Oslo
Metro Power
28.9
of 100 · #137
MPI
28.9
MCC
48.2
MDI
43.4
Pillar profile
Talent44.7
Capital34.7
Research44.3
Infrastructure73.8
Agentic43.4
Indicators
- Population (m)0.7
- GDP ($bn)76.6
- GDP per capita ($k)109.4
- AI investment ($bn)3
- Tech employment %8
- AI talent54
- Research strength70
- Notable AI orgs20
- Compute / data centers56
- Broadband %98
- Tertiary degree %50
- Digital skills83
- Startup ecosystem58
- Agent adoption49
- Patents / 100k44
Nearest peers
- Gothenburg48.3
- Nanjing48.3
- Bristol48.4
- Manchester48.5
- Edinburgh47.8
Metro report · generated from Oslo's indicators
Oslo — metro standing in full
Oslo is the #124 metro by economic size ($77bn) in the panel and ranks #137/162 on absolute Metro Power and #64/162 on per-capita intensity. Its strongest pillar is Infrastructure (73.8, Strong); no single pillar is a binding weakness. Locally it runs below the Norway national average (MCC 48.2 vs CC 62.2).
National context: Norway scores CC 62.2 per-capita; Oslo sits at MCC 48.2.
Economic & scale context ● Eurostat met_10r_3gdp/met_pjanaggr3 (2021/22)
GDP (metro)
$77bn
#124 of 162
GDP / capita
$109k
Population
0.7M
AI investment
$3.0bn
#67 of 162
Notable AI orgs
20
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Oslo's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Oslo sits |
|---|---|---|---|
| MPI Metro Power | 28.9 | Developing · #137/162 | 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 | 48.2 | Moderate · #64/162 | 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 | 43.4 | Moderate · #73/162 | 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 | 44.7 | Moderate · #75/162 | Mid-pack. High would mean a deep talent pool — the scarcest input to building AI; low would mean 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 | 34.7 | Moderate · #74/162 | 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 | 44.3 | Moderate · #65/162 | 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 | 73.8 | Strong · #40/162 | High here — the physical and digital rails to run AI at scale are in place. ▲ 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 | 43.4 | Moderate · #73/162 | 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
- Infrastructure (73.8, Strong) — the physical and digital rails to run AI at scale are in place.
Risk factors
- No acute structural gaps stand out across the metro pillars.
▲
Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.