# The AI Compensation Report 2026

**Orbyt Intelligence Annual Compensation Report 2026. Free Summary edition. National medians, top-paying roles and metros, YoY deltas, and the fastest-growing compensation categories across the AI and technology sector.**
By Justin Bartak · Orbyt Intelligence · Published 2026-04-19
Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Quote freely with attribution.
- Landing page: https://www.orbytjobs.ai/orbyt-intelligence/reports/2026-ai-compensation-summary
- Free Summary PDF: https://www.orbytjobs.ai/api/intelligence/reports/public-download?slug=2026-ai-compensation-summary&edition=free-summary
- Machine-readable JSON: https://www.orbytjobs.ai/api/intelligence/reports/public-json?slug=2026-ai-compensation-summary
- Composite dataset CSV: https://www.orbytjobs.ai/api/intelligence/reports/public-download?slug=2026-ai-compensation-summary&edition=csv
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## Executive thesis

The Q2 2026 compensation market has split into three tiers that no longer move together: AI frontier roles anchor at a $350,000 ceiling, service and trades roles post the fastest quarterly wage growth at 3.1 percent, and mid-tier tech compresses toward a $155,000 cost-adjusted floor shared across New York, San Francisco, Los Angeles, Chicago, and Seattle.

## Headline numbers (Q2 2026)

| Metric | Value | Context |
|---|---|---|
| National median | $150,350 (+8.4% YoY) | Across 3,445 roles |
| Top-paying role | $350,000 | Principal AI Research Scientist |
| Top-paying metro | $213,900 | San Jose |

## What the Orbyt data says


### AI frontier ceiling holds at $350k across three distinct role families

Principal AI Research Scientist, Technical Fellow, and Principal Quantitative Researcher all post a $350,000 median in Q2 2026, $200,000 above the global median of $150,350.

> Dataset path: `macro.topPayingRoles`

### Service labor is outgrowing software quarter over quarter

Home Health Aide and Hotel Clerk / Front Desk each grew 3.1 percent in Q2 2026, while Data Analyst grew 1.3 percent, Frontend Developer 1.7 percent, and Machine Learning Engineer 2.0 percent.

> Dataset path: `macro.fastestGrowingRoles`

### Cost-adjusted medians have collapsed to a single number across the top five metros

New York ($198,400 nominal), San Francisco ($209,250), Los Angeles ($182,900), Chicago ($165,850), and Seattle ($192,200) all land at $155,000 cost-adjusted median despite a $43,400 nominal spread.

> Dataset path: `macro.topValueCities`

### Global median jumped $12,000 in one quarter

Median compensation across 3,445 roles and 81 cities moved from $138,350 in Q1 2026 to $150,350 in Q2 2026, an 8.4 percent quarterly lift concentrated in AI and service categories rather than mid-tier tech.

> Dataset path: `macro.globalMedian`

### San Jose overtook San Francisco on nominal pay

San Jose leads all 81 cities at $213,900 median, $4,650 above San Francisco at $209,250 and $15,500 above Honolulu at $201,500.

> Dataset path: `macro.topPayingCities`

## External corroboration

**Heidi Shierholz (2024)**
Shierholz documents that real wage growth at the 10th percentile outpaced higher percentiles in the post-pandemic labor market, partially reversing four decades of widening. The Q2 2026 Orbyt data extends that pattern: Home Health Aide and Hotel Clerk growth at 3.1 percent is now running ahead of Software Engineer at 2.3 percent and Data Analyst at 1.3 percent, which is the first quarter in our dataset where service roles have cleanly beaten the software stack.

## Forward projection: 2029

**Claim:** By Q4 2029, Principal AI Research Scientist median crosses $500,000 while the cost-adjusted median across New York, San Francisco, Los Angeles, Chicago, and Seattle stays within 5 percent of $155,000, widening the frontier-to-floor ratio from 2.3x today to 3.2x.
**Confidence:** MODERATE
**Methodology:** Frontier roles have compounded at roughly 9 to 11 percent annually in the Orbyt dataset since 2023, driven by a small number of labs competing on a thin supply curve. Cost-adjusted floors have moved less than 2 percent annually because COL multipliers in tier-1 metros absorb nominal gains. Straight-lining those two rates to 2029 yields the claim. Downside risk: a frontier-lab funding reset compresses the ceiling before 2029.

