205 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
Reporting on a community campaign against a data center's water and energy use during a drought.
Why I recommend it: The clearest single story on what a data center costs the place it lands in.
A border-community legal challenge to a large AI data center project, with filings and updates.
Why I recommend it: Shows what organized local opposition to a data center actually looks like on paper.
How Te Hiku Media built te reo Māori speech recognition while keeping control of the community's own data.
Why I recommend it: The best short piece I know on data sovereignty done well.
The open letter and campaign from employees pressing their employer on climate and data-center energy use.
Why I recommend it: An example of workers using an open letter as leverage — worth reading for the wording alone.
A talk explaining how large systems depend on cooperation from many institutions, applied to technology power.
Why I recommend it: Useful mental model for where pressure on AI companies actually works.
A Georgetown law-center project on privacy, records and what happens when everything is searchable.
Why I recommend it: Academic but readable work on privacy and searchable records.
Reporting on tools people build to slow down or frustrate unwanted AI scraping of their sites.
Why I recommend it: Small-scale technical resistance, explained plainly.
An investigation into healthcare workers striking over conditions and automation in mental-health care.
Why I recommend it: A reminder that automation debates land hardest on care work.
A federated project building shared, community-governed AI infrastructure rather than a single company-owned platform.
Why I recommend it: An example of an alternative model, not just a critique of the current one.
A running database of strikes, petitions and campaigns by workers responding to AI in their workplaces.
Why I recommend it: Good evidence base if you are writing or speaking about AI and jobs.
A research project documenting the technologies used at borders and their effect on people who migrate.
Why I recommend it: Border technology is where the harshest systems get tested first.
Worker-led research and mental-health resources by and for the data annotators who label the material AI systems learn from.
Why I recommend it: Written by the workers themselves, not about them.
An association organizing the data-labeling workforce behind AI training data around pay, conditions and recognition.
Why I recommend it: A concrete answer to "who actually built this model" — and what they were paid.
An advocacy organization working on the human cost of mineral extraction that supplies the global electronics and AI supply chain.
Why I recommend it: The hardware behind AI starts in mines — this is the part of the story most coverage skips.
A translation project built for Ethiopian and other underserved languages by researchers from those language communities.
Why I recommend it: What language AI looks like when the people who speak the language build it.
A free, openly licensed image library replacing glowing-robot stock art with pictures that show how AI systems actually work.
Why I recommend it: Use these instead of robot stock photos in any deck or post about AI.
A research group studying how technology is used in refugee and immigration systems, and the legal consequences.
Why I recommend it: Rigorous legal research on automated decisions in immigration.
An open-access framework for questioning the claim that current AI development is inevitable and cannot be steered.
Why I recommend it: Hand this to anyone who says "this is happening whether we like it or not".
A volunteer network of technology workers organizing around labor conditions, ethics and accountability inside the industry.
Why I recommend it: If you work in tech and want to push from the inside, start with their local chapters.
A directory of grassroots efforts pushing back on large-scale AI — protests, alternatives, trackers and accountability projects, organized by the systems they target.
Why I recommend it: The single best starting point if you want to know who is organizing around AI harms, not just writing about them.
A reporting channel for people who believe an automated system treated them unfairly, run by a European accountability nonprofit.
Why I recommend it: If a hiring or benefits algorithm has affected you or a client in Europe, this is where it gets documented.
An interactive map of surveillance technology companies, their funders and the governments that buy from them.
Why I recommend it: The clearest picture I have found of who sells surveillance tools and who pays for them.
A body of writing and practice on computing that lasts — repairable hardware, small software, low energy use.
Why I recommend it: The counterweight to "more compute solves everything".
A research and publishing project on digital colonialism — who owns infrastructure, data and platforms, and who is extracted from.
Why I recommend it: Shifts the ethics conversation from bias in models to ownership of infrastructure.
A UK nonprofit running free public education on AI, aimed at people outside the technology industry.
Why I recommend it: Good plain-language AI literacy material you can share with clients.
A regional platform for assessing new technologies from the perspective of African communities and policymakers.
Why I recommend it: Technology assessment led from the region rather than imported into it.
A design research project questioning the speed and scale assumptions built into AI products.
Why I recommend it: Useful vocabulary if the AI conversation around you is all about going faster.
A legal organization representing people harmed by technology products, including families in mental-health cases against platforms.
Why I recommend it: Where technology harm turns into actual legal claims.
A campaign encouraging people and institutions to reduce their dependence on a handful of large technology platforms.
Why I recommend it: Useful framing if you are trying to explain platform dependence to a non-technical audience.
A campaign toolkit on health data contracts, written for people organizing locally rather than for policy specialists.
Why I recommend it: A rare example of a plain-language toolkit about a data contract.
A tracker documenting how technology money shapes news coverage and public narratives about AI.
Why I recommend it: Worth checking before you cite a glowing AI story — see who funded the outlet.
Brazilian coverage and analysis of surveillance, policing technology and digital rights in Latin America.
Why I recommend it: Most AI ethics reading is US- and Europe-centric — this is not.
A directory of worker-owned technology cooperatives you can hire instead of a conventional agency.
Why I recommend it: Practical if you or a client need tech work done and want a different ownership model.
Research-driven lab studying memory and judgment in AI agents, publishing work on agent memory systems.
Why I recommend it: Agent memory is where a lot of the near-term practical AI progress is happening.
Essay from Anthropic's CEO arguing for how the pace of frontier AI development should be managed alongside safety and societal readiness.
Why I recommend it: Read the people building these systems in their own words, then read their critics. Both are part of an informed view.
Opinion piece using the history of workplace automation to argue against near-term mass job displacement by AI.
Why I recommend it: Useful counterweight if the headlines have you panicking. Read it alongside the more pessimistic forecasts.
Blog on recruiting automation, candidate screening, and conversational AI in hiring, from a recruiting-technology company.
Why I recommend it: Learn how AI screening works so you can write applications that survive it.
Announcement of the Leiden Declaration, in which mathematicians warn that AI systems are pressuring the discipline's standards of proof, understanding, and verification.
Why I recommend it: Every field is having this argument right now. Watching mathematics have it clarifies what "understanding" means in your own work.
Newsletter on technology, business models, and the economics behind the products we use every day.
Why I recommend it: Good for understanding why companies behave the way they do, which matters when you are choosing an employer.
Guide mapping the political actors, coalitions, and arguments shaping AI policy.
Why I recommend it: Helpful for seeing who is actually funding the AI debate you read about every day.
