CognitiveAristocracy
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Overview / Regions / Columbus

Columbus

US Power #95/162Per-capita #57National view: United States →
Metro Power
40.0
of 100 · #95
MPI
40.0
MCC
49.7
MDI
46.5

Pillar profile

Talent44.2
Capital33.9
Research46.7
Infrastructure77.3
Agentic46.5

Indicators

  • Population (m)2.2
  • GDP ($bn)160
  • GDP per capita ($k)74.1
  • AI investment ($bn)4
  • Tech employment %11
  • AI talent56
  • Research strength78
  • Notable AI orgs11
  • Compute / data centers74
  • Broadband %93
  • Tertiary degree %40
  • Digital skills78
  • Startup ecosystem52
  • Agent adoption52
  • Patents / 100k85

Nearest peers

Metro report · generated from Columbus's indicators

Columbus — metro standing in full

Columbus is the #83 metro by economic size ($160bn) in the panel and ranks #95/162 on absolute Metro Power and #57/162 on per-capita intensity. Its strongest pillar is Infrastructure (77.3, Strong); no single pillar is a binding weakness. Locally it runs below the United States national average (MCC 49.7 vs CC 86.7).

National context: United States scores CC 86.7 per-capita; Columbus sits at MCC 49.7.

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

GDP (metro)
$160bn
#83 of 162
GDP / capita
$74k
Population
2.2M
AI investment
$4.0bn
#51 of 162
Notable AI orgs
11

Index & pillar read

For each metro index and pillar: what it means when high (the value) versus low (the gap), and Columbus's own standing.

Index / pillarValueStandingWhat a high vs low value means — and where Columbus sits
MPI Metro Power40.0Moderate · #95/162Mid-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 Coefficient49.7Moderate · #57/162Mid-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 Agentic46.5Moderate · #57/162Mid-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 Talent44.2Moderate · #78/162Mid-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 Capital33.9Moderate · #76/162Mid-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 Research46.7Moderate · #59/162Mid-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 Infrastructure77.3Strong · #25/162High 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 Agentic46.5Moderate · #57/162Mid-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 (77.3, 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.