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.
On the product front, Alibaba’s Qwen team has released Qwen Code version 0.6.0, adding experimental Skills, enhanced VS Code support, new slash commands such as “/compress,” and integrations with external tools. Meanwhile, LlamaIndex rolled out full TypeScript support across its workflow SDK, complete with Express agent tutorials and production deployment patterns. The same team also unveiled several open-source projects—StudyLlama as a NotebookLM alternative, a Gemini filesystem explorer, and a multi-channel processing integration for coding agents.
In related tool news, LangChain shared how Coinbase used LangSmith to build and deploy enterprise AI agents in just six weeks, slashing future build cycles from twelve weeks to under one. Harrison Chase introduced AI Wrapped 2025, an insights agent that analyzes ChatGPT and Claude conversations to surface usage patterns and cluster themes. And LlamaIndex launched LlamaSplit in beta, an AI-driven feature that automatically segments mixed PDFs into clear, targeted sections.
Turning to product management strategy, Lenny Rachitsky reminded us that market fit must exist before launch—no marketing campaign can rescue a product that doesn’t resonate. Shreyas Doshi advised starting customer interviews by asking, “What did you love about the product?” to uncover core value before exploring enhancements.
On the cost side, Paweł Huryn warned that highly engaged AI users can drive unpredictable cost variance, threatening margins even among best customers. On LinkedIn he expanded on this, recommending that pricing be baked into design through cost-shaping mechanisms: token limits, model selection, and orchestration strategies to manage worst-case usage.
At the same time, Claire Vo highlighted the rise of the “IC CPO” model, where product leaders become hands-on builders. At Webflow, Rachel Wolan created a personal AI chief of staff for scheduling and networking prep, leverages multiple models—Cursor and Claude Code—for different tasks, and hosts regular “builder days” to ramp up team-wide AI adoption.
For upskilling, Maria R. spotlighted three free, in-tool tutorials—on Claude Code, Cursor, and Antigravity—each offering three-hour interactive learning experiences powered by the AI itself. Following that, Tal Raviv detailed how to audit token consumption in Linear’s multi-channel processing integration by reprinting tool definitions, cross-verifying UI parameters, and running outputs through OpenAI and Claude Code tokenizers to reveal hidden cost drivers and potential hallucinations.
On the industry front, Google AI published a year-end recap of 2025 breakthroughs in science, mathematics and beyond. In robotics, Clement Delangue showed how his modular, open-source Reachy Mini robot self-repaired over the holidays. Finally, Guillermo Rauch challenged the notion that AI-generated code is sloppy, arguing that agents will produce rigorously tested, type-checked, provably correct software as automated test generation and verification feedback loops become a competitive advantage.
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!