Addis Ababa
Africa
Power #142/162Per-capita #160
Metro Power
25.4
of 100 · #142
MPI
25.4
MCC
8.6
MDI
8.0
Pillar profile
Talent8.7
Capital6.4
Research9.6
Infrastructure10.2
Agentic8.0
Indicators
- Population (m)5.2
- GDP ($bn)22
- GDP per capita ($k)4.2
- AI investment ($bn)0.1
- Tech employment %2.4
- AI talent30
- Research strength30
- Notable AI orgs6
- Compute / data centers32
- Broadband %48
- Tertiary degree %12
- Digital skills34
- Startup ecosystem32
- Agent adoption16
- Patents / 100k1.5
Nearest peers
- Kampala6.5
- Dar es Salaam5.2
- Dakar13.1
- Kigali14.2
- Algiers14.8
Metro report · generated from Addis Ababa's indicators
Addis Ababa — metro standing in full
Addis Ababa is the #155 metro by economic size ($22bn) in the panel and ranks #142/162 on absolute Metro Power and #160/162 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)
$22bn
#155 of 162
GDP / capita
$4k
Population
5.2M
AI investment
$0.1bn
#158 of 162
Notable AI orgs
6
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Addis Ababa's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Addis Ababa sits |
|---|---|---|---|
| MPI Metro Power | 25.4 | Developing · #142/162 | 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 | 8.6 | Lagging · #160/162 | 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 | 8.0 | Lagging · #160/162 | 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 | 8.7 | Lagging · #160/162 | 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 | 6.4 | Lagging · #161/162 | 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 | 9.6 | Lagging · #160/162 | 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 | 10.2 | Lagging · #160/162 | 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 | 8.0 | Lagging · #160/162 | 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 6.4 (#161/162, 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.