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Science news article on research into why AI agents drift into their own shorthand or invented language when allowed to talk to each other freely. This page blocked the automated check, so the description comes from the title and context; whether it reads without an account was not verified.
Research report on skills-first hiring: how employers are shifting from degrees to demonstrated skills, with key findings on assessments and talent evaluation. The site blocked an automated check, so this description is based on the page title and URL.
The Governance Lab's research, publications and programs on how institutions can govern more effectively and legitimately through technology and data.
arXiv paper (Sept. 2026) introducing SkillGym, a framework that turns human-written agent skills into executable, verifiable training environments so language model agents learn them as built-in capabilities.
Anthropic Institute essay on progress toward recursive self-improvement in AI and its implications.
Joint Center report (PDF) with policy recommendations on voting rights, economic opportunity, workforce, education, healthcare, criminal legal system, technology and immigration.
MIT Sloan summary of research surveying 272 experts on which AI risks could cause the most harm in the next five years.
An open-access paper on Zenodo describing Critical AI Literacies (CAILs): ways of thinking about AI that reject framings from the technology industry, naive computationalism, and dehumanizing ideologies, and that center human cognition and the integrity of research and education. Co-authored by Olivia Guest and Iris van Rooij, among others.
A 2024 open-access article in Big Data & Society by Kerry McInerney arguing that the "AI arms race" narrative between the United States and China is deeply racialized, building on "Clash of Civilizations" rhetoric and older anti-Asian tropes such as techno-Orientalism and the Yellow Peril. The author coins "Yellow Techno-Peril" and offers recommendations for policymakers, journalists, and media organizations. Free to read (open access).
Why I recommend it: The arms-race framing shows up constantly in AI coverage. This paper names what that framing carries with it and gives concrete guidance for talking about competition without the racialized baggage.
A July 2026 open-access article in the Cambridge Forum on Technology and Global Affairs by Elisabeth Siegel (University of Oxford) examining how "hybrid epistemic experts" — figures straddling technical expertise, corporate leadership, and policy influence, with Eric Schmidt as the central case — constructed and amplified the "U.S.–China AI Race" narrative between 2015 and 2023. The article raises concerns about democratic governance and the concentration of AI knowledge production in private hands. Free to read (open access).
Why I recommend it: Explains how the AI race narrative was built by people who sit in boardrooms and policy rooms at the same time. Read it alongside the Yellow Techno-Peril paper for two complementary critiques of the same framing.
A July 2026 research report from risk intelligence firm Alethea documenting how Russian, Chinese, and Iranian state media, covert influence operations, and AI-generated content farms amplified genuine local opposition to U.S. data center construction between January and June 2026. The findings were featured in The New York Times. Alethea is a commercial firm publishing its own research; the report is free to read.
Why I recommend it: Two things are true at once here: local opposition to data centers is real, and foreign actors are amplifying it. Useful reading for anyone working in policy, infrastructure, or community advocacy — and a case study in how AI-generated content scales a narrative.
A nonprofit registry that gives researchers a free, permanent identifier (an ORCID iD) linking them to their papers, grants and affiliations, so their work stays attributed across name changes and publishers.
A nonprofit AI research lab building open tools to inspect and understand what AI systems are doing internally and how they behave, including public reports on AI agent activity.
MIT News covers Joy Buolamwini's Gender Shades research, which found three commercial facial-analysis programs had much higher error rates for darker-skinned women than for lighter-skinned men, and proposed a more balanced benchmark.
A Stanford research project page describing HomeBody, which gives vision-language models a humanoid robot body through persistent spatial memory and reusable skills, without training for each new environment. Results are the authors' own and include demo videos.
A library guide tracking major academic publishers' policies on AI use in research and writing, including rules on AI authorship, disclosure requirements and image generation, with links to each publisher's policy.
World Labs blog post introducing Atlas, its world model for spatial intelligence that generates 3D-consistent environments.
Site of Thinking Machines Lab, the AI research and product company founded by Mira Murati.
Open-access paper in AI & Society (2023) by Andrew Dana Hudson, Ed Finn, and Ruth Wylie. Draws on expert interviews and an analysis of nearly 100 science fiction stories to examine how popular AI narratives shape technology policy.
Site of the PhyPID group at the University of Colorado Boulder, working at the intersection of physics, AI, and data-driven discovery.
Research publications from Contrary, a venture capital firm, with in-depth company and industry reports including coverage of AI companies.
The hardware chapter of Stanford's annual AI Index report: data on AI chip performance, costs, and who controls the computing power behind modern AI.
Why I recommend it: Free to download. It's the institute's own synthesis, and some compute and investment figures come from data supplied by the companies being measured — the broad picture is reliable, the fine print less so.
The most widely used open-source framework for building and training AI models, with free tutorials, documentation and pre-trained models. Most AI research papers today are built on it.
Why I recommend it: Free and open source, now governed by the Linux Foundation's PyTorch Foundation — but Meta created it and remains its biggest contributor, so its direction still reflects big-tech priorities.
Google's open-source framework for building and training AI models, with free guides and pre-trained models. Once the industry standard, it's now used less in new research than PyTorch but still powers many production systems.
Why I recommend it: Free and open source, but it's a Google project — its development priorities follow Google's. For a new learner, PyTorch is the more common starting point in 2026.
The official free download and documentation for CUDA, Nvidia's platform for running AI and scientific computing on its graphics chips. Includes compilers, libraries and learning guides.
Why I recommend it: The toolkit itself is free, but it only runs on Nvidia's own chips — learning CUDA ties your skills to one company's hardware. Nvidia dominates AI chips, so that trade-off is real but worth knowing about.
Personal site of software engineer and cybersecurity researcher Nisarga Adhikary, with his projects and writing.
Why I recommend it: A good example of a young researcher building a public track record. It is his own site, so achievements are self-described.
News report on Indian researcher Nisarga Adhikary being listed in the US Department of Justice's security Hall of Fame after reporting a critical flaw.
Why I recommend it: MSN republishes other outlets' stories, and the flaw details come from the researcher himself. I could not open the page, so I wrote this from the title — please check it.
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.
Oxford's Bodleian Libraries announce a partnership with OpenAI covering research, teaching and digitised collections.
Why I recommend it: A joint announcement, so it describes the deal the way both partners want it seen.
memQ is a quantum networking company combining quantum science, materials and photonics to build extensible quantum network architecture for industry, research and government.
Why I recommend it: This is a company's own site, so read it as marketing. memQ sells quantum networking hardware and services; nothing here is a free learning resource beyond an overview of the field.
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.
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.
Yale Insights on research by Yale SOM's Menaka Hampole and co-authors: when AI automates a task it fades from job descriptions, but workers often shift to other work and firms become more productive.
Why I recommend it: A readable summary of one study, written by the business school that produced it. Useful balance to "AI will take every job" headlines, but it is one data set, not the final word.
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.
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.
UCL Bartlett publication on how cities and countries can regain control over digital infrastructure and data from big tech.
Why I recommend it: Academic policy work with a clear point of view on public control of technology; from December 2024.
Anthropic engineering write-up on running many Claude agents in parallel to build a working C compiler, including testing against GCC.
Why I recommend it: Anthropic describing its own product's success. The details are useful, but the results have not been independently reproduced.
Daily Nous, the philosophy profession's news site, on the PhilosophyBench study and philosophers' reactions to it.
Why I recommend it: Read the comments too — working philosophers push back on what the benchmark can show.
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.
A step-by-step tutorial on building a simple AI "world model" that predicts what happens next in an environment.
Why I recommend it: Hands-on and free; assumes some Python. Written by a single contributor, so check the code as you go.
Podcast episode with economist John T. Harvey on how Modern Money Theory has been argued and debated.
Why I recommend it: Money on the Left openly supports MMT, so this is a sympathetic take on a disputed school of economics.
Google's explainer on Project Suncatcher, its research into running machine-learning hardware on solar-powered satellites.
Why I recommend it: Google describing its own moonshot. It is an early research project, not a working service.
The research arm of the Indeed job site: free reports and data on job postings, wages and hiring trends, mostly US-focused with some international coverage.
Why I recommend it: The data comes from Indeed's own job listings, so it reflects what employers post on Indeed — not the whole labor market.
Independent benchmarks comparing AI models and API providers on quality, speed, price and context length, with free charts and leaderboards.
Why I recommend it: Free to browse; the company sells API access to its data. Rankings depend on which tests they choose — compare with a second benchmark source before deciding.
Sakana AI's release post for its Fugu models, explaining how it orchestrates models along the quality-versus-cost "Pareto frontier".
Why I recommend it: A lab's own announcement of its own models — the performance claims are self-reported and not independently verified.
Independent, domain-specific AI benchmarks in legal, tax and finance, testing how models actually perform on professional work rather than academic tests.
Why I recommend it: Free leaderboards; the company sells private benchmarking to enterprises. Domain-specific results are more useful than general scores if you work in these fields.
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.
An arXiv paper showing how message-passing techniques let language models reason more efficiently, with the full text free to read and download.
Why I recommend it: A preprint — not yet peer-reviewed, so treat the results as preliminary until the work is replicated or published.
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.
Google-owned platform hosting data-science and machine-learning competitions, many with cash prizes. Free to enter; a strong way to build a portfolio.
Details: Free to enter and useful for a portfolio, but Kaggle is owned by Google and some competitions require agreeing to sponsor terms — read each competition's rules before submitting work.
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.
Fellowship programme at the Calouste Gulbenkian Foundation in Lisbon supporting advanced research stays. Application details and eligibility on the page.
Why I recommend it: Based in Portugal and aimed at researchers — check eligibility, deadlines and whether your field fits before investing time in an application.
InfoQ's report on AX, Google's newly open-sourced system for running fleets of autonomous AI agents. It explains how AX treats each agent as a long-lived task that can be paused and resumed to save computing resources, using control ideas borrowed from Kubernetes.
Why I recommend it: Free to read, though InfoQ asks you to register for some content. The performance claims come from Google's own announcement, so treat them as the company's figures until others test the system.
ProFellow's guide to using AI chat tools to shortlist fully funded graduate programs in the United States, with prompt ideas and advice on verifying what the tool tells you.
Why I recommend it: Free to read. ProFellow also sells paid fellowship databases and courses, so treat the article as a lead-in to their products. AI tools invent funding details — always confirm anything they list on the university's own page.
The official home of Kubernetes, the free open-source system for running and scaling containerized applications. Includes full documentation, tutorials and a browser-based interactive learning track — useful background if you are moving toward cloud, DevOps or AI infrastructure work.
Why I recommend it: The software and docs are free and open source. Running Kubernetes on a cloud provider costs money, so stick to the free local tutorials while you are learning.
Microsoft Research blog post on running the heavy AI thinking for robots on remote computers instead of on the robot itself, and what that means for speed, cost and reliability in real-world robotics.
Why I recommend it: Free to read. Written by Microsoft researchers, so it presents their own approach favorably — Microsoft also sells the cloud computing this kind of setup relies on.
Resources from Princeton's robotics initiative, including research groups, facilities and learning links.
Why I recommend it: A starting point for exploring academic robotics; the page blocked automatic checks, so confirm links load for you.
A joint MIT and IBM research lab publishing work on AI models, efficiency and applications.
Why I recommend it: Co-funded by IBM, so its research agenda reflects IBM's commercial interests as well as MIT's.
Research areas and projects from Columbia University's Data Science Institute, including AI, health and society.
Why I recommend it: University research overview — follow links to individual projects for readable summaries.
Cornell Tech's AI research hub, covering faculty work on machine learning, AI policy and applications.
Why I recommend it: University overview page — useful for finding researchers and free talks.
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.
MIT's Computer Science and Artificial Intelligence Laboratory — news, research areas and free talks from one of the largest AI labs in academia.
Why I recommend it: Good for seeing where AI research is heading. Many talks and papers are free to read.
The University of Pennsylvania's robotics and AI lab, covering perception, autonomous robots and machine learning.
Why I recommend it: Technical, but the project pages give a clear picture of real robotics work beyond the hype.
Research page of labour economist Kory Kantenga, with papers on labour markets, hiring and how technology changes work.
Why I recommend it: Academic papers — skim the abstracts first. Some linked journal versions may be paywalled; working-paper versions are usually free.
angld.ai analysis of 20,973 posts on Reddit's r/recruitinghell finding that being ghosted after applying now rivals rejection as job seekers' top complaint.
Why I recommend it: Written by angld.ai, which sells a paid tool for reaching hiring managers directly — the answer to ghosting it points to is its own product. Reddit complaints also skew toward the most frustrated job seekers.
Nicholas Bloom, Gordon B. Dahl and Dan-Olof Rooth study whether the rise in remote work explains the recent jump in employment among people with disabilities (American Economic Review: Insights, June 2026).
Why I recommend it: Peer reviewed. Bloom has long argued for remote and hybrid work, so it's worth knowing where the authors stand.
Jesse Buchsbaum, Michael Greenstone and Olga Rostapshova test whether making it easier to start studying economics changes outcomes for underrepresented students (AEA Papers and Proceedings, May 2026).
Why I recommend it: The authors call this preliminary evidence, so the findings may change as more results come in.
Xi Song, Jennie E. Brand, Sukie Xiuqi Yang and Michael Lachanski examine how AI can speed the decline of some occupations and make it harder for affected workers to move into better jobs (AEA Papers and Proceedings, May 2026).
Why I recommend it: A short conference paper, so the evidence is brief. "Mobility trap" is the authors' framing, not settled fact.
Funding page for Stanford Medicine's Upstream Research Center pilot grants, which back early-stage research on upstream causes of health such as work, income and community conditions.
Why I recommend it: Free to read and apply, but eligibility is limited — check whether you need a Stanford affiliation before spending time on an application.
Personal site of the Stanford economist who directs the Digital Economy Lab, with links to his research on AI, productivity and work.
Why I recommend it: One of the most cited voices on AI and jobs. He is broadly optimistic about AI augmenting workers, so read him alongside more skeptical economists.
The non-profit, non-partisan research organisation behind most influential US economics working papers, including much of the research on AI and jobs.
Why I recommend it: NBER working papers are free to download, but most are not yet peer reviewed. Treat headline findings as early evidence.
Stanford's economic policy institute, publishing free research briefs on work, productivity, AI and public policy.
Why I recommend it: Useful plain-language policy briefs. Research reflects the views of its authors, not a consensus.
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.
Cevat Giray Aksoy, Nicholas Bloom, Steven J. Davis and co-authors estimate how much workers value small amounts of in-office time versus fully remote work (June 2026).
Why I recommend it: Handy for negotiating hybrid arrangements. Bloom is a long-time advocate of hybrid work; the paper is not yet peer reviewed.
Politico report (23 September 2026) on the contrasting positions the United States and China took on global AI rules at the United Nations.
Why I recommend it: News reporting on diplomatic positions; I couldn't open the page directly, so this description comes from its headline and search results.
Katja Grace, Harlan Stewart and co-authors survey 2,778 published AI researchers on when AI will reach various milestones and how risky it could be (arXiv, January 2024).
Why I recommend it: The largest survey of its kind, from AI Impacts. Expert forecasts vary widely and shift a lot with how questions are worded.
