No, because the thing that makes you a 10x engineer is not generally writing code. (Let me just take the "10x" term for now as given and not dip into critique I've made elsewhere already.) It is certainly a baseline skill required to get there, but the things that make you 10x are being able to answer questions like, should this be built at all? There are 5 valid architectures I could use to solve this problem, which has the overall best cost/benefits analysis for the business as a whole? (As opposed to the 1x, who will likely run with either the first they come up with, or the one architecture they know.) If I'm working with 5 teams to solve this particular problem, what's the correct application of Conway's Law, in both directions, to solve this problem with the minimum cost in the long term? What's the likely way this system will be deprecated and can we make that transition smoother?
I am abundantly confident you could feed this AI a description of your problem in terms of what I gave above and it will extremely confidently spit out some answer. I am only slightly less confident it'll be total garbage, and most of that confidence reduction is just accounting for the possibility it'll get right by sheer luck. "The average of what the internet thinks" about these issues can't be more than a 2x engineer at best, and that's my very top-end estimate.
I'm not promising no AI will ever crack this case. I'm just saying this AI isn't going to do it. Over-reliance on it is more likely to drop you down the "Xx engineer" scale than raise you up on it.
For that matter, at least at the level I operate at most of the time, coding skill isn't about how fast you can spew it out. It's about how well you understand it and can manipulate that understanding to do things like good, safe refactorings. This tech will not be able to do refactorings. "How can you be so confident about that claim, jerf?" Because most people aren't digging down into how this stuff actually works. These transformer-based technologies have windows they operate on, and then continue. First of all, refactoring isn't a "continuation" anyhow so it's not a very easy problem for this tech (yes, you can always say "Refactor this code" and you'll get something but the nature of this tech is that it is very unlikely to do a good job in this case of getting every last behavior correct), but second of all, anything that exceeds the window size might as well not exist according to the AI, so there is a maximum size thing it can operate on, which isn't large enough to encompass that sort of task.
It really reminds me of video game graphics, and their multiple-orders-of-magnitude improvements in quality, whereas the underlying data model of the games that we are actually playing have grown much, much more slowly. Often late 1990s-era games are actually richer and more complicated than the AAA games of today. But on the surface, a modern game blows away any 1990s game, because the surface graphics are that much better. There's an analog to what transformer-based AI tech is doing here... it is really good at looking amazing, but under the hood it's less amazing an advance than meets the eye. I do not mean to slag on it, any more than I want to slag on graphics technology... both are still amazing in their own right! But part of what they're amazing at is convincing us they're amazing, regardless of what lies beneath the tech.