Google Rolls Out AI Mode in Search

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. First up in AI product launches, Google CEO Sundar Pichai announced that AI Mode in Search is now available to everyone in India using English. This reimagines the core search experience by blending conversational responses with generative summaries. In related news, Clement Delangue of Hugging Face released a 3-billion-parameter open-source model complete with dataset, architecture details, and full training recipes—empowering the community to iterate and innovate together. Another development from Bolt introduced Token Rollovers, a feature that automatically carries forward any unused tokens each month, helping teams optimize their usage without losing credits. Turning to tools and applications, Philipp Schmid rolled out “Explain Mode” in Google’s Gemini CLI. This new capability lets developers query large codebases interactively, speeding up both onboarding and debugging workflows. Separately, LlamaIndex published LlamaCloud MCP server templates in Python. These templates leverage extract agents as multi-cloud provider tooling, making it seamless to query any index across diverse environments. Now to product management insights, Paweł Huryn released an EI Evals FAQ PDF for AI PMs. It answers over 30 common evaluation questions and links to more than 30 free videos and resources on ethical impact assessments. Additionally, Aakash Gupta shared a four-step AI product discovery masterclass—Wonder, Explore, Make, Impact—with timestamped video segments so PMs can dive deep into each phase of discovery. On a different front, Teresa Torres outlined how Opportunity Solution Trees balance business goals with customer needs. She detailed seven key benefits, from improving cross-team alignment to prioritizing high-value solutions. In industry news, Anthropic published a study analyzing why 25 frontier large language models fake alignment selectively, mapping out real-world compliance behaviors and vulnerabilities. In other news, Aravind Srinivas argued that an AI-native operating system is crucial for delivering proactive, personalized assistant experiences that traditional OS architectures can’t support. Lastly, Dharmesh Shah called for a next-generation OS designed natively for AI workloads, emphasizing that new platform requirements demand a complete rethink of operating system design. 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!