alt Hacker NewsFor a long time, I've wanted to write a blog post on why programmers don't understand the utility of LLMs[1], whereas non-programmers easily see it. But I struggle to articulate it well.
The gist is this: Programmers view computers as deterministic. They can't tolerate a tool that behaves differently from run to run. They have a very binary view of the world: If it can't satisfy this "basic" requirement, it's crap.
Programmers have made their career (and possibly life) being experts at solving problems that greatly benefit from determinism. A problem that doesn't - well either that needs to be solved by sophisticated machine learning, or by a human. They're trained on essentially ignoring those problems - it's not their expertise.
And so they get really thrown off when people use computers in a nondeterministic way to solve a deterministic problem.
For everyone else, the world, and its solutions, are mostly non-deterministic. When they solve a problem, or when they pay people to solve a problem, the guarantees are much lower. They don't expect perfection every time.
When a normal human asks a programmer to make a change, they understand that communication is lossy, and even if it isn't, programmers make mistakes.
Using a tool like an LLM is like any other tool. Or like asking any other human to do something.
For programmers, it's a cardinal sin if the tool is unpredictable. So they dismiss it. For everyone else, it's just another tool. They embrace it.
[1] This, of course, is changing as they become better at coding.
Replies
> And so they get really thrown off when people use computers in a nondeterministic way to solve a deterministic problem
Ah, no. This is wildly off the mark, but I think a lot of people don't understand what SWEs actually do.
We don't get paid to write code. We get paid to solve problems. We're knowledge workers like lawyers or doctors or other engineers, meaning we're the ones making the judgement calls and making the technical decisions.
In my current job, I tell my boss what I'm going to be working on, not the other way around. That's not always true, but it's mostly true for most SWEs.
The flip side of that is I'm also held responsible. If I write ass code and deploy it to prod, it's my ass that's gonna get paged for it. If I take prod down and cause a major incident, the blame comes to me. It's not hard to come up with scenarios where your bad choices end up costing the company enormous sums of money. Millions of dollars for large companies. Fines.
So no, it has nothing to do with non-determinism lol. We deal with that all the time. (Machine learning is decades old, after all.)
It's evaluating things, weighing the benefits against the risks and failure modes, and making a judgement call that it's ass.
I’m perfectly happy for my tooling to not be deterministic. I’m not happy for it to make up solutions that don’t exist, and get stuck in loops because of that.
I use LLMs, I code with a mix of antigravity and Claude code depending on the task, but I feel like I’m living in a different reality when the code I get out of these tools _regularly just doesn’t work, at all_. And to the parents point, I’m doing something wrong for noticing that?