AI News
Quietly, without a single headline capturing it, the cost of using capable AI collapsed this year. Not dropped. Collapsed. The kind of model that cost a small fortune to run at scale two years ago now costs cents per million tokens. If your business plan assumed AI would stay expensive, it is time to redo the math.
xAI reportedly acquired the company behind Cursor, the AI coding editor, for $60 billion in all stock. You can argue about the number all day. What you cannot argue with is the message it sends: the smart money has decided the value in AI is no longer in the model. It is in the thing wrapped around it.
Read more: xAI paid $60 billion for the wrapper, not the model. That tells you something.
Most model releases are the same recipe made a little bigger. NVIDIA Nemotron 3 Ultra is a bit more interesting, because it quietly does something different under the hood. It is a 550-billion-parameter model built on a hybrid of Mamba and Transformer architectures, and that architectural choice is the actual story, not the benchmark line.
Read more: NVIDIA Nemotron 3 Ultra quietly bets against the pure Transformer
Moonshot released Kimi K2.7 Code, a trillion-parameter coding model, and buried in the announcement was the number that actually matters: it reaches its answers using roughly 30 percent fewer reasoning tokens than its predecessor. In a world obsessed with capability scores, efficiency is the upgrade people underrate, and it is quietly the more useful one.
Read more: Moonshot Kimi K2.7 Code thinks with fewer tokens, and that is the real upgrade
Every month brings a new open model, and most are footnotes. GLM-5.2 is not. Z.ai released a 753-billion-parameter model, with a one-million-token context window, under a plain MIT license. That last part is what makes it matter.
Read more: GLM-5.2 is a 753-billion-parameter open model with an MIT license. That is a big deal.