How top PMs increase their leverage with AI

2026-06-30 · Source: Lenny's Newsletter · Field: Business & Management — Project & Product Management, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

Lenny's newsletter announces a new course, "Become an AI-Native Builder," co-developed with Colin Matthews, designed to equip Product Managers and other non-technical professionals with skills to amplify their impact using AI. The course focuses on using tools like Codex, Claude Code, and Cursor to support discovery, create prototypes from real codebases, ship changes via GitHub, and set up automated evaluations. The article introduces a "three ladders of influence" framework for AI: personal, product, and systems. Personal influence involves AI assisting with tasks from drafting text (Rung 1) to completing full to-do items by connecting LLMs to external tools like PostHog (Rung 3). Product influence accelerates shipping, progressing from web-based prototypes (Rung 1) to code-based prototypes using real codebases (Rung 2), and ultimately to AI agents shipping production changes as pull requests (Rung 3). This shift enables PMs to move beyond coordination towards hands-on building.

Key takeaway

For Product Managers aiming to enhance their impact and adapt to the AI-driven landscape, you should actively integrate AI tools into your workflow. Focus on connecting your LLMs to core product analytics and development tools to automate tasks like retention analysis or prototype generation. Consider creating a simplified UI codebase to enable AI-driven prototyping with real components, significantly accelerating your ability to validate and ship product changes. This shift allows you to move beyond coordination and directly contribute to building.

Key insights

The PM role is evolving, requiring AI-native building skills to achieve significant personal, product, and systems influence.

Principles

• AI amplifies impact across personal tasks, product development, and systemic automation.
• Connecting LLMs to tools enables AI to complete multi-step tasks end-to-end.
• Prototyping with real codebases accelerates product validation and development.

Method

To achieve personal AI influence, connect LLMs (e.g., Claude, ChatGPT, Gemini) to frequently used products via their connectors marketplace, then create "skills" to automate common multi-step tasks.

In practice

• Use Claude to generate financial models or analyze A/B test results.
• Create UI-only repos for AI-driven code-based prototyping.
• Delegate PRs for small UI/UX changes using AI agents.

Topics

• AI-Native Building
• Product Management
• AI Tools
• LLM Connectors
• AI Prototyping
• Product Development

Best for: Product Manager, Product Designer

Related on AIssential

• The Business Engineer in the Era of AI — The Product Manager role is forking into a "Builder-PM" focused on future AI capabilities within autonomous, taste-driven cells.
• Your PM Can Now Ship Without a Designer. Here's When That's Stupid. — AI shifts product-design from sequential handoffs to deep, cheap exploration, making prototypes primary and PRDs secondary.
• Fragments: June 16 — LLMs are transforming programming and business models, but require careful engineering and decentralized design to avoid pitfalls.
• phuryn / pm-skills — Integrate proven PM frameworks into AI workflows for structured product decision-making.
• More PMs than designers now use Stripe's internal AI prototyping tool — Empowering PMs with design tools improves early-stage prototyping and communication with design teams.
• How PMs should use AI without losing the craft — AI should augment Product Managers' core skills, not replace their craft.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Lenny's Newsletter.