Phoenix
Metro Power
51.5
of 100 · #55
MPI
51.5
MCC
50.2
MDI
47.9
Pillar profile
Talent40.6
Capital38.7
Research43.5
Infrastructure80.3
Agentic47.9
Indicators
- Population (m)5.0
- GDP ($bn)340
- GDP per capita ($k)67.7
- AI investment ($bn)6
- Tech employment %10
- AI talent56
- Research strength70
- Notable AI orgs13
- Compute / data centers82
- Broadband %93
- Tertiary degree %34
- Digital skills76
- Startup ecosystem54
- Agent adoption54
- Patents / 100k85
Nearest peers
Metro report · generated from Phoenix's indicators
Phoenix — metro standing in full
Phoenix is the #31 metro by economic size ($340bn) in the panel and ranks #55/162 on absolute Metro Power and #54/162 on per-capita intensity. Its strongest pillar is Infrastructure (80.3, Leading); no single pillar is a binding weakness. Locally it runs below the United States national average (MCC 50.2 vs CC 86.7).
National context: United States scores CC 86.7 per-capita; Phoenix sits at MCC 50.2.
Economic & scale context ● US Census ACS-1 2022 (population); GDP curated
GDP (metro)
$340bn
#31 of 162
GDP / capita
$68k
Population
5.0M
AI investment
$6.0bn
#34 of 162
Notable AI orgs
13
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Phoenix's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Phoenix sits |
|---|---|---|---|
| MPI Metro Power | 51.5 | Moderate · #55/162 | 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 | 50.2 | Moderate · #54/162 | 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 | 47.9 | Strong · #47/162 | 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 | 40.6 | Moderate · #94/162 | 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 | 38.7 | Moderate · #54/162 | 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 | 43.5 | Moderate · #71/162 | 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 | 80.3 | Leading · #15/162 | 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 | 47.9 | Strong · #47/162 | 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
- Infrastructure (80.3, Leading) — the physical and digital rails to run AI at scale are in place.
- Agentic (47.9, Strong) — agents are actively deployed — an early-mover productivity edge.
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.