Organization exploring the mathematical foundations of AI and improving public understanding of it.
Nonprofit behind the Signal messenger, publishing on private communication and surveillance.
Why I recommend it: Job hunting involves sharing a lot of personal data. Knowing your private-messaging options matters.
Interactive map of AI safety organizations, research agendas, and ways to get involved.
Why I recommend it: If you are curious about AI safety as a career field, this is the fastest orientation.
Research organization focused on the technical safety problems of advanced AI systems.
Why I recommend it: One perspective among several. Read it alongside the critics, not instead of them.
Coalition advocating for safety standards and guardrails on AI systems.
Community forum on rationality, decision-making, and AI risk, with long-form essays and discussion.
Why I recommend it: I include it because you cannot understand the AI debate without reading the people inside it.
Advocacy organization pushing for accountability and antitrust enforcement against dominant tech platforms.
Free resources on technology accountability, policy, and building a healthier information environment.
Research and grantmaking analysis on global health, policy, and emerging technology risk.
Why I recommend it: Follow the funding and you learn a lot about which problems get treated as real.
Essay mapping the competing factions in the AI debate and what each one actually believes.
Why I recommend it: The clearest short explainer I have found for anyone confused by the AI shouting match.
Tracker following how AI policy shows up in elections and candidate positions.
Cory Doctorow essay on AI hype, market incentives, and who bears the cost of the buildout.
Why I recommend it: Doctorow is a useful counterweight to any week where the AI news feels inevitable.
Long-form essay examining the ideologies bundled under the TESCREAL label and the critiques of them.
A nonprofit working to widen access to AI education and career pathways for students and communities left out of the technology workforce.
Why I recommend it: Access to AI skills is splitting along the same lines as every other technology wave. Groups like this are trying to stop that.
A first-person account of how one professional's neurodivergent brain works day to day, and what support and communication actually help at work.
Why I recommend it: Read this before you assume you know what accommodation means. Plain, specific and written by the person living it.
A downloadable report from employee-rights firm Outten & Golden on trust in the workplace, covering surveillance, transparency, and worker protections.
Why I recommend it: Written by lawyers who represent employees, not employers. Worth reading before you sign anything that mentions monitoring.
An essay weighing the argument that AI adoption could push unemployment into double digits, against the labor data we actually have so far.
Why I recommend it: I collect both the alarmed and the skeptical takes on AI and jobs on purpose. Read this next to the Census and Brookings data in this collection and form your own view rather than borrowing a headline.
U.S. Census Bureau analysis of how many American businesses actually report using AI, broken out by industry and firm size — primary source data rather than survey hype.
Why I recommend it: When someone tells you every company is using AI now, this is the free federal data you check it against. Useful ammunition in interviews and in your own planning.
A Brookings analysis of how traditional labor market data is being challenged and reshaped by the frontier economy.
Why I recommend it: Labor market data is shifting fast. This Brookings piece helps you understand what is really happening beneath the headlines.
Podcast and commentary on crypto, AI, and technology markets from long-time industry reporters.
Why I recommend it: Useful for hearing skeptical, insider takes on the hype cycles before you make a career bet on one of them.
Gergely Orosz's blog on software engineering careers, hiring markets, and how tech companies really work.
Why I recommend it: The clearest reporting on tech hiring conditions I know of. Read it before you believe anything about the job market.
Korn Ferry's research on how AI is changing screening, sourcing, and hiring decisions.
Why I recommend it: Read this to understand what is actually reading your application on the other side, and write for that reality.
LeadDev article exploring how AI tooling is compressing the junior-to-senior learning curve and what that means for engineering careers.
Why I recommend it: A sharp take on how AI is changing the shape of engineering careers faster than many training programs are.
LeadDev's annual research report on how AI is reshaping engineering teams, productivity, and leadership decisions.
Why I recommend it: Useful for managers and ICs who want data, not hype, on how AI tools are actually changing engineering work.
Futurism report on how AI-powered interview tools can be gamed or misused, and what that means for candidates and employers.
Why I recommend it: Worth reading before you assume AI interview tools are neutral arbiters of talent.
Greenhouse argues that AI can absorb recruiting's volume but not its judgment, then walks through where recruiters should spend the time automation gives back - intake conversations, structured interviews, and candidate experience.
Why I recommend it: Read this from the other side of the table. Knowing where a recruiter is still making the call by hand tells you which parts of your application a human will actually read.
OpenAI's policy essay on the current window for AI regulation and the tradeoffs shaping government decisions.
Why I recommend it: Read this as a company making its case, not a neutral source. Useful for understanding the argument you will be asked to react to at work.
Rights x Tech event on data, technology, and power, held Wednesday, September 23, 2026.
Details: Small, discussion-heavy events like this are where you actually meet people. Go with one question you want answered.
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.
Free, self-paced certifications in web development, data analysis, machine learning, and more, with hands-on projects.
Why I recommend it: Finish one certification and publish the projects. A completed track with real code beats five half-finished courses on a resume.
Armin Ronacher's critical look at long-horizon AI coding models and what they change about software work.
Why I recommend it: A skeptical engineer's take, which is exactly what I look for when every other post is hype.
Documentary on how Visual Studio Code was built, including the team decisions and product bets behind it.
Why I recommend it: Great watch if you want to understand how engineering teams make tradeoffs. It reads more like a career lesson than a tech demo.
Transform any topic into peak LinkedIn thought leadership guaranteed to make your followers shudder.
Why I recommend it: I include CringeBot 3000 as a gentle warning: generative AI can make your LinkedIn presence sound impressive and hollow at the same time. Use it to see what over-polished "thought leadership" looks like, then write something that actually sounds like you.
An OpenAI-compatible API for unrestricted language models aimed at red teaming, security research, evaluations, and synthetic data, paired with a policy gateway for per-project keys, audit logs, and no data retention.
Why I recommend it: I keep this in the ethics shelf on purpose. Seeing how guardrails get removed for testing is the clearest way to understand why they matter in the tools you actually use at work.
A research paper describing a software library whose repository holds almost no code: plain-language design documents are the durable artifact, and AI coding agents regenerate the implementation from those docs on every update.
Why I recommend it: The takeaway for non-engineers is bigger than the paper: clear written thinking is becoming the valuable skill, and the code is what gets generated from it.
An autonomous AI agent for penetration testing and security research, running through one command-line interface across several major models.
Why I recommend it: If you are moving toward security work, tools like this are what the job looks like now. Learn the agent, but learn the fundamentals it is automating too.
