Monterrey
Metro Power
42.5
of 100 · #88
MPI
42.5
MCC
35.7
MDI
33.8
Pillar profile
Talent37.0
Capital23.7
Research31.2
Infrastructure52.8
Agentic33.8
Indicators
- Population (m)5.3
- GDP ($bn)145
- GDP per capita ($k)27.4
- AI investment ($bn)0.7
- Tech employment %6.2
- AI talent53
- Research strength54
- Notable AI orgs16
- Compute / data centers58
- Broadband %82
- Tertiary degree %38
- Digital skills56
- Startup ecosystem55
- Agent adoption36
- Patents / 100k9
Nearest peers
- Buenos Aires35.7
- Doha35.5
- Santiago35.5
- Kuala Lumpur36.0
- Kansas City36.0
Metro report · generated from Monterrey's indicators
Monterrey — metro standing in full
Monterrey is the #93 metro by economic size ($145bn) in the panel and ranks #88/162 on absolute Metro Power and #120/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is capital. Locally it runs above the Mexico national average (MCC 35.7 vs CC 35.1).
National context: Mexico scores CC 35.1 per-capita; Monterrey sits at MCC 35.7.
Economic & scale context curated v1 estimate
GDP (metro)
$145bn
#93 of 162
GDP / capita
$27k
Population
5.3M
AI investment
$0.7bn
#141 of 162
Notable AI orgs
16
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Monterrey's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Monterrey sits |
|---|---|---|---|
| MPI Metro Power | 42.5 | Moderate · #88/162 | 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 | 35.7 | Developing · #120/162 | Low here — thin intensity per resident — capability is sparse relative to the population. ▲ 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 | 33.8 | Developing · #116/162 | Low here — agentic deployment is shallow — the local agent lever is under-used. ▲ 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 | 37.0 | Developing · #108/162 | Low here — a shallow talent base that constrains how much can be built locally. ▲ 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 | 23.7 | Developing · #133/162 | Low here — thin investment — good ideas struggle to scale locally. ▲ high: abundant capital flowing into building cognitive infrastructure · ▼ low: thin investment — good ideas struggle to scale locally |
| Research Research | 31.2 | Developing · #114/162 | Low here — 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 | 52.8 | Developing · #122/162 | Low here — 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 | 33.8 | Developing · #116/162 | Low here — little agentic deployment — the near-term lever is unused. ▲ high: agents are actively deployed — an early-mover productivity edge · ▼ low: little agentic deployment — the near-term lever is unused |
Strengths to build on
- No pillar stands out as a clear strength yet.
Risk factors
- Binding weakness — Capital 23.7 (#133/162, Developing): thin investment — good ideas struggle to scale locally.
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Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.