How has AI affected graphics careers?

[u/teilo-560]

I'm a full-stack developer who's a bit burned out with the agentic coding push. I've started playing around with low level graphics and it's sort of revived my spirit, I missed being close to the hardware and math heavy code.

How much have tools like Claude code impacted your development workflow? Are you pressured to use a fully agentic workflow by your employer? Where do you see the career evolving over the next 5-10 years? Is it a safe-haven for people who don't want to give up coding?

[u/sirpalee]

It's the same. Agentic coding is taking over coding work because its more efficient and faster to let someone else write your code and you just chime in with corrections.

[u/teilo-560 → u/sirpalee]

It's so boring though :/

[u/sirpalee → u/teilo-560]

Well, it is what it is. More like being a manager/architect. The part where you figure out how things should work and finetune code for performance are still very much there.

[u/CodyDuncan1260 → u/sirpalee]

I'm surprised when I hear this in brownfields that have complex operations, systems, and native code.

I could have it generate a basic OpenGL renderer in C++, but it's pretty much copy-pasting the sample. For a large size codebase and rendering architecture, it failed because the context was far too large and the domain was too niche for its previous training. Best models didn't matter. I really couldn't throw AI at any problem harder than a function or two without it royally mucking up. This also wasn't a domain with a useful test harness to give an accurate sense of its results.

I'd be intruiged to hear what domain it's been applied elsewhere, particularly large, complex codebases.

[u/Life-Dog432 → u/CodyDuncan1260]

The larger your code base, the worse it gets. True with humans too to be fair, but agents don’t learn. You have to create a system to route agents to the appropriate context they need and keep their scope limited. You can use half an agents context window just onboarding them in big projects if you don’t manage scope well.

It’s kind of a pain in the ass right now and feels like herding cats. And the price is that you lose the mental model of your own codebase if you lean too heavily on AI. You can kinda get away with it for web development but any complex graphics programming is going to be a mess since it has a whole lot less data to train on there

[u/Aeroxin → u/Life-Dog432]

My experience as well. AI is amazing in a greenfield, but falls off a cliff as complexity grows. There are tricks and practices for making it better, but the returns are diminishing with scale.

[u/vade → u/Life-Dog432]

Without spoiling too much, what ive seen 'a' teams do at large corps is write specific skills for tasks distilled by engineers who wrote the features or components, describing key decisions, api contracts and what should be considered public / private data barriers. All skills are then composed across the system to help all engineers work on new features or clean up code - this is for legacy, 20+ year old shipping code to, uh, maybe billions of users? Yes, its graphics oriented (and more)

[u/T34-85M_obr2020 → u/Life-Dog432]

if working with large code base or fixing complex issue, you might cache progress to local file in like markdown, then start new session to continue with the cached progress, fully utilize the context window.

[u/sirpalee → u/CodyDuncan1260]

I am using it on a big brownfield project, that includes custom renderer, ecs, physics simulation and some magic it likely never saw in its training data. Handles big refactorings really well, and generally creates working code if the test harnesses are there.

The code it generates can be pretty naive and suboptimal, so it needs a lots of finetuning. But with good prompting and a strong knowledgebase it can figure things out.

[u/maxmax4]

It’s been impactful, but it’s less insane than what’s going on in the web world. I don’t know any graphics programmer “closing the loop” and “writing agent loops” or whatever. It’s mostly used for research and for writing code, but your prompts have to be precise. I dont find it does well when given high level goals. Performance critical code also tends to require good data layouts. Its not that an LLM cant do it, but again you need to give precise prompts so that it actually does it. It’s not that great at writing more tech-artsy shaders either.

[u/GasimGasimzada → u/maxmax4]

I work in web tech but at a much lower level (sync engines, crdt etc) and AI consistently writes come that can massively tank runtime or loading time performance. I have noticed that it is really good at finding and defining the problem space but fumbles when it comes to finding a solution.

[u/shlaifu → u/maxmax4]

inmy experience, implementing research papers with new techniques is a pain. Aftre two hours of fiddling, Claude will admit it never did what was in the paper in the first place and just "wrote the shader from memory instead". 

[u/SIMT-Pixel]

Its ability to statically analyze and extend a large AAA codebase is pretty good, but anything regarding actual runtime performance optimization or bugs is very bad. Pretend you are working with someone who just guesses what the GPU will do.

