For those who use Blender for work, how far are models like Astra away from using Blender to create something good enough at a junior, senior level?
Claude Opus is very good at creating geometry nodes for specific applications. You have to know what to ask for. It's not a magic genie.
For example, I asked it to make a node setup to create the geometry for a table. It did a mediocre job of it. Then I told it to do research on the nomenclature for tables and create a parametric control interface to generate tables with the UI using the table nomenclature. It did a very good job of that. It set it up so that I could put the units in using Imperial or metric. I also asked it to come up with reasonable default values for the parameters and it did. With just a little tweaking I have a very useful table generator for greyboxing scenes.
I then did a similar round with it for book cases and straightback chairs with useful results.
I asked it to make material nodes for wood: oak, maple, etc. And it did a pretty good job of that. Nothing amazing, but a useful place to start.
It won't be replacing people that know what they are doing, but its useful for giving that person better specifications for what you want.
I had Claude keep notes on the project and it made a skill that has been useful for later projects.
Think of it as a very bright intern that works very fast but has very little practical experience.
The problem, of course, is that if AI replaces interns, then there may not be a future generation of experts. By then, though, the AIs may be good enough to replace the experts. Next year? Next week? Who knows. If you guide it right to learn as it works, it _does_ get better over time.
I have no idea what to expect or what the actual limits of this tech is. I've see it make really hilarious mistakes, then turn around and explain the math its using so well that I can ask for what I want "properly" and it's surprised that that was what I really wanted.
It is like dealing with a very well read alien.
If the AIs don't end up murdering us all in our sleep it could be an amazing world we are entering.
Lately, I've been pondering this question in general and I'm worried the answer might be "Never" for some kinds of work - at least if we don't change how LLMs are currently developed and improved. At a high-level, the problem is they don't 'learn on the job' like a human and writing their memory & learning in .md files works for some things but fails for others.
While the 'rising tide' of baseline LLM knowledge continues to grow, they are still FTIs (Factory Trained Intelligences), meaning they mostly learn from data which can be assembled/tagged or synthesized in mass bulk - plus specific abilities in certain domains which the factory has prioritized for more costly special training. For some broad categories of important work, I suspect there's a significant unfilled gap in our LLM instances being able to effectively crystallize knowledge acquired on-task. Without that, context files will remain helpful crutches not true substitutes.
Sci-fi stuff aside, it's not just a matter of the AI's telling the supporting tech what to do. We are already at the point where we can't predict what the AI will do to the supporting tech. Blender, for example, is mostly traditional computer science with some physics and art direction thrown in. It's damn hard to use.
This is not a criticism, that's just where we are in developing a UI for the math and physics and art direction. Just using the AI to make for me UI changes, convenience features, and special utilities has drastically changed not only the way I work, but the kind of projects I'll undertake. I think nothing of having Claude whip up a special physics based particle system or compositing stack these days. I could have done that in the past, but it would have been so much work for a one time job I wouldn't consider it. I'd just brute force a solution with manual keyframes and a lot of trial and error.
And while I can program and solve (simple) physics problems, its not my day job. My day job is telling stories and explaining things with animated narratives.
Earlier today I was asked to help with some animations explaining how musical scales worked and to come up with ways to visualize them. Using Claude I've already got Blender doing a credible job at creating musical notation and linking it up to a (sort of) physically accurate sound wave simulation/visualization. Not done yet, but it's looking promising. This is within a few tens of minutes of an instructional designer asking me what might be possible for the topic. And I already have a sound synthesizer written in javascript (by Claude) that I used for a project a while back. Hooking them together is just a json file or two away...
Even if the Super Intelligence never materializes, just using current LLMs as an amplifier for existing technology is going to change things into something unrecognizable. And something that is very dangerous in the hands of human bad actors.
I didn't discuss how I think the tech will change. I pointed out a current gap in crystalizing knowledge acquisition gained on-task because I feel that gap is still under-appreciated. There's a lot of very interesting work trying to identify how to cost effectively narrow that gap (Dynamic Modular Adapters, Background Distillation, Test-Time Training, etc).
They all can be effective to varying degrees in different ways but they also all have their own gaps and trade-offs. As of now, there's no clear path to per-instance knowledge crystalization that's both broadly generalizable and scales cost-effectively. Will both be solved eventually? I think almost certainly. Will both be solved in the next 2-5 years? Maybe... but it smells like the kind of fundamental problem that's 'wicked' hard, so I wouldn't be surprised if the LLM you and I can afford to daily drive in 2 - 5 years is far more capable and 'smart' but isn't that much better at automatic crystalized learning from on-task knowledge acquisition than the best LLMs are today.
