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How is Decision Intel different from ChatGPT or a general AI assistant?

ChatGPT gives one opinion from one model: ungoverned, untraceable, unaudited. Decision Intel is the reasoning audit platform. Most tools audit your data; we audit your reasoning — and catch the fatal blind spots in strategic memos before the committee does. It measures the noise in your reasoning the same way Kahneman did in the insurance underwriter study, simulates an AI boardroom of CEO, CFO, and board personas, runs what-if interventions against a 143-case public reference library, and compounds every confirmed outcome back into a calibrated Decision Quality Index your audit committee can defend. Not a chatbot; a reasoning audit, checkable from memo to outcome.

Which cognitive biases caused Magic Leap's failure?

5 bias patterns are documented in this case, led by Optimism Bias. The full set: Optimism Bias, Bandwagon Effect, Authority Bias, Confirmation Bias, Planning Fallacy. Each is mapped to Decision Intel's 22-bias R²F taxonomy with stable IDs and academic citations.

What is Decision Intel's reasoning audit?

Decision Intel runs a 12-node Recognition-Rigor Framework (R²F) pipeline on strategic memos, detecting cognitive biases from a 22-bias canonical taxonomy and producing a Decision Quality Index (DQI) score. The audit produces a hashed, tamper-evident Decision Provenance Record (DPR) suitable for audit-committee review.

All case studies

FailureTechnology2010

Magic Leap

Magic Leap Mixed Reality Failure

Estimated impact: $2.6B in investor capital largely destroyed

F30

Decision Quality Index (simulated)

Critical · 5 biases detected, 2 toxic combinations

Magic Leap raised $2.6B on promises of revolutionary augmented reality glasses. After years of secretive development, the Magic Leap One headset launched in 2018 to disappointing reviews — it was bulky, limited, and had no killer app. The company pivoted to enterprise and laid off half its staff.

Decision context

Whether to continue funding a company that maintained extreme secrecy about its product capabilities while raising increasingly large funding rounds based on demo videos.

Decision anatomy

Monetary stakes

Very high

Dissent suppressed

Yes

Time pressure

None

Unanimous consensus

Yes

Decision participants

Dissent encouraged

External advisors

Iterative process

Red = risk factor present · Green = protective factor present

Deep hindsight-stripped analysis coming soon. This case is part of our ongoing backfill to reconstruct what the platform would have flagged at decision time. The summary and bias profile below are drawn from primary sources.

Biases present in the decision

★Optimism Bias

critical

Bandwagon Effect

medium

Authority Bias

high

Confirmation Bias

high

Planning Fallacy

medium

★ Primary driver · Severity estimated from bias type and decision outcome

3D Decision Knowledge Graph

Magic Leap · 5 biases, 2 toxic combinations

Drag to rotate · Scroll to zoom · Click to explore

Decision

Critical

High

Medium

Toxic

Bias Intensity Profile

Severity × confidence across 5 detected biases

Toxic combinations

!Optimism Trap

Overconfidence and availability bias create a systematically positive outlook that ignores base rates.

!Echo Chamber

Group members reinforce each other's views, systematically filtering out dissenting signals.

Reference class base rates

Across all 143 curated case studies in our library:

89%

base failure rate

-0%

partial / mixed

11%

base success rate

■ Failure■ Partial■ Success

Lessons learned

Optimism bias: investors funded a vision rather than validated technology, believing AR would follow smartphone adoption curves
Secrecy as a strategy masked the gap between marketing demos and actual product capabilities
Bandwagon effect among top-tier VCs created social proof that substituted for technical due diligence

Source: Kevin Kelleher, "Magic Leap's Hyped Headset Was a Flop" (Fortune, 2020); The Information investigations (News Investigation)

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Workflows that fire on decisions like Magic Leap’s

The same Recognition-Rigor Framework that documents this case audits memos in the same shape — before the outcome forces the lesson.

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Lessons learned

Optimism bias: investors funded a vision rather than validated technology, believing AR would follow smartphone adoption curves

Secrecy as a strategy masked the gap between marketing demos and actual product capabilities

Bandwagon effect among top-tier VCs created social proof that substituted for technical due diligence