Tunis
Africa
Power #150/162Per-capita #152
Metro Power
23.3
of 100 · #150
MPI
23.3
MCC
22.1
MDI
18.7
Pillar profile
Talent24.9
Capital12.3
Research18.7
Infrastructure35.7
Agentic18.7
Indicators
- Population (m)2.4
- GDP ($bn)24
- GDP per capita ($k)10
- AI investment ($bn)0.1
- Tech employment %4
- AI talent40
- Research strength42
- Notable AI orgs8
- Compute / data centers40
- Broadband %74
- Tertiary degree %32
- Digital skills48
- Startup ecosystem42
- Agent adoption24
- Patents / 100k4
Nearest peers
- Nairobi22.1
- Cairo21.0
- Lagos23.4
- Casablanca23.4
- Panama City24.2
Metro report · generated from Tunis's indicators
Tunis — metro standing in full
Tunis is the #154 metro by economic size ($24bn) in the panel and ranks #150/162 on absolute Metro Power and #152/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is capital.
Economic & scale context curated v1 estimate
GDP (metro)
$24bn
#154 of 162
GDP / capita
$10k
Population
2.4M
AI investment
$0.1bn
#155 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 Tunis's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Tunis sits |
|---|---|---|---|
| MPI Metro Power | 23.3 | Lagging · #150/162 | 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 | 22.1 | Lagging · #152/162 | 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 | 18.7 | Lagging · #152/162 | 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 | 24.9 | Lagging · #147/162 | 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 | 12.3 | Lagging · #155/162 | 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 | 18.7 | Lagging · #149/162 | 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 | 35.7 | Lagging · #149/162 | 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 | 18.7 | Lagging · #152/162 | 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 — Capital 12.3 (#155/162, Lagging): thin investment — good ideas struggle to scale locally.
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Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.