Bogota
Metro Power
51.3
of 100 · #58
MPI
51.3
MCC
28.8
MDI
25.3
Pillar profile
Talent25.1
Capital29.4
Research25.8
Infrastructure38.5
Agentic25.3
Indicators
- Population (m)11.3
- GDP ($bn)180
- GDP per capita ($k)16
- AI investment ($bn)2
- Tech employment %5
- AI talent42
- Research strength42
- Notable AI orgs20
- Compute / data centers44
- Broadband %74
- Tertiary degree %28
- Digital skills50
- Startup ecosystem54
- Agent adoption32
- Patents / 100k3
Nearest peers
- Medellin29.0
- Johannesburg29.3
- Ho Chi Minh City29.4
- Cape Town29.4
- Curitiba28.2
Metro report · generated from Bogota's indicators
Bogota — metro standing in full
Bogota is the #69 metro by economic size ($180bn) in the panel and ranks #58/162 on absolute Metro Power and #143/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is infrastructure. Locally it runs below the Colombia national average (MCC 28.8 vs CC 31.5).
National context: Colombia scores CC 31.5 per-capita; Bogota sits at MCC 28.8.
Economic & scale context curated v1 estimate
GDP (metro)
$180bn
#69 of 162
GDP / capita
$16k
Population
11.3M
AI investment
$2.0bn
#93 of 162
Notable AI orgs
20
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Bogota's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Bogota sits |
|---|---|---|---|
| MPI Metro Power | 51.3 | Moderate · #58/162 | 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 | 28.8 | Lagging · #143/162 | 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 | 25.3 | Lagging · #144/162 | 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 | 25.1 | Lagging · #146/162 | 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 | 29.4 | Moderate · #99/162 | Mid-pack. High would mean abundant capital flowing into building cognitive infrastructure; low would mean 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 | 25.8 | Developing · #131/162 | 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 | 38.5 | Lagging · #148/162 | 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 | 25.3 | Lagging · #144/162 | 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 — Infrastructure 38.5 (#148/162, Lagging): infrastructure gaps cap how much AI can actually be run locally.
▲
Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.