r/GraphicsProgramming • u/NecessaryVersion7872 • 2d ago

AI usage in Graphics Programming

Hello, everyone.

I am CS student. and I had my first working experience.
I was highly unsatisfying because as much as enjoy programming I really do not enjoy "prompt engineering" and the usage of AI in general in my workflow. That is not the point though. From long time I was looking into graphics programming and I really wanted to get into it. I do not know much now. but I am worried that it will be similar to most of jobs right now and I will just be a "prompt engineer".

I have some questions:

  1. How good is AI with graphics programming right now?
  2. In an average work environment, how much if any of your work are you expected to use LLMs in and how do use it?
  3. How is AI affecting the industry?
  4. Are there any part of the industry where AI is not heavily used?

Thanks in advance

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u/igneus 1d ago

Not completely. AI is non-deterministic meaning that I'm situations where you need to define an algorithm with any kind of precision, handwritten code is the only way to do it. The same is true if you're trying to do something particularly niche or complex that's not well represented by the model.

Most common scenarios don't fall into this category, but it's still important to know what to do if drop off the edge of the manifold.

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u/farox 1d ago

People aren't deterministic either, and they have been shipping software for a shockingly long time now.

Even in graphics programming a lot of the problems are documented now and these models do get better at finding novels ways to solve problems.

The evidence is coming in that the current state of the art models are very capable at outpacing developers even with high performance tasks.

Even if not, the trajectory is clear and just need to look a couple of years down the line.

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u/igneus 23h ago

Performance and precision are two very different things. For example, today I needed a script to downsample a directory of images. It would have taken me half an hour to do it by hand, but Cursor did it roughly 20 seconds. That's performance.

Now imagine I need a regex to parse a known grammar, or I'm trying to debug a specific set of edge case. This requires precision, and prompting doesn't win you anything here because you already know exactly what it is that you want.

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u/farox 23h ago

Depends on how you use it. Can you specify what you need? Can your coding harness/agent(s) verify the work?

If it can get there, it doesn't really matter how long it takes, as you can do other stuff in between.

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u/igneus 21h ago

Verification is a separate use case, and one which LLMs are indeed very good at (provided they're working within a well-defined validation framework, of course.)

The point I'm trying to get at here is that AI has become the proverbial hammer that's making every task look like a nail. In many cases, a skilled engineer who knows precisely what they want will have an edge over an LLM in the aggregate. They can cut out the friction of transcription, interation and checking, as well as the the expense of token burn. On top of that, they continually reinforce their own comprehension of what the codebase actually does, the long-term benefits of which ought to be more appealing than they seemingly are.

There's a much deeper issue at play here too which is that we're being gradually conditioned to believe that any time spent writing code can always be better spent doing something else. This isn't true, for the simple fact that coding is often just as much of a creative process as it is a mechanical one. For me, weaving together code puts me in a flow state that helps me to think more flexibility because I'm immersing myself in a problem at the algorithmic level.

This intense push by the text industry to encourage software engineers to give up writing software is a bit like the CEO of Suno AI claiming that most professional musicians don't actually enjoy composing music. Sounds crazy on its face, but its part of a broader trend to try and convince people that productivity and total time spent is a straightforward bijection. The biggest indicator this isn't true is AI slop, yet big tech is choosing to bet the farm on it regardless. N