Teaching & speaking
I turn product practice into ideas and tools other people can use. Start with the current AI course, the DISx example and a concise view of my method.
Product management and AI courses
Transition from Product Manager to AI Product Manager
Udemy: 4.6/5, 387 ratings, 1,920 students. Checked 22 October 2026.
Bestseller · Highest Rated
Who it is for. For product managers moving into AI and builders who want to make model behaviour, context and decision rights part of product design.
What you will practise
- Map an AI feature across Model, Context, Orchestration, Governance and Human responsibilities.
- Run Talk, Decide, Build, Observe and Iterate around a specific customer problem.
- Define evaluation cases, stopping conditions and a human escalation route before granting action authority.
Read a short lesson preview
A useful recommendation is different from permission to act. In the governance lesson, the product question is what should happen when a plausible answer hides an unresolved risk. An evaluation case makes that failure repeatable; a rule outside the model enforces the stop.
A written introduction to the teaching approach. Platform listings provide their own lesson previews.
Try a sample exercise
Use the hypothetical DISx sanctions match: write the ambiguous input, expected escalation and prohibited action as one evaluation case. Then identify which system control must block the payment recommendation from becoming an authorised transfer.
Take away: A small evaluation case and an explicit authority boundary.
Read the public worked example ↗A portfolio exercise based on the teaching material; platform assignments may differ.
Free video previews on Udemy
- Introduction: The New AI Product Management Operating System · 28:34
- The Orchestration Layer · 37:01
Preview availability checked on 22 October 2026.
Udemy and Coursera are separate platform editions. Coursera is offered by LearnKartS and lists 10 modules; use each listing for its curriculum, assignments and included material.
The Fastest Path to Product Management
Udemy: 4.8/5, 162 ratings, 4,881 students. Checked 22 October 2026.
Who it is for. For aspiring and practising product managers who want a clear route from discovery to release, with shared responsibilities and decisions.
What you will practise
- Connect a customer problem to strategic alignment, discovery evidence and a proposed solution.
- Turn the solution into personas, journeys, requirements and success criteria.
- Plan alpha, beta and live release checks, then use feedback to decide the next iteration.
Read a short lesson preview
Before writing requirements, make the problem and its backstory explicit. Explain whose difficulty matters, what evidence supports it and how solving it serves the product direction. The process document gives the team one place to inspect that reasoning before moving to design.
A written introduction to the teaching approach. Platform listings provide their own lesson previews.
Try a sample exercise
Choose a real customer problem. Fill in its backstory, strategic alignment and problem/solution statement, then define one success criterion and the evidence you would need before a wider rollout.
Take away: A short discovery brief and a release-check decision.
Open the process document ↗A portfolio exercise based on the teaching material; platform assignments may differ.
Free video previews on Udemy
- Introduction to the Product Innovation Process · 18:49
- Stage 1/5: Discovery · 10:53
Preview availability checked on 22 October 2026.
This is the product-management foundation course on Udemy. Its five stages describe the process; they are not the module count of the AI course on Coursera.
From Product Manager to AI Product Manager
Coursera · Offered by LearnKartS · 10 modules. Checked 22 October 2026.
View on CourseraAI Made Execution Cheap. High-Stakes Decisions Are Still Expensive.
Data Innovation Summit MEA 2026 · 15 October 2026 · Dubai · M4 · Machine Learning, Generative & Agentic AI
The Stack helps us understand an AI system. The Loop helps us develop it, with humans steering the process.
I presented the AI-Native Product OS, then applied its Stack and Loop to a hypothetical $4.2 billion cross-border payment in a simulated bank workflow. The example shows why an ambiguous sanctions match needs an evaluation case and a system-enforced escalation rule, regardless of the model’s confidence.
Read the DISx talk and hypothetical example
My product method · AI-Native Product OS
Understand the system. Give it a useful job. Keep learning.
AI can make a first working version cheaper to build. It also creates outputs that need to be checked. I use five areas of responsibility and a repeatable development loop to connect the work to a customer problem, clear decision rights and evidence of what happened.
The Stack · what to take responsibility for
- Model
- What can it do?
- Context
- What should it know?
- Orchestration
- What can it act on?
- Governance
- What must hold?
- Human
- Who is accountable?
The Loop · how to develop it
- Talk
- Decide
- Build
- Observe
- Iterate
Governance and human responsibility apply throughout. The checks and release gates depend on the consequences of getting the task wrong.
The framework is this way of thinking. The workspace is the installable GitHub toolkit. The courses teach it on Udemy and Coursera.
Earlier talks
Existentially Viewing your Existential Crisis
A guided attempt to help people make decisions from the reality of existence rather than from daily noise alone.
Use mindfulness and imagination to temporarily see life from a wider cosmic perspective, then let that perspective reorder what matters.
University talk
A personal account of turning a fragmented life and career path into a middle-way system where professional work and creative purpose reinforce each other.
Build a clear story around your experiences, use your 9-5 and your personal projects to compound each other, and create from the vision that keeps you moving through mundane work.
Writing and intellectual foundations
I’m interested in how people find clarity in complex systems. In product work, that means making decisions traceable. In teaching, it means making ideas usable. In fiction, it means making them felt.
About & writing · Explore both books · Framework of Metacognition