Another interesting thing that happens is you don't necessarily know what form your actual optimizations will need to take. Later when your systems grow you discover the suboptimal parts you hadn't optimized for.
Very early on at Cloudflare I worked on part of the DNS infrastructure that took DNS records from the UI and got them in a state for actual authoritative serving. The system had been constructed anticipating Cloudflare having millions of customers with unique domains, but it had not been constructed for a single customer with a single domain with millions of records. This caused a periodic slow down in DNS record updating while the system churned on that one customer.
In a different job I worked on a piece of optimization software that needed to keep track of "node" A is reachable from node "B". This had been implemented as a matrix (literally a malloced NxN matrix of ints storing 0 or 1) which worked really well for small systems. But you'd be out of memory really fast on a large project. I replaced the matrix with a hash table and all was good because the matrix was actually really sparse.
With a rather short prompt, claude/codex will take your code, write a harness, profile it, build experiments, profile those, and give some pretty solid advice which one to pick. Then integrate the changes. It's the kind of goal-directed, bite-sized job that LLMs excel at. Extremely low-commitment.
Except for the whole "making changes in production at scale" problem, of course.
The “evil” of premature optimization is that it’s a misapplication of priority. If I have an acute medical problem that needs attention, it’s not the right time to talk about chloresterol and statins, get my broken leg set.
There’s always a tension between engineering management who needs to deliver a solution to the business and engineers who want to deliver a beautiful object.
Because anyone willing to come in just to design your cache format is going to expect payment that is many multiples more than the engineers you already cannot afford? Long-term employees cost less, which brings them closer to being affordable, but you have to be able to keep them busy for long periods of time to realize that reduction in cost. A engineer who doesn't understand your codebase isn't going to be useful for very long.