AI Labour Market Report 2026 is live
Expansion teams rely on anecdote, broker reports, or government statistics, none of which tell you whether the talent you actually need exists in that city.
Government labour reports and consulting surveys lag the market by 12–24 months. By the time data reaches you, a city's talent profile has already shifted dramatically.
Aggregate "tech talent" numbers hide critical detail, a city may have abundant software engineers but zero AI researchers or compliance specialists. Decisions made on aggregates fail on specifics.
Real-estate cost models ignore wage inflation, market rate drift, and the premium required to attract talent away from local incumbents leading to budget overruns within 18 months of opening.
Composite score across five dimensions: talent density, hiring growth, wage competitiveness, remote adoption, and skill depth. Click column headers to sort.
| Rank ↑ | City | Hub Score | Talent Density | Hiring Growth | Wage Index | Remote % | Tier |
|---|---|---|---|---|---|---|---|
| #1 |
🇺🇸
New York United States |
94 | +14% | 98 | 58% | Tier 1 | |
| #5 |
🇨🇦
Toronto Canada |
74 | +21% | 79 | 61% | Tier 1 | |
| #7 |
🇺🇸
Austin United States |
61 | +42% | 72 | 68% | Tier 2 |
Three-year hiring growth and remote adoption patterns across the world’s most active talent markets.
Four structured signal layers all from live job postings, refreshed continuously across 500+ metro areas.
Total posting volume by role family per city lets you quantify the supply pool before entering a market.
Month-over-month and year-over-year hiring velocity by city, spots accelerating hubs before they peak.
Share of postings offering remote, hybrid, or onsite work by metro, critical for GCC and captive center modelling.
Normalized salary percentiles by role and city, with currency standardization, so you can model true cost-of-talent across borders.
A ready-to-use location scoring framework that combines JobsPikr’s five signal layers into a composite attractiveness score for any metro area, built for strategy and expansion teams evaluating GCC, captive center, or hub-and-spoke footprints.
| Capability | ✦ JobsPikr Dataset | Consulting / Broker Reports | Government Labour Stats |
|---|---|---|---|
|
Data Freshness |
Continuously updated from live postings |
Point-in-time, 12–18 month lag |
Annual surveys, 24+ month lag |
|
Role-Level Granularity |
Specific to job title and skill set |
Broad occupational categories only |
Aggregate sector-level only |
|
Wage Intelligence |
Percentile bands, currency-normalized |
High-level averages, vendor-dependent |
Median wages, no granularity |
|
Remote / Hybrid Signals |
Per-city remote adoption ratio tracked |
Not captured |
Not captured |
|
Speed to Decision |
API or dataset delivery in days |
8–16 week engagement timeline |
Download only, significant processing required |
You're shortlisting cities for a new engineering hub, GCC, or captive center. Brokers push their own inventory. Consultants quote 12-week timelines. JobsPikr gives you live talent intelligence in 48 hours.
Workforce planning without location intelligence means hiring targets that can't be filled. JobsPikr tells you which cities have the role families you need — and which don't, before you open a requisition.
Real-estate cost models miss wage drift. Consultant estimates don't account for local competition. JobsPikr's wage index gives you a ground-truth baseline for every city on your shortlist.
Access role density, wage, growth, and remote signals via API or flat-file delivery across 500+ metro areas, refreshed monthly.
Load cities into the Hub Attractiveness Index Model. Configure dimension weights to match your expansion priorities and see composite scores instantly update.
Export your shortlist as a board-ready summary. Back every location decision with a live data trail, no contested assumptions.
Join 200+ strategy and workforce teams using JobsPikr to make expansion decisions with live market evidence.
Transform job data into market signals for strategic decision-making. From the makers of PromptCloud.
