Somehow this article explains perfectly, visually, how AI generated code differs from human generated code as well.

You see the exact same patterns. AI uses more code to accomplish the same thing, less efficiently.

I'm not even an AI hater. It's just a fact.

The human then has to go through and cleanup that code if you want to deliver a high-quality product.

Similarly, you can slap that AI generated 3D model right into your game engine, with its terrible topology and have it perform "ok". As you add more of these terrible models, you end up with crap performance but who cares, you delivered the game on-time right? A human can then go and slave away fixing the terrible topology and textures and take longer than they would have if the object had been modeled correctly to begin with.

The comparison of edge-loops to "high quality code" is also one that I mentally draw. High quality code can be a joy to extend and build upon.

Low quality code is like the dense mesh pictured. You have a million cross interactions and side-effects. Half the time it's easier to gut the whole thing and build a better system.

Again, I use AI models daily but AI for tools is different from AI for large products. The large products will demand the bulk of your time constantly refactoring and cleaning the code (with AI as well) -- such that you lose nearly all of the perceived speed enhancements.

That is, if you care about a high quality codebase and product...

This reminds me of the latest Ken Stanley paper on representations.

“Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis“

https://arxiv.org/abs/2505.11581

Some of the defects are attributable to the critical:

> AI models generate meshes using "isosurface extraction" or similar volume-to-mesh techniques

This creates the "lumpiness", the inability to capture sharp or flat features, and the over-refinement. Noisy surface is also harder to clean up. How do you define what's a feature and what's noise when there's no ground truth beyond the mesh itself?

Implicit surface methods are expensive (versus if-everything-goes-right of the parametric alternative), but they have the advantage of being robust and simple to implement with much fewer moving parts. So it's a pragmatic choice, why not.

3D generative algorithms might become much better once they can rely on parametric surfaces. Then you can do things like symmetry, flatness, curvature that makes sense, much more naturally. And the mesh generation on top will produce very clean meshes, if it succeeds. That is a crucial missing piece: CAD to mesh is hardly robust with human-generated CAD, so I can't imagine what it'd be with AI-generated CAD. An interesting challenge to be sure.

Trellis is like a year old and practically free. There are already better models to make comparisons to.

Because they all use latent diffusion, and many techniques use voxelized intermediate representations of 3d models, often generated from images, topology is bound to be bad.

There is a lot of ongoing research around getting better topology. I expect these critiques to still be valid as much as 2 years from now, but the economics of modeling will change drastically as the models get better

>If a client asks, "Can you make the handle slightly longer?", on the human model, I can select a loop of polygons and pull. The edit is done in 10 seconds.

>On the AI model, I cannot. There are no loops. I would have to sculpt it like clay, destroying the texture in the process. It is actually faster to rebuild the entire model from scratch than to try and fix the AI's topology.

To play devil's advocate for a second, it seems like you didn't provide a requirement to the AI on how the handle should be made, then got frustrated that the result doesn't conform to unspoken norms. If I made you this model by just starting with a sphere and sculpting it in ZBrush, you'd get frustrated by the same problem too.

On the other hand, I would expect that the AI could perform the task if you just elongated the handle in the reference image. The same procedure would probably work if the client wanted to add cat ears to the top to make a Mario Tennis clone game, while it might be a whole new commission for human modelers.

Now, would the material mapping still be poor, and would it be a questionable use of electricity? Guilty on both counts, but it's exciting to anyone who just wants to make 3D printed items or low-fidelity video games/mods.

I give it two weeks until people start running the meshes through AI:

https://taoofmac.com/space/til/2026/02/16/1334

Claude Opus was able to perfectly replicate an angular/functional part without decimating it, so I would expect the next step to be explicitly instructing AI to clean up meshes.

The tech will catch up in a year or two. Gemini 3.1 pro can now turn a basic raster logo into fairly clean SVG. Six months ago the SOTA models where no where near completing this task.

Those handcrafted "clean UVs" drive me crazy. They're a bunch of clean horizontal and vertical lines, but shapes are overlapping. It looks like what blender does automatically.

I do wonder what the outcome would've been had the 4 hours been spent in perfecting the input to the AI-generator. It's not a fair comparison if the same amount of time is not spent on both.

How good mesh can a human produce in the time that it took for the gen-AI?

Now they need to compare it with Hunyuan 3D 3.0 or other SOTA 3D generator.

Obviously it's not spewing $10,000 3D models, but results are much better than what you would get for under $500 from a human 5 years ago.

So yeah you still need human art director to make sure actual source material used for generation fits your art style, but otherwise "good enough" models are 1000 times cheaper and 10000 times faster to get.

The close but not good enough is what gives us the illusion of productivity in this tools.

That’s why you see a a lot of hype around setups and benchmarks but not a lot of well polished products.

This article make it clear for 3d modeling, but also applies for code. Human touch is necessary for a commercial product. Otherwise it’s nothing more than a prototype.

It is actually much more difficult to maintain Ai code and 3d models than to just make your own.

Either AI can oneshot without human intervention or it becomes a pain really quickly

This article is pretty disingenuous in the parts where it focuses on topology. CAD files are imported all the time into CG software with awful topology - looking very similar to that mess.

