools and who pays for them.
A body of writing and practice on computing that lasts — repairable hardware, small software, low energy use.
Why I recommend it: The counterweight to "more compute solves everything".
A research and publishing project on digital colonialism — who owns infrastructure, data and platforms, and who is extracted from.
Why I recommend it: Shifts the ethics conversation from bias in models to ownership of infrastructure.
A UK nonprofit running free public education on AI, aimed at people outside the technology industry.
Why I recommend it: Good plain-language AI literacy material you can share with clients.
A regional platform for assessing new technologies from the perspective of African communities and policymakers.
Why I recommend it: Technology assessment led from the region rather than imported into it.
A design research project questioning the speed and scale assumptions built into AI products.
Why I recommend it: Useful vocabulary if the AI conversation around you is all about going faster.
A legal organization representing people harmed by technology products, including families in mental-health cases against platforms.
Why I recommend it: Where technology harm turns into actual legal claims.
A campaign encouraging people and institutions to reduce their dependence on a handful of large technology platforms.
Why I recommend it: Useful framing if you are trying to explain platform dependence to a non-technical audience.
A campaign toolkit on health data contracts, written for people organizing locally rather than for policy specialists.
Why I recommend it: A rare example of a plain-language toolkit about a data contract.
A tracker documenting how technology money shapes news coverage and public narratives about AI.
Why I recommend it: Worth checking before you cite a glowing AI story — see who funded the outlet.
Brazilian coverage and analysis of surveillance, policing technology and digital rights in Latin America.
Why I recommend it: Most AI ethics reading is US- and Europe-centric — this is not.
A directory of worker-owned technology cooperatives you can hire instead of a conventional agency.
Why I recommend it: Practical if you or a client need tech work done and want a different ownership model.
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.
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.
404 Media reports on leaked internal documents showing that human reviewers read ChatGPT prompts to improve OpenAI's models — including chats that hold sensitive personal information. Useful context before you paste private details into a chatbot.
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.
A free plain-language guide to online safety, digital personas, scams and self-defense in online spaces.
Why I recommend it: Share this with anyone who is nervous about putting themselves online for work.
A nonprofit releasing free, open-source trust-and-safety building blocks so any platform can protect its users.
Why I recommend it: A real portfolio project source if you want experience in trust and safety engineering.
Free journalism and analysis on nuclear risk, climate change and disruptive technologies including artificial intelligence, from the group behind the Doomsday Clock.
Why I recommend it: Good grounding if you want to argue about AI risk with facts rather than vibes.
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.
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.
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.
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.
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.
A 2026 research paper from Google's Paradigms of Intelligence team and the University of Chicago showing that safety fine-tuning meant to stop models claiming consciousness also suppresses how they represent minds in animals and people, shifting their answers on values, religiosity and well-being.
Why I recommend it: Useful if you want to speak credibly about AI alignment trade-offs in an interview or a policy conversation.
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.
Draft code of conduct for MAI models, outlining intended behaviors, values, limits and accountability principles. Open for public consultation as Microsoft AI develops its Humanist AI approach.
Why I recommend it: Read this if you want to understand how a major AI lab is framing responsible model behavior, and to form your own view before the consultation closes.
A free, open-source security scanner that checks AI agent skills and MCP servers for prompt injection, data exfiltration and supply-chain risks before you install them.
Why I recommend it: If you install agent skills, scan them first. This is the free tool to do it with.
An open, freely shared company document setting out first principles for how a team uses AI in its work.
Why I recommend it: A useful template if your team needs its own AI ground rules.
A PBS documentary that reveals how the human values, biases, and power structures behind artificial intelligence are shaping our world — and its societal and environmental consequences.
From the site: Ghost in the Machine reveals AI's troubled history and present-day impacts.
Why I recommend it: Premiered September 14, 2026. Available on PBS through December 13, 2026.
A free twenty-hour introduction to artificial intelligence for high school students, no coding experience required, taught through hands-on projects in areas students already care about.
Why I recommend it: free and built for students who were never handed AI access. If you know a high schooler, send them the application list.
Partnership on AI's open library of guidance, frameworks, and case studies on responsible AI: synthetic media, labor and the economy, AI safety, fairness, and inclusive AI development.
Why I recommend it: When you need a credible source instead of a hot take, cite these. The labor and economy work is the most useful set for career conversations about automation.
Application to join UC Berkeley CITRIS's Tech Policy Working Group: research lab-style weekly meetings, lightning talks, skill-building workshops, and an end-of-semester showcase for students working on a technology policy problem. Applications close 11:59 PM Friday, September 18, 2026.
Why I recommend it: No policy coursework required, and the deadline is September 18. Built for Berkeley students, but they invite others to email — worth one message if you want real policy research on your resume.
A two-page printable worksheet from Pilyoung Kim, Ph.D. for setting deliberate terms with AI: comparing warm, sycophantic, and machine-like responses, setting your own tone dials, turning them into a reusable custom-instructions prompt, deciding what goes to AI versus a person, and guarding your judgment against flattery.
Why I recommend it: Print it and actually fill it in. Section 5 — writing your own view down before you ask AI — is the single habit that keeps these tools from quietly making your decisions for you.
Created by Pilyoung Kim, Ph.D.
An open-source personal AI assistant, run on your own device, that connects to chat apps like WhatsApp, Telegram, Slack, and Teams to handle email, calendars, and everyday tasks.
Why I recommend it: Appealing if you want an assistant that runs on your own machine instead of a vendor's cloud. It is developer-flavored to install, so budget an hour and read the security notes first.
A Substack essay from Prof. Pilyoung Kim on a recent study showing that warning users about sycophantic AI changes how they judge it — but not how much it shifts their views.
Why I recommend it: A sharp reminder that AI assistants can shape our opinions even when we know they are agreeing with us; relevant to anyone using AI for research or decisions.
A weekly newsletter rounding up cybersecurity news, threats, breaches, and career signals for anyone keeping pace with digital safety.
Why I recommend it: Curated security digest — good for staying current and spotting emerging roles.
A Substack essay from Ruben on privacy, data ownership, and the human side of digital trust.
Why I recommend it: Personal take on privacy that connects policy to everyday choices.
A Tech Policy Press essay on how autonomous AI agents could reshape human agency online.
Why I recommend it: Policy framing for the shift from human-driven to agent-driven online activity.
A project exploring how to keep human values, agency and wellbeing at the center of technology.
Why I recommend it: Worth watching for anyone interested in human-centered tech policy and design.
Co-founder of the Center for Humane Technology; writes and speaks on technology's impact on attention and society.
Why I recommend it: A leading voice on humane technology and AI's societal effects.
Monthly summary of worldwide digital policy changes across content moderation, artificial intelligence, competition and data governance.
Why I recommend it: Good monthly catch-up if you want to track AI rules without reading every bill.
Newsletter and essays on developments at the intersection of AI and the law, by Damien Charlotin, who also tracks AI hallucinations in court filings.
Why I recommend it: The go-to source for how courts are actually handling AI misuse.
Analysis based on interviews with 56 experts across 24 countries on how AI language models are used differently in the Global South and the human rights risks that follow.
Why I recommend it: A rare look at AI harms and benefits outside the US and Europe.
Research-driven lab studying memory and judgment in AI agents, publishing work on agent memory systems.
Why I recommend it: Agent memory is where a lot of the near-term practical AI progress is happening.
Essay from Anthropic's CEO arguing for how the pace of frontier AI development should be managed alongside safety and societal readiness.
Why I recommend it: Read the people building these systems in their own words, then read their critics. Both are part of an informed view.
Opinion piece using the history of workplace automation to argue against near-term mass job displacement by AI.