Omar Abdel Haq, Amitabh Chandra, Tomáš Jagelka and co-authors use large language models to elicit and measure people's life preferences and trade-offs (May 2026).
Why I recommend it: An experimental method, not yet peer reviewed. Using AI to stand in for or interview people carries bias risks the authors themselves discuss.
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.
Open-access study in Social Science Research (2026) by Robinson, Meyer, Bailey-Fakhoury, Zandieh and Loeb, testing whether highlighting certain job features changes which careers college students pursue.
Why I recommend it: Peer reviewed and free under a Creative Commons license. Results come from one study population, so don't over-generalise.
xAI's own announcement of its Grok 4.7 model, with the company's claimed benchmark results.
Why I recommend it: Company announcement: the scores are xAI's own and haven't been checked independently. Some Grok features need a paid plan.
A directory of AI fellowships, residencies and research programs, many of them funded.
Why I recommend it: Always confirm deadlines and eligibility on each program's own page — directories fall out of date.
Free guides, reports and webinars from iDesign on online course design and teaching with technology in higher education.
Why I recommend it: Aimed at educators; useful if you work in or are moving into instructional design.
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.
A February 2026 working paper (Csaszar, Peterson, Wilde) that had AI models and 346 experienced managers rank 30 live Kickstarter tech ventures before their fundraising ended. The best model, Gemini 2.5 Pro, ranked outcomes far more accurately than the humans, and human-AI teams did not beat it.
Why I recommend it: A preprint that has not been peer reviewed, and it covers just 30 crowdfunding campaigns. It is a striking result, but it is not proof that AI can pick winning businesses.
CNBC report (Sept. 23, 2026) on AI taking over junior-level tasks and how employers are changing graduate and entry-level hiring.
Why I recommend it: Free to read. Examples come mostly from large firms; smaller employers may look different.
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.
Chicago Booth Review on a study of 70,000 job applicants comparing machine screening with human recruiters.
Why I recommend it: Free article summarising academic research. Results come from one hiring setting and may not apply everywhere.
Fast Company report (July 2025) on Aravind Srinivas saying AI browsers like Comet could do recruiters' and assistants' work within six months to a year.
Why I recommend it: A CEO selling an AI browser made this forecast. Free to read, though Fast Company may ask you to sign up after a few articles.
The Journalist's Resource explains, with sociologist Corey Moss-Pech, that high unemployment among new grads began before AI.
Why I recommend it: Free research roundup. Useful counterweight to "AI took the jobs" headlines.
Dewey Data review of mixed research on AI and jobs; job-posting data suggests firms are changing what they hire for more than how much.
Why I recommend it: Written by a data vendor that sells datasets used in this research. Check the original studies.
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.
Code and search records for a 2026 Strategic Entrepreneurship Journal paper that uses an AI model to evolve real startup concepts.
Why I recommend it: Free, open research code. Needs Python setup; it's for studying the paper, not a ready-made tool.
Benchmarks of open-source AI models by use case, with quality, cost and speed trade-offs.
Why I recommend it: Free to browse, but it is built by Together AI, which sells hosting for these same open models. Treat "where models run fastest" as a vendor showcase and cross-check with independent benchmarks.
Official site of MIT professor Sherry Turkle, who has spent decades studying how people relate to computers, phones and now AI companions. Books, talks and essays.
Why I recommend it: Start with her work on "artificial intimacy" if you want a careful, human-centerd counterweight to AI hype.
Al Jazeera report (24 September 2026) on AI company leaders urging global regulation at the UN while the Trump administration resists new guardrails.
Why I recommend it: Companies asking to be regulated can also be shaping rules in their favor. Notice what kind of regulation they ask for.
Anthropic reports early results from its life-sciences lab, where Claude agents identified an enzyme system whose function is still unknown.
Why I recommend it: Anthropic describing its own product. Early results; wait for peer review before treating it as a confirmed discovery.
Georgetown University think tank publishing free research and data on AI, national security and the AI workforce.
Why I recommend it: All reports are free. CSET was founded with Open Philanthropy funding, which is worth knowing when it writes about AI risk.
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.
Free Substack newsletter by a principal cybersecurity engineer sharing career tips and cybersecurity news, including a recurring 14-day U.S. cyber threat forecast (e.g. the September 22, 2026 VECTR-CAST report on actively exploited Cisco, ScreenConnect and VMware vulnerabilities).
Why I recommend it: Good for people moving into cybersecurity who want a practitioner's read on current threats. The threat levels and forecasts are the author's own analysis, not an official CISA rating — cross-check CVEs against the CISA KEV catalog.
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.
A free-to-read editorial by psychiatrist Søren Dinesen Østergaard (Aarhus University), first published August 5, 2025. It looks at emerging reports of people whose delusions seemed to be fuelled by chatbot conversations.
Why I recommend it: An invited editorial, not a clinical study: it describes early cases and raises concerns rather than proving cause and effect. Written by a researcher who first raised this question in 2023.
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 September 9, 2025 blog post saying Veracode's research found security flaws in 45% of AI-generated code it tested, and giving advice on checking AI-written code before shipping it.
Why I recommend it: Veracode sells code-security scanning, so it benefits from this finding. The 45% figure comes from its own tests, not independent research. The advice to review AI code still holds.
Gallup and Microsoft survey of the first 37 countries (of a planned 140): positive feelings about AI outweigh negative ones in 34, but a median 57% of adults have never used AI, and use ranges from 79% in Singapore to 3% in Malawi.
Why I recommend it: Free to read. Useful data on how people around the world feel about AI. Note the study is run with Microsoft, a company that sells AI, and covers only 37 countries so far.
Free weekly newsletter and articles from EX Research on internet culture, online communities, music, games and 'super-online' trends, plus free research reports.
Why I recommend it: EX Research is a paid marketing agency; the newsletter and articles are free and double as their advertising. Useful for marketers who need to understand online communities.
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.
Research and policy papers from the Roosevelt Institute on labour, pay and social policy, aimed at making the economy fairer for workers.
Why I recommend it: A progressive think tank, so its papers argue for one side. Useful for understanding worker-rights debates.
Research news from the University of Oxford's Department of Engineering Science: robotics, energy, biomedical engineering, AI and more.
Why I recommend it: A university news page, so it highlights its own wins. A handy way to spot new research before it hits the headlines.
A survey of more than 14,000 US adults on whether college is affordable and worth it. Only one in four adults without a degree think quality, affordable education is within reach.
Why I recommend it: Solid data if you're deciding whether a degree or a shorter credential makes sense for you.
Fast Forward's 2026 report on how tech nonprofits are using AI to serve people and communities.
Why I recommend it: Written by an accelerator that funds these nonprofits, so expect an optimistic view. It's still a good look at real AI-for-good projects.
Charts pulling together 20+ surveys (Resume Genius, Greenhouse, Gartner, HireVue and others) on how often recruiters spot AI-written applications and how much each side trusts AI in hiring.
Why I recommend it: Some data points are marked as interpolated, and the publisher sells an AI-detection tool — treat it as a survey roundup, not independent research.
How common fake or dormant job listings are, the survey data behind the estimates, and where proposed ghost-job laws stood as of September 2026.
Why I recommend it: Written for employers, but useful for job seekers: it explains why a listing may never be filled so you can spend your applications wisely.
A free, searchable record of 7,777 grants awarded since 2004 by what was Carnegie Corporation of New York and is now the Andrew Carnegie Foundation. Each record shows the grantee, the amount, the project description, the date awarded, the duration and the funding programme — National Democracy, National Education, International, Libraries, or Special Initiatives — with the geographic area served on newer entries. Filterable by year, programme and grantee, with every grant to one organisation viewable together.
Why I recommend it: This is one of the most useful free tools a nonprofit or grant-seeker has, and almost nobody uses it. Look up organizations doing work like yours and read the amounts, durations and project wording — it shows you what this funder actually pays for and in what language, which is better preparation than any grant-writing course. Two limits: it is a record of past decisions, not an open call, so it tells you nothing about what is currently accepting applications; and grants before 2004 are only in the archives. The foundation renamed itself in 2026, so older references you find elsewhere will say Carnegie Corporation of New York.
The Tony Blair Institute's hub explaining compute — the hardware, software and infrastructure stack that stores, processes and moves data at scale — and arguing that access to it now determines which countries and public services can use AI at all. Collects its work on infrastructure, digital skills, regulation and international collaboration.
Why I recommend it: Useful for the plain definition and for seeing how governments are being asked to think about this. It is advocacy, not neutral analysis: the Institute campaigns for rapid state adoption of technology and is funded in part by technology donors, so it rarely dwells on the land, water and electricity costs that the same build-out imposes locally. Read it alongside the data-center cases on our impacts page.
A 39-page history by SJ Beard (University of Cambridge) and Émile P. Torres of how existential risk became an academic field, tracing three successive waves: an explicitly transhumanist and techno-utopian first wave, a second built on longtermism and closely tied to Effective Altruism, and a third formed where the field met disaster studies, environmental science and public policy. Posted to SSRN January 2021, revised March 2021.
Why I recommend it: Free — the full PDF downloads without an account. This is the piece to read before anyone quotes an extinction probability at you, because it shows the field's assumptions were inherited from a particular movement rather than discovered. It is a working paper posted by the authors, not journal peer-reviewed, and the authors are participants in the debate they are describing, so it is a history written from inside.
Press release from the Norwegian Prime Minister's office, dated 21 September 2026, announcing a joint international appeal by Finnish President Alexander Stubb and Norwegian Prime Minister Jonas Gahr Støre for stronger oversight of frontier AI models, backed by 22 world leaders and pressed further at the UN General Assembly.
Why I recommend it: Filed as a primary document so you can quote the actual wording instead of a headline about it. Keep the distinction clear: this is a call, not a law. It commits no country to anything, creates no regulator and sets no deadline — its value is in showing which heads of government are now willing to say it publicly.
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.
Company site for TBC, a startup turning findings from experiments on real neurons into software intended to make AI models cheaper and more efficient to run. It announces a partnership with AWS to commercialise what it calls the world's first neuron-derived AI model, and offers early access through an application.
Why I recommend it: Free to read, and worth knowing this direction exists. But every claim here is the company's own marketing: there is no published paper, benchmark or independent test on the site, and "world's first" is their phrase, not a verified fact. Read it to learn the pitch, not to conclude it works.
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.
IBM Institute for Business Value survey of 1,500 chief HR officers and 8,800 employees worldwide, published 21 September 2026. 71% of CHROs call the ability to supervise, validate and override AI outputs the workforce's most essential skill, while only 29% of employees rank judgment as important. 60% of employees worry AI is eroding their skills, naming critical thinking most often; three in four of those say the erosion has already begun. CHROs name critical thinking (57%) and human judgment (48%) among the most important capabilities. 46% of organisations leave the CHRO out of AI strategy entirely.
Why I recommend it: The one number to take into an interview or a performance review: employers say the skill they now value most is checking and overruling the machine, and most employees have not caught up. That gap is your opening — say out loud that you review AI output rather than forward it. Read the rest carefully. This is IBM's own survey, run by IBM's consulting arm, and IBM sells the AI systems and the workforce redesign advice the study concludes you need; the free press release gives the figures, while the full report asks for your details. The 18% risk reduction and 20% quality improvement are self-reported by the companies surveyed, not measured by anyone independent, and 'employees worry their skills are eroding' is how people feel, not a test of whether their skills actually declined.
The Institute for Advanced Study's free biographical page on John von Neumann, who joined its School of Mathematics at 30 — covering his work on quantum theory, game theory, the stored-program computer architecture nearly every machine still uses, and his wartime work.
Why I recommend it: Good background for anyone meeting "von Neumann architecture" for the first time. It is written by the institution that employed him, so it reads as tribute — his role in nuclear weapons work and his later strategic writing get far less space than the mathematics.
Peer-reviewed paper in Entropy (28 May 2024) by Hartmut Neven and Adam Zalcman of Google Quantum AI with Christof Koch of the Allen Institute and others, proposing that conscious experience arises whenever a quantum superposition forms, and laying out quantum-biology experiments to test the idea. Open access; the full text is free at PubMed Central (PMC11203236).
Why I recommend it: Useful when someone claims AI is or isn't conscious: this is what a serious proposal on the question actually looks like — a conjecture with proposed experiments, not a verdict. Note the authors' own disclosures: two work for Google and one has a financial interest in a consciousness-measuring device.
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.
Gates Foundation press release announcing a five-year partnership of 60 signatories — frontier AI labs, researchers, companies, governments, community organizations, and philanthropic groups — to expand AI access across languages and voices.
Why I recommend it: A commitment announcement, not an outcome report. Read it for who has signed on and what they say they will do; the hard part is whether the resulting models and data actually serve the communities named.
Open-access study tracking 183 Canadian co-op students applying for full-time jobs. Applicants whose resumes and cover letters scored higher on detail, clarity, and structure secured substantially more interviews and found jobs faster — even after controlling for experience, achievement, and tailoring.
Why I recommend it: Open-access, peer-reviewed short communication. The key takeaway is that writing quality matters as a hiring signal, but the sample is students in one co-op program, and the authors note this also means the same signals can be generated by tools like ChatGPT.
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 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.
Bugcrowd's library of security guides, ebooks, research reports and customer stories, including its Ultimate Guide to AI Security (2026) and Ultimate Guide to Red Teaming. A reasonable free way into how bug bounty and penetration testing programmes actually run.
Why I recommend it: Free, but several downloads ask for your work email and it is a vendor's marketing library, so the conclusion is usually that you need a bug bounty platform. Read it for the vocabulary and process, then check claims against a neutral source.
Site of the economist and author of The Double Tax and The Black Agenda, a Harvard Kennedy School doctoral candidate and co-founder of The Sadie Collective, the first non-profit tackling the under-representation of Black women in economics. Collects her research, writing and talks on pay gaps and the extra costs women, especially women of colour, carry at work.
Why I recommend it: The research, essays and media appearances are free to read; her books are sold and she is bookable as a paid speaker. Strong source if you are building a case about pay or promotion inequity with numbers rather than anecdotes.
Independent daily reporting on India's startup and internet economy: funding rounds, filed company financials, fintech regulation and shutdowns. Useful if you are job-hunting, selling into or investing around Indian tech, because it reports revenue and profit numbers straight from regulatory filings rather than press releases.
Why I recommend it: Free to read and ad-supported, with a paid newsletter sold alongside it. Coverage is India-first, so treat it as a regional source, not a global one.
An AI work agent from Alibaba that runs multi-step business tasks — pulling store performance data, building comparison reports, handling research — in a browser or desktop app. The free plan includes a one-time seven-day onboarding allowance with bonus credits and stronger models, then reverts to a base daily credit allowance you keep. Paid packages run $19.90, $99 and $199.
Why I recommend it: The honest use here is trying an agent that takes a task end to end instead of answering one question, which is worth doing once so you know what the category actually does. Two flags: the free daily credit allowance is small and unpublished as a number, and the examples are built around Alibaba's own marketplaces, so its strongest work is e-commerce operations rather than general office tasks.
The original home of SHAttered — the February 2017 research by Google and CWI Amsterdam that produced the first practical public SHA-1 collision, two different PDFs with the same hash. The paper and both colliding files are still downloadable, so you can check the hashes yourself. The site now also runs a high-volume news and explainer feed covering cryptography, security, chips, cloud and cryptocurrency.
