The Heart of AI is Not the Code; It is the Weights: Why Model Weights sit outside conventional Intellectual Property doctrine

FOR SEVERAL DECADES, software protection has rested on a fairly stable premise: source code is where the value lives, and copyright doctrine, by protecting expression while leaving function in the public domain, strikes a workable balance between rewarding creators and preserving competitive freedom. Reverse engineering, in that older world, meant a physical act, decompilation, disassembly, close study of internal structures, and courts in the United Kingdom, the European Union, and India built reasonably coherent doctrines around it.

Artificial intelligence (‘AI’) disturbs that settlement. In a contemporary large language model, the economic centre of gravity is no longer the source code, which is often unremarkable, but the weights produced through training. Model weights are numerical parameters that encode the system’s learned behaviour, and represent the largest single capital investment in building the model. They are not authored in any traditional sense, are hard to characterise as a literary work, and influential recent commentary suggests they may not be copyrightable at all. Their behaviour can be observed through any Application Programming Interface (‘API’) and, given enough effort, partly reconstructed in a rival system. The result is an odd mismatch: the asset that matters most commercially is the one that intellectual property doctrine addresses least, and control over its replication has fallen, almost by accident, to the terms of service of whichever company deploys the model.

The Doctrinal Baseline and Its Limits

The classical framework rests on the line between protected expression and unprotected function. The Court of Justice’s judgment in SAS Institute Inc v. World Programming Ltd. (2012) is still the clearest statement of this idea: neither a program’s functionality, nor its programming language, nor the format of its data files amounts to a protected form of expression, and a lawful user is free to observe, study, and test how a program works in order to understand the ideas behind it. 

Indian doctrine reaches a similar starting point through R.G. Anand v. Delux Films (1978), even though that case belongs to a literary and dramatic-work framework rather than a software one. There, the appellant alleged that the defendant had used the idea of his play to create a new movie, after the appellant had narrated the play to the defendant. Commentators, however, have candidly described Indian jurisprudence on the non-literal aspects of computer programs as “shallow,” and Indian courts have not developed anything resembling the abstraction-filtration-comparison framework that emerged in the United States.