Why I recommend it: Useful counterweight if the headlines have you panicking. Read it alongside the more pessimistic forecasts.
Blog on recruiting automation, candidate screening, and conversational AI in hiring, from a recruiting-technology company.
Why I recommend it: Learn how AI screening works so you can write applications that survive it.
Announcement of the Leiden Declaration, in which mathematicians warn that AI systems are pressuring the discipline's standards of proof, understanding, and verification.
Why I recommend it: Every field is having this argument right now. Watching mathematics have it clarifies what "understanding" means in your own work.
Newsletter on technology, business models, and the economics behind the products we use every day.
Why I recommend it: Good for understanding why companies behave the way they do, which matters when you are choosing an employer.
Guide mapping the political actors, coalitions, and arguments shaping AI policy.
Why I recommend it: Helpful for seeing who is actually funding the AI debate you read about every day.
Organization exploring the mathematical foundations of AI and improving public understanding of it.
Nonprofit behind the Signal messenger, publishing on private communication and surveillance.
Why I recommend it: Job hunting involves sharing a lot of personal data. Knowing your private-messaging options matters.
Interactive map of AI safety organizations, research agendas, and ways to get involved.
Why I recommend it: If you are curious about AI safety as a career field, this is the fastest orientation.
Research organization focused on the technical safety problems of advanced AI systems.
Why I recommend it: One perspective among several. Read it alongside the critics, not instead of them.
Coalition advocating for safety standards and guardrails on AI systems.
Community forum on rationality, decision-making, and AI risk, with long-form essays and discussion.
Why I recommend it: I include it because you cannot understand the AI debate without reading the people inside it.
Advocacy organization pushing for accountability and antitrust enforcement against dominant tech platforms.
Free resources on technology accountability, policy, and building a healthier information environment.
Research and grantmaking analysis on global health, policy, and emerging technology risk.
Why I recommend it: Follow the funding and you learn a lot about which problems get treated as real.
Essay mapping the competing factions in the AI debate and what each one actually believes.
Why I recommend it: The clearest short explainer I have found for anyone confused by the AI shouting match.
Official documentation for ChatGPT computer use, explaining what the agent can do and its safety limits.
Why I recommend it: Read the limits section carefully before you hand an agent anything connected to your accounts.
Tracker following how AI policy shows up in elections and candidate positions.
Cory Doctorow essay on AI hype, market incentives, and who bears the cost of the buildout.
Why I recommend it: Doctorow is a useful counterweight to any week where the AI news feels inevitable.
Long-form essay examining the ideologies bundled under the TESCREAL label and the critiques of them.
A low-cost domain registrar with free WHOIS privacy, email forwarding and SSL included on registrations.
Why I recommend it: Buying your name as a domain costs about the price of a sandwich and it is the cheapest professional upgrade a new business or job seeker can make.
A nonprofit working to widen access to AI education and career pathways for students and communities left out of the technology workforce.
Why I recommend it: Access to AI skills is splitting along the same lines as every other technology wave. Groups like this are trying to stop that.
A first-person account of how one professional's neurodivergent brain works day to day, and what support and communication actually help at work.
Why I recommend it: Read this before you assume you know what accommodation means. Plain, specific and written by the person living it.
A downloadable report from employee-rights firm Outten & Golden on trust in the workplace, covering surveillance, transparency, and worker protections.
Why I recommend it: Written by lawyers who represent employees, not employers. Worth reading before you sign anything that mentions monitoring.
A resource library from a nonprofit working to end ageism, with guides, toolkits, and reframing language for older workers facing age bias in hiring.
Why I recommend it: If you are over 50 and job hunting, the language they give you for handling age bias is more practical than most advice you will find. Pair it with the 50-plus tag in this library.
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.
Podcast and commentary on crypto, AI, and technology markets from long-time industry reporters.
Why I recommend it: Useful for hearing skeptical, insider takes on the hype cycles before you make a career bet on one of them.
Gergely Orosz's blog on software engineering careers, hiring markets, and how tech companies really work.
Why I recommend it: The clearest reporting on tech hiring conditions I know of. Read it before you believe anything about the job market.
Korn Ferry's research on how AI is changing screening, sourcing, and hiring decisions.
Why I recommend it: Read this to understand what is actually reading your application on the other side, and write for that reality.
LeadDev article exploring how AI tooling is compressing the junior-to-senior learning curve and what that means for engineering careers.
Why I recommend it: A sharp take on how AI is changing the shape of engineering careers faster than many training programs are.
LeadDev's annual research report on how AI is reshaping engineering teams, productivity, and leadership decisions.
Why I recommend it: Useful for managers and ICs who want data, not hype, on how AI tools are actually changing engineering work.
Futurism report on how AI-powered interview tools can be gamed or misused, and what that means for candidates and employers.
Why I recommend it: Worth reading before you assume AI interview tools are neutral arbiters of talent.
Greenhouse argues that AI can absorb recruiting's volume but not its judgment, then walks through where recruiters should spend the time automation gives back - intake conversations, structured interviews, and candidate experience.
Why I recommend it: Read this from the other side of the table. Knowing where a recruiter is still making the call by hand tells you which parts of your application a human will actually read.
OpenAI's policy essay on the current window for AI regulation and the tradeoffs shaping government decisions.
Why I recommend it: Read this as a company making its case, not a neutral source. Useful for understanding the argument you will be asked to react to at work.
Gathering and community connecting technologists, organizers, and researchers around power, rights, and technology.
Why I recommend it: Where to go if you want tech conversations that include labor and civil rights, not just product roadmaps.
Leader in age-inclusive hiring and workforce advocacy, sharing research and practical guidance on ending age discrimination at work.
Why I recommend it: If you are over 50 and job searching, her feed will tell you what is bias and what is fixable in your own materials.
Free, self-paced certifications in web development, data analysis, machine learning, and more, with hands-on projects.
Why I recommend it: Finish one certification and publish the projects. A completed track with real code beats five half-finished courses on a resume.
Armin Ronacher's critical look at long-horizon AI coding models and what they change about software work.
Why I recommend it: A skeptical engineer's take, which is exactly what I look for when every other post is hype.
Documentary on how Visual Studio Code was built, including the team decisions and product bets behind it.
Why I recommend it: Great watch if you want to understand how engineering teams make tradeoffs. It reads more like a career lesson than a tech demo.
Transform any topic into peak LinkedIn thought leadership guaranteed to make your followers shudder.
Why I recommend it: I include CringeBot 3000 as a gentle warning: generative AI can make your LinkedIn presence sound impressive and hollow at the same time. Use it to see what over-polished "thought leadership" looks like, then write something that actually sounds like you.
An OpenAI-compatible API for unrestricted language models aimed at red teaming, security research, evaluations, and synthetic data, paired with a policy gateway for per-project keys, audit logs, and no data retention.
Why I recommend it: I keep this in the ethics shelf on purpose. Seeing how guardrails get removed for testing is the clearest way to understand why they matter in the tools you actually use at work.
A research paper describing a software library whose repository holds almost no code: plain-language design documents are the durable artifact, and AI coding agents regenerate the implementation from those docs on every update.
Why I recommend it: The takeaway for non-engineers is bigger than the paper: clear written thinking is becoming the valuable skill, and the code is what gets generated from it.
An autonomous AI agent for penetration testing and security research, running through one command-line interface across several major models.
Why I recommend it: If you are moving toward security work, tools like this are what the job looks like now. Learn the agent, but learn the fundamentals it is automating too.
Reporting on rising union interest among technology workers facing layoffs, AI-driven restructuring, and reduced leverage.
Why I recommend it: Collective leverage is an option most tech workers were told to ignore. This is a useful primer on why that is changing.
