Massachusetts
Northeast
State Power #6/51Per-capita #2
State Power
68.7
of 100 · #6
SPI
68.7
SCC
86.6
SDI
87.9
Pillar profile
Talent86.5
Capital76.5
Research94.0
Infrastructure88.2
Agentic87.9
Nearest peers (per-capita)
- Washington80.4
- New York79.6
- California94.9
- District of Columbia73.7
- Virginia68.8
Report · generated from Massachusetts's indicators
Massachusetts — standing in full
Massachusetts is the #12 state by economic size ($700bn GSP) and ranks #6/51 on absolute State Power and #2/51 on per-capita intensity. Its strongest pillar is Talent (86.5, Leading); no single pillar is a binding weakness.
Economic & scale context
GDP
$700bn
#12 of 51
GDP / capita
$100k
Population
7.0M
AI investment
$30.0bn
#4 of 51
Notable AI orgs
90
Index & pillar read
| Index / pillar | Value | Standing | What a high vs low value means — and where Massachusetts sits |
|---|---|---|---|
| SPI State Power | 68.7 | Strong · #6/51 | High here — a heavyweight that anchors national AI capacity and pulls in capital and talent. ▲ 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 | 86.6 | Leading · #2/51 | 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 | 87.9 | Leading · #2/51 | 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 | 86.5 | 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 | 76.5 | Leading · #3/51 | 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 | 94.0 | Leading · #2/51 | 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 | 88.2 | Leading · #2/51 | 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 | 87.9 | Leading · #2/51 | 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 (86.5, Leading) — a deep talent pool — the scarcest input to building AI.
- Research (94.0, Leading) — a strong research base feeding a pipeline of ideas and people.
- Infrastructure (88.2, Leading) — the physical and digital rails to run AI at scale are in place.
Risk factors
- No acute structural gaps stand out across the state pillars.
▲
State values are curated v1 estimates on a consistent scale — not yet measured sub-national data.