Lisbon
Metro Power
37.9
of 100 · #106
MPI
37.9
MCC
40.0
MDI
36.4
Pillar profile
Talent36.5
Capital31.5
Research35.9
Infrastructure59.9
Agentic36.4
Indicators
- Population (m)2.9
- GDP ($bn)82.7
- GDP per capita ($k)28.8
- AI investment ($bn)2
- Tech employment %6.5
- AI talent50
- Research strength62
- Notable AI orgs16
- Compute / data centers48
- Broadband %93
- Tertiary degree %40
- Digital skills68
- Startup ecosystem58
- Agent adoption42
- Patents / 100k12
Nearest peers
- Sao Paulo40.4
- Riyadh39.3
- St. Louis39.3
- Birmingham UK40.7
- Charlotte40.8
Metro report · generated from Lisbon's indicators
Lisbon — metro standing in full
Lisbon is the #122 metro by economic size ($83bn) in the panel and ranks #106/162 on absolute Metro Power and #108/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is infrastructure. Locally it runs below the Portugal national average (MCC 40.0 vs CC 51.6).
National context: Portugal scores CC 51.6 per-capita; Lisbon sits at MCC 40.0.
Economic & scale context ● Eurostat met_10r_3gdp/met_pjanaggr3 (2021/22)
GDP (metro)
$83bn
#122 of 162
GDP / capita
$29k
Population
2.9M
AI investment
$2.0bn
#86 of 162
Notable AI orgs
16
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Lisbon's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Lisbon sits |
|---|---|---|---|
| MPI Metro Power | 37.9 | Developing · #106/162 | 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 | 40.0 | Developing · #108/162 | 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 | 36.4 | Developing · #108/162 | 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 | 36.5 | Developing · #110/162 | 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 · #85/162 | 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 | 35.9 | Moderate · #100/162 | Mid-pack. High would mean a strong research base feeding a pipeline of ideas and people; low would mean 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 | 59.9 | Developing · #112/162 | 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 | 36.4 | Developing · #108/162 | 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 59.9 (#112/162, Developing): 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.