Jeddah
Metro Power
37.8
of 100 · #107
MPI
37.8
MCC
30.9
MDI
27.1
Pillar profile
Talent27.2
Capital22.6
Research20.8
Infrastructure57.0
Agentic27.1
Indicators
- Population (m)4.8
- GDP ($bn)130
- GDP per capita ($k)27
- AI investment ($bn)1.4
- Tech employment %4.5
- AI talent40
- Research strength44
- Notable AI orgs9
- Compute / data centers54
- Broadband %91
- Tertiary degree %36
- Digital skills58
- Startup ecosystem46
- Agent adoption36
- Patents / 100k6
Nearest peers
- Rio de Janeiro30.8
- Kolkata30.5
- Amman30.5
- Bangkok31.5
- San Antonio31.6
Metro report · generated from Jeddah's indicators
Jeddah — metro standing in full
Jeddah is the #97 metro by economic size ($130bn) in the panel and ranks #107/162 on absolute Metro Power and #134/162 on per-capita intensity. No pillar stands out as a clear strength; the binding concern is research. Locally it runs below the Saudi Arabia national average (MCC 30.9 vs CC 48.0).
National context: Saudi Arabia scores CC 48.0 per-capita; Jeddah sits at MCC 30.9.
Economic & scale context curated v1 estimate
GDP (metro)
$130bn
#97 of 162
GDP / capita
$27k
Population
4.8M
AI investment
$1.4bn
#116 of 162
Notable AI orgs
9
Index & pillar read
For each metro index and pillar: what it means when high (the value) versus low (the gap), and Jeddah's own standing.
| Index / pillar | Value | Standing | What a high vs low value means — and where Jeddah sits |
|---|---|---|---|
| MPI Metro Power | 37.8 | Developing · #107/162 | Low here — 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 | 30.9 | Developing · #134/162 | 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 | 27.1 | Developing · #139/162 | 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 | 27.2 | Developing · #141/162 | 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 | 22.6 | Developing · #137/162 | 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 | 20.8 | Lagging · #148/162 | 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 | 57.0 | Developing · #114/162 | 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 | 27.1 | Developing · #139/162 | 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 — Research 20.8 (#148/162, Lagging): a weak research base — fewer home-grown breakthroughs and spinouts.
▲
Metro values are curated estimates (v1) on a consistent global scale — not yet measured sub-national data.