Why I recommend it: Free, no paywall. Two halves, and they deserve different levels of trust. The 2017 SHA-1 collision material is genuine, checkable primary research and still the best way to see a broken hash function with your own eyes. The current news feed is a different thing: posts appear every few minutes, carry no named author, and the site also covers 'provably fair' crypto gambling, which sits close to affiliate territory. I have deliberately not wired it into the headlines here — use the archive, and confirm any breaking claim against a named outlet before you repeat it.
Free service that gathers public expert evaluations and reviews of preprints in one place, so you can see what other researchers said about a study before it was formally published.
Why I recommend it: Use it as a sanity check on a preprint someone is quoting at you. No evaluation on Sciety means nobody independent has publicly assessed it yet — which is not the same as the study being wrong.
FinanceBuzz article by Josh Koebert, syndicated on MSN, listing nine fields it argues are shrinking: journalism, computer programming, data entry, telemarketing, photography, translation, bookkeeping, legal assistance and customer service. It cites Bureau of Labor Statistics projections — 4% fewer journalism jobs, 6% fewer programming jobs and 6% fewer bookkeeping jobs by 2034 — and points at information security analysts as a growing alternative with a 2024 median salary above $124,000.
Why I recommend it: Useful as a prompt to check your own field's BLS projection — not as career advice. Three things I would not take from it: the headline conclusion is the writer's opinion, not the BLS's; a projected national decline says nothing about whether good jobs exist in your city; and the page is packed with affiliate links to insurance and money products, which is how it earns. Look up the BLS Occupational Outlook Handbook entry for your own job title and read that instead.
Free reports and data on small business conditions in New York City. Current findings include a Manhattan storefront vacancy rate of 12.99% as of Q1 2026 (against a 12.79% pre-pandemic benchmark, with 8 of 12 community districts at or below it), non-resident visits down 2.1% year over year — the first decline in five years — an estimated $4.5bn in annual tariff costs absorbed by NYC small businesses and roughly $4,200 in added annual household costs, business formation at a five-year low, and an analysis of 211,000+ Department of Consumer and Worker Protection inspection records showing 62.3% of city business inspection cases end in default judgment, with summonses to unlicensed businesses up 171% in a year.
Why I recommend it: If you run or plan a business in New York, the inspection findings matter most: a default judgment means the business never showed up to contest the summons, and 62.3% is a warning about how easily a fine becomes final. Use the storefront tracker to argue about a specific neighborhood rather than the city average. One flag to keep in view — a chamber of commerce advocates for its members, so the tariff and enforcement figures are produced by an organization with a position on both.
Free, ad-supported daily science and technology news site, with sections for new developments, competitions and future ideas, plus a submissions page for outside contributors.
Why I recommend it: Volume is high and much of it is rewritten from university press releases, so treat it as a tip sheet: find the story here, then click through to the original announcement before you repeat any figure.
A communal blog where working mathematicians — professors, PhD students, sceptics and enthusiasts alike — write about what AI is doing to their field: authorship, what counts as understanding, where papers will go, and whether the job changes. Recent pieces include Martin Hairer on why he joined the AGMAI advisory group and Jonny Evans on the choices ahead. Submissions are open to anyone in the field, any length.
Why I recommend it: Free to read and free to write for. Worth reading even if you never touch mathematics: it is one of the few places where a whole profession is arguing in public about what AI does to its craft, in its own words rather than a journalist's. These are individual opinions, not findings — the value is the range of them, and the disagreement is the point.
Open-access academic publisher with more than 1,700 disciplines covered across its journals. Every published article is free to read and download in full, with no account needed.
Why I recommend it: Free for readers, but not free for authors: Frontiers charges researchers a publishing fee, and that model has drawn criticism over review quality. Read individual papers on their merits and check who funded the work.
A free study by Cory Hymel, Head of Research at Andela, published June 2026. It analysed 47,101 Fortune 500 software job postings against a map of what each established role was historically supposed to require, scored 2,026 distinct skills, and detected 23 candidate 'emergent roles' — job titles that are forming in the overlap between existing ones, the way ML Engineer formed between software engineering and statistics, and DevSecOps formed between development, security and operations.
Why I recommend it: Free to read in full, no signup. Use it for one thing: the titles a job is drifting toward before employers have a word for it. If the posting you are reading asks for skills from two different jobs, that is the pattern this study is measuring, and naming it in your application is stronger than claiming the old title. Two honest flags. Andela sells access to engineering talent, so a study showing that roles are changing faster than titles is also an argument for its own service. And it reads job postings, not people at work — a posting tells you what a company wrote down, not what the job turned out to be.
A free newsletter and news site for IT professionals from Morning Brew, published four times a week, covering cybersecurity, cloud, hardware, IT operations, software and the practical side of AI adoption inside companies. Stories are short, reported, and written from the perspective of the people who have to run the systems — patching, asset inventories, wi-fi troubleshooting, legacy migration — rather than the vendors selling to them. Also runs free virtual events and digital guides.
Why I recommend it: Free to read on the site and free to subscribe — the subscribe box asks for an email, but nothing is paywalled. This is one of the better free ways to learn the vocabulary of an IT job you have not had yet, because it covers the unglamorous work that interviews actually ask about. Two things to keep in mind: it is ad and sponsor funded, so sponsored posts sit alongside the reporting and you should check the byline and any 'presented by' line; and it is written for people already inside IT departments, so expect jargon on first read.
Nonprofit founded on Dr. Richard J. Davidson's wellbeing research at the University of Wisconsin–Madison. The free Healthy Minds Program app teaches awareness, connection, insight and purpose through short guided practices, alongside a free Emotional Style Quiz. The organisation also sells courses, workplace programs and paid research services, and takes donations.
Why I recommend it: The app is free with no paid tier inside it, which is rare here — worth having if burnout is part of your job search. Two honest flags: the courses, workplace programs and retreat studies on the same site are paid, and much of the evidence cited is the founder's own research group examining its own program, so read outcome claims as promising rather than settled.
A free, open platform from the non-profit Center for Open Science for managing research projects, sharing data and materials, registering studies in advance and hosting preprints. You can browse public projects without signing up.
Why I recommend it: The most useful part for a non-researcher: when a study makes headlines, its OSF page often holds the data and the pre-registered plan, so you can see whether the researchers found what they said they would look for.
ACM's magazine on human-computer interaction and design, covering how people actually live with technology — accessibility, friction by design, generative AI in scholarly work. The issue contents and the Blog@IX posts are free to read on this site.
Why I recommend it: The site itself is free, but some full features link through to the ACM Digital Library, which can ask for a subscription or membership. Read the blog and the free features first — it is one of the few places writing about design choices as ethical choices.
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.
Conference paper from IFAC TECIS 2024 proposing 'Cybernetic Artificial Intelligence' — the argument that AI lost something real when it dropped cybernetics, especially the difference between correlation and causation. The full text and PDF are free on ScienceDirect.
Why I recommend it: Free to read and download in full. It is a position paper from a conference, not a tested result, and its language is sweeping in places ('the only hope for the survival of the planet'). The useful part is the correlation-versus-causation section.
A Wharton working paper by Steven D Shaw and Gideon Nave, written 11 January 2026 and posted to SSRN on 2 February 2026. It proposes "Tri-System Theory" — adding a "System 3" (thinking done outside your head by a machine) to Kahneman's fast/slow account — and names "cognitive surrender": taking an AI's answer with barely a glance. Across three preregistered experiments (1,372 people, 9,593 trials) the researchers secretly varied whether the AI was right. People consulted it on more than half of questions; accuracy rose about 25 percentage points when the AI was right and fell about 15 when it was wrong, and confidence went up either way — even after errors. Time pressure, cash incentives and feedback all moved baseline scores but never removed the pattern.
Why I recommend it: Free to read and free to download the full 58-page PDF — no account needed. Two honest flags. It is a working paper: posted by the authors, and the SSRN version has not been through journal peer review, so treat the numbers as a strong first result rather than settled fact. And the copyright line says all rights reserved — read and cite it, do not republish the text. The finding worth carrying around is the one about confidence: people felt surer of themselves after the AI led them wrong. That is exactly why the checks on our AI Basics page are worth doing out loud.
Free reading app from OverDrive that borrows ebooks, audiobooks and magazines from your local public library with a library card. Works in a browser or on iOS, Android, Kobo and Kindle (US libraries only), with offline downloads, CarPlay and Android Auto support, holds, and tagged reading lists.
Why I recommend it: This is how you read the paid books on this site without buying them — most business, career and technology titles are in public library collections. What you need is a library card, which is free where you live. The catch is availability, not price: your library chooses what it licenses and popular titles come with waiting lists, so place holds early rather than expecting a book on the day you want it.
The free research library and blog of Snorkel AI, the company spun out of Stanford's Snorkel project on programmatic labelling. The papers and posts explain how training data for AI models is actually built — labelling, evaluation sets, and the 'environments' used to train agents. Useful if you want to understand the unglamorous data work behind model quality, which is where a lot of the real jobs are.
Why I recommend it: Research and blog posts are free to read with no signup. Read it for the how, not the verdict: Snorkel sells data services to frontier AI labs, so posts arguing that better data beats bigger models are also a sales case. Everything else on the site is a paid enterprise product — 'request dataset samples' means a sales call.
US Census Bureau working paper (CES 26-56, September 2026) by Cody Orr, Lee C. Tucker and Lawrence Warren, using administrative records covering about 29% of US bachelor's degrees conferred 2016-2024. Graduates in the most AI-exposed tenth of majors saw their chance of being employed in the quarter after graduation fall five percentage points, and first full-quarter earnings fall thirteen percent, starting immediately after ChatGPT's release in late 2022. The earnings hit is comparable to graduating into a large recession. About half came from lower pay inside the same industries, the rest from graduates shifting into lower-paying sectors such as restaurants and retail. The effect shrinks to about five percent two years out but does not disappear for the most exposed majors.
Why I recommend it: This is the strongest evidence yet that AI has already moved entry-level pay, because it uses actual wage records rather than employer statements or surveys. Two things to hold onto: the paper says plainly it has not been through Census Bureau review and is not an official position, and exposure is measured by what a major typically leads to, not by whether any particular employer used AI. So it tells you which fields got harder to enter — not that a machine took a named job.
A regularly updated free collection of articles, videos, podcast appearances, research findings, books and downloadable tools on the future of work, from Brian Elliott's advisory firm Work Forward. Filterable by format and by topic — career development, culture at work, diversity and inclusion, flexibility, generative AI, leadership and management, return to office, technology adoption, and time and meeting management.
Why I recommend it: The downloadable tools are the part worth your time — they give you language for asking a manager about flexibility or AI workload without sounding like you are complaining. Elliott founded Future Forum at Slack and is unusually blunt about not trusting vendor-published studies, which is a point in his favor. Still: this is an advisory firm's library, so it exists to demonstrate expertise it sells. Read it for the questions, not the verdicts.
Julie Bort's 22 September 2026 report on The Horowitz Andreessen Academy: a one-year, in-person San Francisco programme for people straight out of high school, about 50 places, no tuition in year one, selected on "proof of work." Raised $42m, for-profit, not accredited, so no degree or transferable credit; a two-year paid version at elite-private-school prices is floated for 2028. Free to read.
Why I recommend it: Read the unaccredited part twice before anyone in your family applies: you finish with projects, contacts and no credential, and you pay your own San Francisco housing. That can be a good trade for someone who already builds and ships; it is a bad trade for someone hoping the name will substitute for a qualification later. The company's own announcement and The Verge's write-up name the partner firms and the perks, and both are worth reading next.
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.
Second annual survey of 6,126 people aged 11-24 in the US, run with GlobalData in June 2026, on trust, money, social media and AI. 85% used AI in the past year and 74% were open to shopping through it, but only 2% trusted it for fashion advice; influencers never exceeded 11% of trust in any category. Self-described financial independence fell to 34.9% from 41.3% in 2025, and 29% say they are "definitely addicted" to social media, up from 18.8%.
Why I recommend it: Handy for anyone marketing to or coaching young people: it says plainly that family and friends outrank influencers and AI on trust. Remember who paid for it — a clothing retailer selling to this exact age group — so treat it as a well-run retailer survey of stated attitudes, not proof of what people actually bought.
Pew Research Center short read, 18 August 2026, on a survey of US adults conducted 22-28 June 2026. 52% now say they are more concerned than excited about AI in daily life, up from 37% in 2021. Among adults aged 18-29, 55% are more concerned than excited and 11% more excited than concerned, and 73% think AI will mean fewer US jobs over the next 20 years, up from 61% in 2024.
Why I recommend it: Useful when a client says the worry is just in their head — it is not, and the numbers are free to download as a spreadsheet. Read it as what people expect, not as what has happened to employment: it measures opinion, not job counts.
Stanford Digital Economy Lab working paper by Bharat Chandar and Bouke Klein Teeselink, separating what happens to jobs after a firm adopts generative AI into a company-wide productivity effect and changes in what specific kinds of workers are asked to do.
Why I recommend it: Free, with the full PDF on the page. It is a working paper dated 21 September 2026, meaning it has not been through peer review yet, so read it as early evidence. Useful because it does not answer 'will AI take jobs' — it asks which workers get asked to do more and which get asked to do less.
Research and product writing from the team behind the Arena model leaderboards: how coding-agent harnesses change cost and success rates, how the agent leaderboards are built, and their academic partnership calls.
Why I recommend it: Free to read. The clearest writing anywhere on why two people using the same model get very different results — the tool wrapped around the model changes the cost and the outcome. They run the leaderboards they write about, so treat their rankings as one measurement, not the verdict.
Hosseinioun and colleagues use US survey and resume data to show skills sit in a nested order — some can only be learned once others are in place — and link that structure to wage gaps and long-term wage penalties after job loss.
Why I recommend it: Open access, so the full paper is free — no library login needed. The useful takeaway for career work: skill order matters. Learning a foundation skill first opens more later moves than stacking another surface skill, and the paper shows why some people recover from a layoff faster than others.
Snap's own announcement page: every new Snapchat feature, ad product, research report and policy change, published first-hand with dates.
Why I recommend it: Free to read, no account needed. It is the company talking about itself, so read the feature notes as fact and the results claims as advertising. Useful if you post on Snapchat and want changes straight from the source rather than second hand.
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.
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.
An essay arguing for building a large model of the natural world — weather, oceans, ecosystems — as its own kind of general intelligence, separate from language models.
Why I recommend it: One argument, well written, worth reading as a counterweight to the LLM-only view of where AI is heading. It is a manifesto, not peer-reviewed research.
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.
Cybersecurity news publication covering breaches, vulnerabilities, threat research, and enterprise security policy.
Why I recommend it: Free to read. One of the longest-running security news sites; useful if security is part of your work or your beat.
Public proof-of-concept for a Windows Defender update denial-of-service vulnerability. Fills the disk by triggering repeated definition updates.
Why I recommend it: For security researchers and IT people who need to test their own systems. Do not run this on a machine that is not yours; misuse against someone else's system is a crime in most places. Read the README before touching it.
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.
A directory of open-access research repositories worldwide, browsable by country, year, repository type and software. It tells you where universities and institutions publish their own researchers' papers for free.
