Study Says AI Reshaping Labor Markets

A new Dallas Federal Reserve study suggests generative artificial intelligence adoption is reshaping the Texas labor market, primarily by reducing demand for some types of work, not so much by eliminating existing positions but by reducing demand for recent college graduates. 

“The most exposed occupations are generally in software development, web design and other computer-heavy occupations,” the report says. “Managers, clerical workers, editors and other white-collar occupations are also subject to some of the highest levels of AI task exposure.”

source: Dallas Federal Reserve

“There is strong evidence that GenAI has decreased labor demand for occupations consisting of tasks that can be performed by these new tools,” the researchers say. Still, “the overall effect on aggregate online job posting behavior thus far has been modest.”

“Recent college graduates, whose unemployment rate rose to unusually high levels during this period of rapid GenAI adoption, are where effects of GenAI on employment and earnings are likely to first appear,” the study suggests. 

So far, AI's labor-market effect is that “fewer people get hired into certain jobs,” rather than “large numbers of existing workers fired from those jobs.” 

Study	Data / period	Main finding	Job impact
Brynjolfsson, Chandar & Chen — “Canaries in the Coal Mine” (Stanford, revised 2026)	ADP payroll data, millions of workers, through June 2026	No widespread economy-wide displacement, but employment of 22–25-year-olds in highly AI-exposed occupations is 19% below the counterfactual. Experienced workers show no comparable gap.	Strong evidence for the hiring channel. The authors say the adjustment occurs primarily through reduced hiring of young workers rather than increased separations. (Stanford Digital Economy Lab)
Hosseini Maasoum & Lichtinger — “Generative AI as Seniority-Biased Technological Change” (Harvard, 2026 revision)	65 million résumés, 280,000+ firms	Junior employment falls following GenAI adoption, particularly in highly exposed occupations; senior employment is largely unchanged.	Decline is driven primarily by slower hiring rather than increased separations. (SSRN)
Tucker — “You're (not) Hired” (U.S. Census, 2026)	Matched employer-employee administrative data	Early-career employment in the most AI-exposed industry/state cells fell 12% over 10 quarters after ChatGPT.	The paper finds the decline in employment was primarily caused by a large decrease in hiring. This is perhaps the clearest administrative-data evidence of the mechanism. (Census.gov)
Liu, Wang & Yu — “Labor Demand in the Shadow of Generative AI” (World Bank, 2026 revision)	285 million U.S. online job postings, 2018–2025	Postings for occupations highly vulnerable to AI substitution fell 9% relative to less-vulnerable occupations, with the differential reaching 15% by the third year.	Direct evidence of reduced labor demand, rather than layoffs. Particularly important because it examines vacancies at enormous scale. (SSRN)
Audoly, Guerin & Topa — New York Fed, “Do Job Postings Show Early Labor-Market Effects of AI?” (2026)	U.S. Lightcast postings	Overall hiring has slowed, but they find little evidence of a distinct AI-driven decline in postings for AI-exposed occupations.	Important counterweight: the aggregate slowdown in postings cannot confidently be attributed to AI. (Liberty Street Economics)
Federal Reserve Board — “AI Adoption and Firms' Job-Posting Behavior” (2026)	Firm/industry AI adoption + job postings	No evidence that firms or industries with greater AI adoption have reduced total job postings.	Suggests that if AI is eliminating some positions, firms may be switching hiring toward other jobs, rather than simply reducing total hiring. (Federal Reserve)
Gimbel, Kendall & Nunn — Yale Budget Lab (2026)	Monthly CPS employment/wage data	After controlling for differences between exposed and unexposed occupations, they find no statistically or economically significant aggregate employment or wage effect.	Again, little evidence of broad existing-job destruction. The effects may be concentrated in particular populations. (The Budget Lab)
Humlum & Vestergaard — “Still Waters, Rapid Currents” (NBER, 2025/26)	Danish administrative records + AI adoption surveys	No detectable effect on earnings or hours, even among early adopters and highly exposed workers. But substantial task restructuring and occupational switching occurred.	Little evidence of job elimination so far. Firms appear initially to be reorganizing work rather than cutting employment. (National Bureau of Economic Research)
Chandar — “Tracking Employment Changes in AI-Exposed Jobs” (2025)	U.S. CPS, Q4 2022–Q1 2025	No substantial aggregate employment/earnings difference in highly exposed occupations, although software and customer-service occupations diverge.	Overall employment effects small; evidence of localized employment declines, not economy-wide displacement. (SSRN)
Frank et al. / related job-posting research summarized by Stanford	U.S. job postings	Several studies find greater declines in postings in AI-exposed occupations. But some declines began before ChatGPT and correlate with interest rates/remote work.	Supports reduced postings, but attribution to GenAI is contested. (Brookings)
“Winners and losers of generative AI” (JEBO, 2025)	Online freelance marketplace	About 10% of postings were judged substitutable by GenAI; demand for those skill clusters fell as much as 50% in short-term roles.	Strong evidence of demand substitution in particular tasks, although aggregate freelance demand did not fall. (ScienceDirect)
PwC 2025 AI Jobs Barometer	Lightcast job postings, 2019–2024	U.S. occupations with greater GenAI exposure experienced substantially slower job-posting growth: roughly 2% vs. 20% for less-exposed occupations.	Strong descriptive evidence of slower demand growth, though not necessarily causal evidence of AI. (PwC)

But trends could, or maybe, should, change over time, as actual job displacement or elimination, plus creation of new jobs, happens. 

