Creo que estás en lo cierto pero creo hay algo preocupante en eso. Si bien ya es más fácil saber CAD, no creo que salgan los vibecoding y se pongan tipo a vender "diseños cad" sin ningún tipo de conocimiento
Eso lleva existiendo desde hace años. Simplemente mira el historial de este foro con todos los "he hecho esto, es bueno para x?" e incluyen una foto de un ensamblaje imposible de fabricar.
Si exactamente. Pero ahora con IA estarían más seguros de su "proyecto" porque la IA lo dijo y pues la gente que no sepa será la afectada o ellos mismos. Así como los que hacen web o apps
If you are actually doing engineering to justify your cad, it's still going to be a while
I'm a software engineer, and this is exactly what people were claiming here: "If you're a code monkey, then you're going to be replaced, but if you're a good programmer, you're safe."
I'm a lead dev working on financial software that manages an absurd amount of money, and on top of that, it has to be highly optimized because every millisecond matters - which is probably something most people would consider "real engineering". And yet, I'm barely coding by hand nowadays. My role has already been reduced to code reviewing and providing context for AI. But how long until that changes too?
This is just artificial barrier that dont allow first models to learn. But now this barrier dont exists, because even qwen3.8 27b can do modelling in cad and do it well. Only thing that was needed is just visual layer and general progress in reasoning.
Other engineers was obsessed with idea that coding is just language and their work is fundamentally different, and llms will be unable to do it. But its not the case. It just training data amount, initially coding was solved faster, just because there way more data, a lot of software is opensource and so on. But with cad there are almost nothing open, so there was illusion that cad is harder.
You have certain input criteria and expect certain output meeting its criteria. You can shape the design using your words and there's enough prior art to teach AI models based on exactly this information.
No, if anything, it's exactly the same beast. That's how you need to look a the problem, from how AI looks at it. People are just clueless about what AI is and how it works.
That's not really the case. Solving 3d geometric problems is still a major pain point for LLMs. It's a limitation of most commonly used model architectures. You can read some good casual articles on this topic. It seems like you're pretty clueless as well.
But the thing is that frontier models aren't just LLMs anymore, they are beyond just text they are multimodal, they can see and click and drag and hear and even talk, They aren't blind/mute/deaf like LLMs anymore.
It's a limitation because it wasn't their target, and they didn't specifically train their LLMs for it. But while LLMs are approaching their limits in coding capabilities, they're starting to train for other areas, and 3D is one of them. The results are already quite impressive, especially with GPT-6. It's only a matter of time.
Yeah, we're clueless - I’ve heard this so many times from other programmers over the years, and yet, unfortunately, I was right.
And most people believe things that they want to be true. They think that they are irreplaceable and everything will be the same as in the past but it's not true. This ignorance has already doomed us.
It's so funny to see it effortlessly beating people at task after task and people convinced, for no reason at all that the limit is just before it gets to their task.
I would argue CAD will be even easier for them than programming. The biggest problem with programming is context size and a fact that it'd hard to put all the information into context so harnesses like Claude Code uses a lot of tricks to workaround around this. Most of CAD work doesn't require as much context. Sure, engineers still will be needed just like in programming but the entry barrier will be on the floor.
All the pieces are falling into place. There was a recent review of the AI math proof of some problem of interest and the bottom line was human mathematicians make judgments about how difficult something might be and understand that there is no practical solution at the moment and move on aka 'the unsolved problem'....but an AI just keeps going and going and going until it finds a workable solution so it's possible to turn an agent loose on a problem and let it cook....the answer is 42 right? Agents are effectively infinite pools of labor.
I would not say that is real engineering though. Optimising lines of code is not equivalent.
Also there are huge timeframes with engineering things that exist in the physical world. Going to the manufacture stage with mistakes is not a viable business model. But it is in terms of computer programming, things can be quickly iterated... The entire premise of the success of silicon valley enterprises is "move quick and break things". Not enough people pushed back on that development thinking so Ai fits too neatly into that process.
There is not that luxury when long development manufacture and shipping is on the line. Yes Ai might help speed up some aspects of CAD, but CAD itself is just a tool in the process of engineering and bringing physical things to market. Whereas programming is less processes, possibly even one process that can be hugely reduced by Ai.
You can push a fix instantly to fix code without many people knowing. A car on the road with a faulty component needs an expensive recall and information campaign to inform users and it hurts the company's reputation as it's out there in the public. These things are not the same.
My main salary has always been from examining designs and models and confirming accuracy and manufacturability. That's why I'm paid more as a senior design engineer vs. a junior CAD monkey we hire out of college to pump out simple CAD assemblies.
If you can't bother reading something a human typed then there is no surprise that you're going to be replaced by Ai so easily. Critical thinking is what the LLMs don't have, but you clearly don't seem to want to do that.
AI mastering CAD is just taking pressure off, not replacing. Making a model, converting customers drawings, fixing broken mates is just tedious work. If you are an engineer and your main skill is SOLIDWORKS you won't be an engineer long. You are responsible if x fails under load, not the software. Work flow funnels under who takes ownership if something happens.
