Lagos
Metro Power
53.8
of 100 · #50
MPI
53.8
MCC
23.4
MDI
21.5
Pillar profile
Talent20.3
Capital31.5
Research23.0
Infrastructure20.9
Agentic21.5
Indicators
- Population (m)16
- GDP ($bn)130
- GDP per capita ($k)8
- AI investment ($bn)2
- Tech employment %4
- AI talent40
- Research strength34
- Notable AI orgs25
- Compute / data centers38
- Broadband %55
- Tertiary degree %22
- Digital skills42
- Startup ecosystem58
- Agent adoption28
- Patents / 100k1
Nearest peers
- Casablanca23.4
- Panama City24.2
- Kuwait City24.5
- Nairobi22.1
- Tunis22.1
Metro report · generated from Lagos's indicators
Lagos — metro standing in full
Lagos is the #95 metro by economic size ($130bn) in the panel and ranks #50/162 on absolute Metro Power and #149/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is infrastructure. Locally it runs above the Nigeria national average (MCC 23.4 vs CC 14.1).
National context: Nigeria scores CC 14.1 per-capita; Lagos sits at MCC 23.4.
Economic & scale context curated v1 estimate
GDP (metro)
$130bn
#95 of 162
GDP / capita
$8k
Population
16.0M
AI investment
$2.0bn
#94 of 162
Notable AI orgs
25
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Lagos's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Lagos sits |
|---|---|---|---|
| MPI Metro Power | 53.8 | Moderate · #50/162 | Mid-pack. High would mean a heavyweight hub where capital, talent and AI organizations concentrate — it can anchor an entire national AI ecosystem; low would mean 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 | 23.4 | Lagging · #149/162 | 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 | 21.5 | Lagging · #148/162 | 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 | 20.3 | Lagging · #152/162 | 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 | 31.5 | Moderate · #88/162 | Mid-pack. High would mean abundant capital flowing into building cognitive infrastructure; low would mean 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 | 23.0 | Lagging · #143/162 | 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 | 20.9 | Lagging · #159/162 | 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 | 21.5 | Lagging · #148/162 | 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 20.9 (#159/162, Lagging): infrastructure gaps cap how much AI can actually be run locally.
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Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.