Build Your Moat as an AI-native PM - Practitioner Certificate

GK Shan

ex-Amazon Sr. PM, running 3 ventures

Jason P. Yoong

Co-Founder, Level Up; $1B+ P&L; led 40+

Orgs are busy investing in AI infra. Make it hard for them to ignore your value

Over 15,000 product professionals signed up for our Lightning Lesson. We intuitively knew what AI-native PMs want.

You have watched customers misuse features, seen promising ideas collapse during rollout, and sat through NPS reviews, and stakeholder debates You know where product works and where it fails.

This your moat.

The reality is that customers never meet the model in the abstract. They meet the product experience it shapes.

The gap between a technically capable product and one that earns trust is almost always a specification problem. Your moat forms when your judgment becomes usable inside AI-assisted product work.

This course helps you

  • embed your moat into artifacts: specs, constraints, decision criteria, evaluation logic, and workflows that engineering can build from.

  • apply principles that GK (as an ML PM) and Jason (as a COO) have seen work at scale, such as Working Backwards, 5-Whys, and Bar Raiser approaches.

  • vibe-coded these on Claude Code (or equivalent), with no technical background

The PMs with the strongest long-term advantage will be the ones who can encode judgment with precision, raise the quality bar for outputs, and make their reasoning legible across teams.

What you’ll learn

Defining your moat through four key pillars as an AI-native PM and raising your technical bar in the process

  • Map customer tripwires, product logic, stakeholder reviews and business constraints into specs that engg directly builds from

  • Leave with CLAUDE.md and system.md set up: the foundation and map your AI systems follow.

  • Working Backwards, Deep Dive, Bar Raiser (proven at $1B+ scale), rebuilt as Claude Code workflows in every live session.

  • Leave with evals, agentic systems that assist you with root cause analyses, briefs, pressure-testing, multi-dimensional rubrics

  • Test your new PM toolkit and track your AI product's performance through observability metrics

  • Develop Leadership Briefs that distill your RCAs & recommendations

  • Leave with meta-prompts and skill files that will help you compound the value of your moat and scale as new AI models arrive

  • Gain a deeper capability: making your judgment legible, transferable, and usable inside AI-assisted PM workflows

Learn directly from GK & Jason

GK Shan

GK Shan

Product & AI Consultant, ex-Amazon Sr PM (ML), currently running three ventures

Amazon
Esade
Typeform
Chargebee
Zoho
Jason P. Yoong

Jason P. Yoong

Co-Founder & COO at Level Up (fmr Amazon, VP at Dentsu, Startup - $8M seed)

Leader / Advisor at
Amazon
Dentsu
Harvard Business Review
IPG Mediabrands
Techstars

Who this course is for

  • Roles: Product Managers, Product Leads, Principal PM, Director of Product, Group PM, Product Designers
  • Experience: 3–10 years in product; at least 2 years shipping features for a real customer base
  • Company type: Mid+, scale-up, or enterprise with an active AI product roadmap, not a solo founder or pre-product startup

Prerequisites

  • Claude Code or Cursor installed on your laptop

    Every session runs inside an AI coding tool. Claude Code or Cursor are recommended. Install before Session 1 and enable file access.

  • Pro (or higher) plan for your chosen tool

    A paid plan (Claude Pro/Max or Cursor Pro) is required before Session 1 for you to test and play with the demo.

  • Have example product docs, problems and data you can work with

    You'll build artifacts from your own context. Leadership reviews, problem scenarios, specs, planning docs, or a Notion/Drive export.

What's included

Live sessions

Learn directly from GK Shan & Jason P. Yoong in a real-time, interactive format.

Certificate of completion

Share your new skills with your employer or on LinkedIn.

Lifetime access

Go back to course content and recordings whenever you need to.

Office hour

1.5 hours of live OH with GK and Jason

Community of peers

Stay accountable and share insights with like-minded professionals.

Hands-on projects

Take home assignments and closing the loop on live Q&A sessions

Maven Guarantee

This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.