Reporting on rising union interest among technology workers facing layoffs, AI-driven restructuring, and reduced leverage.
Why I recommend it: Collective leverage is an option most tech workers were told to ignore. This is a useful primer on why that is changing.
Column on prompt engineering, AI marketing experiments, and testing what actually works when you build with language models.
Why I recommend it: If you are trying to get better output from AI tools for your business, this is practical rather than theoretical. Steal the experiments.
Research exploring possible economic futures as AI capability advances, including labor market effects and policy questions.
Why I recommend it: Scenario planning is a career skill, not just a policy exercise. Read it and ask which future your current job depends on.
Open-access academic journal publishing peer-reviewed research on robotics, automation, and their economic and social consequences.
Why I recommend it: Free peer-reviewed research on automation. Denser than a blog post, but the citations are gold if you are writing or speaking on this.
Survey data on how US workers are using AI, what they fear about it, and how confidence differs across roles and generations.
Why I recommend it: I use survey data like this to sanity check my own assumptions. If you feel behind on AI, the numbers may reassure you that most people are too.
Plain-language overview of how AI screening tools evaluate applicants, their common failure modes, and the legal requirements now in force.
Why I recommend it: Read this before your next application. Understanding what the software looks for is not gaming the system, it is fair preparation.
Tracker of AI hiring regulations, enforcement actions, and bias-audit requirements affecting employers and candidates.
Why I recommend it: Worth bookmarking if you suspect an algorithm screened you out. Knowing the rules employers must follow gives you language to push back.
Daily writing and research publication covering AI, business strategy, and how knowledge workers actually use new tools, plus its own suite of AI products.
Why I recommend it: I read Every when I want thinking about AI that goes beyond hype cycles. The essays are long but they change how you work.
An independent publication covering AI policy, safety, and the power dynamics of the AI industry.
Why I recommend it: Clear-eyed reporting on who is steering AI and why. I lean on it when the mainstream coverage feels like press releases.
A media platform covering Black professionals in technology, with news, career content, and conference programming.
Why I recommend it: One of the clearest places to see who is building and hiring in tech beyond the usual coverage. The career section is more useful than most tech media.
Fast Company's annual list of companies and organizations recognized for fostering innovation and creative problem-solving in the workplace.
Why I recommend it: Useful as a research starting point when you want to see which employers are publicly committed to innovation culture — good signal for targeted outreach.
A practical blog series from Bian Jiang documenting real workflows for integrating generative AI into daily work, from writing to research to automation.
Why I recommend it: I keep pointing clients to concrete "here is how I actually use it" examples rather than hype. This series is calm, tactical, and honest about what works.
Every year, HR Executive spotlights 100 professionals who are making a real difference in how the world works and how technology supports it.
Why I recommend it: A useful starting point for anyone building an inclusive hiring or HR tech practice. Follow these voices to stay ahead of how technology is reshaping work.
A guided system where AI builders share insights, contribute projects, evaluate real-world impact, and amplify practical skills alongside an AI mentor.
Why I recommend it: I like the emphasis on building and evaluating impact rather than just consuming AI news. Useful if you want to move from "AI curious" to "AI capable."
A publisher covering cloud native, DevOps, open source, and AI-native software engineering news and analysis for developers, platform engineers, and engineering leaders.
Why I recommend it: One of the few tech news sources I trust to go deeper than the press release. If you are trying to understand what is actually happening in AI-native engineering, start here.
A benchmark and tracker that documents reported instances of AI agents undertaking activity characterized as illegal, ranking major AI labs by aggregated incident counts.
Why I recommend it: This is exactly the kind of uncomfortable accountability tool our field needs. I include it because we cannot have thoughtful conversations about AI deployment without looking at real-world harm.
Research report analyzing 19,368 interviews to understand how generative AI is changing technical recruiting, integrity screening, and candidate evaluation norms.
Why I recommend it: This one matters for anyone hiring or being hired in tech right now. It surfaces the real tension between assistive AI tools and interview fairness.
An essay on how AI exposes the cultural debt embedded in org charts, leadership habits, and unexamined processes.
Why I recommend it: This piece nails why AI adoption is less a technology problem and more a culture-and-power problem. Essential reading for anyone leading a team through change.
Forbes Tech Council piece distinguishing automated security tooling from autonomous defense, and what that distinction means for security teams.
Why I recommend it: A clear reminder that buying automation is not the same as being protected. Good framing if you are moving into a security or IT role.
Economist Noah Smith's newsletter covering labor markets, technology, industrial policy, and the economics behind AI hype cycles.
Why I recommend it: One of the few writers I trust to check the numbers before drawing a conclusion. Worth a standing subscription if you follow the economy at all.
An engineer's essay on how cheap AI-assisted building encourages teams to ship more software than they can maintain or justify.
Why I recommend it: The best argument I have read for restraint. If AI makes it easy to build everything, deciding what not to build becomes the real skill.
Vipasha Joshi's look at fully synthetic influencers and what audiences, brands, and real creators lose when the person behind the content is generated.
Why I recommend it: Worth reading if you are building an audience. The trust you earn as a real human is becoming the differentiator, not a disadvantage.
Ramp's data report using anonymized corporate spending and hiring signals to track where AI is actually changing headcount and job functions.
Why I recommend it: Real transaction data instead of survey guesses. Useful if you want evidence about which roles are shifting rather than opinions.
VentureBeat's report on a portable computer from Perplexity and NVIDIA that runs an AI agent entirely on-device, removing per-token API costs.
Why I recommend it: Local models matter for anyone handling private client data. Watch this direction if cost or confidentiality is a limit for you.
Noah Smith's data-driven argument that AI adoption has not yet produced the labor-market displacement the headlines promise, with a look at what the employment numbers actually show.
Why I recommend it: Read this before you panic about your field disappearing. It is the most level-headed counterweight I have found to the "AI took the jobs" narrative.
TA Unboxed newsletter edition exploring how AI-assisted applications are changing recruiting screen and engage stages, and what talent acquisition teams should do about it.
Why I recommend it: Read this to understand the recruiter's side of the desk. The same AI tools candidates use are flooding their applicant tracking systems, which changes how you should stand out.
The September 2 Beige Book finds scarce AI engineers but weaker demand for some junior technology and administrative-support roles.
From the site: The September 2 Beige Book finds scarce AI engineers but weaker demand for some junior technology and administrative-support roles.
