Toronto
Metro Power
64.6
of 100 · #24
MPI
64.6
MCC
64.1
MDI
62.1
Pillar profile
Talent67.4
Capital51.8
Research61.3
Infrastructure77.9
Agentic62.1
Indicators
- Population (m)6.4
- GDP ($bn)480
- GDP per capita ($k)75
- AI investment ($bn)8.5
- Tech employment %11
- AI talent78
- Research strength80
- Notable AI orgs55
- Compute / data centers72
- Broadband %94
- Tertiary degree %64
- Digital skills80
- Startup ecosystem72
- Agent adoption58
- Patents / 100k90
Nearest peers
- Boulder63.7
- Los Angeles64.7
- Singapore65.0
- San Diego62.5
- Shanghai66.2
Metro report · generated from Toronto's indicators
Toronto — metro standing in full
Toronto is the #20 metro by economic size ($480bn) in the panel and ranks #24/162 on absolute Metro Power and #14/162 on per-capita intensity. Its strongest pillar is Talent (67.4, Leading); no single pillar is a binding weakness. Locally it runs below the Canada national average (MCC 64.1 vs CC 65.3).
National context: Canada scores CC 65.3 per-capita; Toronto sits at MCC 64.1.
Economic & scale context curated v1 estimate
GDP (metro)
$480bn
#20 of 162
GDP / capita
$75k
Population
6.4M
AI investment
$8.5bn
#23 of 162
Notable AI orgs
55
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Toronto's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Toronto sits |
|---|---|---|---|
| MPI Metro Power | 64.6 | Strong · #24/162 | High here — a heavyweight hub where capital, talent and AI organizations concentrate — it can anchor an entire national AI ecosystem. ▲ 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 | 64.1 | Leading · #14/162 | High here — deep capability per resident — a concentrated, high-intensity ecosystem. ▲ 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 | 62.1 | Leading · #14/162 | High here — agents are widely deployed locally — a near-term productivity multiplier. ▲ 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 | 67.4 | Leading · #7/162 | 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 | 51.8 | Strong · #20/162 | High here — abundant capital flowing into building cognitive infrastructure. ▲ high: abundant capital flowing into building cognitive infrastructure · ▼ low: thin investment — good ideas struggle to scale locally |
| Research Research | 61.3 | Strong · #20/162 | High here — a strong research base feeding a pipeline of ideas and people. ▲ 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 | 77.9 | Strong · #23/162 | 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 | 62.1 | Leading · #14/162 | High here — agents are actively deployed — an early-mover productivity edge. ▲ 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 (67.4, Leading) — a deep talent pool — the scarcest input to building AI.
- Agentic (62.1, Leading) — agents are actively deployed — an early-mover productivity edge.
- Capital (51.8, Strong) — abundant capital flowing into building cognitive infrastructure.
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.