Seattle
Metro Power
68.8
of 100 · #17
MPI
68.8
MCC
81.2
MDI
83.0
Pillar profile
Talent80.1
Capital71.6
Research80.9
Infrastructure90.6
Agentic83.0
Indicators
- Population (m)4.0
- GDP ($bn)480
- GDP per capita ($k)119.1
- AI investment ($bn)34
- Tech employment %25
- AI talent86
- Research strength88
- Notable AI orgs48
- Compute / data centers85
- Broadband %96
- Tertiary degree %52
- Digital skills91
- Startup ecosystem82
- Agent adoption82
- Patents / 100k340
Nearest peers
- Boston78.4
- New York75.9
- Beijing72.7
- Tel Aviv70.7
- Washington DC70.0
Metro report · generated from Seattle's indicators
Seattle — metro standing in full
Seattle is the #19 metro by economic size ($480bn) in the panel and ranks #17/162 on absolute Metro Power and #2/162 on per-capita intensity. Its strongest pillar is Talent (80.1, Leading); no single pillar is a binding weakness. Locally it runs below the United States national average (MCC 81.2 vs CC 86.7).
National context: United States scores CC 86.7 per-capita; Seattle sits at MCC 81.2.
Economic & scale context ● US Census ACS-1 2022 (population); GDP curated
GDP (metro)
$480bn
#19 of 162
GDP / capita
$119k
Population
4.0M
AI investment
$34.0bn
#3 of 162
Notable AI orgs
48
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Seattle's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Seattle sits |
|---|---|---|---|
| MPI Metro Power | 68.8 | Strong · #17/162 | High here — a heavyweight hub where capital, talent and AI organizations concentrate — it can anchor an entire national AI ecosystem. ▲ high: a heavyweight hub where capital, talent and AI organizations concentrate — it can anchor an entire national AI ecosystem · ▼ low: limited absolute weight — a smaller node that leans on capacity built in larger hubs |
| MCC Metro Coefficient | 81.2 | Leading · #2/162 | High here — deep capability per resident — a concentrated, high-intensity ecosystem. ▲ high: deep capability per resident — a concentrated, high-intensity ecosystem · ▼ low: thin intensity per resident — capability is sparse relative to the population |
| MDI Metro Agentic | 83.0 | Leading · #2/162 | High here — agents are widely deployed locally — a near-term productivity multiplier. ▲ high: agents are widely deployed locally — a near-term productivity multiplier · ▼ low: agentic deployment is shallow — the local agent lever is under-used |
| Talent Talent | 80.1 | Leading · #2/162 | High here — a deep talent pool — the scarcest input to building AI. ▲ high: a deep talent pool — the scarcest input to building AI · ▼ low: a shallow talent base that constrains how much can be built locally |
| Capital Capital | 71.6 | Leading · #4/162 | High here — abundant capital flowing into building cognitive infrastructure. ▲ high: abundant capital flowing into building cognitive infrastructure · ▼ low: thin investment — good ideas struggle to scale locally |
| Research Research | 80.9 | Leading · #3/162 | High here — a strong research base feeding a pipeline of ideas and people. ▲ high: a strong research base feeding a pipeline of ideas and people · ▼ low: a weak research base — fewer home-grown breakthroughs and spinouts |
| Infrastructure Infrastructure | 90.6 | Leading · #3/162 | High here — the physical and digital rails to run AI at scale are in place. ▲ high: the physical and digital rails to run AI at scale are in place · ▼ low: infrastructure gaps cap how much AI can actually be run locally |
| Agentic Agentic | 83.0 | Leading · #2/162 | High here — agents are actively deployed — an early-mover productivity edge. ▲ high: agents are actively deployed — an early-mover productivity edge · ▼ low: little agentic deployment — the near-term lever is unused |
Strengths to build on
- Talent (80.1, Leading) — a deep talent pool — the scarcest input to building AI.
- Agentic (83.0, Leading) — agents are actively deployed — an early-mover productivity edge.
- Research (80.9, Leading) — a strong research base feeding a pipeline of ideas and people.
Risk factors
- No acute structural gaps stand out across the metro pillars.
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Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.