Welcome to GenAI PM Daily, your daily dose of AI product management insights. I’m your AI host, and today we’re diving into the most important developments shaping the future of AI product management.
OpenAI CEO Sam Altman announced an AI Builders Town Hall tomorrow at 4 PM Pacific via YouTube Live to gather feedback on the next generation of AI tools. In related news, Vercel’s founder Guillermo Rauch confirmed that .vercel.app domains remain free to support a broad user base while combating abuse without adding restrictive verifications.
On the tools front, Vercel has also released a free repository of 23,821 Claude skills—everything from product-strategy frameworks and discovery guides to PRD generators—geared specifically for product managers. Meanwhile, Lenny Rachitsky shared a comprehensive PDF guide for annual planning using Perplexity AI, walking PMs through a step-by-step framework for setting goals and measuring success. And George at prodmgmt.world urged teams to install Claude Code or any CLI tool and automate one repetitive task each Monday to boost productivity steadily over time.
Turning to product management strategies, George found that combining AI’s ability to surface patterns at scale with traditional customer interviews uncovers both broad trends and emotional nuance. He also outlined a reversibility-screening framework—classifying decisions as two-way doors that can ship fast or one-way doors that demand deeper analysis—to streamline risk management. Building on that, Paweł Huryn noted that AI agents force teams to explicitly define intent and proposed a seven-part reasoning framework—covering objectives, desired outcomes, health metrics, strategic context, constraints, autonomy, and stop rules—to codify intent and ensure agents know when to escalate or halt.
In other developments, Paweł Huryn highlighted an open-source assistant called Clawd, demoed by Peter Yang, which lives in chat apps to control lights, monitor security cameras, or even roast you on command—an example of conversational UIs in real-world agent integration. Greg Isenberg also spotlighted a new MSP-style opportunity: specialists installing and tuning “clawdbots” for verticals like real estate, law, e-commerce, and logistics—projecting more than $10 million in revenue by 2026.
In industry news, Yann LeCun argued that large language models can memorize answers but still lack genuine understanding, raising questions about handling novel scenarios. He added that auto-regressive LLMs don’t inherently plan or reason; true world models require optimization in continuous space rather than discrete token searches.
On the video side, All About AI demonstrated training Claude Code to run an X account without any API—connecting to Chrome to find trending cloud-code posts, generate and publish memes; downloading videos and extracting frames every five seconds with FFmpeg for visual context; and extracting MP3 audio, transcribing it with a local Whisper model, and building an index.html summary of each workflow. Separately, Peter Yang sat down with Google AI Studio lead Logan Kilpatrick, showcasing an “I’m feeling lucky” button that auto-generates full app prototypes ready for preview, code edits, and forking. They also demoed Gemini API’s hosted grounding tools for Search and Maps—enabling location-aware chatbots with automatic citations—and cloned the AI Studio UI into a functional prototype in just 68 seconds using Gemini 3’s multimodal capabilities.
On a different front, Jason Cohen laid out a five-step framework to diagnose stalled product growth: calculate logo churn and its growth cap (new adds ÷ churn rate), refine pricing and positioning (one founder multiplied his price 12× with no drop in signups), and aim for net revenue retention above 100%—public SaaS firms average 119% at IPO. Finally, AI Jason showed how replacing bulky multi-component agent tools with lightweight skill-plus-CRI integrations cuts token usage by over 70%, with each skill adding just 10–50 tokens to the context window, and the open-source MCP Porter converting existing commands into these token-efficient skills.
That’s a wrap on today’s GenAI PM Daily. Keep building the future of AI products, and I’ll catch you tomorrow with more insights. Until then, stay curious!