Casablanca
Africa
Power #133/162Per-capita #150
Metro Power
30.2
of 100 · #133
MPI
30.2
MCC
23.4
MDI
19.1
Pillar profile
Talent23.0
Capital15.2
Research18.6
Infrastructure40.9
Agentic19.1
Indicators
- Population (m)4.0
- GDP ($bn)38
- GDP per capita ($k)9.6
- AI investment ($bn)0.3
- Tech employment %4.2
- AI talent38
- Research strength40
- Notable AI orgs9
- Compute / data centers46
- Broadband %78
- Tertiary degree %30
- Digital skills48
- Startup ecosystem44
- Agent adoption26
- Patents / 100k6
Nearest peers
- Lagos23.4
- Panama City24.2
- Kuwait City24.5
- Nairobi22.1
- Tunis22.1
Metro report · generated from Casablanca's indicators
Casablanca, metro standing in full
Casablanca is the #147 metro by economic size ($38bn) in the panel and ranks #133/162 on absolute Metro Power and #150/162 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)
$38bn
#147 of 162
GDP / capita
$10k
Population
4.0M
AI investment
$0.3bn
#146 of 162
Notable AI orgs
9
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Casablanca's own standing.
| Index / pillar | Value | Standing | What a high vs low value means, and where Casablanca sits |
|---|---|---|---|
| MPI Metro Power | 30.2 | Developing · #133/162 | 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 | 23.4 | Lagging · #150/162 | 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 | 19.1 | Lagging · #151/162 | 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 | 23.0 | Lagging · #149/162 | 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 | 15.2 | Lagging · #149/162 | 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 | 18.6 | Lagging · #150/162 | 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 | 40.9 | Lagging · #144/162 | 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 | 19.1 | Lagging · #151/162 | 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 19.1 (#151/162, Lagging): little agentic deployment, the near-term lever is unused.
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Metro values are curated estimates (v1) on a consistent global scale, not yet measured sub-national data.