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Stanford's August 2026 Canaries update describes an employment shortfall among young workers in highly AI-exposed occupations, primarily through reduced hiring. The authors stress these are descriptive patterns, not proof that AI caused the gap.
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.
CNBC's August 18, 2026 coverage of LinkedIn's AI Talent Divide study, with comments from LinkedIn economist Kory Kantenga. It reports Gen Z makes up more than two-thirds of hires for forward deployed engineer and AI engineer roles (median pay $199,000 and $166,000), millennials 60% of head-of-AI hires, and women 26% of new AI hires, and closes with his advice on computer science degrees, data experience and side projects.
An eight-page August 2026 report from LinkedIn's Economic Graph Research Institute by Matthew Baird, Silvia Lara and Mar Carpanelli. It finds the typical AI job posting lists a $177K salary versus $80K for non-AI roles, women account for 26% of new AI hires versus 50% in non-AI occupations, about 91% of AI roles are held by workers with a bachelor's degree or higher, and Gen Z makes up more than two-thirds of hires for AI Engineer and Forward Deployed Engineer roles.
McKinsey Global Institute report (September 2026) on how AI, automation and structural trends are reshaping US jobs, examining which skills are growing or declining in demand and the pathways workers take into future jobs.
The City Reporter (Sept. 30, 2026) on OpenAI and Anthropic opening large Manhattan offices and smaller AI companies drawn to NYC's talent pool, including City Council discussion of AI jobs.
Investopedia article on hiring rebounds in roles hit by AI and what that means for the value of related degrees. Investopedia blocked the automated check, so this description is based on the title.
Stanford Digital Economy Lab dashboard tracking employment for workers by age and AI exposure, updated as new payroll data arrives.
Ratio Institute Working Paper No. 388 (Lodefalk, Löthman, Koch, Engberg) on how generative AI relates to hiring across age groups. Free PDF.
Stanford Digital Economy Lab paper using payroll data to track employment changes for early-career workers in jobs most exposed to AI.
California Policy Lab report (June 2026, Hyman, von Wachter and co-authors) using unemployment insurance claims to measure job losses linked to AI. Free PDF.
Joshua Gans and Avi Goldfarb's January 2026 NBER working paper modeling how automating some tasks in a chain of interdependent tasks changes the value of the remaining human work. Free PDF.
Stanford Digital Economy Lab follow-up examining whether interest rates and timing, rather than AI, explain employment shifts among young workers.
A free job board focused on AI, machine learning, data science, and data engineering roles, with company listings and a career insights blog. Job seekers can browse and search openings at no cost; employers pay to post roles.
Reporting on New York City's growing AI and tech industry, what the Mamdani administration's plans could mean, and how CUNY students are positioning for the jobs.
Why I recommend it: Local reporting with a policy angle — useful for New Yorkers, less so elsewhere. The page blocked automated checks but opened in a browser.
A breakdown of ten new AI job titles — from agent orchestration to evaluations specialists — with what each role actually does and what hiring managers screen for.
From the site: Discover the 10 fastest-growing AI roles in 2026. Learn who to hire for ethics, UX, prompt engineering, and more. Stay ahead with AI talent.
Why I recommend it: Written to help companies hire, which makes it a clear read on the titles and skills being asked for right now if you are aiming at AI work.
A vetted marketplace matching senior engineers, researchers and AI specialists with companies. Free for engineers to apply and be matched; companies pay only on hire.
From the site: Hire global, AI-first engineers with Index.dev. Skip delays and scale your tech team with pre-verified, secure, and compliant talent to build faster.
Why I recommend it: Free on the candidate side — you are the product being placed. Expect a real vetting process rather than a quick apply button.
Alphabet company applying AI models to drug discovery, spun out of Google DeepMind.
From the site: Isomorphic Labs is building a future where frontier AI can help to unlock deeper scientific insights, faster breakthroughs, and life-changing medicines.
Why I recommend it: A concrete example of AI applied to something other than chat. Worth a look if you are science-trained and wondering where AI hiring is happening outside big tech.
Montreal research institute founded by Yoshua Bengio: publications, research teams, and programs for students and visiting researchers.
From the site: Mila is a Montreal-based artificial intelligence research institute that brings together researchers from Université de Montréal, McGill University, Polytechnique Montréal and HEC Montréal.
Why I recommend it: Check the students and programs pages if you want to move toward research work. Academic institutes publish their entry routes more openly than companies do.
A fellowship with Vals AI, the team that benchmarks how AI models actually perform, for people who want to work on model evaluation.
Why I recommend it: Evaluation work is where a lot of AI hiring is heading — careful, skeptical people do well here even without a PhD.
A residency at Perplexity for people who want to do applied AI research on a working product rather than in a lab.
Why I recommend it: A residency is the friendliest door into research — you are paid to learn on a real team instead of needing the credential first.
A paid AI research fellowship at DoorDash for summer and fall 2026, working on machine learning problems inside a large operating business.
Why I recommend it: Applied AI inside a logistics company teaches you constraints a lab never will — and the posting names its terms up front, which is a good sign.
A job search built on top of an AI recruiting platform, with best-match ranking, saved job preferences, and filters by category, industry, and experience level across a very large aggregated listing pool.
Why I recommend it: Set your job preferences first — the default feed is enormous and only becomes useful once the matching has something to work with.
A connected-candidate recruiting platform that matches job seekers with employers based on skills and fit, with tools for both candidates and hiring teams.
Why I recommend it: A niche job-matching option worth testing alongside the larger boards; set up a profile and see what roles it surfaces for your skills.
Job board dedicated specifically to AI, machine learning, and big data roles — ML engineering, data science, NLP, computer vision, AI research — aggregated from companies worldwide.
Why I recommend it: If you are specifically targeting an AI/ML role rather than "tech in general," this is more signal, less noise than a general tech board.
AI-powered job search engine focused on IT, engineering, and science roles, with profile-based matching rather than manual keyword search.
Why I recommend it: Good complement to ai-jobs.net if you want matching done for you instead of browsing listings yourself.
A free job search engine that indexes listings directly from company career pages instead of relying on job board postings, with unusually deep filters for salary, remote policy, visa sponsorship, and experience level.
Why I recommend it: I like this one because the listings come straight from employers, so you run into fewer ghost jobs and reposted duplicates. Use the filters hard — two or three well-matched searches beat scrolling for an hour.
A marketplace of remote and contract roles, many of them AI training and expert-review work, matched to your skills and availability.
From the site: Explore remote opportunities with top companies worldwide. Find roles that match your skills and work preferences.
Why I recommend it: Good for bridge income between full-time roles, and a way to build recent, verifiable work history. Read the pay terms on each listing carefully before you commit hours.
Startup job board (formerly AngelList Talent) with salary and equity ranges listed up front.
Why I recommend it: Startups read profiles more than resumes here — fill yours out completely and keep the "what I want next" line specific.
AI-driven talent marketplace matching professionals to contract and full-time work via interviews.
Why I recommend it: The intake interview is the whole application — do it when you are sharp, not at midnight.
Apprentices are hired as full-time employees from day one, with 20% of working hours set aside for learning. Tracks include AI/ML engineering and backend engineering.
In plain terms: This paid apprenticeship program helps self-taught coders, bootcamp graduates, and career changers transition into technical roles at LinkedIn. You can apply for full-time engineering positions to gain hands-on experience, receive mentorship, and spend paid work hours building your skills.
Why I recommend it: The application looks past resumes and leans on essays and a take-home project, so this is a strong fit if your resume undersells you.
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.
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.
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.