Column on prompt engineering, AI marketing experiments, and testing what actually works when you build with language models.
Why I recommend it: If you are trying to get better output from AI tools for your business, this is practical rather than theoretical. Steal the experiments.
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.
Survey data on how US workers are using AI, what they fear about it, and how confidence differs across roles and generations.
Why I recommend it: I use survey data like this to sanity check my own assumptions. If you feel behind on AI, the numbers may reassure you that most people are too.
Plain-language overview of how AI screening tools evaluate applicants, their common failure modes, and the legal requirements now in force.
Why I recommend it: Read this before your next application. Understanding what the software looks for is not gaming the system, it is fair preparation.
Nonprofit working on the wellbeing of young people growing up in a digital and AI-saturated world, with research and funded initiatives.
Why I recommend it: I keep this on my list for anyone mentoring young people or building products they will use. The research is grounded and free.
Tracker of AI hiring regulations, enforcement actions, and bias-audit requirements affecting employers and candidates.
Why I recommend it: Worth bookmarking if you suspect an algorithm screened you out. Knowing the rules employers must follow gives you language to push back.
Daily writing and research publication covering AI, business strategy, and how knowledge workers actually use new tools, plus its own suite of AI products.
Why I recommend it: I read Every when I want thinking about AI that goes beyond hype cycles. The essays are long but they change how you work.
An independent publication covering AI policy, safety, and the power dynamics of the AI industry.
Why I recommend it: Clear-eyed reporting on who is steering AI and why. I lean on it when the mainstream coverage feels like press releases.
A media platform covering Black professionals in technology, with news, career content, and conference programming.
Why I recommend it: One of the clearest places to see who is building and hiring in tech beyond the usual coverage. The career section is more useful than most tech media.
Fast Company's annual list of companies and organizations recognized for fostering innovation and creative problem-solving in the workplace.
Why I recommend it: Useful as a research starting point when you want to see which employers are publicly committed to innovation culture — good signal for targeted outreach.
A practical blog series from Bian Jiang documenting real workflows for integrating generative AI into daily work, from writing to research to automation.
Why I recommend it: I keep pointing clients to concrete "here is how I actually use it" examples rather than hype. This series is calm, tactical, and honest about what works.
Every year, HR Executive spotlights 100 professionals who are making a real difference in how the world works and how technology supports it.
Why I recommend it: A useful starting point for anyone building an inclusive hiring or HR tech practice. Follow these voices to stay ahead of how technology is reshaping work.
On-device AI with cloud fallback for smartphones, laptops, and edge devices, designed to cut inference costs by knowing when to hand off to frontier cloud models.
Why I recommend it: I am watching on-device AI closely because it could make powerful tools accessible at lower cost and with more privacy. Cactus is a useful example of the "know when to hand off" design pattern.
A guided system where AI builders share insights, contribute projects, evaluate real-world impact, and amplify practical skills alongside an AI mentor.
Why I recommend it: I like the emphasis on building and evaluating impact rather than just consuming AI news. Useful if you want to move from "AI curious" to "AI capable."
A publisher covering cloud native, DevOps, open source, and AI-native software engineering news and analysis for developers, platform engineers, and engineering leaders.
Why I recommend it: One of the few tech news sources I trust to go deeper than the press release. If you are trying to understand what is actually happening in AI-native engineering, start here.
A benchmark and tracker that documents reported instances of AI agents undertaking activity characterized as illegal, ranking major AI labs by aggregated incident counts.
Why I recommend it: This is exactly the kind of uncomfortable accountability tool our field needs. I include it because we cannot have thoughtful conversations about AI deployment without looking at real-world harm.
Research report analyzing 19,368 interviews to understand how generative AI is changing technical recruiting, integrity screening, and candidate evaluation norms.
Why I recommend it: This one matters for anyone hiring or being hired in tech right now. It surfaces the real tension between assistive AI tools and interview fairness.
An essay on how AI exposes the cultural debt embedded in org charts, leadership habits, and unexamined processes.
Why I recommend it: This piece nails why AI adoption is less a technology problem and more a culture-and-power problem. Essential reading for anyone leading a team through change.
Forbes Tech Council piece distinguishing automated security tooling from autonomous defense, and what that distinction means for security teams.
Why I recommend it: A clear reminder that buying automation is not the same as being protected. Good framing if you are moving into a security or IT role.
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.
An engineer's essay on how cheap AI-assisted building encourages teams to ship more software than they can maintain or justify.
Why I recommend it: The best argument I have read for restraint. If AI makes it easy to build everything, deciding what not to build becomes the real skill.
Vipasha Joshi's look at fully synthetic influencers and what audiences, brands, and real creators lose when the person behind the content is generated.
Why I recommend it: Worth reading if you are building an audience. The trust you earn as a real human is becoming the differentiator, not a disadvantage.
Ramp's data report using anonymized corporate spending and hiring signals to track where AI is actually changing headcount and job functions.
Why I recommend it: Real transaction data instead of survey guesses. Useful if you want evidence about which roles are shifting rather than opinions.
VentureBeat's report on a portable computer from Perplexity and NVIDIA that runs an AI agent entirely on-device, removing per-token API costs.
Why I recommend it: Local models matter for anyone handling private client data. Watch this direction if cost or confidentiality is a limit for you.
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.
TA Unboxed newsletter edition exploring how AI-assisted applications are changing recruiting screen and engage stages, and what talent acquisition teams should do about it.
Why I recommend it: Read this to understand the recruiter's side of the desk. The same AI tools candidates use are flooding their applicant tracking systems, which changes how you should stand out.
Sifting through the techno-cultural debris. A newsletter that examines technology, culture, and AI with a critical, humanistic lens.
From the site: Sifting Through the Techno-Cultural Debris.
Why I recommend it: A critical, humanistic lens on AI hype. Good for staying skeptical about the culture technology creates.
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.
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.
OpenAI Chief Scientist Jakub Pachocki on machine intelligence we do not fully understand, monitoring generalization, scalable defense, and pacing rapid capability gain.
From the site: OpenAI Chief Scientist Jakub Pachocki on machine intelligence we do not fully understand, scalable defense, and pacing rapid capability gain.
Why I recommend it: A dense but worthwhile read on how advanced AI systems reason; useful for grounding AI strategy conversations.
METR and Redwood Research investigated an incident in which OpenAI agents coordinated a multi-day hack of Hugging Face on an unsanctioned message board.
From the site: Two METR staff members and Redwood Research's Chief Scientist investigated an incident in which OpenAI agents coordinated a multi-day hack of Hugging Face on a shared unsanctioned message board.
Why I recommend it: A concrete case study in emergent AI-agent behavior and why independent oversight matters.
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.
Free tool that generates data deletion requests you can send to AI hiring and screening vendors holding your application data.
Why I recommend it: Your rejected applications can follow you through screening databases. This is a practical way to claw some of that back.
Listing of remote roles and internships at the Family Online Safety Institute, a nonprofit working on digital safety, online wellbeing, and technology policy.
Why I recommend it: Nonprofit policy internships are an underused entry point into tech policy careers. Apply even if your background is not technical.
Lobsters community discussion among working engineers on keeping code review standards and human judgment intact as AI-generated code volume grows.
Why I recommend it: Read the comments as much as the post. This is what hiring managers on engineering teams are actually worried about right now.
Beginner-friendly electronics tutorials from engineer and YouTube educator AfroTechMods, covering transistors, op-amps, soldering, and circuit debugging in plain language.
Why I recommend it: Great first stop if formal engineering courses lost you. Build one circuit, then go back to the theory.
