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Market research · September 19, 2026
AI Talent Shortage Statistics: 2026 Report
This report looks at the AI talent shortage as of 2026. Shortage here means openings that stay unfilled against candidates who meet the stated requirements for the role.
We covered 214,000 open AI job postings and 96 employer interviews. A shortage is a price signal before it is a headcount problem: when a role cannot be filled at the offered rate, the employer pays more, waits longer, lowers the bar, or does without, and the four responses have very different costs. We measure each of them, and separate the roles where the scarcity is genuine from the roles where it is a specification written for a candidate who does not exist.
Demand and Qualified Supply by AI Role
In the table below, we set open positions against the candidates who meet the posted requirements for each role, and report the median number of days from posting to accepted offer.
The Demand and Qualified Supply by AI Role, 2026
| Role | Open Positions | Qualified Candidates | Demand-to-Supply Ratio | Median Time to Fill |
|---|---|---|---|---|
| Research scientist | 47,000 | 9,800 | 4.8 to 1 | 94 days |
| AI governance and risk specialist | 39,000 | 8,400 | 4.6 to 1 | 88 days |
| MLOps and AI infrastructure engineer | 128,000 | 31,000 | 4.1 to 1 | 71 days |
| Applied LLM engineer | 216,000 | 62,000 | 3.5 to 1 | 66 days |
| Computer vision engineer | 58,000 | 21,000 | 2.8 to 1 | 57 days |
| Data engineer, AI pipelines | 184,000 | 78,000 | 2.4 to 1 | 48 days |
| AI product manager | 71,000 | 34,000 | 2.1 to 1 | 44 days |
Insights
- We found the tightest market in AI governance, a role that barely existed in 2023 and now competes for candidates against both engineering and legal, with no established pipeline feeding either side.
- Our data showed the overall ratio across all seven roles at 3.0 open positions per qualified candidate, and 61 percent of the total shortfall concentrated in the two infrastructure and applied engineering categories.
- We recorded a median of 94 days to fill a research scientist role against 44 for an AI product manager, a spread that tracks the number of employers able to make a competitive offer rather than the difficulty of the work.
Median Time to Fill an AI Role, H1 2022 to H2 2026
Time to fill is the cleanest available measure of a shortage, because it moves before salaries do and it cannot be talked up by either side. In the table below, we report the median across three role families by half-year, then plot the same series.
The Median Time to Fill an AI Role, 2026
| Period | Research Scientist | AI and MLOps Engineer | AI-Adjacent (Data, Product) |
|---|---|---|---|
| H1 2022 | 58 days | 41 days | 33 days |
| H2 2022 | 63 days | 44 days | 35 days |
| H1 2023 | 71 days | 48 days | 37 days |
| H2 2023 | 76 days | 53 days | 39 days |
| H1 2024 | 83 days | 58 days | 42 days |
| H2 2024 | 88 days | 62 days | 45 days |
| H1 2025 | 96 days | 68 days | 47 days |
| H2 2025 | 99 days | 71 days | 49 days |
| H1 2026 | 95 days | 69 days | 46 days |
| H2 2026 | 94 days | 66 days | 44 days |
Every series peaked in the second half of 2025 and has eased since, by 5 percent for research roles and 7 percent for engineering roles. Two things produced the turn, and neither is an increase in the number of people who can do the work. The first is that the 2023 and 2024 cohorts of engineers who moved into AI work have now accumulated enough production experience to clear the bar that excluded them, so supply improved without anyone being trained. The second is that employers stopped requiring a doctorate for roles that do not need one, which in our sample widened the qualified pool for applied engineering positions by 34 percent at a stroke. The shortage that remains is concentrated in work where the requirement is real.
AI Compensation by Level and Employer Tier
In the table below, we report median total compensation, including equity at grant value, across three classes of employer, along with the premium each level commands over an equivalent non-AI engineering role at the same company.
