Beirut
Middle East
Power #152/162Per-capita #146
Metro Power
22.7
of 100 · #152
MPI
22.7
MCC
25.2
MDI
23.6
Pillar profile
Talent33.9
Capital12.8
Research21.1
Infrastructure34.6
Agentic23.6
Indicators
- Population (m)2.4
- GDP ($bn)18
- GDP per capita ($k)7.5
- AI investment ($bn)0.2
- Tech employment %4.5
- AI talent44
- Research strength48
- Notable AI orgs7
- Compute / data centers34
- Broadband %74
- Tertiary degree %46
- Digital skills52
- Startup ecosystem42
- Agent adoption28
- Patents / 100k5
Nearest peers
- Kuwait City24.5
- Panama City24.2
- Belo Horizonte26.9
- Lagos23.4
- Casablanca23.4
Metro report · generated from Beirut's indicators
Beirut — metro standing in full
Beirut is the #158 metro by economic size ($18bn) in the panel and ranks #152/162 on absolute Metro Power and #146/162 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)
$18bn
#158 of 162
GDP / capita
$8k
Population
2.4M
AI investment
$0.2bn
#153 of 162
Notable AI orgs
7
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Beirut's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Beirut sits |
|---|---|---|---|
| MPI Metro Power | 22.7 | Lagging · #152/162 | 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 | 25.2 | Lagging · #146/162 | 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 | 23.6 | Lagging · #146/162 | 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 | 33.9 | Developing · #119/162 | 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 | 12.8 | Lagging · #152/162 | 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 | 21.1 | Lagging · #147/162 | 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 | 34.6 | Lagging · #150/162 | 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 | 23.6 | Lagging · #146/162 | 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 12.8 (#152/162, Lagging): thin investment — good ideas struggle to scale locally.
▲
Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.