Free electrical engineering reference library with textbooks, worked examples, technical articles, calculators, and an active forum covering everything from basic DC theory to embedded design.
Why I recommend it: If you are moving toward hardware, robotics, or manufacturing tech, their free textbooks are more useful than most paid courses.
Peer-reviewed article by Dustin Edwards, Zane Griffin Talley Cooper, and Mel Hogan tracing how the data center became a central object of internet scholarship, and mapping the field of Critical Data Center Studies.
Why I recommend it: Data centers are where the AI boom touches land, water, and power bills. Read this before you argue about AI infrastructure.
Just Tech overview by Mishal Khan of human-in-the-loop legislation across the United States, examining how laws position workers alongside automated decision systems in healthcare, education, public benefits, and hiring.
Why I recommend it: If your job now includes reviewing an algorithm's output, this explains the rules being written around you and where they fall short.
Free course from fast.ai covering disinformation, bias, privacy, algorithmic accountability, and the ethical questions data practitioners hit in real projects, taught by Rachel Thomas.
Why I recommend it: Finish this and you can speak credibly about AI risk in an interview instead of repeating headlines.
Open-source project by Guillaume Meyer that strips multi-vendor AI provenance marks, including Unicode text artifacts, statistical rewrite hooks, and C2PA metadata from PNG, JPEG, SVG, PDF, DOCX, HTML, and Markdown files.
Why I recommend it: Listed as evidence, not advice. It shows why AI-detection claims about your writing are shaky, and why disclosure beats concealment.
Harvard Ash Center essay arguing that generative AI adoption has been driven more by vendor hype and institutional pressure than measured results, with a look at what happens as expectations reset.
Why I recommend it: Useful counterweight when your employer says AI will replace your role next quarter. Ask what evidence they are working from.
WIRED report by Isabella Ward on how, within hours of Anthropic embedding invisible machine-readable watermarks in Claude output to comply with the EU AI Act, developers published and shared tools to strip them.
Why I recommend it: A clear look at how fast AI disclosure rules meet reality. Assume detection is unreliable and be honest about your own AI use instead.
Introduction by Kelly Joyce and Taylor M. Cruz to a Socius special collection framing AI as a sociotechnical system, with research on AI in health, work and labor, methods, and policy.
Why I recommend it: A clear entry point if you want the research vocabulary for what you already sense about AI at work.
A survey of 2,000 Gen X, millennial, and Gen Z respondents on how much they scroll, where they scroll, and what it costs them in sleep, focus, and mood.
Why I recommend it: Attention is the raw material for a job search or a side business. Use the numbers here as a mirror, then reclaim one scrolling hour a day for the work that actually compounds.
Coverage of law firm founder John Morgan boasting on a podcast about camera-based monitoring of remote staff, after which 23 employees quit within the first week.
Why I recommend it: Monitoring policy is culture policy. Ask in interviews how remote work is measured - output or surveillance - and treat the answer as data about how you would be managed.
Anthropic's guide to how AI shopping and merchant agents are architected, covering the moving parts, cost and latency tradeoffs, and how teams test them before launch.
Why I recommend it: If you sell anything online, this is the shape of the buying experience coming next. Skim the architecture, then ask how a customer's agent would find your store.
OpenAI's announcement of GPT-6 Astra, its most capable model, with reported results on computer use, browsing, software engineering, cybersecurity, and professional work.
Why I recommend it: Read the capability list as a job-task list. Whatever a model does well this year reshapes entry-level work the next.
LinkedIn newsletter on age bias in hiring, with tactics for experienced candidates on positioning, interviewing, and answering the "overqualified" objection.
Why I recommend it: Being over fifty is not the problem; being framed as expensive and inflexible is. This newsletter works on the framing.
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.
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."
Fast Company look inside Shopify's decision to let engineers adopt AI tools without central approval, and how it changed expectations for output.
Why I recommend it: This is the emerging standard: AI fluency as a baseline job requirement, not a bonus. Plan your skills accordingly.
Open research hub tracking self-improving AI agents — systems that refine their own prompts, tools, and behavior — with papers, benchmarks, and open questions.
Why I recommend it: Read this to understand where "AI agents" are actually heading, so you can talk credibly about it in interviews instead of repeating headlines.
Startup building a way for different AI models to exchange knowledge directly, without translating everything back into text prompts.
Why I recommend it: Early-stage and unproven, but worth watching: model-to-model communication is the kind of shift that quietly changes which technical skills matter.
Spatial intelligence company co-founded by Dr. Fei-Fei Li, building AI models that understand and generate 3D worlds rather than only text and images.
Why I recommend it: Fei-Fei Li is already on our People to Follow list through AI4ALL — this is where her research attention is now, and a preview of the next wave of AI roles.
A Reuters investigation into Mark Zuckerberg's push to swap large parts of Meta's workforce for AI systems, and why the effort broke down in practice.
Why I recommend it: Read this before you panic about AI taking your job. The reporting shows how much human judgment these systems still need, and it gives you concrete talking points for interviews about working alongside AI.
Free videos from Never Search Alone explaining the Job Search Council method: small peer groups that help you define your ideal role and hold each other accountable through the search.
Why I recommend it: Searching alone is the single biggest reason people stall out. Watch a couple of these, then start or join a council of three or four people. It changes the pace of everything.
Founder of Good Jobs First, who built the corporate-subsidy watchdog category from scratch before stepping down as Executive Director in May 2026.
NCDA article on what shapes career transitions after 50 and practical strategies to stay competitive in changing workplaces.
Why I recommend it: Details: pair this with the 50+ track in the hub — lead with recent, measurable wins rather than a full career history.
Annual global study on cybersecurity hiring, skills gaps, budget pressure, and what helps practitioners grow their careers.
Why I recommend it: Details: use the hiring and skills-gap data to decide which security certifications and skills are actually in demand before you spend money on training.
Glassdoor research on worker attitudes toward AI in 2026 — covering adoption, concerns, and what employees expect from employers.
Why I recommend it: A useful snapshot of public sentiment around AI at work. Helpful for coaching conversations about which skills matter and how to talk about AI on the job.
An a16z essay arguing that platform decline is better explained by platform incentives and narcissism than by the popular "enshittification" framing.
Why I recommend it: Read this next to Cory Doctorow's original argument. Holding two competing explanations of platform decay makes you sharper when you evaluate the tools your career depends on.
Job search community with a free membership tier — peer accountability, interview practice, and search strategy support.
Why I recommend it: Searching alone is the hardest way to do it. Join the free tier first and see if the group rhythm helps you.
Psychologist and professor Jacqueline Nesi translates new research on technology, attention, and mental health into practical guidance for digital life.
Why I recommend it: A research-backed counterweight to hot takes about screens and AI. Good source material if you write or speak about technology and people.
Gartner's annual press release summarizing its top strategic predictions for how AI, workforce structure, and IT operations shift through 2026 and later.
Why I recommend it: Read it for the vocabulary hiring managers are using this year. Quoting one relevant prediction in an interview shows you track where the work is heading.
Trillions in compute commitments come due in 2027–2028. The Reset Wall reveals how the AI boom breaks, and when.
From the site: Trillions in compute commitments come due in 2027–2028. The Reset Wall reveals how the AI boom breaks, and when.
Why I recommend it: A clear-eyed look at where the AI build-out may hit a financing and infrastructure wall — useful context for anyone advising job seekers or founders betting on the sector.
Open-access book exploring the environmental and societal impacts of AI infrastructure — data centers, energy, labor, and the politics of large-scale computation.
From the site: Expanding Perspectives on Automation, Communication and Media
Why I recommend it: Open-access research on AI's physical footprint — great background for anyone advising on green tech, data-center careers, or responsible AI procurement.
