CognitiveAristocracy
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Overview / Regions / Los Angeles

Los Angeles

US Power #9/162Per-capita #13National view: United States →
Metro Power
74.9
of 100 · #9
MPI
74.9
MCC
64.7
MDI
66.0

Pillar profile

Talent55.1
Capital64.6
Research59.2
Infrastructure78.8
Agentic66.0

Indicators

  • Population (m)12.9
  • GDP ($bn)1,130
  • GDP per capita ($k)87.8
  • AI investment ($bn)24
  • Tech employment %13
  • AI talent72
  • Research strength83
  • Notable AI orgs35
  • Compute / data centers72
  • Broadband %94
  • Tertiary degree %40
  • Digital skills82
  • Startup ecosystem76
  • Agent adoption68
  • Patents / 100k90

Nearest peers

Metro report · generated from Los Angeles's indicators

Los Angeles — metro standing in full

Los Angeles is the #3 metro by economic size ($1,130bn) in the panel and ranks #9/162 on absolute Metro Power and #13/162 on per-capita intensity. Its strongest pillar is Capital (64.6, Leading); no single pillar is a binding weakness. Locally it runs below the United States national average (MCC 64.7 vs CC 86.7).

National context: United States scores CC 86.7 per-capita; Los Angeles sits at MCC 64.7.

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

GDP (metro)
$1,130bn
#3 of 162
GDP / capita
$88k
Population
12.9M
AI investment
$24.0bn
#6 of 162
Notable AI orgs
35

Index & pillar read

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

Index / pillarValueStandingWhat a high vs low value means — and where Los Angeles sits
MPI Metro Power74.9Leading · #9/162High 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 Coefficient64.7Leading · #13/162High 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 Agentic66.0Leading · #9/162High 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 Talent55.1Strong · #29/162High 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 Capital64.6Leading · #8/162High 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 Research59.2Strong · #26/162High 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 Infrastructure78.8Strong · #21/162High 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 Agentic66.0Leading · #9/162High 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

  • Capital (64.6, Leading) — abundant capital flowing into building cognitive infrastructure.
  • Agentic (66.0, Leading) — agents are actively deployed — an early-mover productivity edge.
  • Infrastructure (78.8, 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.