Singapore
Metro Power
63.1
of 100 · #26
MPI
63.1
MCC
65.0
MDI
61.8
Pillar profile
Talent62.3
Capital56.2
Research56.7
Infrastructure87.8
Agentic61.8
Indicators
- Population (m)5.9
- GDP ($bn)510
- GDP per capita ($k)86
- AI investment ($bn)9.5
- Tech employment %12
- AI talent72
- Research strength76
- Notable AI orgs40
- Compute / data centers88
- Broadband %96
- Tertiary degree %58
- Digital skills82
- Startup ecosystem78
- Agent adoption62
- Patents / 100k95
Nearest peers
- Los Angeles64.7
- Toronto64.1
- Shanghai66.2
- Boulder63.7
- Austin67.2
Metro report · generated from Singapore's indicators
Singapore — metro standing in full
Singapore is the #16 metro by economic size ($510bn) in the panel and ranks #26/162 on absolute Metro Power and #12/162 on per-capita intensity. Its strongest pillar is Infrastructure (87.8, Leading); no single pillar is a binding weakness. Locally it runs below the Singapore national average (MCC 65.0 vs CC 73.9).
National context: Singapore scores CC 73.9 per-capita; Singapore sits at MCC 65.0.
Economic & scale context curated v1 estimate
GDP (metro)
$510bn
#16 of 162
GDP / capita
$86k
Population
5.9M
AI investment
$9.5bn
#17 of 162
Notable AI orgs
40
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Singapore's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Singapore sits |
|---|---|---|---|
| MPI Metro Power | 63.1 | Strong · #26/162 | 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 | 65.0 | Leading · #12/162 | 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 | 61.8 | Leading · #15/162 | 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 | 62.3 | Leading · #14/162 | 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 | 56.2 | Leading · #14/162 | 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 | 56.7 | Strong · #29/162 | 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 | 87.8 | Leading · #4/162 | 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 | 61.8 | Leading · #15/162 | 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
- Infrastructure (87.8, Leading) — the physical and digital rails to run AI at scale are in place.
- Talent (62.3, Leading) — a deep talent pool — the scarcest input to building AI.
- Capital (56.2, Leading) — abundant capital flowing into building cognitive infrastructure.
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.