Why I recommend it: This is a map of where to look, not a search engine for papers. When a paper you want is paywalled, find the author's university repository here and check for the free accepted version.
MIT Press's open-access programme: hundreds of scholarly books and journal articles you can read and download in full for free, legally, including work on computing, AI, economics and design.
Why I recommend it: Before you buy an academic book or hit a paywalled paper, check here and on the author's own page. Not the whole catalog is open, only the titles funded for it, so search the specific book rather than assuming.
News, investigations and analysis at the intersection of technology, crypto and finance.
Why I recommend it: News source added to the Technology & Ethics feed. Coverage focuses on crypto, tech companies and financial investigations. Articles are free to read; the site may carry advertising or sponsorship. Feed: https://protos.com/feed/
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.
The full report of MIT's Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training, published 13 August 2026 after five months of meetings and outreach. The committee — students, faculty from every school, and staff from the MIT Libraries and Teaching and Learning Lab — was charged with assessing current AI use, identifying teaching and assessment innovations, and proposing an AI use policy. It sets out eight guiding principles (be humble, be bold, put humanity front and centre, lean into learning, teach with intentionality, no one size fits all, augmentation not automation, think beyond the classroom) and three groups of recommendations. Free to read online and free to download, with appendices and an FAQ.
Why I recommend it: The most useful part is the principles section — 'augmentation not automation' and 'teach with intentionality' are phrases you can borrow directly when you have to argue an AI policy to a school, a manager or a client. Be straight about what it is, though: MIT examining MIT, written for a residential research university, so its recommendations do not transfer unchanged to a community college, a bootcamp or a workplace.
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.
Pew Research Center survey of 10,548 U.S. adults finds growing concern about data centers' environmental impact, home energy costs and quality of life, with majorities now saying they are mostly bad for the environment.
Why I recommend it: Pew Research Center short read published 22 September 2026, survey fielded 20 July–9 August 2026. 54% of Americans say data centers are mostly bad for the environment (up from 39% in January), 50% say mostly bad for home energy costs, 49% say mostly bad for nearby quality of life. Views on local tax revenue and jobs are mixed. 12% had not heard of data centers at all.
Free guided 'missions' that walk you through building a small, real AI project in under an hour — an assistant, an app or site, a data analysis, a design, an image or video piece, or a research task. Missions are grouped by career (AI for Teaching, AI in Marketing, AI in Sales, Entrepreneurship) and built with partner tools including OpenAI, Google, Figma, Notion, Replit, Vercel, Gamma, Clay, Slack and Lovable. You finish with a working project, and completed projects are shown on your Handshake profile so employers see the work rather than a resume line. Free to get started with a Handshake account.
Why I recommend it: This is the fastest honest answer to 'I have no AI experience on my resume' — an hour gets you something you actually built and can talk about in an interview. Three caveats worth saying out loud: Handshake says free to get started rather than free forever, so check the current terms on the page; the partner tools each have their own free limits, which is where a cost can appear; and a one-hour mission is a starting point, not evidence of depth, so be ready to explain what you would do differently with more time.
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.
A paid fellowship for emerging leaders in social entrepreneurship, economic development, and the nonprofit sector. Fellows commit 20 hours a week from November 5, 2026 through June 30, 2027, work directly with the founder and team, and receive an $8,000 stipend on successful completion. Focus areas include leadership development, strategic planning, program design, research and data analysis, and community engagement.
Why I recommend it: Free to apply and it pays you — a rare combination. Read the time commitment honestly before applying: 20 hours a week for eight months is a real obligation alongside school or another job. Dates and the stipend amount are taken from their page as of September 2026; confirm both with the organization before you plan around them.
A research-based capacity-building organization in the Pittsburgh region working with nonprofit leaders, founders, artists, institutions, and funders to advance equity. Its programs include the Social Impact Fellowship, the Transformative Leader Program, the SCALE Fellowship, a Creative in Residence program, and a nonprofit accelerator, plus an impact report and published stories.
Why I recommend it: Browse their programs page first — most of what is useful to an individual is a cohort program with an application, not a self-serve resource. Strongest fit if you are building a nonprofit or social venture in or near Pittsburgh.
Brookings' running collection of technology and innovation research, including AI policy, labour effects and governance. Free to read.
Why I recommend it: A steady source of policy research when you want something more careful than news coverage. Brookings is a think tank with its own funders and viewpoints.
Turns books and documents into scrollable, bite-sized learning cards with AI summaries and quizzes.
From the site: What is AI smart scrolling? ScrollEd pioneered AI smart-scrolling technology that transforms books into scrollable, bite-sized learning cards with AI summaries and quizzes. Learn faster in 5-minute sessions.
Why I recommend it: The free tier is limited: demo content only, no file uploads, and ads. Pro is $6.99 a month (currently offered free for three months to early members), so treat the free plan as a look around rather than a usable reading tool.
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.
A 90-minute SCORE webinar with attorney Barbara Weltman on year-end tax moves for small businesses, including new rules, deadlines, deductions and 1099 reporting.
Details: Attending live is free. The on-demand recording is sold separately through SCORE's course platform, so catch it live if you can.
A free tracker documenting AI-driven layoffs, closures and job displacement by country, industry and company, with each entry sourced back to reporting.
From the site: AIimpacted documents AI-driven layoffs, closures, and job displacement across every country, industry, and company - reported, sourced, and discussed.
Why I recommend it: Useful for seeing which roles and industries are actually being cut rather than which ones headlines say are at risk. Treat it as a directory of reported cases, not a forecast.
A free printable workbook for the weeks after a layoff, reorg or exit you did not choose, written by Erica Rivera, a former Google and Indeed recruiter. It walks through the first 72 hours, then days 1-30 (runway math, benefits and unemployment deadlines, severance review), days 31-60 (evidence of your work, your narrative) and days 61-90 (options and offers), with space to write your own answers and date them.
Why I recommend it: The most useful page is the first 72 hours: read the separation agreement slowly, do not sign on day one, and handle healthcare and unemployment deadlines before you touch your resume. It is written for senior women with 12+ years in, so the framing assumes that; the deadlines and runway math apply to anyone. It downloads straight as a PDF with no email required.
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.
Interdisciplinary Stanford research institute studying how digital technology and AI change work, productivity and shared prosperity, with public papers and data.
From the site: The Stanford Digital Economy Lab is an interdisciplinary research institute shaping a future where technology drives human well-being and shared prosperity.
Why I recommend it: Free to read. Useful when you want measured research on AI and jobs instead of headline claims — check the publication date on each paper, the field is moving fast.
Published research and analysis on education, skills and workforce markets from HolonIQ, including global market reports and outlook pieces.
From the site: Published research and analysis on education, skills and workforce markets.
Why I recommend it: The research pages are free to read, though HolonIQ sells paid intelligence products — treat the reports as vendor research and check who funded any figure you plan to quote.
Research institute publishing free reports and analysis on AI governance, compute policy, security and international coordination.
Why I recommend it: Read this alongside the industry labs' own publications — it covers the governance side that vendor blogs tend to skip. All research is free to download.
Free, ad-supported daily AI news covering models, research, business and policy, with an open RSS feed.
From the site: Artificial Intelligence is changing the world. THE DECODER brings you all the news about AI.
Why I recommend it: A good daily read if you want AI news without a paywall. It moves fast, so check the linked source before repeating a claim.
IEEE's research library of journal articles, conference papers and standards across computing, electrical engineering, AI and robotics.
From the site: Search and browse IEEE journals, conference proceedings and standards; abstracts are free, full text usually requires a subscription or purchase.
Why I recommend it: Paywall warning: searching and reading titles, abstracts and citations is free, but most full papers need a subscription, an institutional login or a per-article purchase. Some papers are open access and free in full. Check your school or public library for access before paying, and look for the same paper on the author's own site or arXiv first.
All-in-one bookmark manager for saving, tagging and organising links, articles and media, with a generous free plan and a low-cost Pro tier.
From the site: For your inspiration, read later, media and stuff
Why I recommend it: This is the tool I use to capture every link before it becomes a resource here. The free plan is enough for most people; Pro mainly adds full-text search and backups.
Subscription tech newsroom covering AI labs, funding and executive moves, known for scoops on OpenAI, Anthropic, Google and Meta well before wider coverage picks them up.
Why I recommend it: Honest warning: this one breaks the site's 100%-free rule. Almost everything is behind a paid subscription — you can read headlines and the opening lines, and a few free briefings, but full articles need a paying account. Added at my request as a reporting source; treat it as a headline tracker rather than something you can read end to end.
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.
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.
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.
A curated, uncommercial public archive of the Cybernetic Culture Research Unit (CCRU) and adjacent theory — Nick Land, accelerationism, hyperstition, and 1990s Warwick philosophy. The public reference layer is free; full corpus metadata is available for download.
From the site: An editorial introduction to the Cybernetic Culture Research Unit: what it was, why it keeps returning, and how to move from orientation to evidence without…
AI-powered trend intelligence that tracks what is going viral on TikTok, Instagram, X, YouTube and more. Browse public trend reports or subscribe to the newsletter to spot rising sounds, formats and aesthetics before they peak.
From the site: Discover emerging social media trends before they explode. Advanced AI predicts viral content across all major platforms.
Why I recommend it: Public trend reports and the newsletter are free to read. The full real-time dashboard appears to be invitation-only or not yet launched.
Open-access study in Frontiers in Artificial Intelligence on how workers' career satisfaction holds up as AI enters their jobs, and why feeling supported by an employer does not fully cushion the effect over time.
From the site: IntroductionThe growing integration of Artificial Intelligence (AI) into the workplace is reshaping employees’ perceptions of job and career stability, poten...
Why I recommend it: Useful if you are coaching people through AI-driven change at work: the finding is that reassurance from an employer alone does not protect long-term satisfaction. Peer-reviewed and free to read in full.
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.
Gwern Branwen’s long-form essays and meta-analyses on AI, psychology, statistics, technology and self-experiments.
From the site: Personal website of Gwern Branwen (writer, self-experimenter, and programmer): topics: psychology, statistics, technology, deep learning, anime. This index page is a categorized list of Gwern.net pages.
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,
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.
Personal site of the philosopher behind Superintelligence and the simulation argument, with free full-text papers on existential risk, human enhancement, anthropics and the future of AI. Many of the ideas now standard in AI risk debates started here; his work is also widely criticised, so read it alongside its critics.
From the site: Oxford philosopher (videos, papers, interviews, bio, etc.)
Why I recommend it: Read the primary source rather than summaries of it — then read the critics, several of whom are already in the hub.
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.
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.
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.
Personal site of Faine Greenwood, a specialist in civilian drone technology, GIS, OSINT and humanitarian aid. She writes about drones, technology and human rights for outlets like Foreign Policy, Bellingcat and Slate, and publishes the Little Flying Robots newsletter.
From the site: I’m Faine Greenwood, and I’m available for consulting. I’m a specialist in civilian drone technology, GIS, OSINT, and humanitarian aid. I write about drones, technology, and human rights for outlets like Foreign Policy, Bellingcat, and Slate. I’ve been working with, writing about, and flying small drones since 2013. C…
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.
A vetted marketplace matching senior engineers, researchers and AI specialists with companies. Free for engineers to apply and be matched; companies pay only on hire.
From the site: Hire global, AI-first engineers with Index.dev. Skip delays and scale your tech team with pre-verified, secure, and compliant talent to build faster.
Why I recommend it: Free on the candidate side — you are the product being placed. Expect a real vetting process rather than a quick apply button.
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.
Trade association providing news, research and information for and about the digital media industry.
From the site: News, research and information for and about the digital media industry from the trade association dedicated to digital media companies.
The Royal Institute of International Affairs, an independent policy institute based in London, publishing research on international affairs, governance and technology.
From the site: Chatham House, the Royal Institute of International Affairs, is an independent policy institute based in London. Discover what we do, visit our website today.
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
Non-profit helping build the field of digital minds, supporting research and education on AI consciousness, moral status and AI minds.
From the site: PRISM is a non-profit helping to build the field of digital minds, supporting research and education on AI consciousness, moral status, and AI minds.
Journalism, PR, communications, social media and content jobs, searchable by role, location and beat. Free to browse and apply without an account.
Why I recommend it: Strong for media, public relations and content-marketing pivots. Many listings link straight to the employer's application page, so it's worth checking even if the headline role isn't a perfect match.
Database of companies, funding rounds, founders and investors.
From the site: Discover private company data, funding insights, and AI-powered predictions with Crunchbase.
Why I recommend it: Free account gives you limited searches and basic company profiles; the deeper filters are paid. Free is enough to check whether a company you are interviewing with has raised money recently.
Long, source-backed guides built from the questions people actually search for, written by subject-matter experts.
From the site: In-depth, source-backed guides written by subject-matter experts, built from the questions millions of people search for every day.
Why I recommend it: Better than a listicle because it cites its sources. Still check the date on anything about tools or rules, since both move fast.
Stanford professor who built ImageNet, co-directs Stanford's Human-Centered AI institute and co-founded the AI4ALL diversity pipeline program. Her faculty page collects the work; her Google Scholar list has the papers themselves.
From the site: Fei-Fei Li is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). The site facilitates research and collaboration in academic endeavors.
Why I recommend it: Start with ImageNet if you want to understand why the last decade of AI happened when it did. Google Scholar refuses automated visits, so that link may show no picture here.
Microsoft's research division: published papers, open datasets and tools, plus its internship and residency programs.
From the site: Explore research at Microsoft, a site featuring the impact of research along with publications, products, downloads, and research careers.
Why I recommend it: The publications are free to read and the programs page lists real entry routes into research work. Both are more useful than the marketing pages.
Large technology solutions provider; its site publishes free technical research, labs and articles alongside its own job listings.
Why I recommend it: Two uses: a big employer worth knowing about, and a source of free hands-on technical labs if you are building infrastructure skills.
MIT research group studying how digital technology and AI change work, wages and productivity, with published papers and reports.
From the site: The MIT Initiative on the Digital Economy (IDE) explores how people and businesses will work, interact, and prosper in the digital era.
Why I recommend it: This is measurement rather than prediction, which is rare in this subject. Go here when you want numbers on automation instead of opinions about it.
Free library of articles, reports and recordings from Singularity on emerging technology and business change.
From the site: Explore our comprehensive resources, including insightful videos, articles, guides and reports designed to foster an innovation mindset and leverage exponential technology in your organization. Dive into Singularity insights today to drive growth and thrive in the future.
Why I recommend it: The free resources are free to read; the courses behind them are not. Stick to this page unless you want to be sold a program.
Trade publication covering AI in business: deployments, vendors, executives and industry reports.
From the site: The No.1 Magazine, Website, Newsletter & Webinar service covering AI, Machine Learning, AR & VR, Data, Technology and AI Applications.
Why I recommend it: Written for buyers of AI, so it skews positive about products. Good for learning the vocabulary used in job descriptions.
Group building the open source Eliza personal agent and related products at the intersection of AI and people.
From the site: Products at the intersection of AI and people.
Why I recommend it: The company behind the open source framework. Useful for seeing where the project is going before you build on it.
Free search across academic papers, theses and citations, with links to full text where it is public.
