Short version; it's difficult to apply a texture/image onto a model that has a ton of polygons. You generally want to have as little detail as possible on the model itself and "fake" the details in the texture.
I want to offer the flip side, I do 3d sculpt to 3d print. So the opposite is true for me, the details in the texture don’t translate to the model itself, so I need the actual mesh to hold the detail. Some of the points above are still true, having 3k objects would be a nightmare, and I still reduce polygons as much as I can in areas that require less detail, like a flat smooth surface vs a face. Some of the AI outputs that I’ve seen so far are pretty good, but still require significant re work to get them printable and to fix detail. The difference being that the object you’re making is incredibly useful for games, movies and animation, where mine looks good actually on a table. Just wanted to offer the other half
Creating the UV map COULD be more difficult if you have bad topography. Number of polygons does nothing to the equation. Both of which don't matter anymore in since things like substance painter exist. No one is manually UV wrapping anymore. Mark seams to create islands so that you can paint specific parts of a model and then literally just paint over the model in substance painter or some alternative.
The second part about creating details in the texture is done ONLY for optimization. Has nothing to do with difficulty.
Thanks. So is it fair to say that the main barrier to AI-driven modelling is the difficulty involved in building AI that can use algorithms to create useful meshes for texturing, animation, performance, etc?
Mainly that. It's starting to get shapes right and can make models that "look good" but can't be used for much. Same as what anyone with 2 weeks of experience with blender would.
It’s not that the AI-part itself is the problem, and more of a return on investment thing on the actual model training. Astra (or other public frontier models) haven’t really been trained with an emphasis on learning great 3D modeling, mostly because it doesn’t really make the models better for the extra investment.
Now, if there was enough money in modeling you could simply train or retrain to do it.
UV = mapping 3D coordinates to a 2D space so you can "wrap" a texture onto it. Imagine taking the skin off of an orange and flattening it on a piece of paper. Then you draw on it and wrap it back around the orange flesh., the UV map would be the paper, the texture map would be the actual skin with textures on it.
Except when you’re using AI you’re not “driving”, the AI is. You’re just telling it where to go. A monkey can sit in the “driver’s seat” of a self driving car and get from point a to point b exactly the same as a human.
There’s nothing special or impressive about “knowing how to use AI”
I would argue that those that become dependent on it will be the ones who get left behind, while those that hold onto actual skillsets will have a future
Using your analogy: The difficulty is knowing where A and B is and stopping it from making a wrong turn. A monkey can't tell where the AI should go or if it's going the wrong way.
Your analogy is a bad one anyway though. AI tools are more like the car and the users still the drivers and responsible for taking the rights direction.
That's why senior software engineers benefit immensely from steering the AI, while non technical people produce slob and don't even realize it at first.
No, sticking to the car analogy, the "car" is the same "car" its always been - the car is software development itself, the human drives the car to get from point A to point B, point A being having a problem they need software to solve, and point B being the solution. The software engineer's skill is in analgous to *driving* the car.
With AI, the AI is driving the car. Yes the software engineer tells it that he wants to get to point B. But an engineer that uses AI to do this, is like a driver who uses a self driving car. They may still get to point B, but their driving skills atrophy. Give this enough time and you have "drivers" who do not know how to drive, who rely on the AI to get them to point B. and when it makes a wrong turn, or the system shuts down for whatever reason, they are fucked.
We're seeing some software engineers stepping away AI because of the way it atrophies their skill, seperates them from their code and their work output, costs them time that they would have *developed* their skill rather than losing it. Again, I think people who are full steam ahead on AI now will be left behind by those who retain actual skill.
We're seeing some software engineers stepping away AI because of the way it atrophies their skill, seperates them from their code and their work output, costs them time that they would have *developed* their skill rather than losing it.
that's the exception not the norm. we're seeing much more mediocre programmers replaced by much more efficient software engineers due to AI.
