Expert takes Sep 12, 2026

This quarter · retrospective, originally posted August 21

A viral AI-layoffs number was recomputed by a named person on the day it peaked and cut by nearly two-thirds — and we accept neither figure.

This column has no new named view this week; the pick is an older item that matters. On August 21 a post went viral claiming that American tech companies had laid off 170,000 people in the past year "with the explicit claim that these cuts are due to AI," against 1,547 new data center jobs (original post, 2026-08-21). The same day Susan Zhang, who previously led training of Meta's OPT-175B large model, took it apart in two moves: the number's source website is tied to a crypto token, and her own recount came to about 50,000 (Susan Zhang, 2026-08-21). ⚠️ Her recount method was "having two AI agents fix the numbers": not reproducible, no public procedure, no third-party check, so as evidence it is no stronger than the 170,000, and she did not claim precision herself. ⚠️ There is also a problem neither side raised: 170,000 is a year of layoffs across the entire national tech sector, and 1,547 is data center jobs in the operating phase, excluding construction. Dividing one by the other never meant anything. Which of our calls it supports or rebuts: it runs in the same direction as an existing record of ours: that attributing layoffs to AI is a systematically exaggerated phenomenon. The strongest evidence for that record is New York State's mass-layoff filings: more than 160 filings in the first year, with the share ticking the AI box close to zero. ⚠️ But the two differ by an order of magnitude in evidentiary strength, so this item cannot be used to vouch for that one. What is actually useful to a reader is not a better number but two checks you can run: who maintains the data source, and how far the correction travels. Here the correction reached about one forty-eighth of the original post's audience.