Live in the future, then build what's missing
Sequoia’s third Generative AI Act report argues that foundation models have plateaued, the focus is shifting to inference‑time reasoning, with OpenAI’s o1 model exemplifying a new scaling law that makes inference compute the key driver of future AI breakthroughs.
The author reviews Google NotebookLM, describing how it aids deep paper reading, boosts chat willingness with guided prompts, maintains conversation coherence through self‑play insights, highlights the audio‑overview feature, and reflects on AI concepts such as the "bitter lesson" and the limits of self‑play in open scenarios.
The talk outlines the evolution of intelligent customer service over three decades, explains how generative large-model AI like ChatGPT has raised service expectations, and presents Alibaba Cloud’s four-stage implementation—experience, efficiency, capability, and insight—through three concrete cases and a roadmap for SMEs to build their own smart service systems.
The author reflects on the large gaps and hidden costs that hinder AI product adoption in non‑internet sectors, comparing AI software to traditional SaaS, questioning data‑driven moats, and outlining practical steps to bridge these challenges.
The article chronicles Alibaba’s five‑year journey from the early CloudDBA diagnostic tool to the self‑driving Database Platform (SDDP), detailing product pivots, data‑driven engineering, AI collaborations, and the technical and organizational challenges overcome along the way.
The author recounts his first year at Alibaba, detailing the transition from a solo newcomer to leading the OceanBase cloud platform, establishing large‑scale real‑time monitoring with JStorm, evolving team processes, and adapting to Alibaba’s fast‑paced, iterative culture.
