Opaque Logic Drift

Users question AI logic and decisions when the reasoning is opaque. Initial trust depletes through repeated encounters with unexplained outputs. Engagement becomes skeptical rather than collaborative.

## Trigger Signals

• Users question AI logic without explanation
• Initial trust depletes with each opaque output
• Engagement becomes skeptical, not collaborative
• "Why did it do that?" unanswered

## Why It Persists

Explainability is expensive to build. Opacity ships faster. The people building the system don't experience the trust erosion the users do.

## Common Misdiagnosis

• "Users need more training"
• "The AI is too complex to explain"
• "Trust will build over time"
• "People are resistant to change"

## Cost of Ignoring

Adoption stalls. Users work around the system rather than with it. The AI becomes a black box that people comply with but don't trust. Value extraction collapses.

• Users question AI logic without explanation
• Initial trust depletes with each opaque output
• Engagement becomes skeptical, not collaborative
• "Why did it do that?" unanswered

## Why It Persists

Explainability is expensive to build. Opacity ships faster. The people building the system don't experience the trust erosion the users do.

## Common Misdiagnosis

• "Users need more training"
• "The AI is too complex to explain"
• "Trust will build over time"
• "People are resistant to change"

## Cost of Ignoring

Adoption stalls. Users work around the system rather than with it. The AI becomes a black box that people comply with but don't trust. Value extraction collapses.

Explainability is expensive to build. Opacity ships faster. The people building the system don't experience the trust erosion the users do.

## Common Misdiagnosis

• "Users need more training"
• "The AI is too complex to explain"
• "Trust will build over time"
• "People are resistant to change"

## Cost of Ignoring

Adoption stalls. Users work around the system rather than with it. The AI becomes a black box that people comply with but don't trust. Value extraction collapses.

AI systems make decisions without explaining their reasoning. Users encounter outputs they can't interpret. Initial trust depletes through repeated encounters with opacity.

## What It Looks Like In the Wild

Users question AI logic and decisions when the reasoning is opaque. Initial trust depletes through repeated encounters with unexplained outputs. Engagement becomes skeptical rather than collaborative.

## Trigger Signals

• Users question AI logic without explanation
• Initial trust depletes with each opaque output
• Engagement becomes skeptical, not collaborative
• "Why did it do that?" unanswered

## Why It Persists

Explainability is expensive to build. Opacity ships faster. The people building the system don't experience the trust erosion the users do.

## Common Misdiagnosis

• "Users need more training"
• "The AI is too complex to explain"
• "Trust will build over time"
• "People are resistant to change"

## Cost of Ignoring

Adoption stalls. Users work around the system rather than with it. The AI becomes a black box that people comply with but don't trust. Value extraction collapses.

• "Users need more training"
• "The AI is too complex to explain"
• "Trust will build over time"
• "People are resistant to change"

## Cost of Ignoring

Adoption stalls. Users work around the system rather than with it. The AI becomes a black box that people comply with but don't trust. Value extraction collapses.

Adoption stalls. Users work around the system rather than with it. The AI becomes a black box that people comply with but don't trust. Value extraction collapses.