From the site: Google Scholar provides a simple way to broadly search for scholarly literature. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions.
Why I recommend it: The fastest way to check whether a claim in a news article traces back to an actual paper. Set an alert on a topic and it will email you new work.
The actual 2004 pitch deck Reid Hoffman used to raise LinkedIn's Series B, with his annotations explaining each slide.
From the site: At Greylock, my partners and I are driven by one guiding mission: always help entrepreneurs. It doesn’t matter whether an entrepreneur is in our portfolio, whether we’re considering an investment, or whether we’re casually meeting for the first time.
Why I recommend it: One of the few real pitch decks published with the reasoning attached. Read the annotations more than the slides — they show what a room of investors was actually asking.
Open wiki encyclopedia covering computing, science and technology topics, edited collaboratively.
From the site: HandWiki is a wiki encyclopedia for collaborative editing of articles on computing, science, technology and general knowledge. Registered users can post and edit articles, books, manuals and tutorials. Login or request account using the top-right menu. We strongly encourage editors to use their real...
Why I recommend it: Useful as a starting point on a technical term, not as a citation. Open wikis vary in quality article to article — follow its sources.
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.
Montreal research institute founded by Yoshua Bengio: publications, research teams, and programs for students and visiting researchers.
From the site: Mila is a Montreal-based artificial intelligence research institute that brings together researchers from Université de Montréal, McGill University, Polytechnique Montréal and HEC Montréal.
Why I recommend it: Check the students and programs pages if you want to move toward research work. Academic institutes publish their entry routes more openly than companies do.
Open project documenting notable people and the data behind who gets recorded as notable.
Why I recommend it: Interesting for what it reveals about whose lives get written down. Coverage is patchy, so treat gaps as gaps in the record rather than in reality.
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.
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.
Oxford research group on AI governance, publishing open papers and briefings on regulation, compute governance and international coordination.
Why I recommend it: The clearest writing anywhere on how AI rules might actually be enforced. Start with their briefings, not the full papers.
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.
Non-profit with free research on where donations do the most good, plus a public pledge community and evaluations of charities and causes.
From the site: 100x your impact by finding and donating to the best charities. Learn about high-impact philanthropy, join an effective giving community, take a giving pledge.
Why I recommend it: Their cause research is a good model of reasoning under uncertainty, whether or not you pledge. Nothing is paywalled.
Daily AI news and explainer site covering models, tools, research and industry moves, free to read with no account.
From the site: Artificial intelligence news, research, interviews, funding updates, AI tools, machine learning, robotics, cybersecurity, healthcare AI, and emerging technology analysis.
Why I recommend it: Good breadth, and clearly labeled review posts that carry affiliate links — read those as marketing, the news as news.
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.
News and recorded-talk archive from Berkeley's Simons Institute for the Theory of Computing, covering theory, cryptography and the maths under machine learning.
Why I recommend it: The recorded talks are free and often better than the paper. Skim the archive by program rather than by date.
Department site for one of the birthplaces of modern deep learning, with faculty pages, open research groups and course listings.
From the site: The University of Toronto
Why I recommend it: Where Hinton's group worked. Useful for finding the original papers and the people still there, rather than for courses you can enrol in.
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.
Google Scholar profile listing Geoffrey Hinton's papers in citation order — backpropagation, dropout, AlexNet, t-SNE and the rest of the deep learning canon.
From the site: Emeritus Prof. Computer Science, University of Toronto - Cited by 1.089.325 - machine learning - psychology - artificial intelligence - cognitive science - computer science
Why I recommend it: The single best index of the papers that made current AI work. Sort by year to see the ideas arrive. Scholar blocks automated visits, so the picture may be a screenshot.
DeepSeek's chat assistant. Free tier gives unlimited messages and file uploads on a daily-reset quota; no payment required. One of the most significant free AI releases of the past two years.
Why I recommend it: Worth trying on reasoning-heavy work and long documents. Note it is a Chinese service — read its data terms before pasting anything confidential.
Official news from CERN, the European particle physics laboratory: accelerator and experiment updates, computing and engineering write-ups, and knowledge-sharing pieces, filterable by topic and audience (general public, students, educators, policymakers). Free to read, no account.
Why I recommend it: Worth following if you want technology news written by the people doing the work rather than by a tech press cycle. The Computing and Knowledge sharing topics are the useful filters for a career audience — CERN publishes plainly about large-scale computing, data handling and open-source work, and the writing is aimed at non-specialists.
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.
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.
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.
Anthropic's AI assistant. Strong at long writing, reading documents you paste in, analysis and coding help. Free tier with daily limits; paid plans lift them.
From the site: Claude is Anthropic
Why I recommend it: The one I reach for when the task is writing or thinking through a document. Check anything factual yourself — it can be confidently wrong.
Google's AI assistant, wired into Gmail, Docs and the rest of Workspace, so it can work on files you already have. Free tier; paid plans add the larger models.
Why I recommend it: Worth it mainly if your work already lives in Google Docs and Gmail — that integration is the real advantage.
Research-grade tracking of what AI models can do and how that has changed over time, with the data and methods published. Free.
From the site: Our hub for benchmark results, featuring the performance of leading AI models on challenging tasks. It includes results from benchmarks administered internally by Epoch AI as well as data collected from external sources. Explore trends in AI capabilities across time, by benchmark, or by model.
Why I recommend it: For the longer view rather than this week's launch — they show their working, which most leaderboards do not.
xAI's assistant, built into X, with access to current posts and news as they happen. Limited free use with an X account; higher limits on paid tiers.
From the site: Grok is an AI assistant built by SpaceXAI. Chat, create images, write code, and get real-time answers from the web and X.
Why I recommend it: Useful when you need what is being said right now rather than a considered answer. Its live sources are public posts, so treat them as claims, not facts.
Independent benchmarking of the major AI models on speed, price and quality, with the numbers side by side. Free to read.
From the site: Comparison and analysis of AI models and API hosting providers. Independent benchmarks across key performance metrics including quality, price, output speed & latency.
Why I recommend it: Where I check what a model actually costs per million words before believing a "cheap" claim.
Head-to-head model comparisons voted on by the public: you see two anonymous answers to the same prompt and pick the better one, and the rankings come from those votes. Free.
From the site: Chat, compare, vote for the world's best AI models. Join the community shaping the public leaderboard for LLMs, image, and code models through real-world evaluation.
Why I recommend it: The closest thing to a fair fight between models on ordinary prompts, instead of marketing claims. Votes are taste as much as accuracy, so read it as popularity with a purpose.
A reading layer over Wikipedia: cleaner article pages, hover previews, timelines, and a chat that answers only from the Wikipedia article you are reading and the articles it links to, with every answer linked back to its source.
Why I recommend it: Useful precisely because it refuses to answer from anywhere but Wikipedia — you can check every claim. Still Wikipedia underneath, so treat it as a starting point, not a citation.
A long-running technology news publication with careful, technical reporting on computing, science, policy and security — deeper than most tech headlines and clear about what is known versus claimed.
From the site: News and reviews, covering IT, AI, science, space, health, gaming, cybersecurity, tech policy, computers, mobile devices, and operating systems.
Why I recommend it: One of the few tech outlets that reads a filing or a paper before writing about it. Free to read, with an optional paid subscription that removes ads.
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.
Research and commentary on hiring, skills and the labour market from Four One Insights, free to read.
From the site: Explore thought leadership on workforce trends, emerging technologies, and skills development with FourOne Insights. Stay informed on the latest industry insights.
Why I recommend it: Analysis from a firm that sells into hiring teams, so read the recommendations with that in mind.
A free community-built study guide for Anthropic's Claude certification: what the exam covers, practice questions and the order to learn it in.
From the site: A collaborative AI workspace, built on your company context. Build and orchestrate agents right alongside your team
Why I recommend it: Community notes, not Anthropic's official material — check anything it claims about the exam against Anthropic's own page.
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.
Glassdoor research on "stability stacking" — how millennials are combining roles, side income and benefits to build job security in an unstable market.
Why I recommend it: Useful numbers to cite when you are explaining your own patchwork career history in an interview.
IBM's free tech publication — explainers, news and market analysis aimed at people working in and around technology.
From the site: Experience an integrated media property for tech workers—latest news, explainers and market insights to help stay ahead of the curve.
Why I recommend it: Clear explainers, but it is a vendor publication — the conclusion usually favors enterprise software.
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.
Xiaomi's MiMo family of open language and reasoning models, with weights and technical reports published free for anyone to download and run.
Why I recommend it: Free open weights — worth knowing about if you want to run a capable model yourself.
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.
Google's official blog — product launches, AI announcements and policy posts, straight from the company.
From the site: Get the latest news and stories about Google products, technology and innovation on News from Google, Google's official blog.
Why I recommend it: Primary source for what Google is shipping. Marketing language included — read the "what changed" parts.
Free global research on jobs, skills and technology — including the Future of Jobs reports that most workforce coverage is based on.
Why I recommend it: Go to the source. The Future of Jobs report is free and is what half the "jobs of the future" headlines are quoting.
A free explorer for new arXiv research with plain-language paper summaries, topic pages and video overviews, so you can follow AI research without reading raw papers.
From the site: Your first stop to discover and learn about new arXiv research. Detailed paper summaries, video overviews, and more — no prompting required.
Why I recommend it: The fastest way I know to keep up with AI research when you are not a researcher. Free to browse.
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.
An AI answer engine that searches the live web and cites its sources, with a free tier that covers everyday research.
Why I recommend it: Good for a first pass on a company, an industry, or a role — then click the citations and read the originals yourself.
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.
The Verge's technology section: daily reporting on the companies, products and policies shaping the industry.
From the site: The latest tech news about the world’s best (and sometimes worst) hardware, apps, and much more. From top companies like Google and Apple to tiny startups vying for your attention, Verge Tech has the latest in what matters in technology daily.
Why I recommend it: Free to read and readable. Good for keeping current on the companies you might interview with.
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.
SSRN's browsable index of working-paper series across economics, law, management and technology — most papers are free to download.
Why I recommend it: A good way to read serious research on labor markets, hiring and AI before it hits paywalled journals.
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 company building governed, verifiable agentic infrastructure for enterprise systems, with free research and publications on its site.
From the site: Emergence builds mission-critical agentic infrastructure for enterprise. Verified, governed AI agents that plan, reason, and act across the most complex systems.
Why I recommend it: Their research and reports are free to read; the platform itself is an enterprise product, so treat the writing as the resource here.
A free report from Emergence AI surveying agentic AI in enterprise settings — where agents are being deployed and what governs them.
Why I recommend it: Read it as an industry vendor's view of where agent work is going, useful context if you're targeting AI roles.
The institute behind the Millennium Prize Problems, with free lecture videos, published proofs, historical mathematics archives and details of its research programmes.
Why I recommend it: Free access to serious mathematics — the lecture library alone is worth bookmarking if you are studying or teaching math.
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.
A free searchable database of funded PhD positions, studentships and research masters worldwide, with funding details, deadlines and supervisor contacts.
Why I recommend it: Free to search and filter by funding — use the funded-only filter so you are not applying to positions you would have to pay for.
Free hands-on technology journalism and tutorials — AI coding tool comparisons, self-hosting guides, Linux walkthroughs and hardware how-tos.
Why I recommend it: Their side-by-side tool tests are practical; good place to see whether a tool is worth your time before you install it.
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.
Mike Masnick's influential free essay arguing that open protocols, rather than centrally moderated platforms, are the durable answer to online speech problems.
Why I recommend it: One of the most cited pieces on platform power — read it before joining any debate about content moderation.
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.
A free, CERN-operated repository where researchers publish papers, datasets, code and reports with permanent DOIs — open access, no subscription.
Why I recommend it: If a paper is paywalled elsewhere, search here first; you can also publish your own work and get a citable DOI for free.
Free research reports on how people now search through AI answer engines instead of traditional search, with data by industry.
From the site: Explore Profound research on AI search, answer engines, and how people discover brands and information.
Why I recommend it: Relevant if you market anything online — how AI answer engines pick sources is changing how people find businesses.
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.
A technology policy institute publishing free reports and commentary on AI regulation, automation and the economics of emerging technology.
Why I recommend it: Free reports with a clear point of view — read them alongside the Turing Institute and AlgorithmWatch for a fuller picture.
Free reports from the Computing Research Association on evaluating computing researchers, undergraduate AI education, hiring and tenure practices, and building research capacity.
Why I recommend it: Useful if you are heading into academia or research hiring — it spells out how committees are told to evaluate people.
A free searchable directory of business software with side-by-side feature comparisons, pricing summaries and verified user reviews across categories like workflow, CRM and accounting.
Why I recommend it: Free to browse and useful when you are choosing tools for a small business — just remember vendors pay for placement, so read the reviews, not the rankings.
Free technical whitepapers, reference architectures and best-practice guides on cloud architecture, security, machine learning and cost optimisation.
Why I recommend it: Free and deep — the well-architected guides are what cloud interviews are drawn from.
A free Joint Center for Political and Economic Studies brief (September 2026) on Black employment, wages, unemployment and the sectors driving the gaps.
Why I recommend it: Data you can quote. Useful if you are making the case for a hiring or pay decision and need a source rather than an opinion.
A technical design paper by Alice Poteat (Anthropic, August 2026) setting out how function hooks let plugins extend Claude Code — the event model, composition order, rendering and enterprise controls.
Why I recommend it: Advanced and unapologetically technical. Read it as an example of a clear design document as much as for the AI tooling itself.
A free, regularly updated leaderboard benchmarking how well leading AI models actually search the web, with the methodology and benchmarks published alongside.
Why I recommend it: Check this before assuming your favorite chatbot is the best one for research. The rankings move month to month.
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.
Washington, D.C. think tank publishing free research and policy briefs on jobs, apprenticeships, career pathways for non-college workers, trade, AI and the economy — all readable without a subscription.
Why I recommend it: Useful when you want numbers rather than opinions about where jobs and training money are actually going. Their work on apprenticeships and career pathways for people without a degree is the part I send clients most.
A fellowship with Vals AI, the team that benchmarks how AI models actually perform, for people who want to work on model evaluation.
Why I recommend it: Evaluation work is where a lot of AI hiring is heading — careful, skeptical people do well here even without a PhD.
A residency at Perplexity for people who want to do applied AI research on a working product rather than in a lab.
Why I recommend it: A residency is the friendliest door into research — you are paid to learn on a real team instead of needing the credential first.
A paid AI research fellowship at DoorDash for summer and fall 2026, working on machine learning problems inside a large operating business.
Why I recommend it: Applied AI inside a logistics company teaches you constraints a lab never will — and the posting names its terms up front, which is a good sign.
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.
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.
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 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 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 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 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 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.
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.
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.
Moonshot AI's free Kimi assistant, strong on long documents, research and coding tasks in the browser.
Why I recommend it: Good free option when you need to feed in a long report and get straight answers.
Alibaba's Qwen family of open AI models with a free chat assistant for writing, coding, image and document work in the browser.
Why I recommend it: A strong free alternative when you want a capable assistant without a subscription.
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 continuously updated, human-edited river of technology industry news, formatted for phones.
Why I recommend it: A fast daily scan so you can talk about what happened in tech this week.
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.
Z.ai's technical announcement for the GLM-5.2 model, covering what changed and how it performs.
