CognitiveAristocracy
Detail
Overview / Regions / Hong Kong

Hong Kong

Asia-Pacific Power #34/162Per-capita #47
Metro Power
57.7
of 100 · #34
MPI
57.7
MCC
52.6
MDI
48.2

Pillar profile

Talent44.7
Capital40.4
Research51.0
Infrastructure78.5
Agentic48.2

Indicators

  • Population (m)7.5
  • GDP ($bn)380
  • GDP per capita ($k)51
  • AI investment ($bn)4.5
  • Tech employment %8
  • AI talent62
  • Research strength74
  • Notable AI orgs28
  • Compute / data centers76
  • Broadband %95
  • Tertiary degree %40
  • Digital skills76
  • Startup ecosystem62
  • Agent adoption50
  • Patents / 100k70

Nearest peers

Metro report · generated from Hong Kong's indicators

Hong Kong — metro standing in full

Hong Kong is the #28 metro by economic size ($380bn) in the panel and ranks #34/162 on absolute Metro Power and #47/162 on per-capita intensity. Its strongest pillar is Infrastructure (78.5, Strong); no single pillar is a binding weakness.

Economic & scale context curated v1 estimate

GDP (metro)
$380bn
#28 of 162
GDP / capita
$51k
Population
7.5M
AI investment
$4.5bn
#47 of 162
Notable AI orgs
28

Index & pillar read

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

Index / pillarValueStandingWhat a high vs low value means — and where Hong Kong sits
MPI Metro Power57.7Strong · #34/162High 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 Coefficient52.6Strong · #47/162High 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 Agentic48.2Strong · #45/162High 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 Talent44.7Moderate · #76/162Mid-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 Capital40.4Strong · #48/162High 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 Research51.0Strong · #40/162High 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 Infrastructure78.5Strong · #22/162High 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 Agentic48.2Strong · #45/162High 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 (78.5, Strong) — the physical and digital rails to run AI at scale are in place.
  • Research (51.0, Strong) — a strong research base feeding a pipeline of ideas and people.
  • Agentic (48.2, 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.