Gurugram-NCR
Metro Power
47.4
of 100 · #71
MPI
47.4
MCC
53.1
MDI
53.8
Pillar profile
Talent60.6
Capital46.8
Research42.2
Infrastructure62.2
Agentic53.8
Indicators
- Population (m)2.5
- GDP ($bn)60
- GDP per capita ($k)24
- AI investment ($bn)3.5
- Tech employment %16
- AI talent70
- Research strength58
- Notable AI orgs40
- Compute / data centers68
- Broadband %80
- Tertiary degree %48
- Digital skills68
- Startup ecosystem78
- Agent adoption52
- Patents / 100k28
Nearest peers
- Lausanne53.1
- Guangzhou53.0
- Salt Lake City53.5
- Copenhagen52.7
- Minneapolis52.6
Metro report · generated from Gurugram-NCR's indicators
Gurugram-NCR — metro standing in full
Gurugram-NCR is the #130 metro by economic size ($60bn) in the panel and ranks #71/162 on absolute Metro Power and #43/162 on per-capita intensity. Its strongest pillar is Talent (60.6, Strong); no single pillar is a binding weakness. Locally it runs above the India national average (MCC 53.1 vs CC 41.0).
National context: India scores CC 41.0 per-capita; Gurugram-NCR sits at MCC 53.1.
Economic & scale context curated v1 estimate
GDP (metro)
$60bn
#130 of 162
GDP / capita
$24k
Population
2.5M
AI investment
$3.5bn
#63 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 Gurugram-NCR's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Gurugram-NCR sits |
|---|---|---|---|
| MPI Metro Power | 47.4 | Moderate · #71/162 | Mid-pack. High would mean a heavyweight hub where capital, talent and AI organizations concentrate — it can anchor an entire national AI ecosystem; low would mean 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 | 53.1 | Strong · #43/162 | 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 | 53.8 | Strong · #28/162 | 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 | 60.6 | Strong · #17/162 | 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 | 46.8 | Strong · #28/162 | 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 | 42.2 | Moderate · #75/162 | Mid-pack. High would mean a strong research base feeding a pipeline of ideas and people; low would mean 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 | 62.2 | Moderate · #104/162 | 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 | 53.8 | Strong · #28/162 | 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 (60.6, Strong) — a deep talent pool — the scarcest input to building AI.
- Capital (46.8, Strong) — abundant capital flowing into building cognitive infrastructure.
- Agentic (53.8, Strong) — agents are actively deployed — an early-mover productivity edge.
Risk factors
- Concentration — Gurugram-NCR runs well above its national average (MCC 53.1 vs India CC 41.0), 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.