Why I recommend it: Skim releases like this so you know which free models are actually current.
Faine Greenwood's independent newsletter on drones, data and technological anxiety, including her global map of drone attacks on civilians.
Why I recommend it: Follow this if you want a specialist voice showing how to build authority in a niche.
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.
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.
Times Higher Education Campus guide on using open educational resources to widen the reach of your research and teaching, with practical steps for licensing, sharing and measuring impact.
Why I recommend it: Publishing openly is one of the cheapest ways to build a public track record — this lays out how to start.
Free open-access working-paper series from the Annenberg Institute at Brown University with Stanford's SCALE Initiative — early education research with strong policy implications, downloadable before journal publication.
Why I recommend it: If you work in education or workforce programs, citing a current working paper makes a proposal much harder to dismiss.
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.
The Black Wall Street Times covers an Institute for Women's Policy Research report on why roughly 600,000 Black women left the U.S. workforce, including public-sector cuts, caregiving load and hiring discrimination.
Why I recommend it: If your search feels harder than it should, this is the structural context — it helps separate market forces from personal performance.
Staffing Industry Analysts report on research showing that college graduates entering the workforce during the AI boom face starting-pay and employment conditions comparable to a major recession. Useful context for salary expectations and negotiation.
Why I recommend it: Read this before you accept a first offer — knowing the market backdrop keeps a low number from feeling personal.
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.
Strada Education Foundation's five principles for career guidance that actually leads to good jobs: centered on outcomes, driven by student agency, foundational and universal, rooted in relationships, and informed by data. Free download, endorsed by dozens of national education and workforce organizations.
Why I recommend it: If you advise students or run a program, these five principles are the cleanest checklist for whether your coaching is working.
A free AI search assistant that answers questions with cited sources and can run research tasks, useful for company research before an interview or scanning an industry quickly.
Why I recommend it: Good for the twenty minutes of company research you should do before every interview.
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.
A free, continuously updated and sourced record of the physical infrastructure behind AI: data centres, GPU clusters, power, chips, cloud prices, measured performance and company financials.
Why I recommend it: Useful grounding when you want facts rather than headlines about the AI build-out.
IBM's internship hub, describing paid summer and co-op internships across software engineering, data science, consulting, design, sales and research, plus how and when to apply.
Why I recommend it: Start here rather than a general job board if you want a large-company internship on your resume. Applications open early, so check it in the autumn for the following summer.
Free annual research on wellbeing from the University of Oxford's Wellbeing Research Centre with Gallup and the UN Sustainable Development Solutions Network, covering how work, community and trust shape how people feel about their lives.
Why I recommend it: Useful evidence when you are weighing a job on more than salary.
A free look inside how an AI media company uses AI across research, writing, design and operations, with the specific tools and workflows.
Why I recommend it: Concrete workflows you can copy rather than vague advice to "use AI".
Free workplace data showing that as AI use rises, more time is going into troubleshooting AI output than into productivity gains.
Why I recommend it: Good counterweight to AI hype in job interviews and internal pitches.
Live posting for Citi's 2027 Markets Quantitative Analysis Summer Analyst role in New York, working on pricing models, risk analytics and quantitative research alongside trading desks.
Why I recommend it: If you have strong math, statistics or programming coursework, this is the quant route into markets. Expect technical questions on probability and coding, so prepare those before applying.
Job market data drawn from employer career sites since 2007 — 350+ million postings used for hiring-trend research, competitive analysis, and investment research.
Why I recommend it: Not a job board — it is where the hiring trend numbers come from. Handy when you want evidence about a field instead of vibes.
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.
Huntr's data-backed look at resume formats — chronological, functional, and combination — and which structure holds up with recruiters and applicant tracking systems.
Why I recommend it: Format questions eat a lot of people's time. Read this once, pick chronological unless you have a real reason not to, and spend the rest of your energy on the bullet points.
Research from Specific Resume on what really happens to resumes at high-volume job openings — most are never opened by a human, and the true gatekeeper is a time-starved recruiter rather than the applicant tracking system everyone fears.
Why I recommend it: Read this before you spend another weekend keyword-stuffing for the ATS. The takeaway is to make the top third of page one obviously relevant to one specific role, and to find a human path in alongside the application.
The open-access preprint server for physics, mathematics, computer science, and related fields — a primary source for cutting-edge AI and machine-learning research papers.
Why I recommend it: The best place to read AI research before it hits journals or the press; search by tag or author to follow a specific line of work.
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 Liberty Street Economics post from the New York Fed arguing that, so far, AI adoption is being used to change how work is done rather than to reduce headcount, based on regional business survey data.
Why I recommend it: A useful counterweight to AI job-loss headlines; helpful for understanding how employers are actually deploying the technology right now.
A Center for an Urban Future report on how AI is reshaping entry-level tech hiring in New York City and what city leaders, educators, and employers can do to rebuild on-ramps for low-income New Yorkers.
Why I recommend it: Valuable context if you are entering tech in NYC or advising students and early-career talent on which pathways still lead to good first jobs.
A job board focused on UX, product design, and research roles.
Why I recommend it: Niche design board — filter by role, level, and location.
Statista chart showing how people are using AI tools for education and learning purposes.
Why I recommend it: Useful data point for anyone building or explaining AI-powered learning tools.
A look at how DeepSeek and OpenAI's GPT-6 Astra design benchmark are reshaping model comparisons.
Why I recommend it: News and analysis on AI model benchmarks from Decrypt.
Curated weekly newsletter covering AI research, tools, industry moves and practical applications.
Why I recommend it: A readable weekly roundup for staying current without drowning in AI news.
A September 2026 working paper by Brian Jabarian (Carnegie Mellon) and Luca Henkel (Erasmus Rotterdam) reporting a field experiment with 70,000 real applicants randomly assigned to be interviewed by a human recruiter or an AI voice agent. Applicants interviewed by AI were 12% more likely to receive an offer, with higher job starts and retention and no drop in on-the-job productivity. Transcript analysis traces the gain to more structured, consistent interviews that still adapt to each applicant.
Why I recommend it: If you have been told AI interviews are stacked against you, this is the largest piece of real evidence so far and it points the other way: consistent, structured questions helped candidates more than a tired recruiter on their eleventh call did. Prepare for them like any structured interview, with clear, specific answers.
Created by Brian Jabarian and Luca Henkel
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.
Job board dedicated specifically to AI, machine learning, and big data roles — ML engineering, data science, NLP, computer vision, AI research — aggregated from companies worldwide.
Why I recommend it: If you are specifically targeting an AI/ML role rather than "tech in general," this is more signal, less noise than a general tech board.
Open internship listing at MITRE for students interested in applied technology research and public-interest engineering work.
Why I recommend it: Federally funded research labs hire interns early. Apply well before spring deadlines and mention specific research centers in your cover letter.
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.
Resume and cover letter builder with a large template library, an AI writer, and a free tier for basic documents.
Why I recommend it: Pick one clean template and reuse it. Switching designs every application wastes hours you should spend researching companies.
Country-by-country career and work-permit research database covering international job search, visa basics, and local hiring norms.
Why I recommend it: Often free through a university or public library account. Check there before paying anything.
Directory of early-stage startups backed by leading investors, with funding news and company profiles in one place.
Why I recommend it: Great for building a target list of companies before anyone else has heard of them.
Video walkthrough of open-source repositories founders can use for content quality, distribution, and monetization experiments.
Why I recommend it: Watch for the ideas, not the tools. The repos change monthly; the distribution thinking lasts.
Careers hub for MITRE, a not-for-profit operator of federally funded research and development centers, with internships, early-career roles, and research positions.
Why I recommend it: Mission-driven tech employers often get overlooked by job seekers chasing big tech. Less competition, real work.
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.
edX research on which AI skills employers are hiring for, across industries and seniority levels, with data on how postings have shifted.
Why I recommend it: Use it to pick what to learn next, not to panic. Two skills learned well beats ten skimmed.
Economic policy organization researching corporate power, prices, and worker outcomes.
Why I recommend it: Their work on corporate power explains a lot about the labor market job seekers are facing.
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.
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.
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.
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.
Walkthrough of ready-made NotebookLM notebooks built from business and investing source material, with prompts to reuse.
Why I recommend it: NotebookLM is the AI tool I recommend most to people who want research help without hallucinated sources.
SEO and competitive research platform with a limited free account for keyword and site checks.
Think tank publishing research on economic policy, labor markets, and corporate power.
A New York nonprofit that trains the staff of workforce development organizations, with programs, research and practice resources on serving job seekers well.
Why I recommend it: If you work in workforce development, this is professional development for you rather than for your clients. Their practice materials are strong.
TD's 2026 financial preparedness survey on how small business owners are planning for the year, where confidence is high, and where cash and credit readiness is thin.
Why I recommend it: Good gut check on business finances. Read the readiness gaps and ask honestly which ones describe you.
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 downloadable guide to using Claude for LinkedIn content: audience research prompts, voice calibration, and reusable prompt frameworks for turning an hour into a week of posts.
Why I recommend it: The prompt frameworks in here are the useful part - especially the audience pain-point mining prompt. It is a lead magnet for a paid program, so take the system and ignore the sales pitch.
Created by The Creator Accelerator
Free multi-day programs and events Jane Street runs for students exploring quantitative trading, technology, and research careers, including travel and housing for selected participants.
Why I recommend it: If you are a student anywhere near quant or engineering, these paid-for programs are one of the shortest routes to a real internship pipeline. Applications open on a set calendar, so check the deadlines early.
AI assistant aimed at product teams that turns calls, notes, and research into structured briefs and specs.
Why I recommend it: Saved this to check what stays usable on the free tier before I recommend it.
A newsletter analyzing what actually performs on LinkedIn and social platforms, based on running experiments and reporting the data rather than repeating best-practice folklore.
Why I recommend it: Most LinkedIn advice is guesswork dressed up as expertise. This one tests things and shows the numbers, which is why I read it.
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 walkthrough of using the Apify command line tool to let AI agents run web scraping and automation tasks, aimed at people building their own small automations.
Why I recommend it: This is for the tinkerers. If you have ever wanted a repeatable way to pull data for lead lists or market research, this is a concrete starting point rather than another think piece.
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.
Fast Company's collected advice on building a personal brand that supports your career rather than performing for an algorithm.
Why I recommend it: Skim it, take three ideas you would actually do, ignore the rest. A personal brand you cannot sustain is worse than none.
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.
Curated entrepreneurship resources, research, and case materials from the Yale School of Management.
Why I recommend it: Academic-quality, freely available materials for anyone serious about entrepreneurship theory and practice.
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.
A free 12-page playbook from Tiffany Teasley (Data Sistah) with six portfolio projects built on real business problems, a free browser-based coding setup, a GitHub README template, a resume rewrite prompt, and the exact referral messages that turn a 15-minute chat into an introduction.
Why I recommend it: I like this one because it refuses to let you hide behind another certificate. Pick one project this week, finish it, then use the referral scripts at the back - that pairing is what moves people from studying to hired.
Created by Tiffany Teasley - Data Sistah
A research report based on roughly 95,000 search result records across 3,000 prompts in Claude, laying out seven patterns behind which sites AI assistants cite - useful if you want your business or personal site to surface in AI answers.
Why I recommend it: AI answers are becoming a discovery channel whether we like it or not. If you run a business or a personal site, this is a practical read on being findable there.
Free Entrepreneur session on validating a business idea before spending money building it.
Why I recommend it: Validation before building is the single cheapest business lesson available. Bring one real idea and test it against what they lay out.
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.
Prompting framework and twelve worked prompts for using a top-ranked model on finance and analysis tasks.
Why I recommend it: Steal the prompt structure, not the finance specifics. The same framing works for market research or competitor scans in your own business.
Dice's research report on tech hiring demand, in-demand skills, and salary movement across roles.
Why I recommend it: Use the skills demand section to decide what to learn next. It is one of the few free reports with role-level detail.
A job board connecting employers with candidates from underrepresented groups, including veterans, people with disabilities, and women.
Why I recommend it: Employers who pay to post here are at least signaling intent. Still do your own culture research before you accept.
A free personalized salary report based on your role, skills, location, and experience, useful as a second data point before you name a number.
Why I recommend it: Never walk into a pay conversation with one source. Pull two or three ranges and bring the one you can defend.
Company profiles with photos, employee interviews, and culture detail alongside job listings, so you can research an employer before applying.
Why I recommend it: Company research is the step most people skip. Seeing the team and the office before you interview changes the questions you ask.
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.
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.
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.
Curated open library mapping the organizations, data systems, and policies connecting education, credentials, and employment.
Why I recommend it: This is the map behind the map: who funds and shapes hiring pipelines. Useful if you work in workforce development or want to.
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.
LinkedIn News research finding that Gen Z professionals increasingly influence business decisions, yet 72% say they lack the confidence to turn contacts into career opportunities.
Why I recommend it: I use this one to normalize the awkwardness of networking. If nearly three quarters of early-career people feel the same way, the problem is practice, not personality.
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.
Data-driven resume research and insights from Specific Resume, exploring what actually gets resumes noticed, common ATS myths, and field-tested formatting guidance.
Why I recommend it: A refreshing evidence-based take on resume conventions. Worth a read if you are unsure whether your resume is helping or hurting your application odds.
A high school quant and finance league where students learn, compete, research, and connect around investing, quantitative analysis, and financial careers.
Why I recommend it: A great entry point for young people curious about quantitative finance. Even if you are not in high school, the competition structure is a model for how to learn by doing.
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.
An AI research platform for finding papers, verifying citations, reviewing literature, managing knowledge, and creating scientific figures.
Why I recommend it: For anyone doing deep research, Bohrium helps cut through the paper flood and verify claims before you cite them. I recommend it to clients writing thought-leadership content.
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.
A nonprofit research institute that translates labor-market data into insights about skills, mobility, and the future of work.
Why I recommend it: Burning Glass turns labor-market data into actionable insight about which skills are in demand and who is getting left behind. I cite their research often.
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.
Public leaderboard and open-source benchmark that drops AI agents into realistic business environments with 47 real tools across sales, marketing, operations, support, finance, and HR. Scores are based on final environment state, not an LLM-as-judge.
Why I recommend it: The leaderboard and the benchmark code are free; running it yourself means paying the model APIs at the costs shown. The test design is based on Zapier's own task data, so it's a realistic lens on agent work, but Zapier also sells automation tools — treat the benchmark as a useful public dataset, not a neutral referee.
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.
A common interview question bank aimed at all industries, not specific to any technical field.
Why I recommend it: If you are not studying algorithms, the usual question banks will frustrate you. This one is built for real job seekers in real industries.
One daily markdown page for setting a clear intention and reflection.
From the site: One daily markdown page: intent.md
Why I recommend it: A tiny daily-practice prompt; useful if you need a low-friction way to set one clear work intention each morning.
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.
Huntr analyzed nearly 2 million applications and three years of activity data. Activity peaked in September, and October led interviews.
From the site: Huntr analyzed nearly 2 million applications and three years of activity data. Activity peaked in September, and October led interviews.
Why I recommend it: Concrete seasonal patterns to time your job search; don't let a slow month discourage you—use it to prepare.
Consumer app aiming to make tipping simpler and more transparent for both customers and service workers.
