You’re just spouting theoretical numbers here, not actual real-world numbers. First, ChaCha20 can easily be around 1-3 cpb, which translates into around 4-12 c

You’re just spouting theoretical numbers here, not actual real-world numbers. First, ChaCha20 can easily be around 1-3 cpb, which translates into around 4-12 c/u32 (source: AVX2 perf of Go ChaCha20 package: https://pkg.go.dev/github.com/aead/chacha20#section-readme https://pkg.go.dev/github.com/aead/chacha20#section-readme). So that’s already a lot better than your theoretical claim of 60 c/u32. Second, a multiply-add PRNG is going to have abysmally poor statistical properties. Those are the kinds of statistical failures that show up real quick if you generate a billion numbers. If you care that little about random quality in your application, why not just increment a counter and be done with it? A more realistic assessment is that it will cost you around 0.3 cpb for a decent quality generator that passes statistical tests. Yes, you want that, at the very least: you don’t want spurious correlations screwing up your billions of Monte Carlo iterations. There’s a plausible claim that you can get down to ~0.1 cpb with AVX (cf https://espadrine.github.io/blog/posts/shishua-the-fastest-prng-in-the-world.html https://espadrine.github.io/blog/posts/shishua-the-fastest-p...). In any case, the best case here is that a CSPRNG is ~5-20x as slow as a decent-quality PRNG. Sure, a crappy RNG is 10x faster than this, but the tradeoff is that sometimes your “randomized” algorithms produce nonsense, and the last thing that you want to debug is stochastic failures in your stochastic algorithms. ChaCha20 implementations don’t usually need to have special handling for side-channel security because they don’t perform data-dependent lookups or branches. Indeed, as the article points out, a lot of these decent PRNGs kinda look like ChaCha-style ARX ciphers with a lot fewer rounds.