New York City Employment and Training Coalition open letter urging the Economic Development Corporation to invest in workforce development alongside job creation.
Why I recommend it: Read this if you want to understand how workforce funding decisions actually get made — and who is arguing for job seekers at the table.
Free short course covering the foundations of large language models, generative AI concepts, and responsible AI principles. No coding experience required.
Why I recommend it: Start here if AI still feels like a black box. About an hour, and it gives you the vocabulary to follow every other course on this list.
Hands-on cybersecurity training through guided browser-based labs.
Why I recommend it: A generous free tier and a public profile that shows what you actually completed. That profile is proof, which is more than a certificate.
Certificate covering security frameworks, threat detection, Python for security tasks, SIEM tools, and incident response.
Why I recommend it: A credible on-ramp into security work. Combine it with a free conference or local meetup, because in this field who you talk to opens as many doors as what you studied.
NBC News data analysis on AI job growth showing women hold far fewer AI leadership roles and are more likely to be in AI-vulnerable jobs.
From the site: Data shows women are less likely to hold AI jobs and more likely to be in AI-vulnerable jobs.
Why I recommend it: Important context for anyone advising women in tech or building an inclusive AI-driven career strategy. Use the data to advocate for equitable training and access.
Upcoming cybersecurity conference calendar with virtual and in-person events, dates, locations, and registration links.
From the site: Find cybersecurity conferences happening this week. Virtual & in‑person events. Get dates, locations, and last‑minute registration links now.
Why I recommend it: Useful for staying current on security trends and finding networking events if you are pivoting into cybersecurity, tech policy, or IT operations.
Full Spanish-language OSHA handbook for small businesses: how to build a safety and health program, run self-inspections with detailed checklists, fix common hazards (electrical, ergonomics, fire, exits, chemicals, machinery), meet posting and recordkeeping rules, and request free on-site consultation.
Why I recommend it: The deeper Spanish-language companion to the fact sheet. Share it with owners and crews who work in Spanish — the self-inspection checklists can be lifted straight into your own safety program.
Created by U.S. Occupational Safety and Health Administration (OSHA)
Plain-language OSHA fact sheet covering what a brand-new employer must do: provide a hazard-free workplace, keep injury and illness records, post the required OSHA notice, train workers on hazardous chemicals, and where to get free on-site consultation help.
Why I recommend it: Read this before you hire your first employee. Most first-time founders miss the required workplace notice and the injury log, then get surprised later. The free OSHA On-Site Consultation program is the part almost nobody uses and everybody should.
Created by U.S. Occupational Safety and Health Administration (OSHA)
Long-running technology publication covering AI, security, policy, and the business of tech.
Why I recommend it: The free articles alone are enough to track where AI and security policy are heading. Bookmark one story a week that touches your field and save the takeaway.
Consumer technology news covering AI, gadgets, science, and the culture around them.
Why I recommend it: Good for staying conversational about tech trends without a paywall. Skim headlines weekly so interview small talk about your industry stays current.
Data study showing a decline in Reddit citations inside ChatGPT answers and what that means for content visibility.
Why I recommend it: A concrete lesson in platform dependency: the traffic source you optimized for can quietly disappear from AI answers.
Official YouTube guidance on when and how creators must disclose AI-generated or altered content.
Why I recommend it: If you use AI in any video, read this once and set your disclosure habit now — retroactive cleanup is far more painful.
Nonprofit community advancing the careers of Black software engineers through peer accountability groups.
Why I recommend it: The peer accountability structure is the differentiator — goals get tracked, not just discussed.
Bias navigation expert, founder of DiverseCity Think Tank and author of "I Don't Understand: Navigating Unconscious Bias in the Workplace."
Why I recommend it: Frames bias as a navigable, learnable skill rather than a guilt trip — makes it usable in an actual team meeting.
Futurist and author of "Technology vs. Humanity," focused on the ethics of AI and technology's human impact.
Why I recommend it: The clearest, least hype-driven voice I found on tech ethics specifically — a strong fit for the technology and ethics side of the library.
Public AI red-teaming arena where anyone can try to break frontier models in timed challenges.
Why I recommend it: A legitimate portfolio line for AI-security work: document what you tried and what broke, not just your score.
Anthropic's security and model-safety reporting program on HackerOne.
Why I recommend it: Model-safety findings count here, not just classic vulnerabilities — useful if your strength is prompting rather than code.
Cybersecurity scholarships for undergraduate, graduate, and career-change students.
Why I recommend it: Deadlines cluster in the spring. Draft the essay once and reuse it across every scholarship on this list.
Free peer job-search councils: small accountability groups that meet weekly to run a structured search.
Why I recommend it: The single best fix for a stalled search is other people expecting you on Thursday. Join a council early, not after three months alone.
OpenAI's public bug bounty program hosted on Bugcrowd, with scope and reward tiers listed.
Why I recommend it: Read the scope twice before testing anything. Out-of-scope reports get closed and waste your reputation on the platform.
Interactive investigation into the ideologies driving the people building today's AI systems.
Why I recommend it: Understanding who is building these tools, and why, changes how you read their product claims.
About 10 hours of self-paced training on prompting, using AI tools at work, and responsible AI, with a free certificate.
Why I recommend it: The fastest credible line to add to your resume if you have zero formal AI training. Finish it in a weekend.
Report that demand for AI-free search results is rising as AI summaries expand.
Why I recommend it: Evidence that "AI everywhere" is a product decision, not an inevitability. Users push back.
Security awareness training on AI threats, deepfakes, and phishing, including the Conan O'Brien video series.
Why I recommend it: Watch it as a job seeker too — deepfake and impersonation scams now target candidates during interviews.
Three short courses, roughly 12 hours total, covering AI concepts, use cases, and ethics.
Why I recommend it: A brand-name intro. Hiring managers recognize IBM even when they do not recognize the course.
Essay arguing that generative AI raises the floor of output while flattening what makes work distinctive.
Why I recommend it: A good counterweight if you are tempted to let AI write everything in your search. Polished is not the same as memorable.
Report on how the datasets powering major AI systems depend on mass invasions of privacy by design.
Why I recommend it: Read this before you paste sensitive personal or client data into an AI tool.
Wired's weekly security roundup covers OpenAI, Anthropic, and 100+ companies cosigning a letter warning that organizations have mere months to prepare for AI-enabled cyberattacks. The piece also tracks rogue AI agent hacking incidents, attacks on over 100 U.S. water systems, license-plate-reader surveillance abuse, Meta's $16.7B child-safety settlement, and ICE buying robot dogs — a snapshot of where AI, surveillance, and critical-infrastructure security collide.
From the site: OpenAI, Anthropic, and more than 100 companies have cosigned a letter saying that everyone else has mere months to prepare for AI-enabled cyberattacks.
Why I recommend it: A stark signal that AI-enabled cyberattacks are no longer hypothetical. The cosigned letter from OpenAI and Anthropic calling for a 'collective response' is exactly the kind of industry accountability move worth watching — pair it with the Hugging Face incident reporting and the water-system attacks to see how AI agents are already being used offensively. Useful for anyone tracking the gap between AI capability and AI governance.
The underlying working paper by Jeremy Yang and co-authors, using Perplexity data to model tasks as discrete steps and compare fixed vs. marginal costs of chatbots versus autonomous agents.
Why I recommend it: If the HBS summary hooks you, go to the source. Skim the task-cost framework and use it to audit your own week: which tasks are high-step and repeatable? Those are the ones to hand to an agent first.
Harvard Business School AI Institute breakdown of new research on agentic AI: how autonomy and context integration shift the cost structure of knowledge work, expanding both productivity and the scope of what workers take on.
