Build vs. Buy vs. Partner (AI) is strategic choice on AI capability sourcing. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0045, within the Strategy family. The core principle: strategic choice on AI capability sourcing. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
Scientific Definition
Strategic choice on AI capability sourcing.
Plain-English Definition
Strategic choice on AI capability sourcing.
Feynman Explanation
Three answers. Different bets. Pick wrong and you wear it.
Core Principle
Strategic choice on AI capability sourcing.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
Three answers. Different bets. Pick wrong and you wear it.
Examples
- Foundation model decisions across enterprises.
- Capital allocation and strategic positioning.
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Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
This element is common enough to feel like human nature and specific enough to be engineered around. The mechanism underneath it operates in the Incentives dimension — what makes behavior more or less likely?. You can recognize it in the field by its signature: three answers. Different bets. Pick wrong and you wear it. Every element in the Incentives dimension changes the perceived payoff of an action before the action happens, which is exactly where incentive design has leverage.
How it gets exploited
Left undesigned, capital allocation and strategic positioning. It is amplified whenever capital allocation and strategic positioning. Inside organizations that shows up as capital allocation and strategic positioning. The pattern is the same one Goodhart's Law describes: the measurable proxy attracts the effort, and the purpose behind it quietly loses funding.
How the Lab designs around it
The redesign move is to decide per layer of the stack. Don't make one decision for all of AI. The test of any redesign here is simple: after the change, can you name what the organization is now doing less of? If not, the payoff structure did not actually move.
Famous Experiments
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Design Principles
- Decide per layer of the stack. Don't make one decision for all of AI.
Measurement Approaches
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Evidence
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The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Decide per layer of the stack. Don't make one decision for all of AI.
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Interactive Mini Network
Click any neighbor to re-center the graph and follow the threads of connection.
Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
Augmentation strategy vs. substitution strategy.
Stages of organizational AI capability.
AI strategy = decisions about which capabilities to build and where.
Augment when judgment matters. Automate when scale matters.
AI capability outpacing organizational ability to use it.
Specialized training on domain data.
Open-weight vs. API-only models.
Concentration risk on a single AI provider.
Operating economics shaped by per-token pricing.
Written rules about how AI may be used internally.
Innovators → early adopters → majority → laggards, AI-specific.
AI system that takes actions to achieve goals, often across tools.
Where Build vs. Buy vs. Partner (AI) is cited in the corpus
Essays, field guides, and diagnostics from The Incentives Lab that apply this element.
- Field guideIncentives: definition, types, examples
The parent field guide for this element.
- ReferenceThe laws of incentives
Goodhart, Campbell, and the Cobra Effect.
- EssayAI Agents Inherit Your Incentives
How this element propagates into automated systems.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about Build vs. Buy vs. Partner (AI)
- What is Build vs. Buy vs. Partner (AI)?
- Build vs. Buy vs. Partner (AI) is strategic choice on AI capability sourcing. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0045, within the Strategy family. The core principle: strategic choice on AI capability sourcing. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
- What is an example of Build vs. Buy vs. Partner (AI)?
- Capital allocation and strategic positioning. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0045).
- How is Build vs. Buy vs. Partner (AI) exploited?
- Capital allocation and strategic positioning.
- How do you design around Build vs. Buy vs. Partner (AI)?
- Decide per layer of the stack. Don't make one decision for all of AI.
- Which behavioral dimension does Build vs. Buy vs. Partner (AI) belong to?
- Build vs. Buy vs. Partner (AI) is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Strategy", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0045 and its evidence grade is C.