St. Louis
Metro Power
38.4
of 100 · #102
MPI
38.4
MCC
39.3
MDI
36.4
Pillar profile
Talent35.4
Capital29.1
Research32.8
Infrastructure62.7
Agentic36.4
Indicators
- Population (m)2.8
- GDP ($bn)185
- GDP per capita ($k)66
- AI investment ($bn)1.8
- Tech employment %6.3
- AI talent50
- Research strength58
- Notable AI orgs13
- Compute / data centers62
- Broadband %90
- Tertiary degree %38
- Digital skills63
- Startup ecosystem55
- Agent adoption42
- Patents / 100k24
Nearest peers
- Riyadh39.3
- Lisbon40.0
- Sao Paulo40.4
- Pune38.1
- Birmingham UK40.7
Metro report · generated from St. Louis's indicators
St. Louis — metro standing in full
St. Louis is the #66 metro by economic size ($185bn) in the panel and ranks #102/162 on absolute Metro Power and #110/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is talent. Locally it runs below the United States national average (MCC 39.3 vs CC 86.7).
National context: United States scores CC 86.7 per-capita; St. Louis sits at MCC 39.3.
Economic & scale context curated v1 estimate
GDP (metro)
$185bn
#66 of 162
GDP / capita
$66k
Population
2.8M
AI investment
$1.8bn
#97 of 162
Notable AI orgs
13
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and St. Louis's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where St. Louis sits |
|---|---|---|---|
| MPI Metro Power | 38.4 | Moderate · #102/162 | Mid-pack. High would mean a heavyweight hub where capital, talent and AI organizations concentrate — it can anchor an entire national AI ecosystem; low would mean limited absolute weight — a smaller node that leans on capacity built in larger hubs. ▲ 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 | 39.3 | Developing · #110/162 | Low here — thin intensity per resident — capability is sparse relative to the population. ▲ 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 | 36.4 | Developing · #109/162 | Low here — agentic deployment is shallow — the local agent lever is under-used. ▲ 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 | 35.4 | Developing · #112/162 | Low here — a shallow talent base that constrains how much can be built locally. ▲ 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 | 29.1 | Moderate · #102/162 | Mid-pack. High would mean abundant capital flowing into building cognitive infrastructure; low would mean thin investment — good ideas struggle to scale locally. ▲ high: abundant capital flowing into building cognitive infrastructure · ▼ low: thin investment — good ideas struggle to scale locally |
| Research Research | 32.8 | Developing · #109/162 | Low here — a weak research base — fewer home-grown breakthroughs and spinouts. ▲ 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 | 62.7 | Moderate · #100/162 | Mid-pack. High would mean the physical and digital rails to run AI at scale are in place; low would mean infrastructure gaps cap how much AI can actually be run locally. ▲ 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 | 36.4 | Developing · #109/162 | Low here — little agentic deployment — the near-term lever is unused. ▲ high: agents are actively deployed — an early-mover productivity edge · ▼ low: little agentic deployment — the near-term lever is unused |
Strengths to build on
- No pillar stands out as a clear strength yet.
Risk factors
- Binding weakness — Talent 35.4 (#112/162, Developing): a shallow talent base that constrains how much can be built locally.
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Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.