Kampala
Africa
Power #161/162Per-capita #161
Metro Power
19.7
of 100 · #161
MPI
19.7
MCC
6.5
MDI
6.2
Pillar profile
Talent6.2
Capital7.2
Research7.1
Infrastructure5.9
Agentic6.2
Indicators
- Population (m)3.6
- GDP ($bn)13
- GDP per capita ($k)3.6
- AI investment ($bn)0.1
- Tech employment %2.4
- AI talent28
- Research strength28
- Notable AI orgs5
- Compute / data centers30
- Broadband %44
- Tertiary degree %9
- Digital skills32
- Startup ecosystem34
- Agent adoption15
- Patents / 100k1
Nearest peers
- Dar es Salaam5.2
- Addis Ababa8.6
- Dakar13.1
- Kigali14.2
- Algiers14.8
Metro report · generated from Kampala's indicators
Kampala — metro standing in full
Kampala is the #161 metro by economic size ($13bn) in the panel and ranks #161/162 on absolute Metro Power and #161/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is infrastructure.
Economic & scale context curated v1 estimate
GDP (metro)
$13bn
#161 of 162
GDP / capita
$4k
Population
3.6M
AI investment
$0.1bn
#162 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 Kampala's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Kampala sits |
|---|---|---|---|
| MPI Metro Power | 19.7 | Lagging · #161/162 | 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 | 6.5 | Lagging · #161/162 | 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 | 6.2 | Lagging · #161/162 | 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 | 6.2 | Lagging · #161/162 | 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 | 7.2 | Lagging · #159/162 | 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 | 7.1 | Lagging · #161/162 | 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 | 5.9 | Lagging · #162/162 | 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 | 6.2 | Lagging · #161/162 | 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 5.9 (#162/162, 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.