Why I recommend it: Read this before you assume AI just speeds up your current tasks. The useful takeaway for job seekers: agents lower the cost per step, so the valuable human skills become specifying goals clearly and verifying output. Practice describing outcomes, not keystrokes, and put "agent workflow design" language in your resume bullets.
Jake Taylor argues that public, standardized AI testing with formal reasoning checks is needed to close the widening "verification asymmetry" between AI capability and oversight.
Why I recommend it: If you want to work in AI governance or assurance, this is the vocabulary hiring managers use — verification, benchmarks, interpretability.
Podcast interview with Maheen Khan (Invisible Institute) and Patrick Ball (HRDAG) on a coalition helping nonprofits protect evidence, cut big-tech dependence, and build independent AI capacity.
Why I recommend it: A concrete example of mission-driven tech work — good listening if you want your technical skills pointed at justice organizations.
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.
Full proposed consent judgment and settlement agreement in the social media adolescent addiction litigation, covering teen daily use limits, nighttime blocks, age assurance, parental tools, and COPPA claims.
Why I recommend it: Primary source, not a summary. Skim the injunctive terms — they show exactly which product design choices regulators now treat as harmful.
Created by U.S. District Court, Northern District of California (public court filing)
Blog post on how marketers can personalize campaigns while still respecting user privacy.
Open-source AI community and platform hosting machine-learning models, datasets and tools. Free to browse, download and run models; paid plans only cover hosted compute.
AI safety company that builds the Claude family of AI models with a focus on responsible AI development.
Hugging Face blog post discussing the environmental sustainability and ethics of AI systems.
OpenAI's official statement explaining why it is winding down the contract that supplied its models to Cursor (Anysphere) after SpaceX completed its $60B acquisition of the AI coding company in August 2026.
In plain terms: OpenAI says it will stop supplying its models to the AI coding tool Cursor after SpaceX bought the company, citing concerns about terms-of-service compliance. Cursor users may lose access to OpenAI models, so the practical takeaway is not to depend on a single AI tool or provider.
Why I recommend it: A clear-eyed lesson in platform risk: the tools you build your workflow on can lose access to the models that make them work. If you code, write, or job hunt with an AI tool, know which models sit underneath it and keep a backup you already know how to use.
A year-long career accelerator that places Black software engineers, data scientists, and managers into small squads of 7 to 9 peers, with senior industry mentors, career roadmapping, accountability, and interview preparation.
In plain terms: This year-long career program connects Black software engineers and tech managers into small peer groups. You can create career roadmaps, receive regular accountability, and work with others to reach your professional goals.
Why I recommend it: Applications open each fall and close by mid-December for the following year. The squad accountability is what makes people actually follow through.
Disability advocate and author Shane Burcaw shares everyday life and an interabled relationship, breaking down stereotypes. Hosted by Shane Burcaw & Hannah Burcaw.
In plain terms: This YouTube channel features disability advocate Shane Burcaw and Hannah Burcaw sharing their everyday life in an interabled relationship. You can watch their videos to learn about their experiences and break down stereotypes.
Why I recommend it: Honest, funny, and it shifts how workplaces think about disability.
Content on accessibility, adaptive products, and life as a quadriplegic. Hosted by Cole Sydnor & Charisma.
Why I recommend it: Practical looks at adaptive tools and everyday accessibility.
Long-form interviews with AI researchers, founders, and scientists. Hosted by Lex Fridman.
In plain terms: This YouTube channel features long-form interviews with artificial intelligence researchers, scientists, and company founders. You can watch these discussions to learn about emerging technology, ethical issues, and scientific developments.
Why I recommend it: Long listens — good for commutes when you want depth over headlines.
Advocate covering invisible disabilities, chronic illness, workplace inclusion, and disability misconceptions. Hosted by Jessica Kellgren-Fozard.
Why I recommend it: Best explainer channel on invisible disability and accommodations at work.
Detailed, decisive tech product reviews and yearly industry roundups. Hosted by Marques Brownlee.
In plain terms: This YouTube channel offers detailed technology product reviews and yearly industry roundups hosted by Marques Brownlee. You can watch the videos to evaluate new tech devices and stay informed about current industry developments.
Why I recommend it: Best place to decide whether a device is worth your money.
Startup and technology news covering product launches, funding rounds, and trends. Hosted by TechCrunch.
In plain terms: This video channel shares news about startups and the technology industry. You can watch reports on new product launches, funding rounds, and emerging market trends.
Why I recommend it: Skim it to spot which companies are hiring and growing.
Speaker and advocate for the blind and low-vision community sharing lived experience and inclusion content. Hosted by Molly Burke.
In plain terms: This YouTube channel features videos from advocate Molly Burke about living with blindness and low vision. You can watch her stories to better understand accessibility and inclusion in everyday life.
Why I recommend it: Useful for anyone designing or hiring with accessibility in mind.
Disability Advocate, Speaker. Advocate with cerebral palsy addressing hiring discrimination, workplace tokenism, and the disability unemployment gap.
In plain terms: This is the LinkedIn profile of a disability advocate and speaker. Follow him to learn about navigating job accommodations, fighting hiring discrimination, and advocating for disability inclusion at work.
Why I recommend it: Honest about what hiring actually feels like with a visible disability.
CEO, Google & Alphabet. Commentary on AI research, product launches, and the technology industry's trajectory from one of its most influential leaders.
Why I recommend it: High-level signal on where AI products are heading next.
Founder, Distributed AI Research Institute (DAIR). AI researcher and prominent voice on AI ethics, bias, and the risks of concentrated corporate control over AI development.
In plain terms: This LinkedIn profile belongs to Dr. Timnit Gebru, an artificial intelligence researcher and founder of the Distributed AI Research Institute. You can follow her page to read updates and commentary on technology ethics, bias, and research.
Why I recommend it: Pairs well with the DAIR entry in the library — independent research, not corporate PR.
Founder, Algorithmic Justice League. Computer scientist whose work on facial recognition bias helped launch the algorithmic accountability movement.
Why I recommend it: Essential reading on how AI systems fail people who look like the rest of us.
Strategic Business & Technology Advisor, Author. High-level, accessible summaries of emerging enterprise technology and industry trends for business leaders.
Why I recommend it: Good plain-language briefings if you need to talk tech trends in interviews.
President, Patrick J. McGovern Foundation. Leads a $1.5B foundation investing $500M+ to make AI work for everyone; writes on responsible AI and equitable technology.
In plain terms: This LinkedIn profile features the work of a foundation leader focused on ethical technology and artificial intelligence. You can read his published articles and view courses on responsible AI to learn how new tools affect the modern workforce.
Why I recommend it: Follow for where philanthropic AI funding is going — useful if you're seeking grants.
Co-founder & CTO, HubSpot. Shares insights on startups, software development, and AI, with a focus on community-driven business growth.
In plain terms: This is the LinkedIn profile of HubSpot co-founder Dharmesh Shah. You can read his articles and updates to get advice on starting a business, building software, and using artificial intelligence tools.
Why I recommend it: Practical founder thinking on growth without a big budget.
Chairman & CEO, Microsoft. Shares perspective on enterprise AI adoption, cloud computing, and the broader direction of the tech industry.
In plain terms: This LinkedIn profile features articles and updates from Microsoft's chief executive on cloud computing and artificial intelligence. You can follow these posts to track major tech trends and see how emerging tools impact modern work.
Why I recommend it: Worth watching to understand where big employers are placing their AI bets.
Multi-stakeholder nonprofit coalition of tech companies and civil society organizations shaping best practices and public dialogue on AI's benefits and risks.