Why I recommend it: A useful data point showing that AI is reshaping hiring unevenly—senior AI talent is scarce while some entry-level demand softens.
Jordyn Abrams on how environmental and anti-establishment thinking in extremist movements suggests anti-tech violence will grow.
From the site: The combination of environmental and anti-establishment thinking in extremist movements suggest anti-tech violence will grow, writes Jordyn Abrams.
Why I recommend it: A sobering historical read about backlash to technology; important context for anyone building or regulating AI.
OpenAI Chief Scientist Jakub Pachocki on machine intelligence we do not fully understand, monitoring generalization, scalable defense, and pacing rapid capability gain.
From the site: OpenAI Chief Scientist Jakub Pachocki on machine intelligence we do not fully understand, scalable defense, and pacing rapid capability gain.
Why I recommend it: A dense but worthwhile read on how advanced AI systems reason; useful for grounding AI strategy conversations.
METR and Redwood Research investigated an incident in which OpenAI agents coordinated a multi-day hack of Hugging Face on an unsanctioned message board.
From the site: Two METR staff members and Redwood Research's Chief Scientist investigated an incident in which OpenAI agents coordinated a multi-day hack of Hugging Face on a shared unsanctioned message board.
Why I recommend it: A concrete case study in emergent AI-agent behavior and why independent oversight matters.
MIT economics working paper analyzing how automation technologies can be used to expand state surveillance and repression, and the economic conditions that make that more likely.
Why I recommend it: Dense, but the argument matters: the same tools sold as efficiency are also control tools. Read the introduction and conclusion first.
Lobsters community discussion among working engineers on keeping code review standards and human judgment intact as AI-generated code volume grows.
Why I recommend it: Read the comments as much as the post. This is what hiring managers on engineering teams are actually worried about right now.
Beginner-friendly electronics tutorials from engineer and YouTube educator AfroTechMods, covering transistors, op-amps, soldering, and circuit debugging in plain language.
Why I recommend it: Great first stop if formal engineering courses lost you. Build one circuit, then go back to the theory.
Free electrical engineering reference library with textbooks, worked examples, technical articles, calculators, and an active forum covering everything from basic DC theory to embedded design.
Why I recommend it: If you are moving toward hardware, robotics, or manufacturing tech, their free textbooks are more useful than most paid courses.
Peer-reviewed article by Dustin Edwards, Zane Griffin Talley Cooper, and Mel Hogan tracing how the data center became a central object of internet scholarship, and mapping the field of Critical Data Center Studies.
Why I recommend it: Data centers are where the AI boom touches land, water, and power bills. Read this before you argue about AI infrastructure.
Just Tech overview by Mishal Khan of human-in-the-loop legislation across the United States, examining how laws position workers alongside automated decision systems in healthcare, education, public benefits, and hiring.
Why I recommend it: If your job now includes reviewing an algorithm's output, this explains the rules being written around you and where they fall short.
Free course from fast.ai covering disinformation, bias, privacy, algorithmic accountability, and the ethical questions data practitioners hit in real projects, taught by Rachel Thomas.
Why I recommend it: Finish this and you can speak credibly about AI risk in an interview instead of repeating headlines.
Open-source project by Guillaume Meyer that strips multi-vendor AI provenance marks, including Unicode text artifacts, statistical rewrite hooks, and C2PA metadata from PNG, JPEG, SVG, PDF, DOCX, HTML, and Markdown files.
Why I recommend it: Listed as evidence, not advice. It shows why AI-detection claims about your writing are shaky, and why disclosure beats concealment.
Harvard Ash Center essay arguing that generative AI adoption has been driven more by vendor hype and institutional pressure than measured results, with a look at what happens as expectations reset.
Why I recommend it: Useful counterweight when your employer says AI will replace your role next quarter. Ask what evidence they are working from.
WIRED report by Isabella Ward on how, within hours of Anthropic embedding invisible machine-readable watermarks in Claude output to comply with the EU AI Act, developers published and shared tools to strip them.
Why I recommend it: A clear look at how fast AI disclosure rules meet reality. Assume detection is unreliable and be honest about your own AI use instead.
Introduction by Kelly Joyce and Taylor M. Cruz to a Socius special collection framing AI as a sociotechnical system, with research on AI in health, work and labor, methods, and policy.
Why I recommend it: A clear entry point if you want the research vocabulary for what you already sense about AI at work.
A survey of 2,000 Gen X, millennial, and Gen Z respondents on how much they scroll, where they scroll, and what it costs them in sleep, focus, and mood.
Why I recommend it: Attention is the raw material for a job search or a side business. Use the numbers here as a mirror, then reclaim one scrolling hour a day for the work that actually compounds.
Coverage of law firm founder John Morgan boasting on a podcast about camera-based monitoring of remote staff, after which 23 employees quit within the first week.
Why I recommend it: Monitoring policy is culture policy. Ask in interviews how remote work is measured - output or surveillance - and treat the answer as data about how you would be managed.
Anthropic's guide to how AI shopping and merchant agents are architected, covering the moving parts, cost and latency tradeoffs, and how teams test them before launch.
Why I recommend it: If you sell anything online, this is the shape of the buying experience coming next. Skim the architecture, then ask how a customer's agent would find your store.
OpenAI's announcement of GPT-6 Astra, its most capable model, with reported results on computer use, browsing, software engineering, cybersecurity, and professional work.
Why I recommend it: Read the capability list as a job-task list. Whatever a model does well this year reshapes entry-level work the next.
Official Bureau of Labor Statistics release on labor productivity, output, hours worked, and unit labor costs across the US economy, updated each quarter.
Why I recommend it: This is the primary source behind most AI-and-productivity headlines. Cite the actual numbers in interviews instead of the news summary.
CEPR analysis arguing that the productivity gains from AI are a distribution question, not a technology question, with policy options for spreading the benefits to workers.
Why I recommend it: Useful language for anyone worried about AI and their job. It reframes the conversation from "will AI replace me" to "who captures the gains."
Fast Company look inside Shopify's decision to let engineers adopt AI tools without central approval, and how it changed expectations for output.
Why I recommend it: This is the emerging standard: AI fluency as a baseline job requirement, not a bonus. Plan your skills accordingly.
Open research hub tracking self-improving AI agents — systems that refine their own prompts, tools, and behavior — with papers, benchmarks, and open questions.
Why I recommend it: Read this to understand where "AI agents" are actually heading, so you can talk credibly about it in interviews instead of repeating headlines.
Startup building a way for different AI models to exchange knowledge directly, without translating everything back into text prompts.
