Foundation Model Concentration
A handful of providers shape the entire AI economy.
"Watch the moats forming. They're forming fast."
What is Foundation Model Concentration? A handful of providers shape the entire AI economy. Long-term strategic positioning.
OpenAI, Anthropic, Google, Meta, plus a few more.
Long-term strategic positioning.
Plan for both worlds: continued concentration and surprise decentralization.
Pick what to reward the model for.
A handful of providers shape the entire AI economy. In the wild: OpenAI, Anthropic, Google, Meta, plus a few more.
Pick a lever. There are no neutral ones — every incentive funds a behavior somewhere.
Pick a reaction to Foundation Model Concentration
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The full taxonomy entry
Every concept in the Atlas uses the same structure — so Foundation Model Concentration can be compared, recombined, and cited like an element on a periodic table.
- Business
- Leadership
- Government
- Healthcare
- Education
- Sales
- Marketing
- AI
- Negotiation
- Media
- Public Policy
- Relationships
- Where in our org would Foundation Model Concentration most often show up unnoticed?
- Which metric, ritual, or contract clause quietly rewards Foundation Model Concentration?
- If we removed every payoff for Foundation Model Concentration, what behavior would replace it?
- Who benefits when Foundation Model Concentration persists — and who pays the cost?
- People defend the status quo using the language of foundation model concentration.
- Decisions cluster around the easiest narrative rather than the strongest evidence.
- New data changes the slide deck but not the decision.
- Anyone naming the pattern is treated as the problem.
Every Atlas entry is a node in a knowledge graph. See the related rail below to follow the connections.
See Foundation Model Concentration through 2 lenses
Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.
Do you actually know Foundation Model Concentration?
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Which best describes Foundation Model Concentration?
Worked example, counter-example & concept map
On-demand AI analysis grounded in the Lab's research. Cached on your device after first run.
When you encounter Foundation Model Concentration, your prefrontal cortex has to do extra work to override the automatic response — and that override budget is finite.
Executive control, planning, impulse override, working memory, System 2. First thing to go offline under stress, fatigue, or low blood sugar. Why your 4pm decisions are worse than your 9am ones.
See Prefrontal in the Brain Atlas →Picked for you, from the Atlas
Ranked by shared learning paths, overlapping chips, and what you've saved.
AI investment outpacing measurable productivity gains.
GPU access and pricing shape what's feasible.
Operating economics shaped by per-token pricing.
Training is a one-time cost. Inference is forever.
Cache common prompt prefixes to reduce cost and latency.
Real cost includes data, ops, monitoring, governance, training.
Send the card, not just the link
A pre-rendered social card with the title, eyebrow, and URL. Copy the link, post it anywhere, or download the SVG for slides.
More definitions to follow
Every term in the Atlas connects to a dozen others. Pick any of these and see where it takes you.
We judge harm by action more harshly than the same harm by inaction.
Distance in time, space, social relation, or hypotheticality changes judgment.
Confidential settlements buy silence and prevent precedent that would deter future harm.
Cherry-picking data to fit a pattern after the fact.
We attribute our own actions to situations but others' actions to their character.
Replacing a hard question with an easier one without realizing it.
Underpriced externalities keep dirty energy artificially competitive.
Retroactive extensions privilege legacy estates over public-domain enrichment.
Prices driven far above intrinsic value by feedback loops of belief and behavior.
Paying providers per procedure rewards more procedures, not better outcomes.
The mythical fully rational, self-interested, utility-maximizing agent neoclassical models assume.
Giving up after repeated exposure to uncontrollable negative events.