Montevideo
Latin America
Power #140/162Per-capita #128
Metro Power
27.0
of 100 · #140
MPI
27.0
MCC
33.6
MDI
32.3
Pillar profile
Talent35.1
Capital19.1
Research25.1
Infrastructure56.2
Agentic32.3
Indicators
- Population (m)1.9
- GDP ($bn)42
- GDP per capita ($k)22.1
- AI investment ($bn)0.3
- Tech employment %6.5
- AI talent50
- Research strength50
- Notable AI orgs10
- Compute / data centers56
- Broadband %88
- Tertiary degree %37
- Digital skills58
- Startup ecosystem52
- Agent adoption36
- Patents / 100k7
Nearest peers
- Mexico City34.0
- Manila34.3
- Istanbul34.6
- Jakarta32.1
- Guadalajara35.2
Metro report · generated from Montevideo's indicators
Montevideo — metro standing in full
Montevideo is the #144 metro by economic size ($42bn) in the panel and ranks #140/162 on absolute Metro Power and #128/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is capital.
Economic & scale context curated v1 estimate
GDP (metro)
$42bn
#144 of 162
GDP / capita
$22k
Population
1.9M
AI investment
$0.3bn
#150 of 162
Notable AI orgs
10
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Montevideo's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Montevideo sits |
|---|---|---|---|
| MPI Metro Power | 27.0 | Developing · #140/162 | Low here — 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 | 33.6 | Developing · #128/162 | 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 | 32.3 | Developing · #123/162 | 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 | 35.1 | Developing · #113/162 | 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 | 19.1 | Lagging · #143/162 | 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 | 25.1 | Developing · #137/162 | 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 | 56.2 | Developing · #118/162 | 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 | 32.3 | Developing · #123/162 | 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 19.1 (#143/162, Lagging): thin investment — good ideas struggle to scale locally.
▲
Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.