The number is 18%. That’s the layoff probability for a tech worker who isn’t regularly using AI, according to a study based on Gallup survey data published June 19, 2026. For those using AI at least monthly, the figure drops to 6%. Three times the risk. Not directionally, not theoretically, statistically, after the researchers controlled for age, education level, and which part of the tech sector the worker is in.
The research drew on Gallup’s database of more than 23,000 U.S. workers, including, according to the study, approximately 660 respondents who reported involuntary job loss. The survey was conducted in February 2026; the analysis and findings were published four months later in June. That lag is standard for peer-reviewed research, and the findings are being treated as new. But readers should know the labor market snapshot is from earlier in the year.
Here’s what makes this study different from the aggregate layoff counts that have dominated coverage of AI displacement. Challenger data and employer-reported figures measure what companies say they’re doing. This study measures what actually happened to individual workers, sorted by a behavioral variable, AI usage frequency, that the employer never publicly cited. The result is a cleaner signal. The catch is one that Gallup flags directly: only 1% of laid-off workers name AI as the primary reason for their job loss. Workers aren’t being told automation was a factor. The 18% figure captures the correlation anyway.
Evidence
The real story is the gap between how AI displacement appears in official data and how it operates in practice. Employers restructure. Roles get eliminated. The official reason is “organizational efficiency.” The statistical reality, per this research, is that the workers who weren’t using AI were three times more likely to be the ones who lost their jobs.
This matters for two audiences at once. Tech workers looking at their own career risk now have an empirically grounded number attached to a behavior they can change. HR and talent teams building upskilling programs have a defensible figure for internal justification, not “AI is coming,” but “18% vs. 6%, controlled for your demographics.”
The methodological detail worth noting: the Gallup and Bloomberg research reports the risk disparity held after controlling for demographic and sub-sector factors, though this specific claim relies on the research’s own reporting rather than independent methodological verification from additional sources.
Who This Affects
What to watch
whether this study triggers further research from independent institutions trying to replicate the finding with different populations or time periods. The 3x risk ratio is striking enough that it will attract scrutiny. Watch also for corporate responses, HR and L&D teams will cite this number in upskilling program proposals, and that creates a paper trail of how employers are interpreting the data.
The accumulating empirical case for AI-driven labor market stratification is getting harder to dismiss as anecdote. Challenger’s May 2026 data established AI as the top self-reported layoff driver among employers. The 113,000-cut aggregate through mid-May showed the volume. This Gallup study adds the individual-level probability layer. Three different methodologies, three different data sources, and they’re pointing the same direction.