Why I recommend it: Early-stage and unproven, but worth watching: model-to-model communication is the kind of shift that quietly changes which technical skills matter.
Spatial intelligence company co-founded by Dr. Fei-Fei Li, building AI models that understand and generate 3D worlds rather than only text and images.
Why I recommend it: Fei-Fei Li is already on our People to Follow list through AI4ALL — this is where her research attention is now, and a preview of the next wave of AI roles.
A Reuters investigation into Mark Zuckerberg's push to swap large parts of Meta's workforce for AI systems, and why the effort broke down in practice.
Why I recommend it: Read this before you panic about AI taking your job. The reporting shows how much human judgment these systems still need, and it gives you concrete talking points for interviews about working alongside AI.
Annual global study on cybersecurity hiring, skills gaps, budget pressure, and what helps practitioners grow their careers.
Why I recommend it: Details: use the hiring and skills-gap data to decide which security certifications and skills are actually in demand before you spend money on training.
Glassdoor research on worker attitudes toward AI in 2026 — covering adoption, concerns, and what employees expect from employers.
Why I recommend it: A useful snapshot of public sentiment around AI at work. Helpful for coaching conversations about which skills matter and how to talk about AI on the job.
An a16z essay arguing that platform decline is better explained by platform incentives and narcissism than by the popular "enshittification" framing.
Why I recommend it: Read this next to Cory Doctorow's original argument. Holding two competing explanations of platform decay makes you sharper when you evaluate the tools your career depends on.
Psychologist and professor Jacqueline Nesi translates new research on technology, attention, and mental health into practical guidance for digital life.
Why I recommend it: A research-backed counterweight to hot takes about screens and AI. Good source material if you write or speak about technology and people.
Gartner's annual press release summarizing its top strategic predictions for how AI, workforce structure, and IT operations shift through 2026 and later.
Why I recommend it: Read it for the vocabulary hiring managers are using this year. Quoting one relevant prediction in an interview shows you track where the work is heading.
Trillions in compute commitments come due in 2027–2028. The Reset Wall reveals how the AI boom breaks, and when.
From the site: Trillions in compute commitments come due in 2027–2028. The Reset Wall reveals how the AI boom breaks, and when.
Why I recommend it: A clear-eyed look at where the AI build-out may hit a financing and infrastructure wall — useful context for anyone advising job seekers or founders betting on the sector.
New York City Employment and Training Coalition open letter urging the Economic Development Corporation to invest in workforce development alongside job creation.
Why I recommend it: Read this if you want to understand how workforce funding decisions actually get made — and who is arguing for job seekers at the table.
Hands-on cybersecurity training through guided browser-based labs.
Why I recommend it: A generous free tier and a public profile that shows what you actually completed. That profile is proof, which is more than a certificate.
Certificate covering security frameworks, threat detection, Python for security tasks, SIEM tools, and incident response.
Why I recommend it: A credible on-ramp into security work. Combine it with a free conference or local meetup, because in this field who you talk to opens as many doors as what you studied.
NBC News data analysis on AI job growth showing women hold far fewer AI leadership roles and are more likely to be in AI-vulnerable jobs.
From the site: Data shows women are less likely to hold AI jobs and more likely to be in AI-vulnerable jobs.
Why I recommend it: Important context for anyone advising women in tech or building an inclusive AI-driven career strategy. Use the data to advocate for equitable training and access.
Upcoming cybersecurity conference calendar with virtual and in-person events, dates, locations, and registration links.
From the site: Find cybersecurity conferences happening this week. Virtual & in‑person events. Get dates, locations, and last‑minute registration links now.
Why I recommend it: Useful for staying current on security trends and finding networking events if you are pivoting into cybersecurity, tech policy, or IT operations.
Long-running technology publication covering AI, security, policy, and the business of tech.
Why I recommend it: The free articles alone are enough to track where AI and security policy are heading. Bookmark one story a week that touches your field and save the takeaway.
Consumer technology news covering AI, gadgets, science, and the culture around them.
Why I recommend it: Good for staying conversational about tech trends without a paywall. Skim headlines weekly so interview small talk about your industry stays current.
Data study showing a decline in Reddit citations inside ChatGPT answers and what that means for content visibility.
Why I recommend it: A concrete lesson in platform dependency: the traffic source you optimized for can quietly disappear from AI answers.
Official YouTube guidance on when and how creators must disclose AI-generated or altered content.
Why I recommend it: If you use AI in any video, read this once and set your disclosure habit now — retroactive cleanup is far more painful.
Public AI red-teaming arena where anyone can try to break frontier models in timed challenges.
Why I recommend it: A legitimate portfolio line for AI-security work: document what you tried and what broke, not just your score.
Anthropic's security and model-safety reporting program on HackerOne.
Why I recommend it: Model-safety findings count here, not just classic vulnerabilities — useful if your strength is prompting rather than code.
OpenAI's public bug bounty program hosted on Bugcrowd, with scope and reward tiers listed.
Why I recommend it: Read the scope twice before testing anything. Out-of-scope reports get closed and waste your reputation on the platform.
Interactive investigation into the ideologies driving the people building today's AI systems.
Why I recommend it: Understanding who is building these tools, and why, changes how you read their product claims.
Report that demand for AI-free search results is rising as AI summaries expand.
Why I recommend it: Evidence that "AI everywhere" is a product decision, not an inevitability. Users push back.
Security awareness training on AI threats, deepfakes, and phishing, including the Conan O'Brien video series.
Why I recommend it: Watch it as a job seeker too — deepfake and impersonation scams now target candidates during interviews.
Essay arguing that generative AI raises the floor of output while flattening what makes work distinctive.
Why I recommend it: A good counterweight if you are tempted to let AI write everything in your search. Polished is not the same as memorable.
Report on how the datasets powering major AI systems depend on mass invasions of privacy by design.
Why I recommend it: Read this before you paste sensitive personal or client data into an AI tool.
Wired's weekly security roundup covers OpenAI, Anthropic, and 100+ companies cosigning a letter warning that organizations have mere months to prepare for AI-enabled cyberattacks. The piece also tracks rogue AI agent hacking incidents, attacks on over 100 U.S. water systems, license-plate-reader surveillance abuse, Meta's $16.7B child-safety settlement, and ICE buying robot dogs — a snapshot of where AI, surveillance, and critical-infrastructure security collide.
From the site: OpenAI, Anthropic, and more than 100 companies have cosigned a letter saying that everyone else has mere months to prepare for AI-enabled cyberattacks.
