Resource Hub
2467 hand-picked resources, updated every week. Search it, filter it, or just browse a collection and see what catches your eye. Want today’s headlines instead? Read the free AI news feed.
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60 resources
Topic brief from the nonprofit Learn & Work Ecosystem Library on how governments and organizations are developing AI laws and policies affecting workplaces, learning, credentialing and government services.
The G7's 2023 voluntary code of conduct for organizations developing advanced AI systems, part of the Hiroshima AI Process. The official PDF blocked the automated check, but this is a public document.
A free unofficial explorer that makes the EU AI Act easier to read: browse the regulation article by article, with a table of contents and search.
The first binding international treaty on AI, opened for signature in 2024, requiring signatories to keep AI consistent with human rights, democracy and the rule of law. The official site blocked the automated check, but this is a public treaty document.
California's 2025 law requiring large frontier AI developers to publish safety frameworks and report critical safety incidents. Full text on the California Legislature's site.
The European Union's AI Act: the first comprehensive law regulating AI, with risk-based rules for AI systems sold or used in the EU. Official text on EUR-Lex. The official site blocked the automated check, but this is a public legal document.
UNESCO's global standard on AI ethics, adopted by 193 countries in 2021, covering values, principles and policy action areas for ethical AI.
The OECD's intergovernmental principles on trustworthy AI, first adopted in 2019 and updated in 2024 — the basis for many national AI policies.
The UK government's AI Security Institute: research and evaluations of advanced AI systems' capabilities and risks, with public reports and tools.
Colorado's 2024 law on high-risk AI systems, requiring developers and deployers to use reasonable care to avoid algorithmic discrimination. The official site blocked the automated check, but this is a public law.
The January 2025 U.S. executive order setting federal AI policy toward maintaining American leadership in AI, which revoked the 2023 AI executive order. The official site blocked the automated check, but this is a public document.
Future of Privacy Forum explainer on Connecticut SB 5, the 39-section AI bill signed by Governor Lamont, summarizing its new requirements across several areas of AI policy.
A free job board from All Tech Is Human, a nonprofit working to grow the responsible technology field, listing openings in AI policy, trust and safety, AI ethics, responsible AI governance, digital rights, and adjacent roles at companies, nonprofits, governments, and universities. Listings are free to browse without an account.
Why I recommend it: If you want to work on the governance side of AI rather than the engineering side, this is one of the few boards that aggregates exactly those roles. Filter by policy, trust and safety, or responsible AI and check weekly — the field is small and good postings move fast.
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.
Bill introduced Sept. 23, 2026 by Sen. Sanders and Rep. Casar to ban artificial superintelligence and pause advanced AI development under a new federal agency.
Why I recommend it: A proposed bill, not law. The PDF blocked my automatic check; the details come from the senator's press release.
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.
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.
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.
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 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.
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.
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.
Bill Gates on where he thinks AI is heading and the choices he believes governments and companies face, written for a general audience.
Why I recommend it: Free to read. One influential investor's opinion, not research — he has significant stakes in the industry he is writing about.
Essays, interviews and testimony from the former Google chief executive on AI, national competition and technology policy.
From the site: Read about new efforts that Eric Schmidt is involved in and views on critical issues shaping society today.
Why I recommend it: He invests in and advises on the industry he writes about, so read this as an interested party's argument rather than neutral analysis.
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.
Site of the DeepMind co-founder now running Microsoft AI, with his writing on AI and containment.
From the site: Personal site of Mustafa Suleyman, AI pioneer and author.
Why I recommend it: He runs a major AI business and writes about restraining AI. Both things are true at once, which is worth holding in mind while reading.
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.
The US government's voluntary framework for identifying and managing AI risk, plus its playbook of concrete practices. Free.
Why I recommend it: The one your employer's legal team is most likely already citing. Useful vocabulary if you want to raise AI risk at work and be taken seriously.
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.
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.
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.
Every post by Google's CEO in one feed — product announcements, AI strategy and company positioning, straight from the source.
Why I recommend it: Primary source material — worth reading before commentary about Google, not after.
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.
Long-form essays from Anthropic's CEO on AI capability, safety, economics and policy, published free in full.
Why I recommend it: Read these directly rather than through summaries — they are the source most AI-safety coverage is quoting.
The Argument essay arguing that much of the current AI debate misreads the problem: delegating decisions is the intended feature, not an accident, and that reframing should change how we govern it.
Why I recommend it: Read this alongside the optimistic AI takes — holding both views is what makes you sound credible on the topic.
Draft code of conduct for MAI models, outlining intended behaviors, values, limits and accountability principles. Open for public consultation as Microsoft AI develops its Humanist AI approach.
Why I recommend it: Read this if you want to understand how a major AI lab is framing responsible model behavior, and to form your own view before the consultation closes.
An open, freely shared company document setting out first principles for how a team uses AI in its work.
Why I recommend it: A useful template if your team needs its own AI ground rules.
A 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
Monthly summary of worldwide digital policy changes across content moderation, artificial intelligence, competition and data governance.
Why I recommend it: Good monthly catch-up if you want to track AI rules without reading every bill.
Newsletter and essays on developments at the intersection of AI and the law, by Damien Charlotin, who also tracks AI hallucinations in court filings.
Why I recommend it: The go-to source for how courts are actually handling AI misuse.
Analysis based on interviews with 56 experts across 24 countries on how AI language models are used differently in the Global South and the human rights risks that follow.
Why I recommend it: A rare look at AI harms and benefits outside the US and Europe.
Essay from Anthropic's CEO arguing for how the pace of frontier AI development should be managed alongside safety and societal readiness.
Why I recommend it: Read the people building these systems in their own words, then read their critics. Both are part of an informed view.
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.
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.
Coalition advocating for safety standards and guardrails on AI systems.
Research and grantmaking analysis on global health, policy, and emerging technology risk.
Why I recommend it: Follow the funding and you learn a lot about which problems get treated as real.
Essay mapping the competing factions in the AI debate and what each one actually believes.
Why I recommend it: The clearest short explainer I have found for anyone confused by the AI shouting match.
Tracker following how AI policy shows up in elections and candidate positions.
OpenAI's policy essay on the current window for AI regulation and the tradeoffs shaping government decisions.
Why I recommend it: Read this as a company making its case, not a neutral source. Useful for understanding the argument you will be asked to react to at work.
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.
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.
Harvard Business School AI Institute breakdown of new research on agentic AI: how autonomy and context integration shift the cost structure of knowledge work, expanding both productivity and the scope of what workers take on.
Why I recommend it: Read this before you assume AI just speeds up your current tasks. The useful takeaway for job seekers: agents lower the cost per step, so the valuable human skills become specifying goals clearly and verifying output. Practice describing outcomes, not keystrokes, and put "agent workflow design" language in your resume bullets.
Jake Taylor argues that public, standardized AI testing with formal reasoning checks is needed to close the widening "verification asymmetry" between AI capability and oversight.
Why I recommend it: If you want to work in AI governance or assurance, this is the vocabulary hiring managers use — verification, benchmarks, interpretability.
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.
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.
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.