In plain terms: This nonprofit website shares research and guidelines on artificial intelligence from tech companies and community organizations. You can explore free reports and frameworks to learn how AI affects the economy, workplace practices, and technology safety.
Why I recommend it: Where industry and civil society actually sit at the same table.
Independent nonprofit researching the social implications of data-centric and automated technologies, informing policy and public understanding.
In plain terms: This nonprofit research institute studies how artificial intelligence, automation, and data technologies affect work and society. You can read free reports, guides, and articles or attend public events to understand how emerging technology impacts labor and the economy.
Why I recommend it: Excellent on automated management and surveillance at work.
MIT research lab exploring human-AI collaboration, alongside a joint fund with Berkman Klein supporting research on AI's ethical and governance challenges.
In plain terms: This academic research site shares news and projects focused on emerging technology, design, and artificial intelligence. You can explore articles on AI ethics and human collaboration, view new inventions, and find related job opportunities.
Why I recommend it: Browse their projects when you want to see what humane technology looks like in practice.
NYU-based research institute examining the social and political implications of AI, publishing influential annual reports on AI's societal effects.
In plain terms: This research institute analyzes the social, economic, and workplace impacts of artificial intelligence. You can read free reports, policy toolkits, and expert analyses to better understand how AI affects society and the economy.
Why I recommend it: Their reports connect AI directly to jobs and worker power.
University of Oxford institute researching the ethical problems arising from AI, from societal downstream effects to how AI systems reflect human values.
In plain terms: This academic website shares research and analysis on the ethical and social impacts of artificial intelligence. You can read publications, attend public events, and search for fellowships, scholarships, and job openings.
Why I recommend it: Philosophy-forward work — useful when you need the "why", not just the "how".
Philanthropic investment firm supporting organizations that harness technology to empower individuals and communities responsibly.
In plain terms: This philanthropic investment firm focuses on responsible technology and its impact on society. You can check their careers page to search for open jobs and learn about their work.
Why I recommend it: Follow their funding to see which responsible-tech ideas are gaining ground.
UK-based independent research institute (established by the Nuffield Foundation) ensuring data and AI work for people and society.
In plain terms: This independent research website examines the ethical and legal impacts of artificial intelligence and data. You can read policy reports, explore industry analysis, and attend events to understand how emerging technology affects society.
Why I recommend it: Clear, public-interest research with plain-language summaries.
Harvard University center studying the ethics, governance, and societal impact of the internet and AI, and anchor institution for the Ethics and Governance of AI Fund.
In plain terms: This research center explores how artificial intelligence and the internet impact society, law, and ethics. You can read free policy publications, watch educational videos, find public events, and check for open job or fellowship opportunities.
Why I recommend it: Decades of open research and fellowships, much of it free to read.
Global foundation committed to social justice, funding technology initiatives that promote equity, inclusion, and accountable AI governance.
In plain terms: This global foundation funds organizations and individuals working on social issues, workers' rights, and technology. You can search for grant opportunities, apply for fellowship programs, and read research reports on the future of work.
Why I recommend it: A major funder of public-interest technology work worth tracking.
Interdisciplinary Stanford institute advancing AI research, education, policy, and practice to improve the human condition, with strong ethics and governance focus.
In plain terms: This university center shares research, policy updates, and educational resources focused on artificial intelligence. You can browse an AI glossary, read industry reports, and explore professional courses or research fellowships.
Why I recommend it: Their policy briefs are readable and free — a good first stop if AI governance feels opaque.
Nonprofit working to align technology design with human wellbeing, addressing extractive incentives in tech and AI.
In plain terms: This nonprofit organization provides educational materials, policy guides, and research on the societal impact of artificial intelligence and social media. You can take courses, listen to podcasts, and use design toolkits to learn about ethical technology practices.
Why I recommend it: Practical framing for anyone rethinking their relationship with their devices.
Research center developing AI systems that are provably beneficial and aligned with human values.
In plain terms: This university research center focuses on creating safe and beneficial artificial intelligence. You can read published research papers, follow recent news and blog updates, and explore opportunities to work with their team.
Why I recommend it: Technical AI safety, explained by the people who defined the field.
Founded by Joy Buolamwini, AJL combines art and research to expose racial and gender bias in AI and mobilize advocates, researchers, and industry toward more accountable algorithms.
In plain terms: This organization researches and exposes bias and discrimination in artificial intelligence systems, including automated hiring tools. You can explore educational materials, learn about the social impacts of technology, and report unfair automated practices.
Why I recommend it: Start here if you have ever been misjudged by an automated system — including a hiring one.
Analysis at the intersection of finance and technology trends, by Byrne Hobart.
In plain terms: This newsletter provides in-depth articles analyzing trends, strategies, and news across the technology and finance industries. You can read detailed company profiles to understand market shifts and explore an included job board.
Why I recommend it: Dense, but it explains where the money behind tech is actually going.
Reporting on the intersection of Silicon Valley and democracy, by Casey Newton.
In plain terms: This publication delivers reporting and analysis on artificial intelligence, social platforms, and the tech industry. You can read free articles to stay updated on how new technology impacts modern work and business.
Why I recommend it: Independent accountability reporting on the platforms we all depend on.
Power dynamics and inside stories from Big Tech, by Alex Kantrowitz.
In plain terms: This newsletter and podcast covers inside reporting on major tech companies and their impact on society. You can read weekly updates and listen to interviews to stay informed about the technology industry.
Why I recommend it: Good on how decisions inside big companies land on workers.
Analysis of the strategy and business side of technology and media, by Ben Thompson.
In plain terms: This website provides articles and podcasts that analyze the business strategy and impact of technology companies. You can explore in-depth commentary to better understand industry trends and how modern tech businesses operate.
Why I recommend it: Teaches you to read industry news as strategy instead of headlines.
Watchdog database tracking corporate subsidies, violations, and job quality commitments by employer and location.
In plain terms: A nonprofit watchdog with free searchable databases on corporate subsidies, tax breaks, and company violation records. Use it to research an employer or a city before you take a job or move for one.
From the site: Good Jobs First promotes corporate and government accountability in economic development, especially around the use of public subsidies.
Why I recommend it: Look up a company before you accept an offer or a relocation. Subsidy and violation records tell you how an employer treats the places it operates in.
Announcement of Spaces, an extension to the open AT Protocol (the tech behind Bluesky) that supports private, permissioned data.
In plain terms: The team behind Bluesky opened an alpha for Spaces, a way to store private or group-only data on their open protocol. If you build community tools, it is an early look at owning your data instead of renting a platform.
From the site: Atproto Spaces, formerly known as “the permissioned data protocol,” is a new extension to atproto that enables non-public data. The alpha is now officially open.
Why I recommend it: If you build community or product, open protocols are a real alternative to renting an audience from a platform. Worth watching early.
Job search guidance written for workers over 50 — resumes, skills, ageism, and flexible work.
In plain terms: This website provides job search guidance and career tools for workers age 50 and older. You can take online courses to build skills, explore flexible work options, and get advice on handling age discrimination.
Why I recommend it: The advice here assumes a long career instead of apologizing for one.
Job listings from employers who have publicly committed to age-inclusive hiring practices.
In plain terms: This directory highlights companies committed to age-inclusive hiring practices and multigenerational workplaces. You can use it to find and connect with employers that actively value experienced workers.
Why I recommend it: Applying where age-inclusive hiring is a stated commitment beats applying everywhere and hoping.
What the ADEA actually protects, what employers may and may not ask, and how to file if a line is crossed.
In plain terms: This guide explains federal rules protecting workers and job seekers age 40 and older from age discrimination. You can use it to recognize illegal workplace practices, check reporting deadlines, and learn how to file a complaint.
