Benchmark partner Chetan Puttagunta said in a post on X that Anthropic’s public call for stronger regulation of AI model distillation is hard to reconcile with the company’s own scale and resources. He argued that if Anthropic is as well funded and technically capable as described, then so-called large-scale distillation attacks should in theory be easier to detect and trace. In his view, the real cost of restricting such activity may not be a lack of technical ability, but the loss of part of its API revenue. Puttagunta wrote that “the only cost seems to be reducing related API business revenue.”
The comments come as Anthropic and other AI companies keep model distillation under close watch. The practice usually refers to using the outputs of a larger “teacher” model to train a smaller “student” model, cutting costs and improving efficiency. Some AI companies worry that rivals could make heavy API calls to collect outputs and use them to train their own systems, bypassing the original research investment. Puttagunta’s argument points to a broader dispute over how the AI industry should balance commercial interests, open competition and intellectual property protection.
Odaily reported that Benchmark partner Chetan Puttagunta said in a post on X that Anthropic’s public push for stronger regulation of AI model distillation is difficult to understand.
Puttagunta said Anthropic is currently a company valued at about $1 trillion and has substantial technical and financial resources. At the scale Anthropic itself describes, he argued, so-called “large-scale distillation attacks” should in theory be relatively easy to identify and track. If Anthropic decides to restrict that activity, the real cost may not be limited technical capability, but giving up part of its API revenue. “The only cost seems to be reducing related API business revenue,” he wrote.
Anthropic and other AI companies have been watching the issue of model distillation for some time. Model distillation usually refers to using the outputs of a large model, or a teacher model, to train another model, or a student model, in order to lower costs and improve efficiency.
Some AI companies are concerned that competitors could make large numbers of API calls to obtain model outputs and then use those outputs to train their own models, sidestepping the original research and development investment. In Puttagunta’s view, the dispute over model distillation in the AI sector comes down to the balance between commercial interests, open competition and intellectual property protection. For leading AI companies, how to balance protection of core technology with an open ecosystem is set to remain a major industry question.
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