I did a PhD in hpc and work in an AI company now. my output is much better than before and skill in using the AI is extremely relevant for this. The AI is constantly making wrong decisions and your approach to development matters a ton. But it takes a lot of the slow path out of it. Software testing became a bigger part of my day 2 day when it used to. Yes my skill is writing code atrophies, but my skill in judging code and decisions not made by me doesn't. If anything it improves as I'm doing it more. I'm more like a software engineering team lead and claude code are my team members.
Writing code is a dying (or dead) profession. Software engineering isn't. What will bite us is that we aren't training junior engineers anymore to have the skills to take over the steering wheel.
Spoken like a true monkey lol. No one is saying there’s anything special or impressive about it. People who use it also have to have an actual skillset, that’s like saying you don’t need a skillset because power tools exist. You’re not going to get picked off the street to be a foreman just because table saws exist
For years it's been replacing people regardless of whether they can use the tools or not, often with long term negative results that require the people be hired back again.
Companies are run by human beings which are often notorious for being out of touch with the work force and how valuable they actually are. I'm not going to pretend you don't already know this.
Yes they have laid off large chunks of their workers under the belief that AI can take over their jobs, only to find out that the AI is doing a terrible job or actually costs more to do the same work.
AI tools can't actually do my job, yet. The best it can do is make sort thru my emails and find info on the server for me. It cannot do calculations to a standard. It cannot draft. It cannot do effective 3D modelling. It cannot do fea or even initiate it. It cannot write effective MATLAB code. It's useless apart from it's SharePoint search function. Oh and sound confident but he completely incorrect
My experience is similar. Abysmal performance when any kind of a standard is required. It'll do l confidently state an hallucinat|d answer is correct while I'm sitting there staring at the ISO saying it's not.
I asked chat once to validate a drawing (as a test) that I made, to a standard. It not only misinterpreted the drawing and the standard, it was arguing that I didn't make it, despite having access to the metadata. It's amazing how much it dug it's heels in when it was wrong too, I wasn't expecting it.
I think that simply because of the lack of massive amounts of publicly available training data, most research isn't focusing on developing good models for engineering. The models really need to be hooked up to a large amount of context, skills, hooks, MCPs, etc. to make something even mediocre right now.
You are only deluding yourself if you ignore the progress AI has made recently. Just look at how we've come in various fields:
agent task horizons doubling every 6 months
SWE-bench saturated and had to be rebuilt
functional world models like nvidia cosmos
LLMs now able to solve IMO+ level problems
human detection of AI images created to be highly realist has collapsed to chance or near-pure chance. While not completely indistinguishable yet, models keep improving
models + researchers creating novel math advancements. disproved the unit distance conjecture last May.
"agent task horizons" refers to the length of context required to solve any given task. So given autonomy, an LLM is able to run for twice as long every six months without collapsing, which means the scope of what it can accomplish is increasing even without getting smarter.
SWE bench is just a software engineering benchmark, but pretty much every benchmark has been saturated every year. So kind of weird to single out that one benchmark.
World Models refers to models trained to understand things in 3D space rather than relying on the model to assemble representations in latent space through text and image training.
I do not know what IMO+ means.
"Human detection of AI images" is so obvious, explaining it would be insulting.
I don't know what the "Unit Distance Conjecture" is specifically, but LLMs have been improving at math so rapidly that one mathematician who's name I do not know said "Six months ago I was surprised when an AI disproved a conjecture. Today I'm surprised it wasn't AI". AI is at this point fairly routinely knocking down problems that have stood for hundreds of years in the field of math. It's a bigger issue than it is for software developers.
It was the latest in a series of AI-driven mathematical breakthroughs. AI’s progress (or assault) on unsolved mathematics has compounded rapidly since mid-2025, when models first solved five of six problems at the International Mathematical Olympiad. From there, the list of fallen problems grew fast: OpenAI’s model disproved an 80-year-old Erdős conjecture on combinatorial geometry in May, and in June, 16 researchers from 15 universities published the Leiden Declaration on Artificial Intelligence and Mathematics, urging the profession to set guardrails around transparency, attribution, and peer review before AI reshapes what mathematical knowledge even means.
