CognitiveAristocracy
Detail
Overview / Regions / Portland OR

Portland OR

US Power #80/162Per-capita #38National view: United States →
Metro Power
44.8
of 100 · #80
MPI
44.8
MCC
54.1
MDI
50.0

Pillar profile

Talent50.3
Capital39.2
Research54.6
Infrastructure76.4
Agentic50.0

Indicators

  • Population (m)2.5
  • GDP ($bn)210
  • GDP per capita ($k)83.7
  • AI investment ($bn)5
  • Tech employment %14
  • AI talent60
  • Research strength72
  • Notable AI orgs14
  • Compute / data centers68
  • Broadband %94
  • Tertiary degree %42
  • Digital skills81
  • Startup ecosystem58
  • Agent adoption54
  • Patents / 100k230

Nearest peers

Metro report · generated from Portland OR's indicators

Portland OR — metro standing in full

Portland OR is the #59 metro by economic size ($210bn) in the panel and ranks #80/162 on absolute Metro Power and #38/162 on per-capita intensity. Its strongest pillar is Infrastructure (76.4, Strong); no single pillar is a binding weakness. Locally it runs below the United States national average (MCC 54.1 vs CC 86.7).

National context: United States scores CC 86.7 per-capita; Portland OR sits at MCC 54.1.

Economic & scale context ● US Census ACS-1 2022 (population); GDP curated

GDP (metro)
$210bn
#59 of 162
GDP / capita
$84k
Population
2.5M
AI investment
$5.0bn
#40 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 Portland OR's own standing.

Index / pillarValueStandingWhat a high vs low value means — and where Portland OR sits
MPI Metro Power44.8Moderate · #80/162Mid-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 Coefficient54.1Strong · #38/162High 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 Agentic50.0Strong · #40/162High 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 Talent50.3Strong · #40/162High 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 Capital39.2Moderate · #52/162Mid-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 Research54.6Strong · #31/162High 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 Infrastructure76.4Strong · #30/162High 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 Agentic50.0Strong · #40/162High 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 (76.4, Strong) — the physical and digital rails to run AI at scale are in place.
  • Research (54.6, Strong) — a strong research base feeding a pipeline of ideas and people.
  • Talent (50.3, Strong) — a deep talent pool — the scarcest input to building AI.

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.