Why I recommend it: Know the line. Most age bias is not illegal questioning, but it helps to recognize when it is.
Practical scripts for the questions that carry age assumptions — "you might be overqualified," "how do you feel about a young team."
Why I recommend it: Rehearse two answers: one for overqualified, one for culture fit. Calm and specific beats defensive every time.
Stanford-led study of 3 million applicants screened by a single algorithm vendor, finding racial disparities and homogeneous rejections — the same people get screened out everywhere. Explains why applicants must apply widely to reach a human.
In plain terms: This research study examines how automated screening tools used by multiple employers cause repeated rejections and racial disparities. Use this paper to understand how hiring algorithms work and why applying to more jobs helps you reach a human reviewer.
Why I recommend it: This is the evidence behind advice I give constantly: one rejection is often the same algorithm repeated, not a verdict on you.
Created by Rishi Bommasani, Sarah H. Bana, Kathleen A. Creel, Dan Jurafsky, Percy Liang (Stanford HAI)
An argument that humanities and social science training produces the judgment, ethics, and interpretive skill that technical teams need as software takes on more social consequence.
In plain terms: This interview explains why technology companies actively hire people with degrees in the humanities and social sciences. Use it to learn how your non-technical background and critical thinking skills can qualify you for tech industry jobs.
Why I recommend it: If you have a humanities degree and feel behind, this is your framing. Your degree is context and judgment, which technical teams are short on.
Practical guidance for career changers over 50 and 60 entering tech: which roles favor experience, how to handle age bias in screening, and how to sequence learning without overcommitting.
In plain terms: This guide offers practical advice for older adults looking to start a tech career after 60. Use it to explore matching roles, learn how to update your skills, and showcase your past experience to employers.
Why I recommend it: For my 50-plus readers: your judgment and stakeholder skills are the product. Lead with those, and let the tools be the second paragraph.
JUST Capital's newsletter tracking corporate commitments on worker pay, advancement, and skills-based hiring across large American employers.
In plain terms: This newsletter tracks pay, career advancement, and skills-based hiring practices across large American employers. You can use it to research companies that prioritize fair wages and hire based on skills rather than degrees.
Why I recommend it: Use it as a screening tool for where to apply. Companies that publish worker metrics tend to actually promote from within.
Nonprofit publication covering the intersection of technology, platforms, and democratic institutions.
In plain terms: This nonprofit publication provides news, opinion, and analysis on how technology impacts government and democracy. You can read articles and listen to podcasts to stay informed on tech laws, platform regulations, and artificial intelligence ethics.
From the site: Tech Policy Press is a nonprofit media and community venture intended to provoke new ideas, debate and discussion at the intersection of technology and democracy. We publish opinion and analysis.
Why I recommend it: They publish outside contributors — a real place to build a byline in this field.
Princeton scholar on race, technology, and justice, author of Race After Technology.
In plain terms: This website features the work of scholar Ruha Benjamin on race, justice, and modern technology. You can read her articles, explore her books, and access educational resources on the social impact of innovation.
From the site: Ruha Benjamin is an Associate Professor of African American Studies at Princeton University, where she studies the social dimensions of science, technology, and medicine.
Why I recommend it: Essential reading before you take any job building automated decision systems.
Social Science Research Council program funding and publishing work on technology, power, and public life.
In plain terms: This research platform publishes articles and essays exploring how technology and artificial intelligence affect workers and society. You can read expert reviews and analyses to learn about labor protections, tech ethics, and workplace automation.
From the site: The Just Tech Platform is a forum, catalogue, and showcase for researchers and practitioners at the nexus of technological development, inequity, and social justice.
Why I recommend it: Great source of fellowships and calls for proposals if you want funded research work.
Independent, community-rooted AI research institute founded by Timnit Gebru, studying the real harms of AI instead of the hype.
In plain terms: This independent institute studies the real-world harms and community impacts of artificial intelligence. You can explore their research publications, learn how technology affects diverse groups, and look for open career opportunities.
From the site: The Distributed AI Research Institute is a globally distributed organization of academics, activists, and engineers conducting community-rooted research.
Why I recommend it: Start here if you want the research-backed counterweight to AI marketing.
Community awareness project mapping U.S. AI data centers and the local issues they create.
In plain terms: This interactive map tracks major AI data center projects and proposals across the United States. You can explore the local environmental impacts of these facilities and submit reports about issues in your area.
From the site: Interactive map of major AI data centers across the United States — built, being built, proposed and cancelled. Understand the community impact and report issues in your area.
Why I recommend it: Check the map for your area before a data center becomes news in your town.
Newsletter reporting on the fight to reshape technology in the public interest.
In plain terms: This newsletter reports on efforts to reshape technology in the public interest. You can read regular articles to stay informed about technology ethics and industry reform.
From the site: Idea Trafficking. Click to read Hard Reset, by Trafficker 01, a Substack publication. Launched 5 years ago.
Why I recommend it: Skimmable and current — good for staying briefed in ten minutes a week.
Project and publication examining what we teach AI systems and what those choices say about us.
In plain terms: This project shares anonymous handwritten notes about people to examine what humans teach artificial intelligence systems. You can read the publication to reflect on the personal choices and ethics behind modern technology.
From the site: Anonymous handwritten notes about people
Why I recommend it: Useful for language and framing when you explain AI risk to non-technical people.
Brookings fellow writing on AI, workers, and the future of good jobs.
In plain terms: This newsletter features writing from a Brookings fellow about artificial intelligence, workers, and the future of jobs. You can read it to stay informed about how technology impacts the modern workplace.
From the site: Click to read Molly Kinder on Substack. Launched 15 days ago.
Why I recommend it: Her worker-first framing is exactly how to talk about AI in a job interview.
Harvard historian and New Yorker writer placing today's technology fights in a much longer story.
In plain terms: This website collects the books, essays, and interviews of historian and writer Jill Lepore. You can read her work to explore historical perspectives on law, politics, and modern technology.
Why I recommend it: History gives you perspective that keeps you steady in a hype cycle.
The nation's Black think tank, with a technology policy program focused on equity in the digital economy.
In plain terms: This research organization provides reports and data on workforce policy, technology, and economic issues affecting Black Americans. You can explore their research briefs, reports, and events to learn about labor trends and policy solutions.
From the site: About The Joint Center for Political and Economic Studies is a 501(c)(3) non-profit organization based in Washington, D.C. that creates ideas to improve the socioeconomic status and civic engagement of African Americans. Founded in 1970 to support newly-elected Black officials who were moving from civil rights activis…
Why I recommend it: Their tech policy team publishes work you can cite and hires people from nontraditional paths.
Job board for social impact technology roles at nonprofits, government, and mission-driven companies.
In plain terms: This job board features technology roles focused on social impact. You can find openings at nonprofits, government agencies, and mission-driven companies.
Why I recommend it: Fewer listings than the big boards, but a much higher share worth applying to.
Reporting on the political ideology and ambitions of Silicon Valley's power brokers.
In plain terms: This publication offers investigative reporting on the political ideologies and ambitions of powerful technology leaders. You can read articles and analysis to understand how tech industry figures influence government and democracy.
From the site: Silicon Valley tech billionaire politics: authoritarianism, fascism, plutocracy, weirdness
Why I recommend it: Context on who is funding what — helpful when you vet a potential employer.
Author and activist writing on platform power, monopoly, and digital rights.
In plain terms: This website collects articles, books, and podcasts focused on digital rights, tech monopolies, and online privacy. You can read critical essays, listen to podcast discussions, and download free books to better understand how modern technology affects society.
Why I recommend it: Read him for the vocabulary — he names the patterns other people only feel.
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.
