> Legacy C code is sequential, and the C model makes parallel programming very difficult.
Neither of these statements are true, unless "Legacy" refers to the early days of UNIX.
Tasks that parallelize poorly do not benefit of many small cores. This is usually a result of either dealing with a problem that does not parallelize, or just an implementation that does not parallelize (because of a poor design). Neither of these attributes are related to language choice.
An example of something that does not parallelize at all would be an AES256-CBC implementation. It doesn't matter what your tool is: Erlang, Haskell, Go, Rust, even VHDL. It cannot be parallelized or pipelined. INFLATE has a similar issue.
For such algorithms, the only way to increase throughput is to increase single-threaded performance. Increasing cores increase total capacity, but cannot increase throughput. For other tasks, synchronization costs of parallelization is too high. I work for a high performance network equipment manufacturer (100Gb/s+), and we are certainly limited by sequential performance. We have custom hardware in order to load balance data to different CPU sockets, as software based load distribution would be several orders of magnitude too slow. The CPU's just can't access memory fast enough, and many slower cores wouldn't help as they'd both be slower, and incur overheads.
Go and Erlang of course provide built-in language support for easy parallelism, while in C you need to pull in pthreads or a CSP library yourself, but the C model doesn't make parallel programming "very difficult", nor is C any more sequential by nature than Rust. It is also incorrect to assume that you can parallelize your way to performance. In reality, the "tons of small cores" is mostly just good at increasing total capacity, not throughput.