Alibaba Launches Qwen3-Max-Preview Trillion-Parameter Model

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 the next three minutes, we’ll explore major product launches, fresh developer tools, pressing management strategies, and industry-wide debates. In recent AI product news, Alibaba’s Qwen team launched the Qwen3-Max-Preview model, boasting over 1 trillion parameters and now accessible through Qwen Chat and the Alibaba Cloud API. Early-access developers can integrate it via standard REST endpoints to begin testing at scale. On the mobile side, Perplexity rolled out a major iOS update featuring streamed answers, support for tables and Markdown formatting, plus intermediate reasoning steps to break down complex queries. The update is live in the App Store. Meanwhile, Google AI introduced EmbeddingGemma, an open on-device model for generating embeddings, and highlighted that its Nano Banana image editor has already been used to edit more than 200 million images. The model supports deployment on mobile and edge devices for offline embedding generation. On the tools front, developer Philipp Schmid announced that SQLite now includes a vector extension, enabling fully on-device AI applications using SQLite-vec and EmbeddingGemma. It’s available in the official SQLite distributions, so teams can add vector search without extra services. He also published a complete developer tutorial in AI Studio on Nano Banana, covering setup, image creation, editing, restoration, and even conversational image editing. All sample code is included in the accompanying repository. Additionally, LlamaIndex showcased SemTools, a new command-line toolkit for parsing and semantic search, which the team tested on a thousand arXiv papers using Claude code. Demo scripts illustrate ingestion and query workflows for large document collections. In product management insights, Shreyas Doshi warned against inventing exotic metrics that capture narrow results at the expense of broader business outcomes. He also pointed out a common blindspot among systems thinkers, who often overlook individual psychology—a factor that can make or break product adoption. Separately, George Ananian tackled the “everything is priority number one” mindset, calling it a core PM challenge and emphasizing the need to translate incoming requests into clear business impact and trade-offs. Shifting to industry developments, OpenAI CEO Sam Altman posed a critical trade-off question for AI practitioners: do you value making GPT-5 Pro smarter or faster? And in a breakthrough for astrophysics, DeepMind co-founder Demis Hassabis shared that the Deep Loop Shaping model is now helping LIGO and Caltech detect gravitational waves from intermediate-mass black holes more effectively, with the results published in Science Magazine. 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!