Johannesburg
Metro Power
45.1
of 100 · #79
MPI
45.1
MCC
29.3
MDI
26.4
Pillar profile
Talent25.4
Capital24.9
Research29.6
Infrastructure40.1
Agentic26.4
Indicators
- Population (m)6
- GDP ($bn)130
- GDP per capita ($k)22
- AI investment ($bn)1
- Tech employment %6
- AI talent44
- Research strength46
- Notable AI orgs24
- Compute / data centers50
- Broadband %70
- Tertiary degree %24
- Digital skills52
- Startup ecosystem54
- Agent adoption32
- Patents / 100k4
Nearest peers
- Ho Chi Minh City29.4
- Cape Town29.4
- Medellin29.0
- Bogota28.8
- Brasilia29.8
Metro report · generated from Johannesburg's indicators
Johannesburg — metro standing in full
Johannesburg is the #96 metro by economic size ($130bn) in the panel and ranks #79/162 on absolute Metro Power and #141/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is infrastructure. Locally it runs below the South Africa national average (MCC 29.3 vs CC 31.6).
National context: South Africa scores CC 31.6 per-capita; Johannesburg sits at MCC 29.3.
Economic & scale context curated v1 estimate
GDP (metro)
$130bn
#96 of 162
GDP / capita
$22k
Population
6.0M
AI investment
$1.0bn
#132 of 162
Notable AI orgs
24
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Johannesburg's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Johannesburg sits |
|---|---|---|---|
| MPI Metro Power | 45.1 | Moderate · #79/162 | Mid-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 Coefficient | 29.3 | Developing · #141/162 | Low 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 Agentic | 26.4 | Developing · #142/162 | Low 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 Talent | 25.4 | Lagging · #145/162 | Low 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 Capital | 24.9 | Developing · #130/162 | Low 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 Research | 29.6 | Developing · #120/162 | Low 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 Infrastructure | 40.1 | Lagging · #145/162 | Low 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 Agentic | 26.4 | Developing · #142/162 | Low 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 40.1 (#145/162, 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.