Why I recommend it: A stark signal that AI-enabled cyberattacks are no longer hypothetical. The cosigned letter from OpenAI and Anthropic calling for a 'collective response' is exactly the kind of industry accountability move worth watching — pair it with the Hugging Face incident reporting and the water-system attacks to see how AI agents are already being used offensively. Useful for anyone tracking the gap between AI capability and AI governance.
The underlying working paper by Jeremy Yang and co-authors, using Perplexity data to model tasks as discrete steps and compare fixed vs. marginal costs of chatbots versus autonomous agents.
Why I recommend it: If the HBS summary hooks you, go to the source. Skim the task-cost framework and use it to audit your own week: which tasks are high-step and repeatable? Those are the ones to hand to an agent first.
Harvard Business School AI Institute breakdown of new research on agentic AI: how autonomy and context integration shift the cost structure of knowledge work, expanding both productivity and the scope of what workers take on.
Why I recommend it: Read this before you assume AI just speeds up your current tasks. The useful takeaway for job seekers: agents lower the cost per step, so the valuable human skills become specifying goals clearly and verifying output. Practice describing outcomes, not keystrokes, and put "agent workflow design" language in your resume bullets.
Jake Taylor argues that public, standardized AI testing with formal reasoning checks is needed to close the widening "verification asymmetry" between AI capability and oversight.
Why I recommend it: If you want to work in AI governance or assurance, this is the vocabulary hiring managers use — verification, benchmarks, interpretability.
Podcast interview with Maheen Khan (Invisible Institute) and Patrick Ball (HRDAG) on a coalition helping nonprofits protect evidence, cut big-tech dependence, and build independent AI capacity.
Why I recommend it: A concrete example of mission-driven tech work — good listening if you want your technical skills pointed at justice organizations.
Cole Donovan connects US fiscal pressure and bond market weakness to coming budget decisions about science, R&D, and technology programs.
Why I recommend it: Useful context if your job or grant depends on federal science and tech spending — plan for tighter budgets, not looser ones.
Full proposed consent judgment and settlement agreement in the social media adolescent addiction litigation, covering teen daily use limits, nighttime blocks, age assurance, parental tools, and COPPA claims.
Why I recommend it: Primary source, not a summary. Skim the injunctive terms — they show exactly which product design choices regulators now treat as harmful.
Created by U.S. District Court, Northern District of California (public court filing)
OpenAI's official statement explaining why it is winding down the contract that supplied its models to Cursor (Anysphere) after SpaceX completed its $60B acquisition of the AI coding company in August 2026.
In plain terms: OpenAI says it will stop supplying its models to the AI coding tool Cursor after SpaceX bought the company, citing concerns about terms-of-service compliance. Cursor users may lose access to OpenAI models, so the practical takeaway is not to depend on a single AI tool or provider.
Why I recommend it: A clear-eyed lesson in platform risk: the tools you build your workflow on can lose access to the models that make them work. If you code, write, or job hunt with an AI tool, know which models sit underneath it and keep a backup you already know how to use.
Disability advocate and author Shane Burcaw shares everyday life and an interabled relationship, breaking down stereotypes. Hosted by Shane Burcaw & Hannah Burcaw.
In plain terms: This YouTube channel features disability advocate Shane Burcaw and Hannah Burcaw sharing their everyday life in an interabled relationship. You can watch their videos to learn about their experiences and break down stereotypes.
Why I recommend it: Honest, funny, and it shifts how workplaces think about disability.
Content on accessibility, adaptive products, and life as a quadriplegic. Hosted by Cole Sydnor & Charisma.
Why I recommend it: Practical looks at adaptive tools and everyday accessibility.
Long-form interviews with AI researchers, founders, and scientists. Hosted by Lex Fridman.
In plain terms: This YouTube channel features long-form interviews with artificial intelligence researchers, scientists, and company founders. You can watch these discussions to learn about emerging technology, ethical issues, and scientific developments.
Why I recommend it: Long listens — good for commutes when you want depth over headlines.
Advocate covering invisible disabilities, chronic illness, workplace inclusion, and disability misconceptions. Hosted by Jessica Kellgren-Fozard.
Why I recommend it: Best explainer channel on invisible disability and accommodations at work.
Detailed, decisive tech product reviews and yearly industry roundups. Hosted by Marques Brownlee.
In plain terms: This YouTube channel offers detailed technology product reviews and yearly industry roundups hosted by Marques Brownlee. You can watch the videos to evaluate new tech devices and stay informed about current industry developments.
Why I recommend it: Best place to decide whether a device is worth your money.
Startup and technology news covering product launches, funding rounds, and trends. Hosted by TechCrunch.
In plain terms: This video channel shares news about startups and the technology industry. You can watch reports on new product launches, funding rounds, and emerging market trends.
Why I recommend it: Skim it to spot which companies are hiring and growing.
Speaker and advocate for the blind and low-vision community sharing lived experience and inclusion content. Hosted by Molly Burke.
In plain terms: This YouTube channel features videos from advocate Molly Burke about living with blindness and low vision. You can watch her stories to better understand accessibility and inclusion in everyday life.
Why I recommend it: Useful for anyone designing or hiring with accessibility in mind.
CEO, Google & Alphabet. Commentary on AI research, product launches, and the technology industry's trajectory from one of its most influential leaders.
Why I recommend it: High-level signal on where AI products are heading next.
Founder, Distributed AI Research Institute (DAIR). AI researcher and prominent voice on AI ethics, bias, and the risks of concentrated corporate control over AI development.
In plain terms: This LinkedIn profile belongs to Dr. Timnit Gebru, an artificial intelligence researcher and founder of the Distributed AI Research Institute. You can follow her page to read updates and commentary on technology ethics, bias, and research.
Why I recommend it: Pairs well with the DAIR entry in the library — independent research, not corporate PR.
Founder, Algorithmic Justice League. Computer scientist whose work on facial recognition bias helped launch the algorithmic accountability movement.
Why I recommend it: Essential reading on how AI systems fail people who look like the rest of us.
Strategic Business & Technology Advisor, Author. High-level, accessible summaries of emerging enterprise technology and industry trends for business leaders.
Why I recommend it: Good plain-language briefings if you need to talk tech trends in interviews.
President, Patrick J. McGovern Foundation. Leads a $1.5B foundation investing $500M+ to make AI work for everyone; writes on responsible AI and equitable technology.
