Accra
Africa
Power #151/162Per-capita #155
Metro Power
23.0
of 100 · #151
MPI
23.0
MCC
18.9
MDI
16.6
Pillar profile
Talent17.7
Capital15.1
Research14.7
Infrastructure30.3
Agentic16.6
Indicators
- Population (m)2.6
- GDP ($bn)18
- GDP per capita ($k)6.9
- AI investment ($bn)0.2
- Tech employment %3.6
- AI talent36
- Research strength35
- Notable AI orgs8
- Compute / data centers40
- Broadband %68
- Tertiary degree %22
- Digital skills44
- Startup ecosystem46
- Agent adoption24
- Patents / 100k3
Nearest peers
Metro report · generated from Accra's indicators
Accra — metro standing in full
Accra is the #159 metro by economic size ($18bn) in the panel and ranks #151/162 on absolute Metro Power and #155/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is talent.
Economic & scale context curated v1 estimate
GDP (metro)
$18bn
#159 of 162
GDP / capita
$7k
Population
2.6M
AI investment
$0.2bn
#152 of 162
Notable AI orgs
8
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Accra's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Accra sits |
|---|---|---|---|
| MPI Metro Power | 23.0 | Lagging · #151/162 | 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 | 18.9 | Lagging · #155/162 | 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 | 16.6 | Lagging · #155/162 | 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 | 17.7 | Lagging · #156/162 | 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 | 15.1 | Lagging · #150/162 | 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 | 14.7 | Lagging · #153/162 | 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 | 30.3 | Lagging · #152/162 | 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 | 16.6 | Lagging · #155/162 | 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 — Talent 17.7 (#156/162, Lagging): a shallow talent base that constrains how much can be built locally.
▲
Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.