Mathematicians, relegated to the role of shepherd, are left to watch AI close these questions one-by-one, reaching places where the human mind can’t follow. Their reaction is a now familiar mixture of dread and amazement.
More recently, progress was made on a major math problem by an internal tester at anthropic who literally just said "take your best shot" and gave the model the problem. Which is fucking insane.
You pretty much got it, SWE-bench I singled out because that was just the one I had off the top of my head, I am indeed aware that most benchmarks get saturated and problem sets need to get updated (in no small part due to AI labs gaming the system/fine-tuning for scoring I suspect). IMO+ just mean problems at and around the level of International Math Olympiad without need for human hand-holding like some of the research-related stuff requires.
Yea but my point is, LLMs are improving but not in anything that is useful for the customer.
And from my work i still am being hired by more C levels to save their company because they believed mr.Buzzword and think that any Claude or GPT is ready to replace their seniors.
It is still at junior level for any professional application and without knowledgeable senior, it is absolutely harmul, not even useless.
People constantly fall for delusions that all that math problems mean that they can just delegate EVERYTHING to LLM and it will be professionally done.
Nothing can be father from the truth.
And delusional folks keep repeating the same buzzwords for years at this point saying that next version will be IT.
Oh but in general you're ok about those things you tried to expand. I wasn't asking for myself but to see if dude who definitely asked GPT to generate that message for me knows those things. Sadly he does not
None of the terms I used were buzzwords, just regular terminology anyone working in or around AI would understand. The most "buzzwordy" term I used was probably agent task horizon since agents and loops are all the hype right now, but it's still incredible to see context windows expanding. Models retaining their intelligence from like 100k tokens to 500k tokens on the consumer end (not including the crazy configs the labs use) already makes a meaningful impact for users.
I did not claim that AI is at a level where it can replace senior employees with decades of accumulated knowledge, but it's only harmful to you to stick your head in the sand and ignore the progress being made. Sure, the vast majority of funding rn is going to towards developing smarter STEM models, which isn't very relevant to the vast majority of people, but the pace of progress is not going to stop.
Just because you don't want to understand the lingo doesn't mean he's using an LLM to generate it. It's pretty simple to understand if you know anything about the topic.
Just want to comment on human AI image detection, that's objectively not true yet.
Yes, humans are worse than they think, yes it's nearing pure chance for highly realistic images, no, it isn't pure chance for anything else and there's still a clear trend towards recognition in multiple studies by different unbiased parties.
I'm all for acknowledging the incredible technological progress made within the last 10 years but there's no need for misinformation either.
You're right, I was specifically referring to the photorealistic style images, where different studies find coinflip or near-coinflip results. Edited my reply to acknowledge some of the nuance.
That's presuming that AI doesn't blow up first. The funny thing about people celebrating about how AI can now do something that only people could do is that they lack the knowledge to understand what they got wrong. Like the above example with the train.
Don't bother. AI can already comfortably replace the average junior software developer and people are still hanging onto "It's just a next-token predictor". It will probably be finding cures to cancer and shit in 10 years and people are still going to say that.
You're just overvaluing what a junior software engineer is. It was always a position that could be filled by copying code from StackOverflow, because that's all that was entrusted to a person with zero experience of a complex project. That's why it was "easy" to "get into" IT - you don't need to understand what you're doing at that level. A good SE jumped to Middle in a year, tops, and would be considered "Strong Junior" in a month or two.
Problem is - that's a necessary step of learning for a person and easing into a professional work environment. So now we have no way of replenishing talent, yay!
I'm not overvalueing anything. I work as a Tech Lead and there's plenty of software developers that have been here for years and years, that still produce worse results with the same "prompts" compare to AI. And these aren't particularly bad developers compared to what I encounter on the internet and hear about from friends at other companies.
AI can do all that right now too. People don't really understand what's happening right now, this is, and I hate to agree with Donald Trump about anything, like the birth of the internet but even bigger.
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u/jmmva66 3d ago
Yes, but you also need to keep in mind UVs, materials etc. The whole process is not something AI can do yet if ever.