Effect	Evidence so far
Mass layoffs caused by GenAI	🔴 Little evidence
Economy-wide employment decline	🔴 Little/no evidence
Reduced total hiring because of AI	🟡 Weak/mixed
Reduced hiring in particular AI-exposed occupations	🟢 Increasing evidence
Reduced entry-level hiring	🟢 Significant evidence, but causality disputed
Reduced job postings in AI-substitutable occupations	🟢 Fairly strong evidence
Task substitution/reorganization	🟢 Strong evidence
Productivity increases without employment reductions	🟢 Strong evidence
Wage effects	🟡 Generally small so far
Long-term displacement	❓ Still largely unknown

On balance, it is possible net job creation could happen, as AI creates new jobs and roles, despite AI-induced reductions. 

Study	Period / data	Main finding	Implication
Autor, Chin, Salomons & Seegmiller, “New Frontiers: The Origins and Content of New Work” (QJE, 2024)	U.S., 1940–2018; ~35,000 Census occupations	About 60% of employment in 2018 was in occupations that did not exist in 1940. New work emerged alongside technological change, particularly in professional and service occupations after 1980. (National Bureau of Economic Research)	Perhaps the strongest evidence that technology doesn't simply redistribute a fixed number of jobs. It creates new categories of work.
Autor, “Why Are There Still So Many Jobs?” (JEP, 2015)	Historical review	Automation substitutes for labor in particular tasks but also complements workers, raises productivity and output, and increases demand for labor elsewhere. (TopCat)	Explains why technological progress can eliminate particular jobs without eliminating work generally.
Acemoglu & Restrepo, “Automation and New Tasks” (JEP, 2019)	U.S. historical/employment evidence	Automation creates a displacement effect, but creation of new tasks produces a reinstatement effect that raises labor demand. (AEA Publications)	Provides a theoretical framework for understanding why job creation can offset automation.
Acemoglu & Restrepo, “The Race Between Man and Machine” (AER, 2018)	Long-run economic model	Technology that automates existing tasks reduces labor demand, while creation of new tasks has the opposite effect. (American Economic Association)	Net employment depends on the balance between automation and new-task creation.
Bessen, “Automation and Jobs: When Technology Boosts Employment” (2017)	Historical industries, including textiles, steel and automobiles	Industries experiencing rapid productivity growth sometimes experienced employment growth, because lower costs stimulated demand sufficiently to offset labor-saving technology. The OECD reviews this evidence. (OECD)	Lower prices can create enough additional demand to more than compensate for labor-saving technology.
Mann & Puttmann, “Benign Effects of Automation” (2018)	U.S. counties/industries, patent data	Automation innovations were associated with declining manufacturing employment but increasing employment in services. (OECD)	Job destruction and creation can occur in completely different industries and locations.
Autor & Salomons, “Is Automation Labor-Displacing? Productivity Growth, Employment, and the Labor Share” (2018)	28 industries, 19 advanced economies, 1970–2015	Productivity improvements can reduce employment within an industry, but positive spillovers to other industries more than offset those losses. The OECD summarizes the evidence. (OECD)	This is especially important: you shouldn't look only at the industry where automation occurs.
OECD, Employment Outlook 2019	OECD countries, long historical perspective	Despite substantial technological displacement, overall employment has generally grown. The OECD concludes that historically the net effects of major technological revolutions on employment have been positive. (OECD)	Broad institutional review supporting the historical pattern.
OECD, Technology, Productivity and Job Creation (1998)	OECD historical evidence	Technological change destroys jobs in some industries while creating jobs in others; historically the process produced net job creation as new industries replaced old ones and demand expanded. (OECD)	An earlier, broad cross-country examination reaching the same conclusion.

U.S. Court Rejects Structural Remedies for Google AdX: Perhaps Not a Surprise

The U.S. District Court in Virginia has rejected the U.S. Department of Justice structural remedies in the case of Google advertising antitrust, and instead ordered behavioral remedies. 

The DoJ had asked for divestiture of AdX, among other remedies. Some would have questioned whether divestiture and untangling was feasible, in any case.  

