In short it seems like virtually all of the improvement in future AI models will come from better algorithms, with bigger and better data a distant second, and more parameters a distant third.
Of course, this claim is itself internally inconsistent in that it assumes that new algorithms won't alter the returns to scale from more data or parameters. Maybe a more precise set of claims would be (1) we're relatively close to the fundamental limits of transformers, i.e., we won't see another GPT-2-to-GPT-4-level jump with current algorithms; (2) almost all of the incremental improvements to transformers will require bigger or better-quality data (but won't necessarily require more parameters); and (3) all of this is specific to current models and goes out the window as soon as a non-transformer-based generative model approaches GPT-4 performance using a similar or lesser amount of compute.