Kigali
Africa
Power #162/162Per-capita #158
Metro Power
13.2
of 100 · #162
MPI
13.2
MCC
14.2
MDI
11.8
Pillar profile
Talent11.5
Capital11.6
Research11.3
Infrastructure24.8
Agentic11.8
Indicators
- Population (m)1.3
- GDP ($bn)6
- GDP per capita ($k)4.6
- AI investment ($bn)0.1
- Tech employment %3.2
- AI talent32
- Research strength31
- Notable AI orgs7
- Compute / data centers42
- Broadband %60
- Tertiary degree %14
- Digital skills40
- Startup ecosystem42
- Agent adoption20
- Patents / 100k1.5
Nearest peers
Metro report · generated from Kigali's indicators
Kigali, metro standing in full
Kigali is the #162 metro by economic size ($6bn) in the panel and ranks #162/162 on absolute Metro Power and #158/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is research.
Economic & scale context curated v1 estimate
GDP (metro)
$6bn
#162 of 162
GDP / capita
$5k
Population
1.3M
AI investment
$0.1bn
#160 of 162
Notable AI orgs
7
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Kigali's own standing.
| Index / pillar | Value | Standing | What a high vs low value means, and where Kigali sits |
|---|---|---|---|
| MPI Metro Power | 13.2 | Lagging · #162/162 | 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 | 14.2 | Lagging · #158/162 | 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 | 11.8 | Lagging · #157/162 | 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 | 11.5 | Lagging · #158/162 | 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 | 11.6 | Lagging · #156/162 | 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 | 11.3 | Lagging · #159/162 | 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 | 24.8 | Lagging · #156/162 | 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 | 11.8 | Lagging · #157/162 | 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, Research 11.3 (#159/162, Lagging): a weak research base, fewer home-grown breakthroughs and spinouts.
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Metro values are curated estimates (v1) on a consistent global scale, not yet measured sub-national data.