Shenzhen
Metro Power
77.9
of 100 · #6
MPI
77.9
MCC
69.7
MDI
66.0
Pillar profile
Talent60.9
Capital63.3
Research78.3
Infrastructure79.9
Agentic66.0
Indicators
- Population (m)17.6
- GDP ($bn)500
- GDP per capita ($k)28
- AI investment ($bn)14
- Tech employment %12
- AI talent80
- Research strength72
- Notable AI orgs70
- Compute / data centers80
- Broadband %94
- Tertiary degree %45
- Digital skills76
- Startup ecosystem84
- Agent adoption62
- Patents / 100k390
Nearest peers
- Washington DC70.0
- London69.2
- Tel Aviv70.7
- Austin67.2
- Beijing72.7
Metro report · generated from Shenzhen's indicators
Shenzhen — metro standing in full
Shenzhen is the #17 metro by economic size ($500bn) in the panel and ranks #6/162 on absolute Metro Power and #8/162 on per-capita intensity. Its strongest pillar is Research (78.3, Leading); no single pillar is a binding weakness. Locally it runs below the China national average (MCC 69.7 vs CC 69.9).
National context: China scores CC 69.9 per-capita; Shenzhen sits at MCC 69.7.
Economic & scale context curated v1 estimate
GDP (metro)
$500bn
#17 of 162
GDP / capita
$28k
Population
17.6M
AI investment
$14.0bn
#13 of 162
Notable AI orgs
70
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Shenzhen's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Shenzhen sits |
|---|---|---|---|
| MPI Metro Power | 77.9 | Leading · #6/162 | 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 | 69.7 | Leading · #8/162 | 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 | 66.0 | Leading · #10/162 | 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 | 60.9 | Leading · #16/162 | 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 | 63.3 | Leading · #9/162 | 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 | 78.3 | Leading · #5/162 | 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 | 79.9 | Strong · #17/162 | 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 | 66.0 | Leading · #10/162 | 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 (78.3, Leading) — a strong research base feeding a pipeline of ideas and people.
- Capital (63.3, Leading) — abundant capital flowing into building cognitive infrastructure.
- Agentic (66.0, Leading) — agents are actively deployed — an early-mover productivity edge.
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.