Dar es Salaam
Africa
Power #156/162Per-capita #162
Metro Power
21.9
of 100 · #156
MPI
21.9
MCC
5.2
MDI
5.0
Pillar profile
Talent5.0
Capital5.0
Research5.0
Infrastructure6.1
Agentic5.0
Indicators
- Population (m)5
- GDP ($bn)18
- GDP per capita ($k)3.6
- AI investment ($bn)0.1
- Tech employment %2.2
- AI talent27
- Research strength27
- Notable AI orgs4
- Compute / data centers30
- Broadband %46
- Tertiary degree %8
- Digital skills30
- Startup ecosystem30
- Agent adoption14
- Patents / 100k1
Nearest peers
- Kampala6.5
- Addis Ababa8.6
- Dakar13.1
- Kigali14.2
- Algiers14.8
Metro report · generated from Dar es Salaam's indicators
Dar es Salaam — metro standing in full
Dar es Salaam is the #160 metro by economic size ($18bn) in the panel and ranks #156/162 on absolute Metro Power and #162/162 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)
$18bn
#160 of 162
GDP / capita
$4k
Population
5.0M
AI investment
$0.1bn
#161 of 162
Notable AI orgs
4
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Dar es Salaam's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Dar es Salaam sits |
|---|---|---|---|
| MPI Metro Power | 21.9 | Lagging · #156/162 | 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 | 5.2 | Lagging · #162/162 | 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 | 5.0 | Lagging · #162/162 | 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 | 5.0 | Lagging · #162/162 | 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 | 5.0 | Lagging · #162/162 | 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 | 5.0 | Lagging · #162/162 | 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 | 6.1 | Lagging · #161/162 | 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 | 5.0 | Lagging · #162/162 | 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 5.0 (#162/162, 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.