CognitiveAristocracy
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Overview / Regions / Indianapolis

Indianapolis

US Power #121/162Per-capita #113National view: United States →
Metro Power
33.9
of 100 · #121
MPI
33.9
MCC
37.0
MDI
34.2

Pillar profile

Talent33.2
Capital26.8
Research28.5
Infrastructure62.2
Agentic34.2

Indicators

  • Population (m)2.1
  • GDP ($bn)165
  • GDP per capita ($k)79
  • AI investment ($bn)1.5
  • Tech employment %6.2
  • AI talent47
  • Research strength53
  • Notable AI orgs11
  • Compute / data centers62
  • Broadband %90
  • Tertiary degree %37
  • Digital skills62
  • Startup ecosystem53
  • Agent adoption41
  • Patents / 100k22

Nearest peers

Metro report · generated from Indianapolis's indicators

Indianapolis — metro standing in full

Indianapolis is the #81 metro by economic size ($165bn) in the panel and ranks #121/162 on absolute Metro Power and #113/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is research. Locally it runs below the United States national average (MCC 37.0 vs CC 86.7).

National context: United States scores CC 86.7 per-capita; Indianapolis sits at MCC 37.0.

Economic & scale context curated v1 estimate

GDP (metro)
$165bn
#81 of 162
GDP / capita
$79k
Population
2.1M
AI investment
$1.5bn
#107 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 Indianapolis's own standing.

Index / pillarValueStandingWhat a high vs low value means — and where Indianapolis sits
MPI Metro Power33.9Developing · #121/162Low 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 Coefficient37.0Developing · #113/162Low here — 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 Agentic34.2Developing · #114/162Low here — 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 Talent33.2Developing · #121/162Low here — 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 Capital26.8Developing · #120/162Low here — 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 Research28.5Developing · #123/162Low here — 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 Infrastructure62.2Moderate · #103/162Mid-pack. High would mean the physical and digital rails to run AI at scale are in place; low would mean infrastructure gaps cap how much AI can actually be run locally.
▲ 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 Agentic34.2Developing · #114/162Low here — 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

  • No pillar stands out as a clear strength yet.

Risk factors

  • Binding weakness — Research 28.5 (#123/162, Developing): a weak research base — fewer home-grown breakthroughs and spinouts.
Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.