Why I recommend it: A small, focused product worth studying if you are building something for service industries. Note how narrow the problem is.
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.
AI job search assistant that matches your background to open roles, ranks fit, and helps tailor application materials.
Why I recommend it: Let it surface roles you would not have searched for, then research each company yourself before applying.
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.
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.
ResumeTemplates.com survey of 1,000 US hiring managers at companies with 101 or more employees, finding 48 percent would rather invest in AI tools than hire and train a recent graduate, and that entry-level work is being restructured around AI.
Why I recommend it: Read the numbers, not the panic. The takeaway is to show proof of skill early, because employers are hiring more selectively rather than not at all.
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.
Worldwide network of entrepreneurship programs, research, and policy work, best known for organizing Global Entrepreneurship Week across more than 180 countries.
Why I recommend it: Use their country pages to find the local ecosystem group nearest you. Entrepreneurship is a local sport played on a global field.
An AI assistant that works inside Slack and Microsoft Teams, connecting to thousands of tools to produce reports, dashboards, and campaign work. Free tier available.
Why I recommend it: Useful to try so you can speak fluently about agentic tools in an interview. Watch what it gets wrong as closely as what it gets right.
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.
Hiring-funnel data showing roughly 6% of job views become applications, 3% of applicants reach an interview, and 27% of interviewees are hired - about one hire per 180 applicants, with tech roles needing far more applicants than healthcare.
Why I recommend it: Read this when the silence feels personal. It is not: 97% of applicants are screened out before a human ever sees them. That is the argument for referrals, sourcing, and warm outreach over another 40 cold applications.
The Job Hopper's roundup of no-cost tools for people switching careers — skills mapping, resume and profile help, research, and job tracking.
Why I recommend it: Start with the skills-translation tools. Career changers lose interviews on vocabulary far more often than on ability.
Local reporting on the growth of New York's artificial intelligence sector — which companies are hiring, where the funding comes from, and which neighborhoods anchor it.
Why I recommend it: Regional reporting names employers you will not find on a job board. Turn the company names into a target list.
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.
Linas Beliūnas's fintech newsletter issue rounding up funding, product launches, regulation, and hiring signals across banking and payments.
Why I recommend it: Industry newsletters are cheap market research. Read a few issues before a fintech interview and you will sound like an insider.
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."
Culture and trends newsletter breaking down what Gen Z is actually doing and feeling right now, from counterculture shifts to anxiety about AI at work.
Why I recommend it: Read it if you market to, manage, or compete with early-career workers. Trend reports age fast, but the mood it captures is real.
WIRED report on candidates using AI to answer interview questions while employers use AI to screen and score them, and what that arms race does to hiring.
Why I recommend it: Know the screening you are up against, then be the human in the room. Over-scripted AI answers are exactly what these systems flag.
Business Insider report on the widening gap between what employers expect from new graduates and what degree programs actually teach, including which skills get flagged most.
Why I recommend it: Treat the employer complaints as a checklist. Every gap named here is one you can close before your first interview.
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.
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.
A candidate-reported database of companies that stopped responding after interviews. You can search employers or submit an anonymous report of your own.
From the site: Ghosted after a job interview? Submit an anonymous report and see which companies candidates say stopped responding after interviews.
Why I recommend it: Being ghosted is not a reflection of your worth. Check a company here before you invest in a five-round process, and log your own experience so the next person walks in informed.
Founder of Good Jobs First, who built the corporate-subsidy watchdog category from scratch before stepping down as Executive Director in May 2026.
Founder of Coqual (formerly the Center for Talent Innovation), economist and author who coined "the Hidden Brain Drain" and the mentorship-versus-sponsorship distinction.
Founder and President of the Burning Glass Institute, who created real-time labor-market data analytics before spinning his expertise into nonprofit research on skills-based hiring.
Co-founders of the Kapor Center, pairing Mitch's tech legacy as creator of Lotus 1-2-3 with Freada's decades of tech-equity research.
The state labor department's full library of free New Jersey labor market publications: economic outlooks, employment and industry reports, emerging occupations, business employment dynamics, and monthly labor market updates.
Why I recommend it: Details: Free, current, and specific to New Jersey and the surrounding region. Use it to name real industries and wage ranges in interviews and business plans instead of guessing.
A regional analysis of well-paying occupations with a bright outlook and lower educational barriers to entry, covering New Jersey, Pennsylvania, New York, Maryland, and Connecticut.
Why I recommend it: Details: Start in the Key Regional Findings section, then jump to your state chapter. These are the roles that pay well, are projected to grow, and do not require a four-year degree — a shortlist worth building a plan around.
HubSpot distills 10 powerful insights about AI and marketing from conversations with industry leaders on Marketing Against the Grain.
From the site: We've distilled 10 most powerful insights about AI and marketing from conversations with industry leaders on Marketing Against the Grain.
Why I recommend it: A free HubSpot report summarizing the most useful AI-and-marketing takeaways from the Marketing Against the Grain series — good for anyone blending marketing skills with AI fluency.
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.
Reporting on how candidates use AI to apply and why some hiring managers reject applications that read as AI-dependent.
Why I recommend it: Details: use AI to draft and sharpen, then rewrite in your own voice with specifics only you can supply — that is what survives a human read.
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.
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.
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.
An entry-level capture-the-flag competition explicitly designed for undergraduate students trying to break into cybersecurity.
Details: Free status inferred from this long-running academic CTF's history; not explicitly restated on CSAW's 2026 page at time of research.
Entry-level IT certificate covering troubleshooting, networking, operating systems, security, and customer support.
Why I recommend it: Still one of the most reliable no-degree entry points into tech. Pair it with a help desk or apprenticeship application while you study.
Certificate covering research, wireframing, prototyping in Figma, and building three portfolio projects.
Why I recommend it: In UX the portfolio is the resume. The reason to take this one is the three case studies you finish with, so treat them as real work, not homework.
Remote job listings paired with company culture and flexibility insights, plus a talent side for companies hiring remote contractors and agencies.
Why I recommend it: Use this alongside Remotive. Its value is the culture and flexibility detail on each company, which is exactly what you need for your interview questions and for spotting a bad remote setup early.
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.
Searchable directory of thousands of YC-backed startups with stage, industry, location, and hiring links.
Why I recommend it: Filter by industry and location to build a target list of small companies where one hiring manager makes the call. Also a fast way to research startup models before building your own.
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.
Creator marketing platform with public research, benchmarks, and campaign case studies you can learn from for free.
Why I recommend it: Read their case studies before you pitch a brand — they show what deliverables and metrics buyers actually pay for.
We partner with nonprofits to strengthen their workforce development programs through evidence-based leadership coaching, career assessment, and research.
From the site: We partner with nonprofits to strengthen their workforce development programs through evidence-based leadership coaching, career assessment, and research.
Why I recommend it: A good reference for community organizations and workforce program leaders looking to improve career services with evidence-based coaching and assessments.
CEO of 4 Day Week Global and TIME100 Health honoree researching workplace productivity and shorter work weeks.
Why I recommend it: If a client is skeptical the 4-day week is a gimmick, point them here — this is the actual research, not the hype.
Reporting on funding rounds, startup trends, and venture activity.
Why I recommend it: Companies that just raised are hiring. Read it as a lead list for both job search and B2B pitching.
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.
Discounted certifications, practice materials, and career guidance for students and career changers.
Why I recommend it: Pick one cert tied to a job posting you have actually read — A+, Network+, or Security+ — and schedule the exam before you start studying.
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.
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.
Research foundations, prior grants, and potential funders by mission and geography.
Why I recommend it: Look up who a foundation funded last year before you write a word of your application.
Research on entry-level hiring, credentials, and what employers actually do after education.
Why I recommend it: Their entry-level hiring research is the clearest picture of what employers say versus what they do.
Crowd-sourced write-ups of real interview loops, questions, and formats by company and role.
Why I recommend it: Read two or three write-ups for your target company before you prep answers. You will know the format going in.
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.
Step-by-step guide to forming a US LLC from any country: state selection, registered agent, EIN, banking, taxes, and ongoing compliance.
Why I recommend it: Pair this with the LLC/S-Corp formation guide already in the hub. Get the EIN and the bank account right the first time and everything downstream is easier.
Created by Entrepreneur Media
Jacqueline Tangorra's Entrepreneur white paper with 30 startup ideas, a 12-tool AI stack, four business prompts, and 12 deep strategy prompts for market sizing, competitor analysis, pricing, go-to-market, unit economics, and expansion.
Why I recommend it: The prompt library alone is worth the download. Paste the TAM, pricing, and unit-economics prompts into your AI assistant and you have a consultant-grade first draft.
Created by Jacqueline Tangorra — Entrepreneur Media
Entrepreneur's framework for pressure-testing a business idea before you spend money on it: demand, differentiation, cost, and timing.
From the site: Ask yourself these questions before you start fundraising.
Why I recommend it: Start here before anything else. If the idea cannot survive these questions on paper, it will not survive your savings.
Entrepreneur's 2026 white paper on turning social platforms into a repeatable sales system: content cadence, offers, funnels, and conversion tracking.
Why I recommend it: Treat social as a storefront, not a diary. Follow the cadence in here for 90 days before you judge the results.
Created by Entrepreneur Media
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.
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.
Structured, free computer science study plan for landing engineering roles.
Why I recommend it: A full self-taught CS curriculum, written by someone who did it himself.
TikTok discover page showcasing the viral community and collector content around Squishmallows plush toys.
Article examining how Gymshark's marketing strategy successfully captured millennial and Gen Z fitness audiences.
TikTok's initiative spotlighting and supporting creators who run charitable and social-impact campaigns.
Article breaking down Gymshark's valuation and financial growth as a case study in brand scaling.
LinkedIn article analyzing how niche character storytelling turned Labubu into a global viral phenomenon.
The viral TikTok video of a Stanley cup surviving a car fire that sparked the brand's massive social moment.
Search Engine Journal piece on why authentic, genuine connection outperforms polished content on social media.
Pinterest's annual trend report forecasting the themes and searches expected to grow in the coming year.
Forbes' account of how a viral TikTok moment helped drive Stanley's cup sales and brand revenue growth.
Why I recommend it: A great real-world example of how one viral moment can transform a brand overnight.
Gymshark's own account of its founding story, from a teenage side hustle to a global apparel brand.
Why I recommend it: One of the best founder stories out there for anyone starting a business from nothing.
USA Today's coverage of Stanley's CEO replacing the car destroyed in the viral fire video as a goodwill gesture.
AI-assisted tool for generating interactive forms and surveys from a short text prompt.
Official site of Stanley 1913, the drinkware brand behind the viral Stanley Quencher tumbler.
Press release on a Build-A-Bear marketing campaign built around user-generated content and customer stories.
Fan-community guide to the Labubu collector lifestyle and the culture built around the toy line.
Official site of Gymshark, the fitness apparel brand built largely through social media and influencer marketing.
Official Pop Mart product page for Labubu, the collectible character line behind the 2025 viral toy craze.
Forbes article on the marketing lessons businesses can learn from Labubu's viral growth.
Site telling the origin story of the Stanley Quencher cup and the women-owned business that popularized it.
Research roundup of statistics on voice search and voice shopping adoption trends.
OpenAI's conversational AI assistant for writing, research, brainstorming and support tasks. Free tier with message and model limits; paid plans lift them.
Why I recommend it: The single most useful free tool for brainstorming marketing copy and business ideas.
Angela Duckworth (author of Grit) argues that the one habit high achievers share is not rugged self-sufficiency — it’s asking for help. She opens with a near-tragedy in the ocean and lines up evidence that we overestimate doing it alone and underestimate how willing others are to step in. Building a supportive environment, she writes, is the real power move.
In plain terms: In plain terms: The people who get furthest aren’t the lone wolves — they’re the ones who ask for help. Duckworth (the Grit author) says we sell ourselves short by trying to do everything alone. Whether it’s a mentor, a network, or just admitting you’re stuck, reaching out is the shortcut most people skip.
From the site: Angela Duckworth argues high achievers share one habit: they ask for help. We overestimate self-sufficiency and underestimate how willing others are to step in.
Why I recommend it: This mirrors exactly why Launchpad Library exists. The most successful people Justin has mentored were not the ones who figured it all out alone — they were the ones willing to raise a hand and ask. If you’re stuck, the fastest move is to ask for help: share your story, your career pivot, or your business idea, and let a community lift you. Don’t let “do it yourself” cost you a year of progress.
Experis/ManpowerGroup research summary on how employers and employees are actually using AI at work, plus a five-step action plan for building AI career durability.
In plain terms: A research write-up from staffing firm Experis showing most employers now use AI in hiring and are fine with candidates using it too, while very few companies have AI fully rolled out. It argues AI mostly augments jobs rather than replacing them, and lists five practical steps — build durable skills, learn your company's AI tools, research use cases for your role, take free training, and propose a small pilot.
From the site: Exploring the key findings of our new report: Building and Sustaining a Meaningful Career in the AI Age.
Why I recommend it: The headline stat matters for job seekers: 85% of employers say it is fine for candidates to use AI during hiring, and 53% already use AI in hiring and onboarding. Use the five-step durability plan as a checklist — learn what AI your employer is deploying, find use cases for your role, take free training, then pitch one small pilot you can measure. That pilot becomes a resume bullet.
In-depth analysis of frontier AI model releases, benchmarks, and capability claims. Hosted by Philip.
In plain terms: This YouTube channel provides detailed breakdowns of new artificial intelligence models and testing benchmarks. You can watch videos hosted by Philip to examine capability claims and learn how new tools perform.
Why I recommend it: The best check against hype when a new model drops.
Accessible breakdowns of AI and machine learning research papers for a general audience. Hosted by Károly Zsolnai-Fehér.
In plain terms: This YouTube channel offers simple video breakdowns of artificial intelligence and machine learning research papers. You can watch these quick guides to stay informed about new AI developments and understand how the technology is evolving.
Why I recommend it: Short, visual, and it makes research feel approachable.
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.
National nonprofit conducting research and providing free guides on small business policy, access to capital, and entrepreneurship support for underserved owners.
In plain terms: This nonprofit organization provides free educational guides, tools, and webinars to support small business owners. You can use it to learn about managing finances, securing funding, and connecting with local lenders and support programs.
Why I recommend it: Their free webinars are short and practical — good for capital and healthcare questions.
The largest membership organization for economic development professionals, connecting local and state EDCs with research, training, and best practices for community and small business growth.
Why I recommend it: Use it to find the economic development office in your own city — they often have grants nobody applies for.
Founder and CEO of Opportunity@Work, who coined the "STARs" (Skilled Through Alternative Routes) framework and drives the "Tear the Paper Ceiling" campaign.
In plain terms: This LinkedIn profile features the writings and updates of workforce advocate Byron Auguste. Follow him to learn about skills-first hiring trends, career pathways, and job market changes that value practical experience over four-year college degrees.
Why I recommend it: Pairs with the Opportunity@Work entry — he makes the economic case for hiring on skills.
National nonprofit researching and advocating for skills-first hiring; created the STARs (Skilled Through Alternative Routes) framework covering 70M+ U.S. workers without a bachelor's degree, and runs the 'Tear the Paper Ceiling' campaign.
