Panama City
Latin America
Power #154/162Per-capita #148
Metro Power
22.4
of 100 · #154
MPI
22.4
MCC
24.2
MDI
22.9
Pillar profile
Talent24.0
Capital12.8
Research14.3
Infrastructure47.2
Agentic22.9
Indicators
- Population (m)1.9
- GDP ($bn)48
- GDP per capita ($k)25.3
- AI investment ($bn)0.2
- Tech employment %4
- AI talent40
- Research strength38
- Notable AI orgs6
- Compute / data centers60
- Broadband %78
- Tertiary degree %30
- Digital skills47
- Startup ecosystem42
- Agent adoption30
- Patents / 100k4
Nearest peers
- Kuwait City24.5
- Lagos23.4
- Casablanca23.4
- Beirut25.2
- Nairobi22.1
Metro report · generated from Panama City's indicators
Panama City, metro standing in full
Panama City is the #141 metro by economic size ($48bn) in the panel and ranks #154/162 on absolute Metro Power and #148/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is research.
Economic & scale context curated v1 estimate
GDP (metro)
$48bn
#141 of 162
GDP / capita
$25k
Population
1.9M
AI investment
$0.2bn
#154 of 162
Notable AI orgs
6
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Panama City's own standing.
| Index / pillar | Value | Standing | What a high vs low value means, and where Panama City sits |
|---|---|---|---|
| MPI Metro Power | 22.4 | Lagging · #154/162 | 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 | 24.2 | Lagging · #148/162 | 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 | 22.9 | Lagging · #147/162 | 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 | 24.0 | Lagging · #148/162 | 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 · #153/162 | 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 | 14.3 | Lagging · #154/162 | 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 | 47.2 | Developing · #133/162 | 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 | 22.9 | Lagging · #147/162 | 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, Research 14.3 (#154/162, Lagging): a weak research base, fewer home-grown breakthroughs and spinouts.
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Metro values are curated estimates (v1) on a consistent global scale, not yet measured sub-national data.