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The Incentives Lab
AI Incentives · Adoption

Sandbagging Adoption

Teams under-reporting AI capability to protect comp or status.

"Quiet quitting goes both ways."

Quick answer

What is Sandbagging Adoption? Teams under-reporting AI capability to protect comp or status. Predictable when adoption threatens individual interests.

In the wild

Salespeople not adopting tools that reveal pipeline health.

Why it matters in the room

Predictable when adoption threatens individual interests.

Counter-move

Align personal upside with adoption upside.

Visual · Reward gradient
REWARD ↑OPTIMIZER →
Sandbagging Adoption shows where an optimizer climbs vs where we want it to go.
Live example · Train around Sandbagging Adoption

Pick what to reward the model for.

Teams under-reporting AI capability to protect comp or status. In the wild: Salespeople not adopting tools that reveal pipeline health.

● Live

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Human Behavior Element™ · HBE Spec

The full taxonomy entry

Every concept in the Atlas uses the same structure — so Sandbagging Adoption can be compared, recombined, and cited like an element on a periodic table.

About the standard →
A
SA
HBT-A3606
Official name
Sandbagging Adoption
AI Incentives · Adoption
Identity
HBT ID
HBT-A3606
Symbol
SA
Official name
Sandbagging Adoption
Synonyms
Adoption
Keywords
AI Incentives, Adoption, human behavior, incentive design
Version
v1.0
Last updated
Maintained by The Incentives Lab
Classification
Kingdom
Systems
Domain
Machine Behavior
Family
AI Alignment & Incentives
Class
Adoption
Element
Sandbagging Adoption
Definition
Scientific
Teams under-reporting AI capability to protect comp or status.
Plain-English
Teams under-reporting AI capability to protect comp or status.
Feynman
Quiet quitting goes both ways.
Core principle
Teams under-reporting AI capability to protect comp or status.
One-sentence summary
Predictable when adoption threatens individual interests.
Mechanisms
Psychological
Teams under-reporting AI capability to protect comp or status.
Behavioral econ.
Predictable when adoption threatens individual interests.
Neurological
Reward, threat, and salience circuits bias attention toward the cue.
Evolutionary
Heuristics that paid off in ancestral environments now misfire in modern systems.
Sociological
Group norms and status incentives reinforce the pattern across a team.
Computational
Models trained on biased human signals will replicate and amplify the pattern.
Systems thinking
Feedback loops between metrics, incentives, and behavior lock the pattern in place.
Signals & signature
Inputs (activators)
Salespeople not adopting tools that reveal pipeline health.
Outputs (observable)
Predictable when adoption threatens individual interests.
Behavioral signature
You see Sandbagging Adoption when the explanation for a decision sounds reasonable but the outcome keeps repeating.
Behavioral molecules
Often combines with related Atlas entries — see the rail below.
Pathways · before
A goal, metric, or contract clause makes the behavior rational locally.
Pathways · after
Locally rational choices accumulate into a systemic distortion.
Domains where it shows up
  • Business
  • Leadership
  • Government
  • Healthcare
  • Education
  • Sales
  • Marketing
  • AI
  • Negotiation
  • Media
  • Public Policy
  • Relationships
Examples
Everyday
Salespeople not adopting tools that reveal pipeline health.
Modern
Predictable when adoption threatens individual interests.
Historical
A pattern repeatedly documented since the foundational behavioral science literature on ai alignment & incentives.
Famous experiments
See the References block — primary papers in the Atlas link out to the original studies.
Design principles
How to leverage
Predictable when adoption threatens individual interests.
How to reduce
Align personal upside with adoption upside.
How to redesign
Align personal upside with adoption upside.
The Perverse Incentive Lens™
How it's exploited
Organizations weaponize sandbagging adoption — sometimes deliberately, often by accident — when metrics reward the symptom rather than the outcome.
Common perverse incentives
Volume metrics, short review windows, bonus cliffs, and contracts that pay on activity rather than impact.
Failure modes
When Sandbagging Adoption dominates, teams optimize for the dashboard while the real outcome quietly degrades.
Incentive redesign
Align personal upside with adoption upside.
Ethical considerations
Don't engineer sandbagging adoption into customers, employees, or citizens as a manipulation tactic — design for informed choice instead.
Diagnostic questions
  • Where in our org would Sandbagging Adoption most often show up unnoticed?
  • Which metric, ritual, or contract clause quietly rewards Sandbagging Adoption?
  • If we removed every payoff for Sandbagging Adoption, what behavior would replace it?
  • Who benefits when Sandbagging Adoption persists — and who pays the cost?
Organizational warning signs
Metrics
A KPI is hit while the underlying outcome stalls or worsens.
Behaviors
People route around the rule rather than challenge it.
Language
'That's just how we do it here.' / 'The system requires it.'
Culture
Naming the pattern is treated as disloyalty.
Red flags
  • People defend the status quo using the language of sandbagging adoption.
  • 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.
Intervention playbook
Immediate
Make the perverse payoff visible to the people creating it.
30-day
Run a small pilot that pays for the outcome, not the proxy.
Long-term
Rewrite the comp plan, contract, or ritual so the right behavior becomes the easy behavior.
AI considerations
Detect
Audit training data and reward signals for the same pattern this element describes.
Avoid amplifying
Don't optimize models on metrics that already encode the perverse incentive.
Counteract
Use the model to surface where the pattern is most active, then redesign the incentive — not the model.
Measurement
Metrics
Outcome-to-proxy ratio over time.
Assessment
The Incentives Lab III Diagnostic.
Survey
Calibrated pulse questions on rules vs. outcomes.
Behavioral signals
Where people work around the system.
Observational
Where the dashboard and the lived experience disagree.
Scientific evidence
Evidence grade
Synthesized from the behavioral science literature; see Atlas references.
Replication
Tracked in the Atlas as primary, replicated, or contested.
Intervention confidence
Moderate — patterns generalize, mechanisms vary by context.
Research consensus
Broad agreement on the pattern; ongoing debate on boundary conditions.
Known limitations
Local context, culture, and incentive structure all change the strength of the effect.
Open questions
How does Sandbagging Adoption interact with AI-mediated decisions at scale?
References
Meta-analyses
Tracked in the Atlas registry.
Seminal authors
Kahneman, Tversky, Thaler, Ariely, Cialdini, Ostrom, Simon — and the field they built.
Cross references

Every Atlas entry is a node in a knowledge graph. See the related rail below to follow the connections.

Disciplinary layers

See Sandbagging Adoption through 5 lenses

Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.

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Worked example, counter-example & concept map

On-demand AI analysis grounded in the Lab's research. Cached on your device after first run.

How this lands in you

Your nervous system has a region for this.

Primary region
VTA & Dopamine Pathway

When you encounter Sandbagging Adoption, your dopamine system is tracking the gap between what you expected and what you got — and that gap is what's driving the next move, not the reward itself.

Wanting, anticipation, prediction error, motivational salience. Predictable rewards stop motivating. The phone buzz fires dopamine; the message itself rarely does.

See Dopamine in the Brain Atlas →
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