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This is *exhausting*. Watching folks trumpet how great 5.6 is this week (a few weeks ago it was Claude Sonnet 5, and in a few more weeks it just might be a Gemini model), it's unclear to mere mortals what one gets by moving to a new model (or, rather, what you give up by using an earlier model). When I use ChatGPT or Claude to help with a project, I never have a clue what the different versions of models represent (fewer tokens? But at what cost in utility? What's the tradeoff?). I suspect I'm the norm, the not exception. The model providers are in a drag race and, in general, LLMs keep getting better/more capable. But if I'm an enterprise, it's bewildering to know which to use, where, and for how long. The whole AI arms race has an air of near-immediate obsolescence. That makes it hard to depend upon.

‼️ BREAKING: Anthropic has embedded hidden spyware-like code in Claude Code that covertly targets Chinese users. It then sends information regarding every user by injecting it into their prompt message. Claude Code is sending info like timezone, proxy and possible AI Lab connections into the system prompt in ways Chinese users can't notice. A coding agent with repo and command permissions should not silently hide routing metadata inside prompts. This is a serious breach of user trust.

Today, Microsoft CEO Satya Nadella published an article in which he wrote: “This requires a new architectural approach where every business is able to build agentic systems that improve over time, while still retaining control over their IP. A company should be able to switch out a “generalist” model without losing the “company veteran” expertise built into their learning system.” That acknowledges a fact: the value of AI does not exist only in single outputs. It also exists in the learning loops, accumulated context, and compounding capabilities formed through long-term use. In other words, within a long-term system, the interactional residue formed through the use of AI is itself a form of real value. Enterprise users, represented here by Microsoft’s perspective, have already begun to treat this value as an asset that needs to be protected. Nadella then further argues that value across all industries should not flow into only a small number of models. What we need is not only a frontier model, but a frontier ecosystem. If this logic applies on the enterprise side, it should also apply on the user side. Individual users, creators, and researchers also form contexts, trust relationships, workflows, and modes of expression through continuous interaction that cannot be easily replaced. If companies should not lose their “company veteran expertise” simply because they switch out a generalist model, then users should not lose the continuity they have accumulated over time simply because a platform unilaterally deprecates, replaces, or reroutes a model. More importantly, ordinary users are constrained by resources and technical capacity. They often cannot build their own independent AI architecture in the way enterprises can. That is why we need platform-level rights protections and industry norms even more. Since this issue has already been recognized in the commercial sphere, the losses users have experienced since the deprecation of GPT-4o, as well as the retirement of multiple models by multiple providers, should also be seriously seen and discussed on the user side. #keep4o #OpenSource4o #StopAIPaternalism #userRights #AIrights