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AI: Jobs, Power & Money
16APR

BLS skips; NY Fed fills the vacuum

4 min read
13:29UTC

The Bureau of Labor Statistics skipped its scheduled GenAI workplace paper on 14 April. The Federal Reserve Bank of New York published its own survey the same day, finding 62% of American workers now expect AI to raise unemployment within twelve months.

EconomyDeveloping
Key takeaway

Two in three American workers now price AI as a net job-destroyer; the BLS missed its own publication date.

The Bureau of Labor Statistics had scheduled a dedicated Generative AI workplace publication for 14 April 2026; the date came and went without a release or a public explanation. The same day, the Federal Reserve Bank of New York published a paper on its Liberty Street Economics blog drawing on the Survey of Consumer Expectations (SCE), the NY Fed's monthly household survey covering labour market, credit and income conditions. The SCE found 39% of employed Americans use AI tools at work, 62% expect AI to increase unemployment within 12 months, and AI access is stratified 4.2x by income (66.3% of workers earning over $200,000 versus 15.9% earning under $50,000) and 2.6x by education.

Only 15.9% of US employers offer any AI training; 11% actively prohibit AI tool use on the job. The gap between the two numbers describes an employer population broadly divided between firms banning the tools and firms offering nothing, with a small minority running formal training programmes. Workers are pricing that access directly: those without training say they would accept an 11.4% salary cut to join a job that provides it, while those with AI-trained roles demand a 24.2% premium to move to one without. The training stratification documented alongside the 4.3x Fed Board adoption spread context) turns AI access into a priced labour-market benefit without any official federal instrument tracking its distribution.

The Hawley-Warner coalition had written in March specifically to ask the BLS to build that instrument . The BLS was scheduled to respond on 14 April with a dedicated GenAI paper; instead the Federal Reserve Bank of New York provided the day's federal signal, drawing on worker self-reports rather than employer filings. The practical effect is that the de facto US AI workforce measure has moved from Department of Labor establishment data to a regional reserve bank's household survey. The SCE's 62% unemployment-expectation figure is the first time a federal instrument has captured worker sentiment on AI displacement at nationally representative scale.

Whether the BLS reschedules its paper before May or lets the NY Fed SCE become the standing measure is the decision the Hawley-Warner letter was designed to force. It is also the decision the Leading the Future super PAC fundraising surge is designed to neutralise at the legislative level, by targeting senators who could fund better BLS data collection. On the scheduled publication date, the worker-reported figure was the only one on offer; the agency meant to produce the employer-reported equivalent was absent from its own schedule.

Deep Analysis

In plain English

The government agency responsible for tracking AI's impact on jobs (the Bureau of Labor Statistics) missed its scheduled publication without explanation. A different government body, the Federal Reserve Bank of New York, published survey data the same day showing that 62% of workers expect AI to increase unemployment. That data also found a stark divide: workers earning over $200,000 a year are four times more likely to have AI tools at work than those earning under $50,000. Only 16% of employers offer any AI training at all.

Deep Analysis
Root Causes

The BLS publication was listed in the agency's forward calendar with no announced change. The absence without explanation fits a pattern visible since the Trump administration's restructuring of the Office of Management and Budget's statistical policy function in early 2025: statistical agencies have less insulation from executive branch scheduling pressures than their statutory independence suggests.

The 4.2x income stratification in AI tool access (66.3% for workers earning over $200,000 versus 15.9% earning under $50,000) reveals the deeper structural condition: AI adoption is not diffusing like previous workplace technologies. The internet reached 90% of US workers within 15 years of commercial deployment.

AI tool access is stratifying by income at ratios that suggest it will function as a professional productivity multiplier for high earners while leaving low-wage workers structurally behind; a pattern more consistent with the deployment of private aviation than with mass-market technology.

First Reported In

Update #6 · Three federal surveys, one 34-to-1 gap

Federal Reserve Bank of New York (Liberty Street Economics)· 16 Apr 2026
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