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▲ bwfan123 4 hours ago

At a startup I worked, there was an engineer whose code was incoherent and buggy. So, we were literally better off if that engineer did nothing because their net output was negative. Engineers like that become weaponized with LLMs, and negative numbers become larger negative numbers when scaled up.

▲icedchai 3 hours ago | parent | next [-]

I've seen similar. They wasted weeks of senior engineering time, between reviews, meetings, and follow up in Slack, only to have the PR closed without merge. The offending individual was eventually moved to another project.

▲ilaksh 3 hours ago | parent | prev [-]

Is that the fault of AI or management for not firing them?

▲bwfan123 3 hours ago | parent [-]

> Is that the fault of AI or management for not firing them?

How does the system behave in a variety of scenarios including failures and restarts. How is state maintained coherently. There are the kinds of systems problems that an engineer needs to reason through, and if there are bugs in such decisions, they end up becoming costly. I dont expect AI or LLMs to solve these problems at all, since each of them has nuances and tradeoffs which are specific to each system. In short, there is specification complexity in precisely describing system wide behaviors, and unfortunately, there is no lean/tla+ to meaningfully describe systems at scale. You could then ask: How can a system have guaranteed behaviors if they cannot be even stated or proved formally ? The answer to this is how protocols like raft/paxos initially convinced us of their behaviors which is in human review and understanding. That begs the question: How can human review and understanding be reliable, and the answer is that it is not reliable, but humans have ability and processes to continuously learn from experience in the real world. So, our understanding is grounded not only by whats out there in books etc, but also by our own interactions with the world.

Long story short: The responsibility for system-wide behaviors of software systems relies on human review and understanding, which while imperfect can continuously learn.