Daejeon
Metro Power
32.4
of 100 · #127
MPI
32.4
MCC
50.0
MDI
46.5
Pillar profile
Talent55.8
Capital27.6
Research47.5
Infrastructure72.4
Agentic46.5
Indicators
- Population (m)1.5
- GDP ($bn)50
- GDP per capita ($k)33
- AI investment ($bn)1.1
- Tech employment %9
- AI talent64
- Research strength74
- Notable AI orgs18
- Compute / data centers60
- Broadband %98
- Tertiary degree %60
- Digital skills76
- Startup ecosystem58
- Agent adoption46
- Patents / 100k72
Nearest peers
Metro report · generated from Daejeon's indicators
Daejeon — metro standing in full
Daejeon is the #138 metro by economic size ($50bn) in the panel and ranks #127/162 on absolute Metro Power and #56/162 on per-capita intensity. Its strongest pillar is Talent (55.8, Strong); the binding concern is capital. Locally it runs below the South Korea national average (MCC 50.0 vs CC 74.3).
National context: South Korea scores CC 74.3 per-capita; Daejeon sits at MCC 50.0.
Economic & scale context curated v1 estimate
GDP (metro)
$50bn
#138 of 162
GDP / capita
$33k
Population
1.5M
AI investment
$1.1bn
#125 of 162
Notable AI orgs
18
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Daejeon's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Daejeon sits |
|---|---|---|---|
| MPI Metro Power | 32.4 | Developing · #127/162 | 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 | 50.0 | Moderate · #56/162 | Mid-pack. High would mean deep capability per resident — a concentrated, high-intensity ecosystem; low would mean 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 | 46.5 | Moderate · #59/162 | Mid-pack. High would mean agents are widely deployed locally — a near-term productivity multiplier; low would mean 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 | 55.8 | Strong · #27/162 | High here — a deep talent pool — the scarcest input to building AI. ▲ 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 | 27.6 | Developing · #114/162 | 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 | 47.5 | Moderate · #57/162 | 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 | 72.4 | Moderate · #49/162 | Mid-pack. High would mean the physical and digital rails to run AI at scale are in place; low would mean 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 | 46.5 | Moderate · #59/162 | Mid-pack. High would mean agents are actively deployed — an early-mover productivity edge; low would mean 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
- Talent (55.8, Strong) — a deep talent pool — the scarcest input to building AI.
Risk factors
- Binding weakness — Capital 27.6 (#114/162, Developing): 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.