District of Columbia
South
State Power #28/51Per-capita #5
State Power
38.1
of 100 · #28
SPI
38.1
SCC
77.1
SDI
81.8
Pillar profile
Talent90.1
Capital53.6
Research63.5
Infrastructure96.5
Agentic81.8
Nearest peers (per-capita)
- New York80.5
- Washington81.5
- Virginia69.9
- New Jersey68.9
- Massachusetts85.4
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 (90.1, Leading); no single pillar is a binding weakness.
Economic & scale context
GDP
$165bn
#35 of 51
GDP / capita
$246k
Population
0.7M
AI investment
$8.1bn
#16 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 | 38.1 | 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 | 77.1 | 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 | 90.1 | Leading · #1/51 | 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 | 53.6 | Strong · #14/51 | 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 | 96.5 | Leading · #1/51 | 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 (90.1, Leading), a deep talent pool, the scarcest input to building AI.
- Infrastructure (96.5, Leading), the physical and digital rails to run AI at scale are in place.
- Agentic (81.8, Leading), agents are actively deployed, an early-mover productivity edge.
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.