Jakarta
Metro Power
51.9
of 100 · #53
MPI
51.9
MCC
32.1
MDI
28.8
Pillar profile
Talent29.3
Capital31.5
Research26.7
Infrastructure44.0
Agentic28.8
Indicators
- Population (m)11.2
- GDP ($bn)280
- GDP per capita ($k)13
- AI investment ($bn)2
- Tech employment %6
- AI talent46
- Research strength48
- Notable AI orgs14
- Compute / data centers58
- Broadband %70
- Tertiary degree %30
- Digital skills52
- Startup ecosystem58
- Agent adoption34
- Patents / 100k8
Nearest peers
- Ankara31.9
- San Antonio31.6
- Manama31.6
- Bangkok31.5
- Jeddah30.9
Metro report · generated from Jakarta's indicators
Jakarta — metro standing in full
Jakarta is the #41 metro by economic size ($280bn) in the panel and ranks #53/162 on absolute Metro Power and #129/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is infrastructure. Locally it runs above the Indonesia national average (MCC 32.1 vs CC 29.3).
National context: Indonesia scores CC 29.3 per-capita; Jakarta sits at MCC 32.1.
Economic & scale context curated v1 estimate
GDP (metro)
$280bn
#41 of 162
GDP / capita
$13k
Population
11.2M
AI investment
$2.0bn
#89 of 162
Notable AI orgs
14
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Jakarta's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Jakarta sits |
|---|---|---|---|
| MPI Metro Power | 51.9 | Moderate · #53/162 | Mid-pack. High would mean a heavyweight hub where capital, talent and AI organizations concentrate — it can anchor an entire national AI ecosystem; low would mean 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 | 32.1 | Developing · #129/162 | 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 | 28.8 | Developing · #131/162 | 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 | 29.3 | Developing · #135/162 | 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 · #86/162 | 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 | 26.7 | Developing · #130/162 | 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 | 44.0 | Developing · #141/162 | 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 | 28.8 | Developing · #131/162 | 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 44.0 (#141/162, 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.