There's lots of software and tooling, automated and otherwise, to significantly improve topology. This is a very common problem in this space and not acknowledging that is silly. It's not perfect, and remodeling things is indeed a common solution - but retopo addons and software are big business because they're good enough for a whole lot of use cases.

Trellis isn't and has never been state of the art. It's not a good choice for comparison; there has been progress on a lot of these problems. There are models that can do clean topo and PBR textures, for example.

I am in agreement with many commenters here (https://news.ycombinator.com/item?id=47158240, https://news.ycombinator.com/item?id=47158573 and others) that this article is a clear illustration of failure on part of AI to capture the structure of material in a useful way. As addressed in the article, the effect is very visible in visual space, 3D modeling. I would argue it is very much present in LLM space too, just less prominent due to certain properties of the medium - text-based language. I also believe the effect is fundamental, rooted in the design of those models.

I'll leave here the note I've written down recently, while thinking about this fundamental limitation.

- The relationship between sentient/human thinking and its expression ("language") is similar to the one between abstract/"vector" image specification and its rendered form (which is necessarily pixel-based/rasterised)

- "Truly reasoning" system operates in the abstract/"vector" space, only "rendering" into "raster" space for communication purposes. Today's LLMs, by their natural design, operate entirely in the "raster" space of (linguistic) "tokens". But from outside point of view the two are indistiguishable, superficially.

- Today's LLMs is a brute force mechanism, made possible by availability of sheer computing power and ample training material.

- The whole premise of LLMs ("Large" and "Language" being load-bearing words here) is that they completely bypass the need to formalize the "vector" part, conceptualize in useful manner. I call it "raster-vector impedance".

- Even if not formalized, it can be said that internal "structures" that form within LLM somehow encode/capture ("isomorphic to" is the word I like to use) the semantics ("vector"). I believe the same can be said about "computer vision" ML systems which learn to classify images after being fed billions of them.

- However, I believe that, by nature, such internal encoding is necessarily incomplete and maybe even incorrect.

- Despite the above, LLM can still be a useful tool in many domains. I think language translation is a task that can be very successfully performed without necessarily "decoding" the emerging underlying structures. I.e. a sentence in source language can be mapped onto a region of latent space; an isomorphic region of latent space based on target language can be used to produce an output in the target language which will be representative of an equivalent meaning, from human perspective. All without explicit conceptual decoding of underlying token weight matrices. "Black-box" translation, so to speak. I am amazed (and disturbed, and horrified too!) that producing a viable code in a programming language from casual natural language prompt turned out to be a subset of general translation task, largely. Well, at least on lower levels.

- To me it is intuitive that such design (brute-force transforms of "rasterized" data instead of explicitly conceptualizing it into "vector" forms) is very limited and, essentially, a dead-end.

The article should analyze Rodin that in my opinion is probably the best one in generating 3d assets.

Don't complain about tangential annoyances, I know, I know... but how the hell am I supposed to judge the difference between the images in the post if you disabled zoom and the images are incredibly small? And when I open them in a new tab they automatically download?

On the plus side, I like the informal writing of the post. You can be serious about business and still be human

Edit: firefox reader mode works wonders on this article

The most important two words in this article are the last two: for now.

Indeed, for now generative models generate triangle soup without much thought. The same was true for 2D illustrations where generative models like Deep Dream came up with horrendous images with eyes all over, dogs with multitudes of heads and oh did I mention the eyes? That was about 10 years ago. Things changed, models improved, the eyes were tamed. Yes, people had too many or too few fingers but that also changed. From nightmare fuelling imagery with many-eyed dog heads sticking out where you don't want them to fully animated hi-res video only took a decade and things are still speeding up. The triangle soup of current 3D generative models is like the eye soup of Deep Dream, something to remember somewhat fondly which is no longer relevant now.

I've found Trellis specifically to be very "over-promise and under-deliver".

Nothing i tried with it got even close to th level of quality that they were advertising - felt like a bunch of examples were hand-picked, at best.

>Why AI 3D Generation Fails eCommerce Standards

I wish I had his confidence (in eCommerce Standards)

"The 'autopsy' of 3D slop highlights a critical failure in the current AI supply chain: The Illusion of Completeness.

We are living in an era of 'Statistical Harvest' where models prioritize a 'good enough' surface over structural integrity. In the spiritual supply chain of value, this is called Cutting Corners. A 3D model that breaks down upon closer inspection lacks what I call Internal Agency—it doesn't understand the 'Seed' of its own geometry. As we move towards an agent-centric world, we must distinguish between 'Generative Noise' and 'Authentic Creation'. True value definition requires a 'Watchman' who can see beyond the first-glance polish to the underlying breakdown of utility."

Nice copium. These things are going to get there fast. Even what has been shown can be a good start with a decimator at hand; We've seen this with photogrammetry before. Irony is not lost on the fact that text, which complains about it, went through AI itself.

Everyone needs to quit trying to one-shot, and quit assuming AI can’t do it because it can’t one-shot it.

Since the author can enumerate the problems and describe them, it’d be interesting to just use the one-shot pickleball racket model as a starting point. Generate it, look at the problems, then ask an agent to build “fixers” for each problem - small scripts (that they don’t need to build themselves!) which address each problem in turn. Then send the first pass AI output through a pipeline of fix scripts to get something far better but not quite there - and do final human tuneups on the result.