San Francisco Bay Area
Metro Power
85.4
of 100 · #2
MPI
85.4
MCC
98.6
MDI
100.0
Pillar profile
Talent95.5
Capital100.0
Research100.0
Infrastructure97.4
Agentic100.0
Indicators
- Population (m)4.6
- GDP ($bn)1,050
- GDP per capita ($k)229.3
- AI investment ($bn)190
- Tech employment %30
- AI talent100
- Research strength99
- Notable AI orgs120
- Compute / data centers92
- Broadband %97
- Tertiary degree %58
- Digital skills97
- Startup ecosystem100
- Agent adoption96
- Patents / 100k420
Nearest peers
Metro report · generated from San Francisco Bay Area's indicators
San Francisco Bay Area, metro standing in full
San Francisco Bay Area is the #5 metro by economic size ($1,050bn) in the panel and ranks #2/162 on absolute Metro Power and #1/162 on per-capita intensity. Its strongest pillar is Talent (95.5, Leading); no single pillar is a binding weakness. Locally it runs above the United States national average (MCC 98.6 vs CC 86.7).
National context: United States scores CC 86.7 per-capita; San Francisco Bay Area sits at MCC 98.6.
Economic & scale context ● US Census ACS-1 2022 (population); GDP curated
GDP (metro)
$1,050bn
#5 of 162
GDP / capita
$229k
Population
4.6M
AI investment
$190.0bn
#1 of 162
Notable AI orgs
120
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and San Francisco Bay Area's own standing.
| Index / pillar | Value | Standing | What a high vs low value means, and where San Francisco Bay Area sits |
|---|---|---|---|
| MPI Metro Power | 85.4 | Leading · #2/162 | 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 | 98.6 | Leading · #1/162 | 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 | 100.0 | Leading · #1/162 | 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 | 95.5 | Leading · #1/162 | 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 | 100.0 | Leading · #1/162 | 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 | 100.0 | Leading · #1/162 | 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 | 97.4 | Leading · #1/162 | 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 | 100.0 | Leading · #1/162 | 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 (95.5, Leading), a deep talent pool, the scarcest input to building AI.
- Capital (100.0, Leading), abundant capital flowing into building cognitive infrastructure.
- Research (100.0, Leading), a strong research base feeding a pipeline of ideas and people.
Risk factors
- Concentration, San Francisco Bay Area runs well above its national average (MCC 98.6 vs United States CC 86.7), a sign cognitive capacity is concentrating in the metro.
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Metro values are curated estimates (v1) on a consistent global scale, not yet measured sub-national data.