You were talking about the number of parameters on existing models. Like the history of Deep Learning has shown, simply throwing more computing power at an existing approach will plateau and not result in a fundamental breakthrough. Maybe we'll find new architectures, but the point was that the current ones might be showing their limits, and we shouldn't expect the model suddenly become good at something they are currently unable to handle because "more parameters".
Yes you're right I only mentioned the size of the model. The rate of progress has been astonishing and we haven't reached the end, in terms of both of size and algorithmic sophistication of the models. There is no evidence that we have reached a fundamental limit of AI in the context of deep learning.
