Comment by dawidloubser | Hacker News Reader

Comment by dawidloubser | Hacker News Reader

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I think that anybody who finds the process of clumsily describing the above examples to an LLM in some text box using english and waiting for it to spit out some code which you hope is suitable for your given programming context and codebase more efficient than just expressing the logic directly in your programming language in an efficient editor, probably suffers from multiple weaknesses:

- Poor editor / editing setup

- Poor programming language and knowledge thereof

- Poor APIs and/or knowledge thereof

Mankind has worked for decades to develop elegant and succinct programming languages within which to express problems and solutions, and compilers with deterministic behaviour to "do the work for us".

I am surprised that so many people in the software engineering field are prepared to just throw all of this away (never mind develop it further) in exchange for using a poor "programming language" (say, english) to express problems clumsily in a roudabout way, and then throw away the "source code" (the LLM prompt) entirely such to simply paste the "compiler output" (code the LLM spewed out which may or may not be suitable or correct) into some heterogenous mess of multiple different LLM outputs pasted together in a codebase held together by nothing more than the law of averages, and hope.

Then there's the fun fact that every single LLM prompt interaction consumes a ridiculous amount of energy - I heard figures such as the total amount required to recharge a smartphone battery - in an era where mankind is racing towards an energy cliff. Vast, remote data centres filled with GPUs spewing tonnes of CO₂ and massive amounts of heat to power your "programming experience".

In my opinion, LLMs are a momentous achievement with some very interesting use-cases, but they are just about the most ass-backwards and illogical way of advancing the field of programming possible.

There's a new mode of programming (with AI) that doesn't require english and also results in massive efficiency gains. I now only need to begin a change and the AI can normally pick up on the pattern and do the rest, via subsequent "tab" key hits as I audit each change in real time. It's like I'm expressing the change I want via a code example to a capable intern that quickly picks up on it and can type at 100x my speed but not faster than I read.

I'm using Cursor btw. It's almost a different form factor compared to something like GH copilot.

I think it's also worth noting that I'm using TypeScript with a functional programming style. The state of the program is immutable and encoded via strongly typed inputs and outputs. I spend (mental) effort reifying use-cases via enums or string literals, enabling a comprehensive switch over all possible branches as opposed to something like imperative if statements. All this to say, that a lot of the code I write in this type of style can be thought of as a kind of boilerplate. The hard part is deciding what to do; effecting the change through the codebase is more easily ascertained from a small start.

Provided that we ignore the ridiculous waste of energy entailed by calling an online LLM every time you type a word in your editor - I agree that the utility of LLM-assisted programming as "autocomplete on steriods" can be very useful. It's awfully close to that of a good editor using the type system of a good programming language providing suggestions.

I too love functional programming, and I'm talking about Haskell-levels of programming efficiency and expressiveness here, BTW.

This is quite a different use case than those presented by the post I was replying to though.

The Go programming language has this mantra of "a little bit of copy and paste is better than a little bit of dependency on other code". I find that LLM-derived source code takes this mantra to an absurd extreme, and furthermore that it encourages a though pattern that never leads you to discover, specify, and use adequate abstractions in your code. All higher-level meaning and context is lost in the end product (your committed source code) unless you already think like a programmer _not_ being guided by an LLM ;-)

We do digress though - the original topic is that of LLM-assisted writing, not coding. But much of the same argument probably applies.

At the time I'm writing this, there are over 260 comments to this article and yours is still the only one that mentions the enormous energy consumption.

I wonder whether this is because people don't know about it or because they simply don't care...

But I, for one, try to use AI as sparingly as possible for this reason.

You're not alone. With the inclusion of gemini generated answers in google search, its going down the road of most capitalistic things. Where you see something is wrong, but you have no option to use it even if you don't want it.

I like to idealistically think that in a capitalistic (free market) society we absolutely have the option to not use things that we think are wrong or don't like.

Change your search engine to one that doesn't include AI-generated answers. If none exist any more, all of Google's customers could write to them telling them that they don't want this feature and are switching away from them because of it, etc.

I know that internet-scale search is perhaps a bad example because it's so extremely difficult and expensive to build and run, but ultimately the choice is in the consumers' hands.

If the market makes it clear that there is a need for a search engine without LLM-generated answers at the top, somebody will provide one! It's complacency and acceptance that leads apparently-delusional companies to just push features and technologies that nobody wants.

I feel much the same way about the ridiculous things happening with cars and the automotive sector in general.

when you take energy into account its like anti engineering. What if we used a mountain of effort to achieve a worse result?

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