Sacramento
Metro Power
35.6
of 100 · #117
MPI
35.6
MCC
37.1
MDI
34.0
Pillar profile
Talent34.5
Capital26.7
Research27.8
Infrastructure62.7
Agentic34.0
Indicators
- Population (m)2.4
- GDP ($bn)175
- GDP per capita ($k)72
- AI investment ($bn)1.6
- Tech employment %6.5
- AI talent48
- Research strength52
- Notable AI orgs11
- Compute / data centers60
- Broadband %91
- Tertiary degree %38
- Digital skills64
- Startup ecosystem52
- Agent adoption40
- Patents / 100k20
Nearest peers
- Indianapolis37.0
- Boise36.6
- Rome36.3
- Tampa36.3
- Pune38.1
Metro report · generated from Sacramento's indicators
Sacramento — metro standing in full
Sacramento is the #73 metro by economic size ($175bn) in the panel and ranks #117/162 on absolute Metro Power and #112/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is research. Locally it runs below the United States national average (MCC 37.1 vs CC 86.7).
National context: United States scores CC 86.7 per-capita; Sacramento sits at MCC 37.1.
Economic & scale context curated v1 estimate
GDP (metro)
$175bn
#73 of 162
GDP / capita
$72k
Population
2.4M
AI investment
$1.6bn
#102 of 162
Notable AI orgs
11
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Sacramento's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Sacramento sits |
|---|---|---|---|
| MPI Metro Power | 35.6 | Developing · #117/162 | Low here — 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 | 37.1 | Developing · #112/162 | 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 | 34.0 | Developing · #115/162 | 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 | 34.5 | Developing · #116/162 | 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 | 26.7 | Developing · #121/162 | Low here — 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 | 27.8 | Developing · #125/162 | 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 · #98/162 | 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 | 34.0 | Developing · #115/162 | 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 — Research 27.8 (#125/162, Developing): a weak research base — fewer home-grown breakthroughs and spinouts.
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Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.