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.
Starting with game development, Peter Yang unveiled a major update to Studio MCP Server. It now lets AI agents iteratively plan, write, test, and modify games via APIs and skills, and supports bring-your-own keys from Anthropic, OpenAI, or Google Gemini, allowing teams to accelerate development and tailor workflows.
In related news, Fei-Fei Li announced SparkJS 2.0 is now in developer preview, bringing level-of-detail rendering and streaming support for Gaussian Splats to the open-source library.
Shifting to collaboration tools, Guillermo Rauch is simplifying the deployment of Slack-based agents on Vercel with a new AI infrastructure suite including a gateway, workflows, and sandbox, now available in version zero.
Separately, Teresa Torres highlighted ShowMe, an AI sales development rep teammate trained on multiple reps’ calls and onboarding materials to consistently follow playbooks.
Another development comes from Peter Yang, who cut through the hype around OpenClaw’s autonomous AI. He contrasted two camps—enthusiasts building DIY agent clusters and skeptics calling it overhyped—and shared that an agent named Felix generated $14,700 in three weeks. His no-nonsense tutorial walks through building skills, memory layers, and security workflows for real-world use.
In other tech news, Tanner Lindsay rolled out TanStack Start, a CLI that scaffolds a full-stack framework with built-in VEST testing, Tailwind styling, file-system routing, streaming server-side rendering, server functions, and end-to-end TypeScript type safety in a single command.
On the product side, Lenny Rachitsky quoted the head of Claude Code urging teams to think in an AGI-forward way as new models arrive, since they’re completely different and require fresh mindsets.
Additionally, Peter Yang suggested that in the AI agent era, PMs should eliminate user time spent to zero by exposing rich APIs, skills, and multi-agent coordination so tasks happen seamlessly behind the scenes.
Addressing talent challenges, Ben Erez noted that experienced product recruiters are scarce—most are either at top firms or working independently—and recommended that PM leaders build internal growth paths for high-leverage recruiting roles instead of expecting ready-made hires.
In industry developments, Santiago Pino flagged a new book by Dr. Arsanjani and Juan Pablo Bustos on AI agent patterns, architecture, and design decisions, aimed at experienced practitioners.
Logan Kilpatrick emphasized that ensuring AI accessibility and benefits for all humanity is the defining challenge of this century.
Finally, DeepLearning.AI reported that Dr. CaBot, a medical AI agent, outperformed human internists in diagnosis and provided explanations that doctors often preferred, as summarized in the latest issue of The Batch.
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