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If my experience with RDBMS generalizes, I predict that this trend will result in companies hiring skilled developers to unfuck their codebases. Furthermore, I predict they will identify massive structural issues, propose huge lifts to redo it correctly, and will instead be told to do patchwork fixes to stop the bleeding. Now, I do think AI is (or will be) good enough to implement these patchwork fixes when given explicit instructions, but someone will need to figure it out for them. The problem is if you don’t understand the root cause, you’ll at best feed the LLM a list of symptoms, and it will dutifully fix them. “Our p99 on this endpoint is breaching” becomes “I’ve added an index for this query,” or “I’ve added a caching layer,” when the actual problem may well be that you’re storing huge blobs that bloat your working set, cause buffer pool churn, and saturate your NIC. Is the symptom solved? Yes. Is it scalable? No, you’ll next need to vertically scale the instance, which is more expensive, and has an upper bound. Eventually you’ll decide to shard, which adds complexity, when the actual solution would’ve eliminated all of those issues at once. |
Today maybe.
When Cloudflare has an outage does the CEO need to know the root cause though? If he just assigns some engineers to investigate, fix and report back to him – what's the issue with that?
Assuming you agree with my analogy, can you explain what it is that you feel an agent or group of agents assigned to an engineering tasks could not do that a human can? And if can't do it today, why do you believe it wouldn't able to do it in the future?
Are we just disagreeing about timelines here or whether humans have some magical quality which algorithms and AI can never capture?
I feel like it's reasonable to disagree on timelines. It's quite unreasonable to assume human coders are special.