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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