## What I would do in 2026

**For:** founders and comp teams
Stop benchmarking to San Francisco nominal. Benchmark to the $155,000 cost-adjusted floor for mid-tier engineering and product, and pay the $350,000 AI frontier number without apology for the one or two roles that actually move your roadmap. In my experience running comp at three companies, the most expensive mistake in 2026 is paying San Francisco nominal to a Data Analyst (1.3 percent growth, commoditizing fast) while underpaying the single Principal AI Research Scientist who decides whether your product ships. Split the band. Publish the split internally.

## Top-paying roles

| Rank | Role | Base median |
|---|---|---|
| 1 | Principal AI Research Scientist | $350,000 |
| 2 | Technical Fellow | $350,000 |
| 3 | Principal Quantitative Researcher | $350,000 |
| 4 | Senior VP of Security | $333,000 |
| 5 | Senior VP of Product | $333,000 |

## Top-paying metros (nominal)

| Rank | Metro | Base median |
|---|---|---|
| 1 | San Jose | $213,900 |
| 2 | San Francisco | $209,250 |
| 3 | Honolulu | $201,500 |
| 4 | New York | $198,400 |
| 5 | Washington DC | $193,750 |

## Best-value metros (cost-adjusted)

| Rank | Metro | Cost-adjusted median |
|---|---|---|
| 1 | New York | $155,000 |
| 2 | San Francisco | $155,000 |
| 3 | Los Angeles | $155,000 |
| 4 | Chicago | $155,000 |
| 5 | Seattle | $155,000 |

## Fastest-growing roles (YoY)


| Rank | Role | YoY delta |
|---|---|---|
| 1 | Home Health Aide | +3.1% |
| 2 | Hotel Clerk / Front Desk | +3.1% |
| 3 | Accountant | +2.9% |
| 4 | Aircraft Mechanic | +2.9% |
| 5 | Recruiter / Talent Acquisition | +2.9% |


## Methodology

The Orbyt Intelligence dataset anchors this report on 3,445 roles across 81 U.S. metros, representing 279,045 role-by-city data points. The Q2 2026 snapshot is the analytic baseline. Previous-quarter deltas reference the Q1 2026 snapshot.

Primary sources are the U.S. Bureau of Labor Statistics Occupational Employment and Wage Statistics program (BLS OES), the Department of Labor's H-1B Labor Condition Application disclosures, SEC 10-K and proxy-statement compensation data, and a proprietary aggregation across more than 50 job-posting platforms. Cost-of-living adjustments use the Bureau of Economic Analysis Regional Price Parities.

Figures are rounded to the nearest $1,000 unless otherwise noted. Experience-band estimates scale off the role median using a fixed multiplier set disclosed in the Appendix of the Enterprise Annual edition. Percentile bands (25th, 50th, 75th) derive from the underlying distribution rather than a model fit.

My read on the limitations: the Orbyt dataset reflects posted compensation and disclosed compensation, not realized compensation at the individual level. Equity values reflect grant-date fair value, not mark-to-market value, which understates realized comp in up markets and overstates it in down markets. Geographic coverage is strongest for the top 30 U.S. metros and weaker for secondary markets. The dataset does not capture private-company pre-IPO equity that is not disclosed through H-1B or SEC filings, which materially understates AI-native startup compensation at the senior-IC and staff level.

Projections through 2030 use an employer-posting panel, an occupational displacement model keyed off BLS category mappings, and an AI-capability premium regression run per role archetype. Confidence levels are disclosed in-line with every projection. High confidence means the projection would need a structural break to miss. Moderate confidence means the projection is sensitive to one or two known inputs. Low confidence means the data supports the direction but not the magnitude, and the claim is made to advance the conversation rather than to be defended as precise.

## Suggested citation

**APA:** Bartak, J. (2026). The AI Compensation Report 2026. Orbyt Intelligence. https://doi.org/10.5281/zenodo.19653006
**Chicago:** Justin Bartak, "The AI Compensation Report 2026," Orbyt Intelligence, 2026, https://doi.org/10.5281/zenodo.19653006.
**BibTeX:**
```bibtex
@techreport{bartak2026orbyt,
  author    = {Bartak, Justin},
  title     = {The AI Compensation Report 2026},
  institution = {Orbyt Intelligence},
  year      = {2026},
  url       = {https://www.orbytjobs.ai/orbyt-intelligence/reports/2026-ai-compensation-summary},
  doi       = {10.5281/zenodo.19653006},
  note      = {Free Summary edition, CC BY 4.0}
}
```
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*Orbyt Intelligence publishes one Annual Compensation Report per calendar year. Every edition is free in 2026.*