100% agreed. It should not be used as a replacement but rather as an augmentation to get the real benefits.

I study AI, and have developed plenty of software. LLMs are great for using unfamiliar libraries (with the docs open to validate), getting outlines of projects, and bouncing ideas for strategies. They aren’t detail oriented enough to write full applications or complicated scripts. In general, I like to think of an LLM as a junior developer to my senior developer. I will give it small, atomized tasks, and I’ll give its output a once over to check it with an eye to the details of implementation. It’s nice to get the boilerplate out of the way quickly.

Don’t get me wrong, LLMs are a huge advancement and unbelievably awesome for what they are. I think that they are one of the most important AI breakthroughs in the past five to ten years. But the AI hype train is misusing them, not understanding their capabilities and limitations, and casting their own wishes and desires onto a pile of linear algebra. Too often a tool (which is one of many) is being conflated with the one and only solution–a silver bullet–and it’s not.

This leads to my biggest fear for the AI field of Computer Science: reality won’t live up to the hype. When this inevitably happens, companies, CEOs, and normal people will sour on the entire field (which is already happening to some extent among workers). Even good uses of LLMs and other AI/ML use cases will be stopped and real academic research drying up.

this “cameras for everything!” idiocy.

That’s why I’m so impressed with how well it’s actually working. When they get off that really weird self-imposed restriction, it could be an interesting technology.

Not great performance at all.

That’s better than I was expecting to be perfectly honest.

I’m pretty impressed with the technology, but clearly it’s not ready for field use.

It’s also not all-or-none. Someone who otherwise is really interested in learning the material may just skate through using AI in a class that is uninteresting to them but required. Or someone might have life come up with a particularly strict instructor who doesn’t accept late work, and using AI is just a means to not fall behind.

The ones who are running everything through an LLM are stupid and ultimately shooting themselves in the foot. The others may just be taking a shortcut through some busy work or ensuring a life event doesn’t tank their grade.

I see both points. You’re totally right that for a company, it’s just the result that matters. However, to Bradley’s, since he’s specifically talking about art direction, the journey is important in so much as getting a passable result. I’ve only dabbled with 2D and 3D art, but converting to 3D requires an understanding of the geometries of things and how they look from different angles. Some things look cool from one angle and really bad from another. Doing the real work allows you to figure that out and abandon a design before too much work is put in or modify it so it works better.

When it comes to software, though, I’m kinda on the fence. I like to use AI for small bits of code and knocking out boilerplate so that I can focus on making the “real” part of the code good. I hope the real, creative, and hard parts of a project aren’t being LLM’d away, but I wouldn’t be surprised if that’s a mandate from some MBA.

Ah, gotcha. I didn’t go too deep into the code, just did a cursory look. I think it’s still an interesting concept.

I don’t know why this is getting downvoted. It seems like an interesting concept for certain use cases, and it looks like it’s just a tiny team.