AI-linked roles have accounted for more than 750,000 new jobs created in the US since 2023, according to LinkedIn data cited by The Kobeissi Letter. "AI-related employment is surging," the firm wrote on X, pointing to a shift already visible across hiring trends in the labor market.
AI-related employment is surging:
— The Kobeissi Letter (@KobeissiLetter) October 4, 2026
AI-linked roles have accounted for more than +750,000 new jobs created in the US since 2023, according to LinkedIn estimates.
This surge has been led by data annotators, with +282,000 new positions created, followed by data center jobs at… pic.twitter.com/wQALBnA4Vj
Data annotators lead the hiring surge by a wide margin
Data annotators topped the list with 282,000 new positions, more than double the next closest category. Data center jobs followed with 117,000 new roles, and AI engineers added 105,000. Combined, those three categories accounted for 504,000 new jobs, roughly two-thirds of the total AI-related hiring tracked since 2023. Smaller but notable categories included Head of AI roles at 29,000 and forward deployed engineers at 15,000, according to the same LinkedIn and Wall Street Journal-sourced data.
Data annotation, the practice of labeling images, text, and audio so machine learning models can be trained on structured examples, has become one of the AI industry's most labor-intensive and fastest-growing functions. Every large language model and computer vision system depends on enormous volumes of human-labeled training data, and as frontier labs push toward more specialized and higher-quality datasets, demand for annotators with subject-matter expertise, not just general labeling, has grown accordingly. Scale AI, one of the largest annotation-focused companies, built a multi-billion dollar valuation almost entirely around this labor category before Meta took a $14.3 billion stake in the company in June 2025, a deal that itself signaled how central annotation work had become to frontier AI development.
AI pay sits well above the broader labor market
AI-related positions carry a median salary of approximately $180,000 on LinkedIn, more than double the $80,000 median across all US jobs. That pay gap reflects more than scarcity of technical talent. It also captures how concentrated the current AI buildout is among a small number of well-capitalized companies willing to pay a premium for roles tied directly to model development, infrastructure, and deployment. AI engineer compensation in particular has been pushed upward by a competitive hiring environment among frontier labs, where OpenAI, Anthropic, Google DeepMind, and Meta have each disclosed aggressive recruitment packages for senior researchers over the past two years, a dynamic that filters down into median compensation figures across the broader AI engineering category even for roles outside those specific companies.
Data center construction is driving a parallel hiring wave
The report ties this labor surge directly to the physical buildout of AI infrastructure, particularly data centers. The 117,000 new data center jobs show a construction and operations boom running in parallel with the software and model-development side of the AI industry.
Data center capital expenditure has become one of the largest single categories of corporate investment in the US economy. Estimates place cumulative AI-related capital spending above $5 trillion between 2025 and 2030, a figure cited in BlackRock's own recent research on AI's intersection with digital asset markets. That spending translates directly into construction, electrical, and operations hiring that extends well beyond the software engineers typically associated with the AI boom. McKinsey's data center demand modeling projects non-AI, AI training, and AI inference workloads will together drive total data center power demand from current levels toward 250 gigawatts by 2030, a scale of physical infrastructure that requires sustained hiring across skilled trades, not just computer science graduates.
What this data signals about the shape of the AI labor market
The concentration of new hiring in data annotation and data center roles, rather than purely in AI engineering, suggests the AI boom's employment effects are broader and more labor-intensive than a narrow reading of "AI jobs" as software engineering positions alone would suggest. Annotation work, in particular, often requires less specialized technical training than engineering roles, meaning the AI labor boom has created entry points into the industry for a wider range of workers than frontier model development alone would allow.
Whether this employment growth continues at the same pace depends heavily on the trajectory of AI infrastructure spending, which multiple forecasts, including BlackRock's recent machine-native economy research, project will continue accelerating through the end of the decade as inference workloads scale alongside the continued growth of enterprise and consumer AI adoption.
Gunel Ismayilova
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