New large language models now ship faster than most readers can track — open-weight releases, closed frontier updates, and the industry response to both. This cluster follows the models actually worth tracking and the wider industry shifts around them, keeping benchmark claims and vendor claims separated from independently verified specifications.
This is the index to everything we publish on it. Where a model’s benchmark numbers or licensing terms are vendor-reported rather than independently reproduced, we say so rather than stating them as settled fact.
The timelines
- Qwen3.8-Max: Alibaba’s Open-Weight Flagship, Explained — architecture, benchmarks, licensing status and deployment paths for Alibaba’s largest AI model to date
- Kimi K3 History and Timeline — how Moonshot AI went from Kimi Chat to a 2.8-trillion-parameter open-weight frontier model
- GPT-5 History Timeline & OpenAI Evolution — from OpenAI’s founding to GPT-5, ChatGPT, APIs and enterprise AI
- Did Apple Lose the AI Race? — Siri, Apple Intelligence, and how Apple’s AI timeline compares to ChatGPT and Gemini
- The AI Boom — how artificial intelligence reshaped technology, markets and the global economy
- Artificial Intelligence Development & Ethics Timeline — history, breakthroughs, machine learning, generative AI and responsible-AI regulation
- AI Agents History Timeline — the evolution of autonomous AI and multi-agent systems, from 1950s research to enterprise agents
How to read this cluster
Qwen3.8-Max and Kimi K3 are the two open-weight releases worth reading side by side — both from Chinese labs, both competing on parameter count and context window rather than closed-weight access. GPT-5 and Apple’s AI timeline are the closed-weight counterpoint: one setting the pace, the other visibly playing catch-up. The AI Boom and AI Ethics timelines are the wider lens — what all of this is doing to markets, jobs and regulation once the model-by-model race is set aside.
What we are watching
Whether Alibaba and Moonshot keep shipping open-weight frontier models at this pace, how enterprise adoption of AI agents moves past pilot projects, and where AI-specific regulation actually lands once the current wave of model releases slows down.