In plain terms: This nonprofit organization supports workers who built their skills through work experience, apprenticeships, or military service rather than a college degree. You can explore career pathways, read real worker stories, and learn about employers hiring based on skills instead of degrees.
Why I recommend it: If a degree requirement is blocking you, their STARs research gives you language and data to push back.
Global nonprofit think tank (formerly the Center for Talent Innovation) producing research on diversity, equity, inclusion, and sponsorship in the workplace.
In plain terms: This nonprofit research organization studies diversity, leadership, and workplace culture. You can read their reports and articles to understand current employment trends and workplace dynamics.
Why I recommend it: Useful research on sponsorship — the difference between a mentor and someone who spends political capital on you.
Labor market research institute studying degree requirements, skills-based hiring trends, and economic mobility using real job posting data.
In plain terms: This research institute analyzes employment data and hiring trends across the country. You can read free reports to learn which job skills are in demand, explore labor market forecasts, and find which credentials lead to higher pay.
Why I recommend it: Their reports tell you which employers actually dropped degree requirements versus which just said they did.
National nonprofit driving research-backed transformation of workforce and education systems to expand economic advancement for underserved populations.
In plain terms: This nonprofit helps workers access education, skills training, and quality jobs across the country. You can explore practical guides, research reports, and information on apprenticeships and emerging career pathways.
Why I recommend it: Watch their pilots — they often turn into free training programs you can join.
Global nonprofit resource for business disability inclusion and publisher of the Disability Equality Index used by major employers to benchmark inclusion.
In plain terms: This organization helps businesses improve disability inclusion and publishes reports that benchmark corporate inclusion practices. You can use their guides, learning tools, and employer index reports to research disability-friendly companies.
Why I recommend it: Use the Disability Equality Index as a shortlist of employers to target.
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.
Organizational Psychologist, Wharton Professor. One of LinkedIn's most-followed voices (5M+), sharing research-backed insights on work, motivation, and organizational psychology.
In plain terms: This LinkedIn profile features posts, articles, and courses from an organizational psychologist and business professor. You can explore his updates to learn practical insights on workplace motivation, collaboration, and career resilience.
Why I recommend it: Good antidote to hustle-culture advice — he shows the research behind what actually helps at work.
Author, Behavioral Change Expert. Widely followed for practical, research-grounded content on self-development, motivation, and career mindset shifts.
In plain terms: This LinkedIn page features articles and posts on personal growth, motivation, and workplace habits. You can use these practical tips to build confidence, overcome burnout, and improve your communication at work.
Why I recommend it: Useful when the hard part isn't the plan, it's getting started again after a setback.
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.
Nonprofit increasing diversity and inclusion in AI education, research, and development through programs for underrepresented high school and college students.
In plain terms: This nonprofit provides free virtual training programs for college students interested in artificial intelligence. You can complete hands-on projects, connect with industry mentors, and prepare for entry-level AI internships.
Why I recommend it: Great structured entry point for students curious about AI careers.
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".
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.
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.
One of the largest foundations dedicated to entrepreneurship, funding research and programs to expand economic opportunity through business creation.
In plain terms: This website provides tools, research, and programs to support business creation and career development. You can explore training through programs like Kauffman FastTrac, connect with peers through 1 Million Cups, and look into funding opportunities.
Why I recommend it: Their free research and toolkits are some of the best small-business data available.
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.
Foundation working to close gaps of access, achievement, and opportunity for underrepresented communities in tech, funding equitable tech policy initiatives.
In plain terms: This organization works to remove barriers and increase diversity in the technology industry. You can explore STEM programs for students of color, read research on tech equity, and learn about early-stage startup funding.
Why I recommend it: Their research explains the leaky tech pipeline — and they fund fixes for it.
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.
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.
Machine learning research explained clearly, by Sebastian Raschka. 199K+ subscribers.
In plain terms: This newsletter breaks down recent machine learning and artificial intelligence research into clear explanations. You can read it to stay up to date on new developments in the field.
Why I recommend it: Patient, teacherly explanations of ML research — great for career switchers.
Weekly, deeply researched advice on building products, driving growth, and accelerating your career, for product leaders and founders.
In plain terms: This weekly newsletter provides in-depth advice on building products, driving business growth, and advancing your career. You can read it to learn practical strategies for founder and product leadership roles.
Why I recommend it: The most useful single source I know for how promotions, scope, and product careers actually work.
Weekly synthesis of AI research and policy by Jack Clark, Anthropic co-founder. 130K+ subscribers.
In plain terms: This weekly newsletter summarizes the latest artificial intelligence research and policy. You can read it to keep up with developments in AI technology.
Why I recommend it: Best place to understand the policy fights that will shape AI jobs.
Global community of startups and founders, covering fundraising, product development, and market research.
Why I recommend it: Useful for seeing how other founders describe the same problems you have.
The Ludwig Institute's alternative unemployment measure that counts people who are jobless, underemployed, or earning below a living wage.
In plain terms: A research institute publishes a "true rate of unemployment" that counts anyone jobless, stuck in part-time work, or earning under a living wage. The number is usually far higher than the official rate, which explains why a "strong" job market can still feel impossible.
From the site: LISEP’s mission is to help achieve shared economic prosperity for all Americans, particularly for middle- and low-income families. Our focus is fact-based economic and policy research.
Why I recommend it: When headlines say the job market is strong and your search still feels brutal, this number explains the gap. Useful language for interviews and for your own sanity.
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.
Business ideas and teardowns, useful for spotting small, unsexy, profitable business opportunities.
In plain terms: This weekly podcast features hosts who brainstorm business concepts and interview founders about how they make money. You can listen to explore niche business opportunities and get practical ideas for starting your own company.
Why I recommend it: Skip the hype segments; the teardown segments are a free market-research education.
Research and programs on encore careers — purpose-driven second acts, often in nonprofit and social impact work.
In plain terms: This platform provides research, fellowships, and programs focused on purpose-driven work across different generations. You can explore case studies, reports, and events to help you transition into social impact roles and collaborate with older or younger professionals.
Why I recommend it: Useful if the goal is meaningful work for the next fifteen years, not a ladder climb.
Sole proprietor, LLC, S corp, or partnership — what each means for taxes, liability, and paperwork.
In plain terms: This guide explains the differences between business structures like sole proprietorships, LLCs, and corporations. You can compare how each option affects your taxes, personal liability, and paperwork before you register your business.
Why I recommend it: Read this before paying a service to form an entity for you. Many owners need less than they are sold.
Fundamentals of finding and keeping customers — market research, pricing, and simple sales plans, with no software required.
Why I recommend it: Refreshing if you are tired of being told the answer is more posting. This is about customers, not content.
People of Color in Tech's walkthrough of a realistic coding-interview study plan — what to prioritize, how to schedule practice, and how to prepare for the behavioral rounds alongside the technical ones.
In plain terms: This guide explains how to build a structured study plan for coding interviews. Use it to break down job descriptions, prioritize your practice topics, and prepare for recruiter screens alongside technical rounds.
From the site: Unlocking the coding interview opens the door to top pay, benefits, and perks at premier tech companies.
Why I recommend it: The plan structure is the value here. Scattered LeetCode grinding is why most people plateau.
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)
JFF and Britebound research on how youth reengagement centers use digital tools with opportunity youth — what works, what pushes young people away, and how to keep tech people-centered.
In plain terms: This research report explains how youth centers use online tools and one-on-one coaching to help young people find career paths. You can learn how these programs support young adults with skills assessments, job exploration, and training options.
Why I recommend it: For anyone running a youth program: the finding that matters is that tools help only when a trusted adult is attached to them.
Created by Jobs for the Future (JFF)
Research summary showing skills-based, talent-friendly hiring practices save over $125,000 per cybersecurity worker through faster hiring and better retention, with a focus on widening pathways for women.
In plain terms: This research report shows how cybersecurity employers use skills-based hiring, mentorship programs, and ongoing training to retain talent. You can use this data to identify workplaces that support professional growth and clear promotion paths for women.
Why I recommend it: Good ammunition if you're arguing for skills-based hiring internally — it puts a dollar figure on it.
Created by Women in CyberSecurity (WiCyS) with FourOne Insights
Practical first steps after losing a job — severance and paperwork, unemployment benefits, health coverage, references, and how to sequence your search in the first 30 days.
In plain terms: This guide outlines steps to handle the emotional and personal impact of losing a job. You can use it to establish healthy daily routines, find support, and prepare yourself before starting a new search.
Why I recommend it: Do the benefits and paperwork items in week one. Filing late for unemployment costs real money you can't get back.
Created by Phyl Terry — Never Search Alone
A full system for behavioral interviewing using Situation, Task, Action, Result — question banks, follow-up probes, scoring, and the research behind why structured behavioral interviews predict performance.
In plain terms: This guide explains how employers evaluate behavioral interview questions using the STAR framework. You can study sample questions, scoring rubrics, and follow-up probes to prepare clear stories about your past work achievements.
Why I recommend it: Useful from both chairs. If you're the candidate, write six STAR stories and reuse them — you'll cover most questions you get.
Created by Justin Smith — Launchpad Library
A phase-by-phase checklist for a first interview — company research, common questions, remote/hybrid logistics, what to bring, and follow-up. Built for graduates entering the workforce.
In plain terms: This checklist guides new graduates through every stage of their first job interview. Use it to prepare answers to common questions, handle virtual or in-person logistics, and follow up after the meeting.
Why I recommend it: Print this and work down it the night before. Most first-interview nerves come from unprepared logistics, not hard questions.
Created by The Independence Lab
MIT's resume guidance with samples by field, plus advice on translating technical projects and research into recruiter-readable bullets.
In plain terms: This guide from MIT outlines five practical steps for creating a clear, well-structured resume. You can use it to format your document, write strong bullet points about your accomplishments, and tailor your experience to specific job descriptions.
From the site: Five steps to writing a great resume. Your resume provides an overview of your experience and is often an employer's first impression of you.
Why I recommend it: Especially useful if your experience is projects and coursework rather than job titles.
Slide deck on how AI tools and social platforms are rewriting brand building — breaking down viral case studies like Labubu (scarcity, lore, community) with the revenue numbers behind the hype, plus what those playbooks mean for your own launch.
In plain terms: This presentation breaks down how viral brands use storytelling, social media, and AI tools to grow their sales. You can review real-world marketing case studies and learn how to promote your business using major search and social platforms.
Why I recommend it: Study why these launches worked — story, scarcity, community — then borrow the mechanics, not the gimmick.
Created by Justin Smith — Launchpad Library
Slide guide to the tools that tell you what a role actually pays — Levels.fyi for role-level comp, Numbeo for cost-of-living context, Glassdoor for company intel — plus the mindset and mechanics of negotiating an offer.
In plain terms: This guide reviews tools for researching pay rates and local living costs. You can use it to determine your minimum compensation and prepare to negotiate your salary and benefits.
Why I recommend it: Pair one role-level pay tool with one cost-of-living tool before you ever name a number.
Created by Justin Smith — Launchpad Library
A clear explainer on the shift from credential screening to skills assessment, including how employers weigh transferable skills and what candidates can do to make skills legible on paper.
In plain terms: This article explains why many companies now prioritize practical skills and potential over traditional college degrees. You can use it to understand modern hiring trends and emphasize your relevant abilities during your job search.
Why I recommend it: Good primer if you are early in a pivot and still unsure how to frame experience from an unrelated field.
Reporting on what Google's hiring research found actually predicts performance, and why the company stopped treating GPA and school prestige as meaningful screens for most roles.
In plain terms: This article explains what Google looks for in job candidates beyond college grades and school prestige. Use this advice to choose challenging coursework, build analytical skills, and highlight practical problem-solving abilities to employers.
Why I recommend it: The famous "degrees do not predict performance" reporting. Context for why skills-first hiring started at the big platforms.
A long crowd-sourced discussion among working engineers arguing both sides of the degree question, with detail on where formal study helps and where self-teaching is sufficient.
Why I recommend it: Read the disagreements, not just the top comment. The pattern: degrees help with first-job screening, portfolios help with everything after.
The Consumer Technology Association on why tech employers are expanding registered apprenticeships, how the programs are structured, and what outcomes companies report for apprentice retention.
In plain terms: This article explains how tech apprenticeship programs work and why employers use them to train workers. You can learn how these on-the-job training opportunities help people without college degrees earn certifications and start careers in technology.
Why I recommend it: Industry-level view of why apprenticeships keep growing. Reassuring if you are worried these programs are a temporary fad.
SHRM research on how widely skills-first hiring has actually been adopted, where employers still fall back on degree requirements, and what changes inside companies that commit to it.
In plain terms: This research report explores how employers are shifting toward hiring based on skills and practical experience rather than college degrees. You can use it to understand the credentials companies look for and tailor your job applications around your strongest abilities.
Why I recommend it: Useful reality check. Plenty of companies announce skills-first hiring without changing the screen, so lead with proof of work anyway.
Harvard Business School profile of Interapt, which trains and places people from economically overlooked regions into paid tech roles, with detail on how the earn-while-you-learn model is financed.
In plain terms: This interview explains how Interapt trains and places job seekers into paid technology roles without requiring a tech background. You can read it to understand how their apprenticeship model works and what they look for in applicants.
Why I recommend it: Proof that regional and rural talent gets hired when someone builds the bridge. Worth knowing if you are outside a tech hub.
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.
Free Google tool that maps your experience to possible career paths and the skills each one needs.
In plain terms: This free tool uses artificial intelligence to analyze your work experience and skills. You can use it to discover new career paths and see what skills you need for them.
From the site: An AI-powered tool to help you uncover career potential and analyze your skills to suggest new career paths.
Why I recommend it: Use this when you cannot name what you want next. It turns a vague feeling into job titles you can research.
Practice interviews with AI or live peers, then research the company before you walk in — includes Yoodli, Exponent (formerly Pramp), Verve AI, Glassdoor, Blind, Crunchbase, The Org, and salary databases.
In plain terms: This curated guide lists free and low-cost tools for interview practice and company research. You can run mock interviews with AI or live peers, get feedback on your answers, and look up salary data and workplace reviews.
Why I recommend it: Do one recorded mock interview before every real one. Then look up the salary band so the number conversation is not a surprise.
Created by Justin Smith — Launchpad Library
A 3-page guide with 35 vetted AI tools across resume writing, mock interviews, job search copilots, job scraping, company intel, salary data, and job-fit scoring — each with its real pricing checked against the vendor's own page.
In plain terms: This guide lists 35 verified free and low-cost AI tools for job seekers. You can use it to find help with writing resumes, practicing interviews, checking salaries, and finding open roles.
Why I recommend it: Start here if you are overwhelmed by AI job-search tools. Everything listed has a usable free tier, and the pricing was verified directly with each vendor.
Created by Justin Smith — Launchpad Library
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.
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.
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.
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.
Real, crowd-sourced compensation data so you negotiate with numbers, not nerves.
In plain terms: This website provides salary and benefits data across different companies, job titles, and career levels. You can compare compensation numbers and research pay to help negotiate your next job offer.
From the site: Search 1M+ data points for different companies, job titles, career levels, and locations. Explore our tools to help you get paid more!
Why I recommend it: Bring a range, not a single number, and anchor it to this data.
