AI as autocomplete evolution

The discussion around AI as an evolution of autocomplete highlights a shift where large language models (LLMs) are increasingly viewed as essential productivity "e-bikes" that liberate developers from the drudgery of boilerplate and repetitive character-by-character toiling. While many contributors liken this transition to historical leaps such as the move from assembly to compilers or the invention of the printing press, a sharp debate persists regarding the fundamental nature of these tools. Some users emphasize a critical distinction between the formal, deterministic logic of traditional compilers and the non-deterministic, informal nature of LLM prompts, warning that true engineering still requires rigorous human ownership to avoid quality "nightmares" during code review. Ultimately, the prevailing sentiment is that while LLMs function as sophisticated assistants that handle the bulk of implementation, they remain tools—much like a power drill for a carpenter—that require a skilled practitioner to guide the final output and maintain the integrity of the work.

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I was in the same camp until a few months ago. I now think they're valid tools, like compilers. Not in the sense that everyone compares them (compilers made asm development a minuscule niche of development).

But in the sense that even today many people don't use compilers or static analysis tools. But that world is slowly shrinking.

Same for LLMs, the non LLM world will probably shrink.

You might be able to have a long and successful career without touching them for code development. Personally I'd rather check them out since tools are just tools.

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"What an LLM is to me is the most remarkable tool that we've ever come up with, and it's the equivalent of a e-bike for our minds"

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Depends on what level of abstraction you're comfortable with. I have no problem driving a car I didn't build.

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You could make the same argument about the printing press. Some people like forming the letters by hand, others enjoy actually writing.

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Try tailwind. Very amenable to LLM generation since it's effectively a micro language, and being colocated with the document elements, it doesn't need a big context to zip together.

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Flipping toggle switches went out of fashion many, many, many years ago. We've been describing to trainees (compilers) the dish we want for longer than most on HN have been alive.

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Actually, we’ve been formally declaring the logic of programs to compilers, which is something very different.

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(Replying to myself because hn)

That’s not the only difference at all. A good use of an LLM might be to ask it what the difference between using an LLM and writing code for a compiler is.

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Equally a good use for a legacy compiler that compiles a legacy language. Granted, you are going to have to write a lot more boilerplate to see it function (that being the difference, after all), but the outcome will be the same either way. It's all just 1s and 0s at the end of the day.

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Sorry friend, if you can’t identify the important differences between a compiler and an LLM, either intentionally or unintentionally (I can’t tell), then I must question the value of whatever you have to say on the topic.

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The important difference is the reduction in boilerplate, which allows programs to be written with (often) significantly less code. Hence the time savings (and fun) spoken of in the original article.

This isn't really a new phenomenon. Languages have been adding things like arrays and maps as builtins to reduce the boilerplate required around them. The modern languages of which we speak take that same idea to a whole new level, but such is the nature of evolution.

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No, when we write code it has a an absolute and specific meaning to the compiler. When we write words to an LLM they are written in a non-specific informal language (usually English) and processed non-deterministically too. This is an incredibly important distinction that makes coding, and asking the LLM to code, two completely different ball games. One is formal, one is not.

And yes, this isn’t a new phenomenon.

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It's different in some ways (such is evolution), but is not a distinction that matters. Kind of like the difference between imperative and declarative programming. Different language models, but all the same at the end of the day.

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The only difference is that newer languages have figured out how to remove a lot of the boilerplate.

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Ok, maybe my analogy wasn't the best. But the point I was trying to make is that using AI tools to write code doesn't meant you didn't write the code.

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Well, don’t go around calling me a compiler!

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If that's what you do, then the name is perfectly apt. Why shy away from what you are?

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Little bit of a sweeping generalization there. There are a huge range of ways in which LLMs are being leveraged for software development.

Using a drill doesn’t make you any less of a carpenter, even if you stopped using a screwdriver because your wrists are shot.

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For most of my AI uses, I already have an implementation in mind. The prompt is small enough that most of the time, the agent would get it 90% there. In a way, it's basically an advanced autocomplete.

I think this is quite nice cause it doesn't feel like code review. It's more of a: did it do it? Yes? Great. Somewhat? Good enough, i can work from there. And when it doesn't work, I just scrap that and re-prompt or implement it manually.

But I do agree with what you say. When someone uses AI without making the code their own, it's a nightmare. I've had to review some PRs where I feel like I'm prompting AI rather than an engineer. I did wonder if they simply put my reviews directly to some agent...

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Exactly. AI freed me from the boring character by character toiling

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