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.
In API news, OpenAI’s GPT-5.2 processed over a trillion API tokens on day one, according to Sam Altman. Google AI added speech-to-speech translation to Gemini Audio beta in Translate. Thinky Machines made Tinker available with advanced vision inputs, the Kimi K2 engine, and simpler sampling.
Cursor AI rolled out UI refinements based on user feedback. Google added the Gemini Deep Research agent to its Interactions API, backed by DeepSearchQA, a 900-task benchmark across 32 fields. Peter Yang outlined a three-step Google AI Studio flow: convert UI into a template, clone it for AI-driven feature sketches, and circulate the mockup for feedback before detailed design. v0 now allows you to create custom shadcn/ui components as the base for v0 apps.
Claire Vo argues that teams perform best without traditional PM layers or with PMs serving as unblockers. Shreyas Doshi shows teaching can scale advising, letting leaders share expertise sustainably. Aakash Gupta highlights core AI/ML concepts PMs must master. Marc Baselga identifies four organizational cultures—metrics, design, narrative, and sales—and urges PMs to find where their skills fit to avoid burnout, while Paweł Huryn offers a five-step guide for choosing autonomous agents over automation or RAG, recommending reserving agents for cognition tasks, keeping them small, and using code or visual workflows for simple deterministic tasks.
Demis Hassabis reported the first LLM interaction from space using open-source Gemma models, crediting Philip Johnston’s Starcloud team. Sebastian Raschka revealed Mistral 3 Large uses a DeepSeek V3 design with half as many but twice-sized expert modules for greater efficiency. Google DeepMind released a podcast with Shane Legg on a “golden age” of AGI and the societal changes it will require.
Fireship reports OpenAI declared a code red after Google’s Gemini 3 surge and countered with GPT-5.2, which boosted ARC benchmark efficiency by 390× and landed a $1 billion Disney deal for Star Wars and Toy Story content. AI Explained notes GPT-5.2 matches professionals on 71 percent of GDP Validation tasks, exceeds 90 percent on ARGI1 with extra compute, but underperforms common-sense benchmarks. All About AI shows Claude Code’s subagent pipeline—with image, style, screenshot, and iteration agents—to build a Path of Exile 2 guide from a single prompt, while AI Jason shows combining Gemini 3 and Nano Banana for landing page planning, high-res mockups, and pixel-perfect code handoff.
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!