In plain terms: This LinkedIn profile features the work of a foundation leader focused on ethical technology and artificial intelligence. You can read his published articles and view courses on responsible AI to learn how new tools affect the modern workforce.
Why I recommend it: Follow for where philanthropic AI funding is going — useful if you're seeking grants.
Chairman & CEO, Microsoft. Shares perspective on enterprise AI adoption, cloud computing, and the broader direction of the tech industry.
In plain terms: This LinkedIn profile features articles and updates from Microsoft's chief executive on cloud computing and artificial intelligence. You can follow these posts to track major tech trends and see how emerging tools impact modern work.
Why I recommend it: Worth watching to understand where big employers are placing their AI bets.
Multi-stakeholder nonprofit coalition of tech companies and civil society organizations shaping best practices and public dialogue on AI's benefits and risks.
In plain terms: This nonprofit website shares research and guidelines on artificial intelligence from tech companies and community organizations. You can explore free reports and frameworks to learn how AI affects the economy, workplace practices, and technology safety.
Why I recommend it: Where industry and civil society actually sit at the same table.
Independent nonprofit researching the social implications of data-centric and automated technologies, informing policy and public understanding.
In plain terms: This nonprofit research institute studies how artificial intelligence, automation, and data technologies affect work and society. You can read free reports, guides, and articles or attend public events to understand how emerging technology impacts labor and the economy.
Why I recommend it: Excellent on automated management and surveillance at work.
MIT research lab exploring human-AI collaboration, alongside a joint fund with Berkman Klein supporting research on AI's ethical and governance challenges.
In plain terms: This academic research site shares news and projects focused on emerging technology, design, and artificial intelligence. You can explore articles on AI ethics and human collaboration, view new inventions, and find related job opportunities.
Why I recommend it: Browse their projects when you want to see what humane technology looks like in practice.
NYU-based research institute examining the social and political implications of AI, publishing influential annual reports on AI's societal effects.
In plain terms: This research institute analyzes the social, economic, and workplace impacts of artificial intelligence. You can read free reports, policy toolkits, and expert analyses to better understand how AI affects society and the economy.
Why I recommend it: Their reports connect AI directly to jobs and worker power.
University of Oxford institute researching the ethical problems arising from AI, from societal downstream effects to how AI systems reflect human values.
In plain terms: This academic website shares research and analysis on the ethical and social impacts of artificial intelligence. You can read publications, attend public events, and search for fellowships, scholarships, and job openings.
Why I recommend it: Philosophy-forward work — useful when you need the "why", not just the "how".
Philanthropic investment firm supporting organizations that harness technology to empower individuals and communities responsibly.
In plain terms: This philanthropic investment firm focuses on responsible technology and its impact on society. You can check their careers page to search for open jobs and learn about their work.
Why I recommend it: Follow their funding to see which responsible-tech ideas are gaining ground.
UK-based independent research institute (established by the Nuffield Foundation) ensuring data and AI work for people and society.
In plain terms: This independent research website examines the ethical and legal impacts of artificial intelligence and data. You can read policy reports, explore industry analysis, and attend events to understand how emerging technology affects society.
Why I recommend it: Clear, public-interest research with plain-language summaries.
Harvard University center studying the ethics, governance, and societal impact of the internet and AI, and anchor institution for the Ethics and Governance of AI Fund.
In plain terms: This research center explores how artificial intelligence and the internet impact society, law, and ethics. You can read free policy publications, watch educational videos, find public events, and check for open job or fellowship opportunities.
Why I recommend it: Decades of open research and fellowships, much of it free to read.
Global foundation committed to social justice, funding technology initiatives that promote equity, inclusion, and accountable AI governance.
In plain terms: This global foundation funds organizations and individuals working on social issues, workers' rights, and technology. You can search for grant opportunities, apply for fellowship programs, and read research reports on the future of work.
Why I recommend it: A major funder of public-interest technology work worth tracking.
Interdisciplinary Stanford institute advancing AI research, education, policy, and practice to improve the human condition, with strong ethics and governance focus.
In plain terms: This university center shares research, policy updates, and educational resources focused on artificial intelligence. You can browse an AI glossary, read industry reports, and explore professional courses or research fellowships.
Why I recommend it: Their policy briefs are readable and free — a good first stop if AI governance feels opaque.
Nonprofit working to align technology design with human wellbeing, addressing extractive incentives in tech and AI.
In plain terms: This nonprofit organization provides educational materials, policy guides, and research on the societal impact of artificial intelligence and social media. You can take courses, listen to podcasts, and use design toolkits to learn about ethical technology practices.
Why I recommend it: Practical framing for anyone rethinking their relationship with their devices.
Research center developing AI systems that are provably beneficial and aligned with human values.
In plain terms: This university research center focuses on creating safe and beneficial artificial intelligence. You can read published research papers, follow recent news and blog updates, and explore opportunities to work with their team.
Why I recommend it: Technical AI safety, explained by the people who defined the field.
Founded by Joy Buolamwini, AJL combines art and research to expose racial and gender bias in AI and mobilize advocates, researchers, and industry toward more accountable algorithms.
In plain terms: This organization researches and exposes bias and discrimination in artificial intelligence systems, including automated hiring tools. You can explore educational materials, learn about the social impacts of technology, and report unfair automated practices.
Why I recommend it: Start here if you have ever been misjudged by an automated system — including a hiring one.
Analysis at the intersection of finance and technology trends, by Byrne Hobart.
In plain terms: This newsletter provides in-depth articles analyzing trends, strategies, and news across the technology and finance industries. You can read detailed company profiles to understand market shifts and explore an included job board.
Why I recommend it: Dense, but it explains where the money behind tech is actually going.
Reporting on the intersection of Silicon Valley and democracy, by Casey Newton.
In plain terms: This publication delivers reporting and analysis on artificial intelligence, social platforms, and the tech industry. You can read free articles to stay updated on how new technology impacts modern work and business.
Why I recommend it: Independent accountability reporting on the platforms we all depend on.
Power dynamics and inside stories from Big Tech, by Alex Kantrowitz.
In plain terms: This newsletter and podcast covers inside reporting on major tech companies and their impact on society. You can read weekly updates and listen to interviews to stay informed about the technology industry.
Why I recommend it: Good on how decisions inside big companies land on workers.
Analysis of the strategy and business side of technology and media, by Ben Thompson.
In plain terms: This website provides articles and podcasts that analyze the business strategy and impact of technology companies. You can explore in-depth commentary to better understand industry trends and how modern tech businesses operate.
