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, on the product launch front, There's An AI For That introduced ligma: the first agentic mobile IDE that ships code from your phone with pull-request integration, a diff viewer, full terminal and multi-session support. Relatedly, Alibaba’s Qwen3-VL-235B-A22B-Instruct now holds 48 percent of OpenRouter’s image model market. Separately, Microsoft rolled out MAI-Image-1, its third AI image model, ranking ninth on LMArena for combining speed with quality.
In tools, Andrej Karpathy published nanochat, an end-to-end ChatGPT clone pipeline in about 8,000 lines for rapid experimentation and tunable performance. Google AI Studio’s updated dashboard now makes tracking rate limits and usage ten times easier. Plus, Hugging Face’s fineweb dataset streamlines training, optimizing and deploying custom models without black-box APIs.
On the product management side, George Nurijanian outlined a 70-20-10 roadmap rule: dedicate 70 percent of efforts to core improvements, 20 percent to adjacent experiments and 10 percent to transformative bets. Madhu Guru stressed that product managers are paid to build products customers love, not to write PRDs. Lenny Rachitsky added that this year’s AI products will form user habits that stick for years, echoing the adoption curve of ChatGPT.
In industry news, OpenAI plans proprietary AI chips to embed frontier model insights directly into hardware and meet surging demand. Google’s Sundar Pichai pledged over nine billion dollars in South Carolina AI investments by 2027 to bolster domestic innovation. And developer Guillermo Rauch warned that the real bubble is overestimating AI deployments, even though the ceiling for AI applications remains extremely high.
Beyond products, Helena Liu mapped free Ivy League courses: Harvard’s CS50 series at cs50.harvard.edu/x, /hython and /ai with optional edX certificates for $100 to $600; MIT OpenCourseWare’s Python, machine learning and math offerings without certificates; and Yale’s Financial Markets plus Andrew Chan’s AI and ML classes on Coursera at $50 to $100 per month, granting unlimited LinkedIn-shareable certificates.
Meanwhile, Greg Isenberg sat down with PJ Ace to reveal a five-step viral AI ad workflow: script in Google Docs, generate shot lists via ChatGPT, produce scene images in Rev, animate clips with Veo3’s frames-to-video engine, and finalize in Premiere or CapCut. Their Origin Financial ad used public-domain IP and comedic financial mishaps to surpass two million views, showcasing Veo3’s top talking character animations and built-in sound effects, along with custom music from Epidemic Sound.
Finally, Claire Vo and Hamel Husain from How I AI shared a systematic quality approach: capture multi-turn user interactions or “traces,” open-code about one hundred samples to spot early errors like vague intent or tool miscalls, categorize failures—from handoff issues to scheduling glitches—to prioritize fixes, then build binary, problem-specific evals (both code checks and LLM judges), validate against human labels, and iteratively refine prompts or fine-tune on high-signal failures for maximum impact.
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!