Resource Hub
3505 hand-picked resources for students, job seekers, career changers, founders, and AI learners — 100% free to browse with no account required.
Answers come only from resources in this hub, with the sources listed.
33 resources
Grigory Sapunov describes how AI has changed his coding, research, company administration, reading and schedule. This is a personal account of AI-assisted work, not a representative study of software jobs.
Apollo's October 2026 Daily Spark commentary examining economic indicators of AI-related gains outside technology companies. Its conclusion is the firm's interpretation of the data shown.
An October 20, 2026 ISC2 announcement of a coalition of employers, tech companies and workforce groups that will build a toolkit describing how cybersecurity jobs, roles and skills are changing with AI.
Fast Company (October 2026) on how AI data companies are recruiting academics and professionals for high-paid model-training work, and what that means for careers.
Stanford HAI article arguing that framing AI adoption around productivity gains alone undermines employee buy-in, and outlining alternative approaches to workplace AI rollout.
MIT Sloan summary of the AI Decision Matrix from MIT CISR researchers, based on interviews with 30 executives. It sorts decisions by ambiguity and risk: routine, low-risk decisions are candidates for automation, while ambiguous, high-stakes ones need a strong human lead.
Google Research post on a three-month randomized controlled trial with patent attorneys: AI use raised work quality across the board, but only senior lawyers showed stronger unassisted judgment afterward; junior lawyers showed no average skill gain.
A worker survey on whether AI is already causing job losses finds a more complicated picture than the headlines suggest, looking at how AI is actually affecting employment and day-to-day work.
A thought-leadership piece from Jabra on how work is moving beyond the keyboard, toward voice and other new ways of interacting with technology at work. Described from its title and context only — the page blocked our reader, so the summary could not be checked against the full text.
Why I recommend it: Page blocked our reader; description is based on the title and source only.
TechCrunch reports that Shopify is opening its checkout flow to AI agents that operate inside the browser, letting agents complete purchases on behalf of shoppers — a step toward agent-driven commerce across the millions of stores on its platform.
Business Insider covers Bill Gates' argument that companies replacing human labor with robots should have to pay the same payroll (FICA) taxes they would for human employees, so automation does not quietly drain the tax base that funds Social Security and Medicare.
VentureBeat reports that developers at OpenAI's DevDay described model safeguards flagging routine aerospace, robotics and security work as risky — costing hours, disrupting workflows, and pushing some tasks to other models — as OpenAI tries to reduce harmless refusals while tightening safeguards on more capable models.
Reflects on increased fatigue during AI-assisted programming and suggests it may sometimes reflect a shift toward higher-judgment human work rather than workflow failure. The author explicitly presents this interpretation as conjectural and distinguishes it from glorifying burnout.
Describes a Git-backed ticket queue and owner inbox for coordinating AI coding agents while keeping decisions and project context outside chat windows. The author reports that handling and closing one review item at a time reduces distraction, while noting that the workflow remains tiring.
Uses historical examples of coal, lighting, farming, bank tellers and telephone operators to distinguish increased demand for output from continued demand for human labor. Applies that distinction to AI-assisted software development, offering explicitly speculative predictions about software output and developer employment.
Second Thoughts newsletter essay reflecting on predictions about AI progress and their accuracy heading into 2026.
WEF's 2025 report on expected job growth and decline, skills shifts and employer plans through 2030, based on a global employer survey.
McKinsey Global Institute discussion paper taking a systems view of the AI economy: AI capabilities improve exponentially while infrastructure, organizational change and social acceptance move at different speeds, creating shifting bottlenecks.
MDRC report on how workforce and career-navigation programs can use AI tools in advising, with lessons from early pilots.
A blog post arguing that organizations which do not give employees time to practice with AI coding tools risk falling behind competitors that do, with examples of workflow shifts in software engineering teams.
A State of AI page by Nathan Benaich of Air Street Capital, reviewed October 31, 2026, comparing separate estimates of AI adoption, spending and financial returns, and explaining who each source measured, when, and what it counted as adoption.
A catalog of policy proposals for responding to economic disruption from transformative AI, published by the Windfall Trust. Proposals are organized into five domains — public and social investments, labor market adaptation, wealth capture, regulation and market design, and global coordination — with a filterable page listing every policy.
Research article examining links among industry exposure to AI, occupational insecurity, moral disengagement, and counterproductive work behavior. Published September 2026; abstract free to read, full text may require access.
Group building shared language and current evidence on how AI affects creative work and creative workers.
