Why?
Edit: Thanks for all the replies. It seems this applies to data-parallel workloads only. I'd use a GPU for this. An RTX 3090 has around ~10000 CUDA cores (10000 simultaneous operations) v/s just ~10 for CPUs.
This creates a new problem: how do you balance load across cores? What if the workload is not evenly distributed across the data held by each core? Real workloads are like this! Fortunately, over the last decade, architectures and techniques for dynamic cross-core load shedding have become smooth and efficient while introducing negligible additional inter-core coordination. At this point, it is a mature way of designing extremely high throughput software.
Scheduling Parallel Programs by Work Stealing with Private Deques https://hal.inria.fr/file/index/docid/863028/filename/full.p...
Without a requirement of utility it's easy to come up with counterexamples from math, eg "does the Collatz starting from Graham's number reach one?" Once you've exhausted cores that can be used for the actual arithmetic, you are still gated by the decision-making at each step so cores cannot work too far "ahead" of each other. There may well be much smarter things we can do than brute force, but that's not "just coding work" at that point.
Theoretically, it doesn't hold - at some point you have split apart everything that can be split, and you are left with some essential chains of data dependency that cannot be further parallelized.