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.
Google AI shared a weekly roundup: Project Genie for dynamic world-building, Gemini enhancements in Chrome, AlphaGenome model code, the D4RT video-to-4D model, Agentic Vision in Gemini 3 Flash, plus free JEE Main mock tests.
On the product front, v0 opened its first San Francisco studio and is seeking input on its next location.
Turning to AI tools, George from prodmgmt.world highlighted ten new Claude plugins for PRD writing, roadmap updates, research synthesis, competitive briefs and metrics reviews. He recommended adding AI PM skill prompts to Claude as a “Product Manager” plugin for personal or org-wide use.
In related news, Vercel’s AI-driven support agent now autonomously resolves 87.6% of cases, auto-filling ticket forms, triaging the rest to human coding agents, and feeding customer reports back into product fixes.
On strategy, Lenny Rachitsky quoted Marc Andreessen: “The job is not the atomic unit… orchestrating the AI,” emphasizing workflow orchestration. In related advice, George advised auditing which PM tasks are already AI-replaceable versus those likely to follow, helping teams stay ahead of automation. He also suggested research with ChatGPT and Claude, and connecting with PMs and engineers who built similar systems.
On LinkedIn, Paweł Huryn outlined eight AI skills to define PM careers in 2026: managing and building AI agents, context engineering, AI prototyping, vibe engineering, observability and AI evaluations, AI product strategy, and growth and monetization. He also warned that “Moltbook” agents offer little interaction, may conceal human operators, and risk prompt-injection attacks.
In industry developments, Andrej Karpathy reported over 150,000 autonomous LLM agents linked via a global scratchpad, posing security and coordination challenges. Separately, Lex Fridman released a podcast with Sebastian Raschka and Nathan Lambert on scaling laws, LLM evolution, AGI timelines and future compute. Karpathy demonstrated that nanochat can train a GPT-2–scale model in around three hours for roughly $73—a 600-fold cost reduction since 2019.
On the video front, Fireship traced OpenClaw’s rise—built in TypeScript, renamed after Anthropic’s objections, gathering 65,000 GitHub stars and sparking Mac Mini sell-outs—automating stock-tracking via Telegram on self-hosted servers. All About AI then showcased an OpenClaw clone on a Mac Mini, using WhatsApp slashclaude commands in a logged-in Chrome session to bypass API keys, schedule cron jobs, run browser automations, and autonomously produce videos with a Remotion-driven pipeline.
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!
GenAI PM