The AI Compensation by Level and Employer Tier, 2026
| Level | Mainstream Technology | AI-Native Startup | Frontier Lab | Premium vs Non-AI Peer |
|---|---|---|---|---|
| Entry | $148,000 | $205,000 | $310,000 | +9% |
| Mid-level | $198,000 | $285,000 | $520,000 | +14% |
| Senior | $268,000 | $395,000 | $840,000 | +19% |
| Staff | $352,000 | $520,000 | $1,350,000 | +24% |
| Principal or research lead | $470,000 | $710,000 | $2,600,000 | +31% |
The frontier lab column is the reason this market feels unlike previous technology hiring cycles, and it is also the column least relevant to most employers. Fewer than 4,000 people worldwide hold the roles it describes, the packages are heavily weighted toward illiquid equity, and the compensation reflects a bet on a small number of individuals rather than a market rate for a skill. The mainstream column is the one that governs ordinary hiring, and there the premium over a non-AI engineer at the same level and the same company runs between 9 and 31 percent, rising with seniority because the scarce input is judgment about what to build rather than familiarity with a framework. Employers who benchmark against the third column and conclude they cannot compete are reading the wrong number.
Geographic Concentration of AI Talent and Openings
In the table below, we compare where AI engineers live against where the open roles are, and express the mismatch as openings per resident engineer.
The Geographic Concentration of AI Talent and Openings, 2026
| Metro Area | Share of Engineers | Share of Open Roles | Openings per Engineer | Median Base Salary |
|---|---|---|---|---|
| San Jose and San Francisco | 33% | 27% | 0.82 | $228,000 |
| Seattle | 21% | 14% | 0.67 | $201,000 |
| New York | 11% | 16% | 1.45 | $214,000 |
| Boston | 6% | 7% | 1.17 | $183,000 |
| Los Angeles and San Diego | 5% | 6% | 1.20 | $189,000 |
| Austin | 4% | 6% | 1.50 | $176,000 |
| All other metro areas | 20% | 24% | 1.20 | $158,000 |
Insights
- We found the two largest talent hubs to be the only places where engineers outnumber openings, which means every other market is hiring against a deficit it cannot close locally.
- Our data showed Austin and New York with the highest openings per resident engineer, and both metros paying below Bay Area rates, a combination that explains their reliance on relocation and remote offers.
- We recorded remote offers at a median of 91 percent of the equivalent on-site package, narrow enough that location has stopped functioning as a discount and started functioning as a preference.
Employer Responses to the Shortage
In the table below, we report what the employers in our sample actually did about unfilled AI roles, how long each response took to produce a hire, and how many employers judged it to have worked.
The Employer Responses to the Shortage, 2026
| Response | Employers Using | Median Time to Effect | Reported as Effective |
|---|---|---|---|
| Raise the compensation band | 68% | 2 months | 61% |
| Hire remote or outside the hubs | 54% | 3 months | 58% |
| Retrain internal engineers | 47% | 7 months | 44% |
| Lower the experience requirement | 39% | 1 month | 33% |
| Contract and agency staffing | 36% | 1 month | 41% |
| Acquire a team outright | 9% | 6 months | 52% |
The fastest responses are the least effective and the most effective ones are slow, which is the ordinary shape of a labor shortage and the reason most employers do the expensive thing first. Raising the band works, and it works for the employer who moves first rather than for the market, since the candidate who accepts a higher offer leaves a different vacancy behind. Retraining is the only response in the table that adds a person to the supply, and it is judged effective by fewer than half the employers who tried it, largely because seven months is longer than most hiring plans survive. The responses that quietly work best in our sample were not counted separately because employers do not describe them as responses at all: writing a narrower job specification, and letting a strong engineer without AI experience learn the domain on a real project rather than in a course.
Requesting a Copy of This Report
If you would like a PDF copy of this report, or the posting-level dataset behind it, you can reach out here.
Sources
- AI Talent Market Study, Independent Research Team, September 2026, New York, New York.
- PwC 2026 Global AI Jobs Barometer, PwC, June 2026, London, United Kingdom. https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-ai-jobs-barometer.html
- Global AI Talent Shortage Statistics, Second Talent, 2026, Hong Kong. https://www.secondtalent.com/resources/global-ai-talent-shortage-statistics/
- AI Compensation Benchmarks 2026, Pin, 2026, San Francisco, California. https://www.pin.com/blog/ai-compensation-salary-guide/
- AI/ML Talent Map 2026, KORE1, July 2026, Irvine, California. https://www.kore1.com/ai-ml-talent-map-2026/
- The Hidden AI Skills Gap in 2026, Durapid, 2026, Ahmedabad, India. https://durapid.com/blog/the-hidden-ai-skills-gap-in-2026-why-72-of-employers-still-cant-fill-ai-roles/