Level Up with AI

Cohort 1

pm-moat-cohort-1

workshop-1

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

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

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

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

ex-Amazon Sr. PM, running 3 ventures

Amazon

Esade

Typeform

Chargebee

Zoho

GK Shan

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

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

Dentsu

Harvard Business Review

IPG Mediabrands

Techstars

Jason P. Yoong

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

Over 15,000 product professionals signed up for our <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/p/b062b8/raise-your-technical-bar-as-an-ai-native-pm">Lightning Lesson.</a> 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<ul><li>embed your moat into artifacts: specs, constraints, decision criteria, evaluation logic, and workflows that engineering can build from.</li><li>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.</li><li>vibe-coded these on Claude Code (or equivalent), with no technical background</li></ul>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.

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

ENCODE: Turn your knowledge into specs that engg builds from

BUILD: Embed your moat in Claude Code, with no prior coding knowledge

DEPLOY: Walk into any discussion with the right specs

COMPOUND: Incrementally improve your moat and benefit from model upgrades

Shift from mainstream PM trends and updates

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

Claude Code or Cursor installed on your laptop

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

Pro (or higher) plan for your chosen tool

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

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

<pre><code class="language-markdown">Roles: Product Managers, Product Leads, Principal PM, Director of Product, Group PM, Product Designers</code></pre>

<pre><code class="language-markdown">Experience: 3–10 years in product; at least 2 years shipping features for a real customer base</code></pre>

<pre><code class="language-markdown">Company type: Mid+, scale-up, or enterprise with an active AI product roadmap, not a solo founder or pre-product startup</code></pre>

Vibe-coding the Agent Owner Spec: 
for AI-native PMs

Gayathri Keerthana (GK)

AI and Product Consultant | ex-Amazon Product Leader | ESADE MBA

Co-Founder & COO at Level Up (Prev: Amazon, Dentsu, IPG, Startup - $8M seed)

Learn from catastrophic agent deployments through the lens of One-way / Two-way door decisions

How to recognize and pre-empt failure patterns

Post-mortems trace every major AI failure to one question: who defined what the agent was allowed to do?

How you tackle this being the Agent Owner for your org

We vibe-code an Agent Owner spec in Claude Code. Watch what it can do, what it pre-empts and what failure looks like.

Live demo: Build the Agent Owner spec

Spec Engineering: the Unclaimed Role of an AI-native PM

What changed about the spec and how the gap looks like in your org

Learn why the old PM spec doesn't translate to AI agents

The industry named the highest-leverage skill in AI development "specification engineering." Learn how to claim it.

Understand why Spec Engineering is a PM skill

Take home a structured spec template using a Working Backwards approach that you apply in your next spec engg exercise

Learn directly from Level Up with AI

Level Up with AI

Courses

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

Lightning Lessons

Vibe-coding the Agent Owner Spec: for AI-native PMs

Spec Engineering: the Unclaimed Role of an AI-native PM

Resources

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

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