District of Columbia
South
State Power #28/51Per-capita #5
State Power
36.1
of 100 · #28
SPI
36.1
SCC
73.7
SDI
81.8
Pillar profile
Talent85.1
Capital51.4
Research63.5
Infrastructure86.7
Agentic81.8
Nearest peers (per-capita)
- Virginia68.8
- Colorado68.6
- New Jersey68.5
- New York79.6
- Washington80.4
Report · generated from District of Columbia's indicators
District of Columbia — standing in full
District of Columbia is the #35 state by economic size ($165bn GSP) and ranks #28/51 on absolute State Power and #5/51 on per-capita intensity. Its strongest pillar is Talent (85.1, Leading); no single pillar is a binding weakness.
Economic & scale context
GDP
$165bn
#35 of 51
GDP / capita
$246k
Population
0.7M
AI investment
$6.5bn
#17 of 51
Notable AI orgs
55
Index & pillar read
| Index / pillar | Value | Standing | What a high vs low value means — and where District of Columbia sits |
|---|---|---|---|
| SPI State Power | 36.1 | Moderate · #28/51 | Mid-pack. High would mean a heavyweight that anchors national AI capacity and pulls in capital and talent; low would mean limited absolute weight — a smaller node leaning on capacity built in larger states. ▲ high: a heavyweight that anchors national AI capacity and pulls in capital and talent · ▼ low: limited absolute weight — a smaller node leaning on capacity built in larger states |
| SCC State Coefficient | 73.7 | Leading · #5/51 | High here — deep capability per resident — a concentrated, high-intensity AI economy. ▲ high: deep capability per resident — a concentrated, high-intensity AI economy · ▼ low: thin intensity per resident — capability is sparse relative to population |
| SDI State Agentic | 81.8 | Leading · #5/51 | High 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 Talent | 85.1 | Leading · #2/51 | High here — a deep talent pool — the scarcest input to building AI. ▲ 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 | 51.4 | Strong · #13/51 | High 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 Research | 63.5 | Strong · #7/51 | High 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 Infrastructure | 86.7 | Strong · #6/51 | High 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 Agentic | 81.8 | Leading · #5/51 | High 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
- Talent (85.1, Leading) — a deep talent pool — the scarcest input to building AI.
- Agentic (81.8, Leading) — agents are actively deployed — an early-mover productivity edge.
- Infrastructure (86.7, Strong) — the physical and digital rails to run AI at scale are in place.
Risk factors
- Depth without scale — high per-capita intensity (SCC #5) but limited absolute weight (SPI #28).
▲
State values are curated v1 estimates on a consistent scale — not yet measured sub-national data.