Source: arXiv:2506.00058 — "Prompt Engineer: Analyzing Skill Requirements in the AI Job Market" (LinkedIn job postings scraped April 7, 2025)
The takeaway: "Prompt engineer" was never a job category the market supported. It's more like a transitional label, similar to "computer operator" in 1985. Eventually, we surmise, everyone will use AI the way everyone uses computers now. The jobs that are emerging are building the systems, not using them as consumers (which everyone will likely be).
The Split
Two Markets, Two Stories
Here's the twist: AI specialist roles (ML engineers, LLM engineers, AI researchers) are still growing. General software hiring is down significantly—the exact causes are debated (interest rates? AI productivity? post-ZIRP correction?), but the divergence is real. (ML engineers, LLM engineers, AI researchers) are still growing. General software hiring is down significantly—the exact causes are debated (interest rates? AI productivity? post-ZIRP correction?), but the divergence is real.
The numbers: Q2 productivity rose 3.3% while hours worked grew just 1.1%. General software postings dropped 25% month-over-month in September. Meanwhile, ML engineer salaries hit 8K average. Many factors are at play, but the split highlights a trend. Q2 productivity rose 3.3% while hours worked grew just 1.1%. General software postings dropped 25% month-over-month in September. Meanwhile, ML engineer salaries hit 8K average. Many factors are at play, but the split highlights a trend.
The Adoption
88% of Companies Now Use AI
McKinsey's 2025 Global Survey shows AI adoption jumped from ~50% (where it hovered for years) to 88% in just two years.
55%
2023
72%
2024
88%
2025
23%already scaling AI agents in production
39%experimenting with AI agents
~6%qualify as "high performers" with enterprise-wide AI impact
The gap: Nearly everyone is using AI, but almost nobody has figured it out. Only ~6% qualify as high performers seeing significant EBIT impact. Nearly everyone is using AI, but almost nobody has figured it out. Only ~6% qualify as high performers seeing significant EBIT impact.
The Risk
Who's Getting Displaced?
Stanford researchers analyzed millions of ADP payroll records—the largest real-time study of AI's labor impact. The finding that made headlines: young workers in AI-exposed jobs are seeing employment decline while older workers in the same roles are growing. while older workers in the same roles are growing.
Source: Stanford Digital Economy Lab — "Canaries in the Coal Mine?" (Brynjolfsson, Chandar, Chen — ADP payroll data, Aug 2025)
The pattern: Erik Brynjolfsson offered one possible explanation: "Older workers have tacit knowledge—tricks of the trade learned from experience that may never be written down anywhere. They have knowledge that's not in the LLMs, so they're not being replaced by them." Other factors could include older workers migrating to fractional work or early retirement—the full picture is still emerging.
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The Skills
What AI Roles Require
The jobs are on the creator side—building, deploying, and maintaining AI systems. Machine learning engineer salaries jumped 53% in 15 months while general software engineer pay crept up only 4%.—building, deploying, and maintaining AI systems. Machine learning engineer salaries jumped 53% in 15 months while general software engineer pay crept up only 4%.
75%+want domain specialists, not generalists
40%ML engineer projected growth (WEF 2023-2027)
Source: 365 Data Science — Analysis of 1,000+ AI engineer postings (2025)
Hiring insight: The roles that matter: MLOps, model deployment, LLM fine-tuning, production systems. The roles that matter: MLOps, model deployment, LLM fine-tuning, production systems.
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About this data: All statistics are from peer-reviewed research, published reports, or primary data sources linked throughout. The Stanford/ADP study represents the largest real-time analysis of AI's employment impact using actual payroll data from millions of workers. LinkedIn job posting data comes from academic research analyzing 20,000+ listings. We update this page as new data becomes available. All statistics are from peer-reviewed research, published reports, or primary data sources linked throughout. The Stanford/ADP study represents the largest real-time analysis of AI's employment impact using actual payroll data from millions of workers. LinkedIn job posting data comes from academic research analyzing 20,000+ listings. We update this page as new data becomes available.