A forced divestiture would likely have meant:

• Possible loss of about 4.1 percent of Google's revenue and 1.5 percent of operating profit (2020 estimate)

• Loss of vertical integration (ability to run the ad server Google Ad Manager, the exchange (AdX) and the buy-side tools all in one stack)

• Losing AdX privileged access to ad server auction data and demand

• Losing capabilities such as  "last look" that advantaged AdX bids over rival exchanges

• Losing the ability to steer publisher and advertiser demand toward its own exchange by default

• Losing a business moat compared to Xandr (Microsoft), PubMatic, Magnite or OpenX.

The financial hit from losing AdX's direct revenue arguably would have been modest. The larger implications were competitive: 

• Losing the ability to internally route demand and auction advantages toward its own exchange

• Losing market share to rivals in the near term

• Facing execution risk from a messy technical separation.

Divestiture would not have affected Google's dominant position in the broader digital ad market (search, YouTube, Google Ads), as none of those were alleged to be monopolies. 

The actual behavioral remedies will be agreed upon by Alphabet and DoJ over the next month. 

Court watchers might have bet on behavioral rather than structural remedies. 

In modern U.S. computing history, courts and agencies overwhelmingly settle on behavioral remedies even after finding liability, and the handful of times a true structural breakup was ordered, it either got overturned on appeal or never survived to implementation. 

The one clean exception is AT&T in 1982 (not a "computing" company, but the antecedent case for how computing cases are usually discussed).

Case	Period	Allegation	Remedy Sought	Outcome	Type
United States v. AT&T (1956 consent decree)	1949–1956	Monopolizing telecom equipment	DOJ sought breakup	Settled: AT&T confined to regulated telephone business, barred from computing/commercial ventures	Behavioral
United States v. IBM	1969–1982	Monopolizing mainframe computing	DOJ sought full breakup	DOJ voluntarily dismissed the case in 1982 as "without merit"	None (dropped)
United States v. AT&T	1974–1982	Monopolizing local/long-distance telephony	DOJ sought breakup	Settled via consent decree: AT&T split into seven regional "Baby Bells"	Structural
United States v. Microsoft	1998–2001	Monopoly maintenance (browser tying)	DOJ sought company split (OS vs. applications)	District court ordered breakup (2000); reversed on appeal; settled 2001 on conduct terms	Behavioral (final)
European Commission v. Microsoft	2004	Abuse of dominance (Windows Media Player tying, interoperability)	Conduct remedies + unbundling	Fine + required unbundled Windows version and interoperability disclosures	Behavioral (with a quasi-structural unbundling element)
FTC v. Intel	2009–2010	Exclusionary dealing with OEMs	Behavioral remedies	Settled via consent order; no divestiture	Behavioral
FTC v. Qualcomm	2017–2020	Exclusionary licensing practices	Injunctive/behavioral remedies	9th Circuit reversed district court; FTC lost entirely	None (FTC lost)
EU v. Google (Shopping, Android, AdSense)	2017–2019	Self-preferencing, Android bundling, ad exclusivity	Conduct remedies + fines	Fines (~€8B combined) plus behavioral conduct changes; no breakup	Behavioral
United States v. Google (Search)	2020–2025	Illegal monopoly via default-placement deals	DOJ sought Chrome/Android divestiture	Judge Mehta (Sept. 2025) denied divestiture; ordered data-sharing and end to exclusive default contracts	Behavioral
United States v. Google (Ad Tech)	2023–2026	Illegal tying of ad server and exchange	DOJ sought AdX divestiture	Judge Brinkema (Sept. 2026) denied divestiture; ordered behavioral remedies	Behavioral
FTC v. Meta	2020–2025	Illegal monopoly via "buy or bury" acquisitions	FTC sought Instagram/WhatsApp divestiture	Judge Boasberg (Nov. 2025) ruled FTC failed to prove current monopoly power; case dismissed	None (FTC lost)

Of eleven major computing/telecom cases spanning roughly 70 years, only the 1982 AT&T case resulted in an actual, implemented structural remedy. 

Microsoft's breakup was ordered but reversed before it took effect. 

Three of the most recent, highest-profile cases (Google Search, Google Ad Tech, Meta) all had the DOJ or Federal Trade Commission explicitly request divestiture, and in every one of them the court either declined to order it or ruled the government hadn't proven its case at all.

Courts in Microsoft, Google Search, and Google Ad Tech all cited the risk of "incredibly messy and highly risky" separations of deeply integrated software/data systems. Judge Amit  Mehta used almost that exact language on Chrome, and Judge Lconic Brinkema's opinion in the AdX case echoed Google's own arguments about technical infeasibility.

Judge Mehta explicitly distinguished growth from "superior product, business acumen, or historic accident" versus growth from illegal conduct, and found Google's dominance wasn't attributable enough to the violation to justify divestiture.

In the Google ad tech case, testimony raised real doubt about whether a workable buyer even existed for AdX, since a divested asset built to be part of one company's stack often isn't viable standing alone.

Fast-moving markets are another issue. Judge James Boasberg's Meta ruling leaned on the idea that computing markets change too quickly for old monopoly findings to still describe today's competitive reality, undermining the case for any remedy, structural or not.