CognitiveAristocracy
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Overview / Regions / Nairobi

Nairobi

Africa Power #100/162Per-capita #151National view: Kenya →
Metro Power
39.1
of 100 · #100
MPI
39.1
MCC
22.1
MDI
20.5

Pillar profile

Talent18.3
Capital26.0
Research21.2
Infrastructure24.6
Agentic20.5

Indicators

  • Population (m)5.3
  • GDP ($bn)60
  • GDP per capita ($k)11
  • AI investment ($bn)1
  • Tech employment %4
  • AI talent38
  • Research strength34
  • Notable AI orgs20
  • Compute / data centers40
  • Broadband %58
  • Tertiary degree %20
  • Digital skills44
  • Startup ecosystem56
  • Agent adoption28
  • Patents / 100k1

Nearest peers

Metro report · generated from Nairobi's indicators

Nairobi — metro standing in full

Nairobi is the #129 metro by economic size ($60bn) in the panel and ranks #100/162 on absolute Metro Power and #151/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is infrastructure. Locally it runs above the Kenya national average (MCC 22.1 vs CC 17.1).

National context: Kenya scores CC 17.1 per-capita; Nairobi sits at MCC 22.1.

Economic & scale context curated v1 estimate

GDP (metro)
$60bn
#129 of 162
GDP / capita
$11k
Population
5.3M
AI investment
$1.0bn
#130 of 162
Notable AI orgs
20

Index & pillar read

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

Index / pillarValueStandingWhat a high vs low value means — and where Nairobi sits
MPI Metro Power39.1Moderate · #100/162Mid-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 Coefficient22.1Lagging · #151/162Low 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 Agentic20.5Lagging · #150/162Low 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 Talent18.3Lagging · #154/162Low 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 Capital26.0Developing · #123/162Low 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 Research21.2Lagging · #145/162Low 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 Infrastructure24.6Lagging · #157/162Low here — 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 Agentic20.5Lagging · #150/162Low 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 — Infrastructure 24.6 (#157/162, Lagging): infrastructure gaps cap how much AI can actually be run locally.
Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.