My guess is, that in the future, we'll have computers writing some highly optimized software for certain things. I'm not saying all of use software people will be replaced 100% but some stuff will be replaced by automation.
Will the specifications for the software also be machine-generated?
If the specifications are human-generated, then they're just a form of high-level source code, and your prediction boils down to future programming languages simultaneously improving programmer productivity and reducing resource usage. That's not a controversial prediction.
If I understand you correctly, I think you're correct that over time, we'll see an increase at the abstraction level at which most programming is done. I think the effort put into making compilers better at optimizing will largely follow market demand, which is a bit harder to predict.
One interesting direction is the Halide[0] domain-specific language for image/matrix/tensor transformations. The programs have 2 parts: a high-level description, and a set of program transformations that don't affect results, but make performance tradeoffs to tune the generated code for particular devices. The Halide site has links to some papers on applying machine learning to the tuning and optimization side of things.
I can imagine a more general purpose language along these lines, maybe in the form of a bunch of declarative rules that are semantically (though perhaps not syntactically) Prolog-like, plus a bunch of transformations that are effectively very high-level optimization passes before the compiler ever starts looking at traditional inlining, code motion, etc. optimizations.
At some point, maybe most programmers will just be writing machine learning objective functions, but at present, we don't have good engineering practice for writing safe and reliable objective functions. Given some of the degenerate examples of machine learning generating out-of-the-box solutions with objective functions (throwing pancakes to maximize the time before they hit the ground, tall robots that fall over to get their center of mass moving quickly, etc.), we're a long way from just handing a machine broad objectives and giving it broad leeway to write whatever code it deems best.
I suspect in the medium-term, we'll see a 3-way divergence in programming: (1) safety/security-critical programs generated from proofs (see Curry-Howard correspondence, and how the seL4 microkernel was developed) (2) performance-critical programs that are very intensive in terms of human expert time and (3) lots of cookie-cutter apps and websites being generated via machine learning from vague human-provided (under-)specifications.
No, Google's AI is floorplanning[0] (basically routing and layout) human-designed logic in 6 hours. That headline is misleading.
It's kind of like having a compiler will billions of optimization flags and using AI to select a pretty near-optimal set of flags for a particular human-generated source file. We wouldn't call the output an AI-designed program, even though such AI would be really helpful.
Google is using AI as a heuristic for decently fast approximate solutions to the floorplanning problem, where (IIRC) optimal solutions are NP-hard.
It's an important step forward, and presumably a big time saver, but they're nowhere near giving the AI an instruction set spec or examples of inputs and outputs ad having the AI generate the logic.
That's my prediction.