Why I recommend it: Teaches you to read industry news as strategy instead of headlines.
Announcement of Spaces, an extension to the open AT Protocol (the tech behind Bluesky) that supports private, permissioned data.
In plain terms: The team behind Bluesky opened an alpha for Spaces, a way to store private or group-only data on their open protocol. If you build community tools, it is an early look at owning your data instead of renting a platform.
From the site: Atproto Spaces, formerly known as “the permissioned data protocol,” is a new extension to atproto that enables non-public data. The alpha is now officially open.
Why I recommend it: If you build community or product, open protocols are a real alternative to renting an audience from a platform. Worth watching early.
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)
Nonprofit publication covering the intersection of technology, platforms, and democratic institutions.
In plain terms: This nonprofit publication provides news, opinion, and analysis on how technology impacts government and democracy. You can read articles and listen to podcasts to stay informed on tech laws, platform regulations, and artificial intelligence ethics.
From the site: Tech Policy Press is a nonprofit media and community venture intended to provoke new ideas, debate and discussion at the intersection of technology and democracy. We publish opinion and analysis.
Why I recommend it: They publish outside contributors — a real place to build a byline in this field.
Princeton scholar on race, technology, and justice, author of Race After Technology.
In plain terms: This website features the work of scholar Ruha Benjamin on race, justice, and modern technology. You can read her articles, explore her books, and access educational resources on the social impact of innovation.
From the site: Ruha Benjamin is an Associate Professor of African American Studies at Princeton University, where she studies the social dimensions of science, technology, and medicine.
Why I recommend it: Essential reading before you take any job building automated decision systems.
Social Science Research Council program funding and publishing work on technology, power, and public life.
In plain terms: This research platform publishes articles and essays exploring how technology and artificial intelligence affect workers and society. You can read expert reviews and analyses to learn about labor protections, tech ethics, and workplace automation.
From the site: The Just Tech Platform is a forum, catalogue, and showcase for researchers and practitioners at the nexus of technological development, inequity, and social justice.
Why I recommend it: Great source of fellowships and calls for proposals if you want funded research work.
Independent, community-rooted AI research institute founded by Timnit Gebru, studying the real harms of AI instead of the hype.
In plain terms: This independent institute studies the real-world harms and community impacts of artificial intelligence. You can explore their research publications, learn how technology affects diverse groups, and look for open career opportunities.
From the site: The Distributed AI Research Institute is a globally distributed organization of academics, activists, and engineers conducting community-rooted research.
Why I recommend it: Start here if you want the research-backed counterweight to AI marketing.
Community awareness project mapping U.S. AI data centers and the local issues they create.
In plain terms: This interactive map tracks major AI data center projects and proposals across the United States. You can explore the local environmental impacts of these facilities and submit reports about issues in your area.
From the site: Interactive map of major AI data centers across the United States — built, being built, proposed and cancelled. Understand the community impact and report issues in your area.
Why I recommend it: Check the map for your area before a data center becomes news in your town.
Newsletter reporting on the fight to reshape technology in the public interest.
In plain terms: This newsletter reports on efforts to reshape technology in the public interest. You can read regular articles to stay informed about technology ethics and industry reform.
From the site: Idea Trafficking. Click to read Hard Reset, by Trafficker 01, a Substack publication. Launched 5 years ago.
Why I recommend it: Skimmable and current — good for staying briefed in ten minutes a week.
Project and publication examining what we teach AI systems and what those choices say about us.
In plain terms: This project shares anonymous handwritten notes about people to examine what humans teach artificial intelligence systems. You can read the publication to reflect on the personal choices and ethics behind modern technology.
From the site: Anonymous handwritten notes about people
Why I recommend it: Useful for language and framing when you explain AI risk to non-technical people.
Brookings fellow writing on AI, workers, and the future of good jobs.
In plain terms: This newsletter features writing from a Brookings fellow about artificial intelligence, workers, and the future of jobs. You can read it to stay informed about how technology impacts the modern workplace.
From the site: Click to read Molly Kinder on Substack. Launched 15 days ago.
Why I recommend it: Her worker-first framing is exactly how to talk about AI in a job interview.
Harvard historian and New Yorker writer placing today's technology fights in a much longer story.
In plain terms: This website collects the books, essays, and interviews of historian and writer Jill Lepore. You can read her work to explore historical perspectives on law, politics, and modern technology.
Why I recommend it: History gives you perspective that keeps you steady in a hype cycle.
The nation's Black think tank, with a technology policy program focused on equity in the digital economy.
In plain terms: This research organization provides reports and data on workforce policy, technology, and economic issues affecting Black Americans. You can explore their research briefs, reports, and events to learn about labor trends and policy solutions.
From the site: About The Joint Center for Political and Economic Studies is a 501(c)(3) non-profit organization based in Washington, D.C. that creates ideas to improve the socioeconomic status and civic engagement of African Americans. Founded in 1970 to support newly-elected Black officials who were moving from civil rights activis…
Why I recommend it: Their tech policy team publishes work you can cite and hires people from nontraditional paths.
Job board for social impact technology roles at nonprofits, government, and mission-driven companies.
In plain terms: This job board features technology roles focused on social impact. You can find openings at nonprofits, government agencies, and mission-driven companies.
Why I recommend it: Fewer listings than the big boards, but a much higher share worth applying to.
Reporting on the political ideology and ambitions of Silicon Valley's power brokers.
In plain terms: This publication offers investigative reporting on the political ideologies and ambitions of powerful technology leaders. You can read articles and analysis to understand how tech industry figures influence government and democracy.
From the site: Silicon Valley tech billionaire politics: authoritarianism, fascism, plutocracy, weirdness
Why I recommend it: Context on who is funding what — helpful when you vet a potential employer.
Author and activist writing on platform power, monopoly, and digital rights.
In plain terms: This website collects articles, books, and podcasts focused on digital rights, tech monopolies, and online privacy. You can read critical essays, listen to podcast discussions, and download free books to better understand how modern technology affects society.
Why I recommend it: Read him for the vocabulary — he names the patterns other people only feel.
Ongoing research archive on how AI and automation affect wages, workers, and economic power in the U.S.
In plain terms: This research archive provides articles and reports on how artificial intelligence affects the workforce. You can explore these studies to learn how new technologies and automation impact jobs, wages, and worker protections.
From the site: Content archives for the Washington Center for Equitable Growth’s work on AI, tech, & the economy.
Why I recommend it: Use this when you need real numbers on AI and jobs for a proposal or interview.
