Your premise is wrong. This has nothing to do with 10x-ing parameters. One could argue the current parameter sizes are good enough as we observe "large breadth, shallow depth" behavior from LLM and to some extent, diffusion models.
This suggests the problem is the depth of inference, which is single pass "hot takes" for all language models right now, due to cost of inference and our limited understanding of what makes a model's response high quality.
Yes, you don't need more parameters to increase the depth. You need to iterate, instead. Loop. Imagine programming if looping was not allowed, nor recursion, or not even defining functions and calling them. Everything you write runs at most once during program execution and that's it. This is what an AI model is right now during inference. One big flat, single-pass, directed acyclic graph. And soon it won't be.
Research into Chain of Thought, Tree of Thought reveals this dimension. This means you can take existing models and make them perform much more complex tasks with much better precision, though various ways of letting them iterate. Think of how you'd perform if you always had exactly 5 seconds to answer a question. Now imagine if you have 5 minutes. 5 hours. 5 days. Lo and behold, turns out an AI isn't different in that aspect.
We also need more iterations of training (on the same amount of data), we need larger context windows, and we need new architectures, like Meta's MEGABYTE, for example.
Parameter count and data size could hypothetically have already hit a hard wall (they haven't) and AI will keep exponentially improving regardless. There's too much low hanging fruit and more grows by the nanosecond.