Shanghai
Metro Power
80.8
of 100 · #4
MPI
80.8
MCC
66.2
MDI
63.5
Pillar profile
Talent58.2
Capital59.7
Research70.5
Infrastructure79.3
Agentic63.5
Indicators
- Population (m)24.9
- GDP ($bn)750
- GDP per capita ($k)30
- AI investment ($bn)15
- Tech employment %9
- AI talent78
- Research strength80
- Notable AI orgs65
- Compute / data centers82
- Broadband %93
- Tertiary degree %48
- Digital skills74
- Startup ecosystem76
- Agent adoption60
- Patents / 100k210
Nearest peers
- Austin67.2
- Singapore65.0
- Los Angeles64.7
- Toronto64.1
- Boulder63.7
Metro report · generated from Shanghai's indicators
Shanghai, metro standing in full
Shanghai is the #10 metro by economic size ($750bn) in the panel and ranks #4/162 on absolute Metro Power and #11/162 on per-capita intensity. Its strongest pillar is Research (70.5, Leading); no single pillar is a binding weakness. Locally it runs below the China national average (MCC 66.2 vs CC 69.9).
National context: China scores CC 69.9 per-capita; Shanghai sits at MCC 66.2.
Economic & scale context curated v1 estimate
GDP (metro)
$750bn
#10 of 162
GDP / capita
$30k
Population
24.9M
AI investment
$15.0bn
#11 of 162
Notable AI orgs
65
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Shanghai's own standing.
| Index / pillar | Value | Standing | What a high vs low value means, and where Shanghai sits |
|---|---|---|---|
| MPI Metro Power | 80.8 | Leading · #4/162 | 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 | 66.2 | Leading · #11/162 | 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 | 63.5 | Leading · #11/162 | 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 | 58.2 | Strong · #20/162 | 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 | 59.7 | Leading · #11/162 | 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 | 70.5 | Leading · #9/162 | 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 | 79.3 | Strong · #19/162 | 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 | 63.5 | Leading · #11/162 | 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
- Research (70.5, Leading), a strong research base feeding a pipeline of ideas and people.
- Capital (59.7, Leading), abundant capital flowing into building cognitive infrastructure.
- Agentic (63.5, Leading), agents are actively deployed, an early-mover productivity edge.
Risk factors
- No acute structural gaps stand out across the metro pillars.
▲
Metro values are curated estimates (v1) on a consistent global scale, not yet measured sub-national data.