Course syllabus

5 live sessions • 7 lessons • 3 projects

Week 1

Apr 28—May 3

    Working Backwards — Build products your customers trust

    • Apr

      28

      From Customer Failure to Shippable Spec

      Tue 4/285:30 PM—6:30 PM (UTC)
    2 more items

    Deep Dive — Own the diagnosis of any issue with agents before leadership asks

    • Apr

      30

      Live Demo: From Business Problem to Root Cause using Agents

      Thu 4/305:30 PM—6:30 PM (UTC)
    2 more items

Week 2

May 4—May 10

    Leadership Brief: Make your moat legible to the people who decide

    • May

      5

      How Leaders Decide When the Data Runs Out — Live with Jason Yoong

      Tue 5/55:30 PM—6:45 PM (UTC)
    1 more item

    Teach Your AI the Eight Persuasion Factors

    • May

      7

      Live Demo: Run every persuasion layer on your AI-output

      Thu 5/75:30 PM—7:00 PM (UTC)
    3 more items

Free resource

Raise Your Technical Bar as an AI-Native PM (63min Lightning Lesson) cover image

Raise Your Technical Bar as an AI-Native PM (63min Lightning Lesson)

What you'll learn

  • What "becoming technical" means. You must write structured logic engineers can test against
  • Live demo of a system.md that translates a PRD. Show how the AI thinks, using the Working Backwards approach
  • evals.md created with multi-dimensional thresholds. With binary criteria, through a Bar Raiser's lens
  • Q&A (we answered a lot of questions)

Schedule

Live sessions

3-4 hrs / week

    • Tue, Apr 28

      5:30 PM—6:30 PM (UTC)

    • Thu, Apr 30

      5:30 PM—6:30 PM (UTC)

    • Tue, May 5

      5:30 PM—6:45 PM (UTC)

Projects

1-3 hrs / week

Async content

1 hr / week

Testimonials

  • GK was excellent to work with. She supported us across AI workshops and AI course tutoring, and consistently delivered high-quality, well-structured materials and outputs. Communication was clear and proactive, and she genuinely cared about achieving strong outcomes, not just “completing tasks.” She also interacted brilliantly with students and workshop participants: engaging, supportive, and very clear in explanations and facilitation. Everything was delivered on time, with strong attention to detail, and she stayed flexible and responsive as priorities and timelines evolved[...]she’s thoughtful, reliable, and simply a great person to collaborate with. I strongly recommend her for training delivery/support and for helping early-stage startups, especially across AI, marketing, and product

    Testimonial author image

    Vitaly

    Director at Aon STG | AI & Digital Strategy | Venture Builder
  • We were seeking a product owner who was able to translate the rather abstract concept of [a model idea] into a product that is valuable to the larger business unit. They had to help us understand how the ML model can be leveraged to solve concrete business problems and surface actionable insights for business users. This is where Gayathri shone. She laid out and executed a strategy for business application… She orchestrated the document with simple and easy-to-understand language and used relatable examples… Gayathri's contributions have not only advanced the team's understanding of customer behavior but have also set new benchmarks for how we leverage machine learning in business.

    Testimonial author image

    Principal Applied Scientist

    Stores Economics & Science
  • In my 10 years of product experience in the technology sector, working with PMs from the US and Europe, I've not seen a lot of young leaders exhibiting the same level of innovation, leadership, and problem-solving that GK consistently demonstrates… She exemplified 'Invent and Simplify' by creating an override mechanism… GK has proven herself to be an innovative leader in a large organization while driving technological solutions growth from start to finish… Her customer obsession, innovative problem-solving abilities, and potential to develop scalable solutions make her an exceptional candidate.

    Testimonial author image

    Sarah

    Principal PMT (Applied Sciences / ML )
  • I have not come across many such product leaders in my 14 years of experience in tech and product management, including a decade at Amazon… Her strategy was validated when the findings from the econometric model aligned with the insights from the ML model. That's when I witnessed that she has the acumen to integrate different methodologies into a cohesive strategy… She transformed raw ML outputs into a user-friendly interface that allowed non-technical users to extract predictions on demand impact… She consistently pushed the operations teams to look beyond immediate hurdles and focus on broader customer behavior trends… Her entrepreneurial spirit, customer obsession, and innovative problem-solving skills will undoubtedly enable her to continue making significant contributions

    Testimonial author image

    Shiv

    Principal PMT
  • She set a precedent amidst several retail orgs by successfully applying advanced econometric model outcomes to practical product features that delight customers. She takes a bold and innovative approach to strategic decision-making to live testing… She led this at a time when the team was facing a centralized funding freeze and managed to secure sponsorship from senior leadership… From an external research team standpoint, GK's execution helped us get a full picture of connecting customer needs to product solutions and commit to innovation — which is challenging to achieve in a large organization

    Testimonial author image

    Tom

    Principal Economist & Head of Economics and Decision Science

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Frequently asked questions

$1,300

USD

Apr 28May 12
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