Sandbagging Adoption is teams under-reporting AI capability to protect comp or status. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0243, within the Adoption family. The core principle: teams under-reporting AI capability to protect comp or status. 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
Teams under-reporting AI capability to protect comp or status.
Plain-English Definition
Teams under-reporting AI capability to protect comp or status.
Feynman Explanation
Quiet quitting goes both ways.
Core Principle
Teams under-reporting AI capability to protect comp or status.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
Quiet quitting goes both ways.
Examples
- Salespeople not adopting tools that reveal pipeline health.
- Predictable when adoption threatens individual interests.
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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
Most organizations meet this element as a personnel problem. It is not one. 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: quiet quitting goes both ways. 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, predictable when adoption threatens individual interests. It is amplified whenever predictable when adoption threatens individual interests. Inside organizations that shows up as predictable when adoption threatens individual interests. 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 align personal upside with adoption upside. Watch for it at the boundaries: handoffs, promotions, incident reviews, and budget cycles are where this element gets its power.
Famous Experiments
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Design Principles
- Align personal upside with adoption upside.
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.
- Align personal upside with adoption upside.
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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.
Innovators → early adopters → majority → laggards, AI-specific.
Discounting algorithmic advice even when superior.
AI bolted onto existing workflows to look forward-leaning.
Redesigning roles around AI capability.
AI pilots that succeed and never scale.
Pre-deployment analysis of who loses what.
Employees using unauthorized AI tools to get work done.
AI doesn't just replace tasks — it threatens identities.
Written rules about how AI may be used internally.
AI system that takes actions to achieve goals, often across tools.
When the agent acts, who's responsible?
Augmentation strategy vs. substitution strategy.
Where Sandbagging Adoption 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.
- EssayThe Comp Plan Is the Strategy
Where this element meets compensation design.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about Sandbagging Adoption
- What is Sandbagging Adoption?
- Sandbagging Adoption is teams under-reporting AI capability to protect comp or status. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0243, within the Adoption family. The core principle: teams under-reporting AI capability to protect comp or status. 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 Sandbagging Adoption?
- Predictable when adoption threatens individual interests. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0243).
- How is Sandbagging Adoption exploited?
- Predictable when adoption threatens individual interests.
- How do you design around Sandbagging Adoption?
- Align personal upside with adoption upside.
- Which behavioral dimension does Sandbagging Adoption belong to?
- Sandbagging Adoption is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Adoption", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0243 and its evidence grade is C.