Some things just take time

From speaking to my friends in the industry, it seems like uptake for code is happening slowly, but unevenly, and the results are largely dependent on the level of documentation, which is often lacking. (I know of a few people using AI for (high-quality!) work on Godot, and their AIs struggle with many of the implicit conventions present in the codebase.)

With that being said, I would say that LLMs have generally been quite the boon for the (limited) gameplay work that I have done of recent. Because the cost of generation is so cheap [0], it is trivial to try something out, experiment with variations, and then polish it up or discard it entirely.

This also applies to performance work: if it's a metric that the AI can see and autonomously work on, it can be optimised. This is, of course, not always possible - it's hard to tell your AI to optimise arbitrary content - but it's often more possible than not, especially if you get creative. (Asking it to extract a particularly hot loop out from the code it resides within, and then optimising that, for example: entirely feasible.)

I think there are still growing pains, but I'm confident that LLMs will rock the world of gamedev, just like they're doing to other more well-attested fields of programming.

[0]: https://simonwillison.net/guides/agentic-engineering-pattern...

If you need to spend actual humans searching every 10 degrees (or more if they need to go far), the cost might not be worth it. What if they don't come back? Every human scouting is not foraging for the group or protecting them from whatever is in the jungle,

Now if you have a near infinite capacity of autonomous drones you can send one every degree from your position and get an approximate estimate whether that direction is safe or not.

It's the same with programming, you can do a TON of exploratory crap with near zero extra cost. Just to see if that's the right direction or not. Basically lean in to the "fast but inaccurate" aspect of AI.

Like I had a hare-brained idea of a local web-based file manager / media browser that would use YOLOv8 and CLIP to tag and classify images and videos as you browse. Took me about 30 minutes of active time over a few evenings while doing other stuff to conclude that, yes, it IS doable. Now I can focus my actual brain on it.

Yeah, that sums up a lot of my thoughts with AI c. 2026.

I do take some schedenfreude knowing that AI training also struggles with the utter lack of documentation here. That may be a win in and of itself if this paradigm forces the games industry to properly care for tech writing.

>Because the cost of generation is so cheap [0], it is trivial to try something out, experiment with variations, and then polish it up or discard it entirely.

Well, that's another thing I'm less confident about. The cost is low, for now. But we also know these companies are in loss leader mode. It'll probably always be cheap for a company to afford agents, but I fear reliance on these giant server models will quickly price out ICs and smaller work environments.

That might be something China beats us too. They seem to be focusing on optimizing models that works on local machines out of necessity, as opposed to running tens of billions of dollars of compute. My other big bias is wanting to properly own as much of my pipeline as possible (to the point where my eventual indie journey is planning around OS tools and engines, despite my experience in both Unity and UE), and current incentives for these companies don't want that.