The AI Trap
A Masterclass on AI Decision-Making
Most AI projects don't fail because the models are weak.
They fail because the decisions around them are.
The AI Trap is a masterclass document on AI decision-making — what to build, what to constrain, and when to stop.
This is not about prompts or tools.
It is about judgment.
Why This Exists
The same patterns repeat across applied AI systems:
- Critical decisions deferred until cost compounds
- Confident output masking fragile systems
- Metrics optimized without asking whether they should be
This masterclass focuses on the decisions that determine whether AI creates value — or simply creates activity.
What This Is
A concise, structured masterclass document focused on AI decision-making.
It distills recurring system failures into:
- Clear mental models
- Decision frameworks
- Constraint definitions
- Stop / no-go criteria
This is not about how to build models.
It is about thinking clearly before you deploy them.
What This Is Not
This is intentionally not:
- A beginner course
- A certification
- A prompt collection
- A tool walkthrough
- Motivational content
- A cohort, community, or live program
There is no hand-holding.
There are no templates pretending to be answers.
This is deliberate.
Who This Is For
This masterclass is for people who:
- Already use AI in real work
- Are accountable for outcomes, not demos
- Make decisions under uncertainty
- Are tired of surface-level AI narratives
You likely work in:
- Engineering / Data / ML
- Security / Quantitative Research
- Technical & Product Leadership
Judgment does.
What You Will Learn
You will learn how to:
- Decide when AI should not be used
- Frame problems before automation distorts them
- Define decision boundaries AI must never cross
- Separate signal from convincing noise
- Evaluate systems without being misled by metrics
- Recognize early when a project should be stopped
You will not learn how to:
- "10× productivity"
- Build chatbots
- Impress stakeholders with demos
- Justify AI use after the fact
What You Get
A structured masterclass document you can read at your own pace.
~15,000 words · 7 chapters · 2–3 hours reading time
Each chapter includes:
Core principles grounded in applied experience
Practical frameworks you can use immediately
Decision tools: checklists, templates, audit questions
Pattern recognition drawn from real systems
Chapter Overview
Thinking Before AI
Why most AI initiatives are misframed from the start.
Includes: The Pre-AI Decision Framework — five questions to answer before any AI project.
Decision Boundaries
What AI is allowed to decide — and what it never should.
Includes: Boundary Definition Template
Signal Discipline
Separating useful signals from convincing noise.
Includes: Signal vs. Noise Audit Checklist
Evaluation Without Illusions
Why common metrics lie — and what to use instead.
Includes: Evaluation Reality Check — ten questions before trusting any metric.
Applied Case Patterns
Recurring failure patterns from real systems, including:
- Trading research
- Detection systems (text, image, video)
- Generative AI deployments
Includes: Pattern Recognition Guide
Operating AI Long-Term
What happens after the hype phase ends.
Includes: Long-Term Operations Checklist
Strategic Restraint
Why not building is sometimes the highest-leverage decision.
Includes: Restraint Decision Framework
How This Is Different
Access
One-time access
- Structured masterclass document
- All frameworks and checklists included
- Read at your own pace
- Lifetime access
No subscriptions. No upsells.
Before you purchase, read this carefully.
A Note Before You Buy
This masterclass is not for everyone.
If you need step-by-step instructions, constant feedback, or ready-made answers, you will likely be disappointed.
If you value clear thinking under uncertainty and prefer responsibility over hype, this may serve you.
AI does not remove responsibility.
It concentrates it.
If you are willing to think before you deploy, this will serve you.
One-time payment · Lifetime access