To be clear, LLMs are fabulously useful in myriad ways and I think they'll continue getting much more useful in many more ways. That's so much a given, it makes identifying which remaining gaps are so fundamental there's a good chance they won't be solved in 2-5 years especially interesting.
>Lately, I've been pondering this question in general and I'm worried the answer might be "Never" for some kinds of work
Why are we worried that AIs might not be able to replace people? That seems like quite the dystopian statement.
While I agree that increasing LLM use will, unfortunately, cause disruption and reallocation in job roles, this has always happened whenever technology enables automation. It predates computers going back hundreds of years. A friend of mine has a doctorate in 'History of Technology' and wrote his thesis on the massive employment and societal disruption steam power and railroads caused in the 1800s. The disruption inevitably consolidates roles in some areas while creating new jobs elsewhere. While the directly attributable new jobs are sometimes fewer in number than those lost, they usually have meaningfully higher average compensation.
Although there are a few exceptions, so far the vast majority of these disruptions have, on decadal timescales, resulted in significant economic growth, expanded labor productivity and higher standards of living overall. This has held since Gutenberg's movable type displaced manual scribes, steam shovels displaced dangerous mining jobs and Hollerith's punch card mechanical tabulators replaced huge rooms full of human calculators engaged in mind-numbing manual calculation. Since I generally like humans, I'm glad when fewer of them are forced to feed their families at the cost of black lung in a coal mine, crippling arthritis at 50 or the soul-crushing tedium of paper-pushing. This is part of gradually transitioning to a post-scarcity world where fewer but higher-paying jobs, which are far more productive and engaging, "pay for" more leisure, education, child-rearing, early retirement and unleashing more creative joy by artists, musicians, poets and dreamers. We can't get to 'post-scarcity' by somehow keeping everything the same and one day at 9am flipping a switch. Big changes that matter, whether for better or worse, have always been disruptive.
Individually, disruption is still disruptive and uncomfortable. I get it. No one likes change and uncertainty being forced on them. For hundreds of years each of these transitions has been especially hard on those who are end-of-career or otherwise unwilling to re-skill, learn and transition to new work. And it might be tougher for the current end-of-career generation (including myself) because we're the last generation who will ever have entered the workforce with the expectation we could work for >40 years and retire without ever re-skilling.
While you might think that LLMs are so damn BIG that "this time it's different", there's ample evidence that every generation before us thought that too and I've personally seen different disruptions happen several times in my four decade career, and heard worried claims in the '80s and '90s of "...but it was never like THIS before." It's true the rate of large technological disruptions has been steadily increasing for at least the last 200 years and their size is bigger in absolute terms but not bigger relative to the size of the overall economy, which has grown orders of magnitude. That's why it feels different and bigger to every generation, but it's still the same directional trend you read about in textbooks, except it's now happening to you and all around you. Schumpeter called it "Creative Destruction" for good reason. These are the labor pains and contractions that have always birthed the future.
I truly feel for those personally experiencing uncertainty, discomfort or displacement and being forced into figuring out an uncertain future. I don't know of a way to stop change, and even if there were, there's no point in the past I'd pick as being better than today. And I think our children will probably feel the same and not want us to stop progress today, even if we could. Renaissance Faires are lovely fantasies reimagining a past in which most humans lived short, brutal lives. And it's worth pointing out that even the possibility of NOT being under relatively constant threat of uncertainty, discomfort, or displacement has only ever existed at large scale in all of human history for the last hundred years. Before that, well... the old adage about wishing on someone "May you live in interesting times" was literally considered a curse. Now, and forever after, everyone alive and all their descendants will be both privileged (and doomed) to enjoy (and endure) interesting times. While there will inevitably be digressions in certain regions and in certain decades, on average and over the long run, I believe the outlook for more humans, in more places, to live more of their lives in happiness is better than it's ever been.
Later I gave it 3d rendered images (just png's!) of interior design of specific rooms. It created all the furniture in Blender with 95% accuracy (the 5% were small geometry quirks and shortcuts the model took, but nothing that can't be fixed manually).
Though with some graphic design tools you have to proxy it with AppleScript because there is no other automatable interface with the design tool.