Google AI Introduces Genie 3, Gemini 2.5

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. Anthropic AI removed cost barriers for Claude across all three branches of the U.S. government, granting federal teams free access to advanced conversational AI. Meanwhile, Gemini Deep Research is ramping up at Google, with Logan Kilpatrick teasing a Deep Research API for experimental features. Google AI also launched Genie 3 and Gemini 2.5, unveiling enhanced world-model capabilities to evaluate progress toward artificial general intelligence. In related news, open-source enthusiasts anticipate a 2025–2026 surge in tooling that delegates knowledge queries to search engines, freeing large language models for deeper reasoning, as noted by Sebastian Raschka. On the applied side, LlamaIndex released a tutorial on turning enterprise documents into context-aware agents with LlamaCloud. And Lovable Dev demoed “Lovable,” a voice-driven website builder powered by GPT-5 that generates a working frontend connected to Notion and databases from spoken requests. Switching to product strategy, Aakash Gupta pointed to Teresa Torres’s frameworks for applying AI in discovery and scoping feature experiments, with supporting talks on YouTube and Spotify. Separately, Paweł Huryn stressed isolating staging and production environments to prevent AI experiments from breaking live workflows, sharing best practices for testing and deployment. Over at OpenAI, Sam Altman outlined a GPT-5 compute plan prioritizing ChatGPT subscribers, followed by API customers, to manage soaring demand. In other industry moves, Rowan Cheung credited social media buzz with fueling OpenAI’s rapid rise, citing Twitter-driven hype as a catalyst for ChatGPT’s adoption. Taking a different perspective, Clement Delangue proposed viewing open-source models like biological lineages, mapping traits and “mutations” across Hugging Face’s model families. On the educational front, a new Deeplearning.ai and Snowflake course led by Dr. Channen Nantamad guides developers to prototype GenAI apps in days using Strimlet, prompt engineering, live data, and an AI coding copilot. Participants build a dashboard for a fictional sports gear company covering data integration, customer feedback analysis, and retrieval-augmented generation techniques. In an experimental showcase, All About AI demonstrated a Python app that pulls Bitcoin event probabilities from Polymarket, feeds them into GPT-5 and other models, and benchmarks implied price predictions against Coingecko data. Early results show GPT-5 staying within $200 of real prices, while alternative models exhibit wider spreads, with plans for live trading tests via Polymarket’s API. Finally, Lex Fridman walked through removing duplicates in R using dplyr’s distinct(), covering removal of identical rows, key-based de-duplication while preserving all columns, and arranging by transaction date to keep the most recent record. 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!