Model 'distillation' accusations are getting way overblown at this point

The news about Anthropic settling a class action lawsuit for 1.5B over training data isn't just a legal headache for them, it's a massive warning sign for engineering teams relying entirely on closed API vendors. When you route core business logic, proprietary codebases, and customer data through third party APIs, you're exposing your stack to three massive risks: The massive fine(not rly massive for anthropic) is not gonna come out of their pockets, they gonna charge you more to make up the cost, thats what corpos do. Compliance & IP Exposure: Unclear data provenance and changing vendor terms. Data Leakage: Passing raw prompts and context windows to external servers. Vendor Lock in: Being at the mercy of sudden API deprecations(remember the whole fable saga?) or price hikes. The obvious move for privacy conscious teams is migrating toward self hosted open weight models (like Llama, Qwen, or DeepSeek) inside a private VPC to keep data completely in house. However, moving to local open weight models only solves the data privacy side of the equation. You still face the runtime execution problem: how do you ensure an autonomous open weight model doesn't execute bad code, leak env tokens, or make unauthorized calls? To make local models actually production ready, you need strict governance at the gate. Wiring local agent runtimes through Lyzr Control Plane or smth like Azure AI Foundry which provides that deterministic circuit breaker, letting you run open weight models locally with automatic PII redaction, and policy enforcement. Also 1.5B for 7 million books feels extremely cheap, like unfair even. Also the whole crusade against open weight model screams greed,I feel like Anthropic has almost made everybody forget the original Hinton paper that describes the technique of distillation using full logits for the student model to better learn the teacher's internal representation. That is, I think Anthropic is changing the language here. Claude does not give logits. Generating training data is the more correct term IMO, not distillation. Put another way, if generating training data were always distillation, then even Anthropic's own new models are "distilled" since they surely use older models to generate training data for new models. That really stretches the meaning of the word. Used grammarly for formatting submitted by /u/UsedMorning9886 [link] [comments]

Source: r/LocalLLaMA | 2026-07-23

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