Dakar
Africa
Power #157/162Per-capita #159
Metro Power
21.8
of 100 · #157
MPI
21.8
MCC
13.1
MDI
9.9
Pillar profile
Talent11.4
Capital9.7
Research11.3
Infrastructure23.0
Agentic9.9
Indicators
- Population (m)3.2
- GDP ($bn)20
- GDP per capita ($k)6.2
- AI investment ($bn)0.1
- Tech employment %2.8
- AI talent31
- Research strength33
- Notable AI orgs6
- Compute / data centers38
- Broadband %62
- Tertiary degree %16
- Digital skills38
- Startup ecosystem38
- Agent adoption18
- Patents / 100k2
Nearest peers
- Kigali14.2
- Algiers14.8
- Quito16.6
- Addis Ababa8.6
- Accra18.9
Metro report · generated from Dakar's indicators
Dakar — metro standing in full
Dakar is the #157 metro by economic size ($20bn) in the panel and ranks #157/162 on absolute Metro Power and #159/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is agentic.
Economic & scale context curated v1 estimate
GDP (metro)
$20bn
#157 of 162
GDP / capita
$6k
Population
3.2M
AI investment
$0.1bn
#159 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 Dakar's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Dakar sits |
|---|---|---|---|
| MPI Metro Power | 21.8 | Lagging · #157/162 | 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 | 13.1 | Lagging · #159/162 | 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 | 9.9 | Lagging · #159/162 | 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 | 11.4 | Lagging · #159/162 | 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 | 9.7 | Lagging · #157/162 | 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 · #158/162 | 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 | 23.0 | Lagging · #158/162 | 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 | 9.9 | Lagging · #159/162 | 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 — Agentic 9.9 (#159/162, Lagging): little agentic deployment — the near-term lever is unused.
▲
Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.