OpenAI shipped its superapp. Meta dropped Llama API pricing to near-zero with Llama 3.3 70B at $0.59 per million tokens. A Brown University professor publicly accused students of cheating with ChatGPT, then had to walk it back. Three separate stories, one thesis: AI is compressing into fewer, cheaper, more contested products.
The convergence question is the real story here. When every major AI product starts doing the same things, price becomes the battlefield. Meta confirming it is considering a cloud compute business matters because renting GPUs to the same competitors you are trying to beat is a contradiction worth examining. Roy and Kantrowitz work through whether that economics makes any sense, and the answer is not obvious.
The Brown cheating incident is worth reading in full because it is not just an anecdote. It is a pressure test for how institutions detect, accuse, and punish AI use when the tools for doing so are unreliable. The professor was wrong. How wrong, and what that means for classrooms using AI detection software, is the actual argument.
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