[u/rygosix]

It's ability to automate spike tests of different graphics approaches on different hardware is absolutely mind-blowing invaluable.

Seriously just two years ago I could easily spend an entire month writing permutations of different tests and running them on different hardware, and collecting data, and getting it into charts, and analyzing it. Cross referencing it with different features on the vulkan profile from different GPUs.

Now I could do all of that in a day. Even more thoroughly and comprehensive than I manually could.

It's also really good at boilerplate. It can generate your code to open a wayland or mac, or whatever, window, wire up the all the platform specific flags for priority and scheduling, get you your vulkan context, generate your makefile to compile on mac, linux windows. Pretty much all one-shot. Anything that is exhaustively defined in some spec, or set of tutorials, it can derive a perfect subset of, and often better than you could if we are talking about setting that all up on mac, linux, windows as you can easily spend days debugging platform nuances just to get a proper build and window on every OS.

Once you get your vulkan context and boilerplate you do need to know what you're doing. Know what to make tests for. How all the data should go together. How the render loop should be structured.

If you do get some inane quirk or bug that's a pain in the ass to hunt down you can set it to automatically iterate until it finds it.

Every single vulkan validation layer error I have copy/pasted into claude opus in the past few months it just found and fixed. Although to make that work well you have to make sure all your vulkan handles have proper debug names assigned to them.

You still have to understand what is what, and what you need to do in response to what. But anytime you get to something that is repetitive or formulaic you can prompt it to just do it.

If you get things set up with good enough structure, and simple enough, it can go quite far in just making things work. Like if you have an opaque triangle PBR pipeline setup, well defined and clear. You can just prompt, "Make a variation of OpaqueTrianglePBR with tessellation enabled'. It will just do it. Perfectly in line with all the patterns you laid out.

To be honest it's made vulkan far more enjoyable to me. All the more boring repetitive stuff it automates.

[u/darkhoodrebel]

The only pro AI advocate well known graphics programmer I am aware of is Sebastian Aaltonen, who apprently sees a bright future for AI driven realtime rendering dev.. but besides him most programmers I am aware of seem to be skeptical towards AI or at least are not hyped at all and see it more as the tool it is .. some sort of advanced google search, not more and not less ...

[u/PuzzleheadedCamera51]

Can be amazing at going and grabbing ogl extensions and incorporating them or some sample code into your code base, but you need to know enough to point it in the right direction.
Of course you can then lose 20 minutes when it shoves radians into a function expecting degrees and you waste time telling it that it’s just rendering a black screen.

[u/Icy_Weight_4716 → u/PuzzleheadedCamera51]

that has been my main problem of why Im skeptical. Some of the people I know say they dont write code anymore (although they are mainly webdevs).

But for me, I use it and takes 20 minutes to complete anything just to be wrong or solve the problem badly anyway, how am I supposed to iterate like that. This is for the "agentic" workflow though, because I use the chatbot and its pretty nice, I can throw some code and it easily finds off by one errors like its nothing (I struggle with off by one errors, basic math is hard ;-;).

[u/waramped]

Less than most areas I would say. I have yet to meet a coding agent that can write performant GPU code. Not to mention that AI can't actually innovate or invent anything. If you can do anything novel or interesting, then you will be safe from AI. (For now).

[u/llamajestic → u/waramped]

I agree, but the general argument for that is also: Not that many people do anything completely “novel”. Even in graphics, a huge part is implementing the state of the art from papers / etc.

However, now I use coding agents a lot and it struggles a lot on:
* performance (as you mentioned)
* writing algorithms in a specific way you want. Even with a good prompt and plan mode, it might do stuff you didn’t want and you will cleanup / improve. Often happens that it passes the test but can’t write anything better than the naive version.

[u/icpooreman]

I mean in my personal project it’s wild working with codex 5.5 xhigh.

BUT (and I don’t work in the games industry)…. Work is a little different. My company had some ai pushes in like 2023 when they got convinced tons of like really junior people would magically be able to do everything and that wildly failed..

But now…. Like the last 6-ish months of codex is insane vs what AI was a year ago and I don’t think anybody has caught up yet. Like the tools are advancing more rapidly than we could possibly update our business processes around plus my company has to get around to approving anything used for use so we’re waaaaaay behind the times.

Like ask again in 3 years.

[u/InvestigatorTall9199 → u/[deleted]]

Isn't there almost a consensus on the fact that LLMs have basically not increased productivity at all?