AI's Behavioral Failure Modes
Where human bias meets machine scale
AI doesn't remove cognitive bias — it amplifies and automates it. These terms are the failure modes that show up six months after deployment, when nobody can remember who actually decided.
- 01Mental Models · Decision
Automation Bias
We over-trust machines that look confident.
Favoring suggestions from automated systems over conflicting human judgment.
Open the full definition →Try it · 30 secondsLive example · Apply Automation BiasUse the model. Pick the move.
Favoring suggestions from automated systems over conflicting human judgment. You've just seen this: Ignoring a sales rep's intuition because the forecast model disagrees. Which lever does the model recommend?
● LivePick a lever. There are no neutral ones — every incentive funds a behavior somewhere.
- 02AI Incentives · Adoption
Algorithmic Aversion
And then we abandon them the moment they're wrong once.
Discounting algorithmic advice even when superior.
Open the full definition →Try it · 30 secondsLive example · Train around Algorithmic AversionPick what to reward the model for.
Discounting algorithmic advice even when superior. In the wild: Even when algorithms outperform, users prefer humans.
● LivePick a lever. There are no neutral ones — every incentive funds a behavior somewhere.
- 03Perverse Incentives · AI
AI Productivity Mirage
Activity up, output flat — at scale.
Individual productivity gains hide collective output degradation.
Open the full definition →Try it · 30 secondsLive example · Re-architect AI Productivity MirageFlip the incentive. Watch the side-effect move.
Individual productivity gains hide collective output degradation. Caught in the wild: AI-generated code that's faster to write and harder to maintain.
● LiveWhat gets measuredHeadline number the org is paid on088100What quietly moves with itQuiet damage the proxy hides074100In the room: Local optimization producing global quality erosion.
Counter-move from the Atlas: Measure outcomes, not throughput.
- 04Neuroscience · AI Interaction
Anthropomorphism (AI)
Treating tools like teammates. Costs follow.
We treat AI as more humanlike than it is.
Open the full definition →Try it · 30 secondsLive example · Anthropomorphism (AI) in the bodyWhere does anthropomorphism (ai) fire hardest?
We treat AI as more humanlike than it is. Real scene: Users attributing intent and emotion to LLMs. Drag through a workday and notice which zone your team actually lives in.
● LiveSafety / predictabilityStimulation / novelty050100Flow bandIn the room: Risk of misreading model behavior as agency.
- 05AI Incentives · Strategy
Capability Overhang
What the model can do that nobody's noticed yet.
AI capability outpacing organizational ability to use it.
Open the full definition →Try it · 30 secondsLive example · Train around Capability OverhangPick what to reward the model for.
AI capability outpacing organizational ability to use it. In the wild: Most enterprises sitting on capabilities they haven't integrated.
● LivePick a lever. There are no neutral ones — every incentive funds a behavior somewhere.
- 06AI Incentives · Governance
AI Governance Vacuum
Nobody owns the decision. That is the decision.
Agents deployed before anyone owns the consequences.
Open the full definition →Try it · 30 secondsLive example · Train around AI Governance VacuumPick what to reward the model for.
Agents deployed before anyone owns the consequences. In the wild: Customer-facing agents with unclear escalation and liability paths.
● LivePick a lever. There are no neutral ones — every incentive funds a behavior somewhere.
If you're building an AI strategy, treat this path as a pre-mortem checklist.
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