CognitiveAristocracy
Detail
Overview / Regions / Prague

Prague

Europe Power #104/162Per-capita #93National view: Czechia →
Metro Power
38.2
of 100 · #104
MPI
38.2
MCC
43.1
MDI
40.6

Pillar profile

Talent44.2
Capital28.7
Research38.3
Infrastructure63.7
Agentic40.6

Indicators

  • Population (m)2.7
  • GDP ($bn)101.0
  • GDP per capita ($k)38.0
  • AI investment ($bn)1.1
  • Tech employment %8
  • AI talent58
  • Research strength64
  • Notable AI orgs18
  • Compute / data centers58
  • Broadband %91
  • Tertiary degree %44
  • Digital skills68
  • Startup ecosystem60
  • Agent adoption42
  • Patents / 100k16

Nearest peers

Metro report · generated from Prague's indicators

Prague — metro standing in full

Prague is the #113 metro by economic size ($101bn) in the panel and ranks #104/162 on absolute Metro Power and #93/162 on per-capita intensity. No pillar stands out as a clear strength; no single pillar is a binding weakness. Locally it runs below the Czechia national average (MCC 43.1 vs CC 52.5).

National context: Czechia scores CC 52.5 per-capita; Prague sits at MCC 43.1.

Economic & scale context ● Eurostat met_10r_3gdp/met_pjanaggr3 (2021/22)

GDP (metro)
$101bn
#113 of 162
GDP / capita
$38k
Population
2.7M
AI investment
$1.1bn
#124 of 162
Notable AI orgs
18

Index & pillar read

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

Index / pillarValueStandingWhat a high vs low value means — and where Prague sits
MPI Metro Power38.2Moderate · #104/162Mid-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 Coefficient43.1Moderate · #93/162Mid-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 Agentic40.6Moderate · #92/162Mid-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 · #79/162Mid-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 Capital28.7Moderate · #105/162Mid-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 Research38.3Moderate · #91/162Mid-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 Infrastructure63.7Moderate · #96/162Mid-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 Agentic40.6Moderate · #92/162Mid-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

  • No pillar stands out as a clear strength yet.

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.