There's a persistent illusion within the open-source fine-tuning community: the belief that distilling models from the Chain of Thought (CoT) traces offered by commercial APIs is a free path to advanced reasoning capabilities. The reality, increasingly hard to ignore, is that those traces are sanitized, filtered, or censored versions of the model's actual thought process. Training on them is no different from studying a transcript where the key logical steps have been omitted: the resulting model will be structurally worse than the one it started from.

The root of the problem lies in the safety architecture and competitive strategy of labs like Anthropic. What the user sees in the chat as "reasoning" is an after-the-fact reconstruction, summarized and stripped of potentially sensitive or dangerous details. The internal process that generates the final answer follows far more intricate paths, which remain inaccessible to outsiders. Fine-tuning on those traces means injecting the model with a distorted representation of the inference process, introducing systematic bias that degrades performance precisely on the reasoning tasks it was meant to improve.

This has a second-order effect weighing on the entire self-hosted model ecosystem. Many companies that choose an on-premise LLM do so for data sovereignty and to avoid lock-in to cloud APIs. If the available open models often result from degraded distillations, the risk is ending up with a system that ostensibly meets data residency requirements but actually produces less reliable reasoning, compromising critical decisions in regulated domains. Instead of breaking free from proprietary providers, the organization exposes itself to errors that are hard to diagnose because they are rooted in the training data.

There is also a vicious economic cycle: the pressure to release ever better models at zero cost pushes toward distillation at any cost, but the drop in quality ends up discrediting the open-source alternative, strengthening the position of those who offer closed APIs with "full reasoning" for a fee. The result is a paradox: the pursuit of digital sovereignty collides with an even deeper dependency, because fixing the flaws introduced by poor data requires resources and access that only the major labs possess.

For those designing on-premise deployments, the lesson is that the value of a fine-tuning is not just measured by benchmarks but by the transparency of the source data. Relying on official CoT traces without understanding their filtering mechanisms is like building on shaky foundations. While the ecosystem matures toward more verifiable training modalities, caution toward "magical" distillations remains the first line of defense for anyone unwilling to mortgage their AI stack.