Quito
Latin America
Power #153/162Per-capita #156
Metro Power
22.4
of 100 · #153
MPI
22.4
MCC
16.6
MDI
15.6
Pillar profile
Talent18.3
Capital9.0
Research11.8
Infrastructure28.2
Agentic15.6
Indicators
- Population (m)2.9
- GDP ($bn)38
- GDP per capita ($k)13.1
- AI investment ($bn)0.1
- Tech employment %3
- AI talent34
- Research strength36
- Notable AI orgs5
- Compute / data centers42
- Broadband %66
- Tertiary degree %27
- Digital skills40
- Startup ecosystem36
- Agent adoption24
- Patents / 100k3
Nearest peers
Metro report · generated from Quito's indicators
Quito — metro standing in full
Quito is the #148 metro by economic size ($38bn) in the panel and ranks #153/162 on absolute Metro Power and #156/162 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)
$38bn
#148 of 162
GDP / capita
$13k
Population
2.9M
AI investment
$0.1bn
#156 of 162
Notable AI orgs
5
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Quito's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Quito sits |
|---|---|---|---|
| MPI Metro Power | 22.4 | Lagging · #153/162 | 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 | 16.6 | Lagging · #156/162 | 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 | 15.6 | Lagging · #156/162 | 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 | 18.3 | Lagging · #155/162 | 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 | 9.0 | Lagging · #158/162 | 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 | 11.8 | Lagging · #157/162 | 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 | 28.2 | Lagging · #153/162 | 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 | 15.6 | Lagging · #156/162 | 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 9.0 (#158/162, Lagging): 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.