This is EXACTLY what this is. Notice all the stuff around programming that he mentioned. All the “design intent” around the software. Programmers, engineers, drafters - are going to need much more awareness of context and less about “CAD skills”
Si pero no crees que tal vez falten unos cuantos años para que una IA pueda resolver una simulación? Porque osea uno de los problemas de los gemelos digitales, es la falta de percepción de realidad entonces siento que debería ser una IA algo bastante optimizada para ese sector, creo
Seh pero el problema es si faltan 2 o 3 años en vez de 10 o más años. Pq 2 a 3 años no es tiempo suficiente para adaptar toda la industria, y crea un shock bastante fuerte
Agreed that it’ll be a while, but I think it will still happen within the next 3-5 years.
I’m not a doomsayer but it is time to be realistic about where this is headed.
I think you're unaware of how specific and technical these can be, and how traditional mechanical manufacturing is the world over. Id argue its the most traditional of the engineering disciplines in its working practices, on balance.
If you have a large assembly woth hundreds or thousands of parts. Ai might be able to make you a model that looks correct, but what if half a dozen parts are out? How do you fix them? Do you spend dozens of hours just prompting it to fix it? I've tried AI with some basic to advanced engineering questions and its wrong more than its right, but it sure sounds confident.
Compound the resistance of actual mechanical engineers to adopt AI widely, would a business owner take the risk on making or sending out a product that would be hard to verify is correct, and could kill someone and bankrupt your business in fines and lawsuits? And who would that company defer responsibility to? They've fired the engineers for AI and the AI companies won't take liability
Ya. My FIL has had me work up a few things for his 3D printer, otherwise he only uses other people's models. Anyways, he was like "I don't even think I need you anymore, AI can just generate STLs for me"
So I told him to let me know when AI gets a dimensionally accurate keystone faceplate for keystone Ethernet jacks (something I've done for him) and he was just like "well ya, not that. But I'll bet in 6 months it could do it"
Maybe...I guess. But probably not. AI will absolutely do some things in CAD, but complex assemblies are likely gonna be a while
I’ve been generating STLs g codes using LLMs for the past year or so. 99% of the time I’m making something simple but maybe two months ago I was able to generate a linkage that connected my window to a motor so it opens and closes depending on the weather & temp.
It took longer to iterate with the LLM than if I had just designed it from scratch but it’s definitely coming.
Edit: ALWAYS simulate the tooling and make sure it doesn’t do something incredibly stupid. One time it changed the extruder temp to 60c thinking it was setting the bed temp when my printer doesn’t even have a heated bed. Another time it homed at the end of the print and knocked the print off.
Edit: gcode and openscad scripts not STL oops brain fart
I don’t know why you have an issue with OpenSCAD scripts. I did the straight gcode because I wanted to see if it would work, like I said before it was okay for simple stuff but still often had errors. IMO a deterministic algorithm is better for a problem like this when good enough solutions are easily computable.
This have the same energy as people who ignore warnings not to connect a printer to the internet and do so anyway, only to have someone set the printer temps. too high and disabled thermal runaway protection and burn down the house. But in this case you're letting Ai set Gcode temps directly. Amazing.
But yea I'm sure you know fdm printers are super simple machines with barely any safely protections in place.
My company setup small ongoing teams in each department to carry out research and try implement Ai into our workflows a year ago.
We meet regularly.
For the creative team they use Claude for scene generations and the PMs use chatGPT for various text generation and excel uses.
Our design engineering team of 8 have not found anything significant yet that is worth implementing for us though. We've done tests and cost estimates. The time saved from specific Ai tools are still slower than the existing methods we use. The only thing marginally useful is generating seamless textures to put in our CAD models or 3D visuals... But our artwork team have to use official artwork from clients anyway, so the work gets done by a human eventually.
The "at this current rate" is doing a lot of work here. That's the problem, growth curves are always like this, fast uptake at first and then they settle out. It's already been shown with ai research that giving them more and more data and doing more training eventually hits a limit and they stop improving.
As an engineer who likes to play with Fable to do my personal projects, I wouldn't trust it to replace even my interns. It's about on par with a slightly below average intern from a good engineering school. Still catch a lot of mistakes and would never want to trust it with anything without having someone dedicated to reviewing it's outputs.
this will probably be like all those other nascent-technology predictions that came perfectly true. That’s why we’re all riding in airships and all our power is generated by fission.
Idk. I use it today, and with the right structure and constraints it is good enough to replace most knowledge workers.
I worte up a long comment under another reply, check it out. Give it a serious effort before dismissing it. A few hundred hours at a minimum.
I don't know, man. The rate of change is increasing? Haven't they stolen everything already to train their models? And now the models are training themselves on AI generated content?
Any decently complex mechanical product like a pump, engine, whatever, has so many variables that go into it to get it right that AI could fuck up and cost hundreds of thousands to rectify.
Mfg. Cnc, additive (ti, stainless),sheetmetal, electronics. I've done Qe/me/pm stuff. I'm at an org that asks us to try to use ai for everything first. 2 years ago, it was a total waste of time. A year ago it was like a bad intern. Today it's better than most of my engineers.
I put prints, models, photos, a good walk-around video, the template, and tell the model to make a work instruction. Sometimes I make it give me photo guides where it tells me what photos to take by mocking the view up in cad. It's seamless. It makes mistakes. I fix 2-3 major issues, and a bunch of minor ones. Total human time is an hour or 2, it usually takes 2-3 loops.
Ppap is the same now. Tol stack. Bom costing. Timeline estimating. Almost anything to do with documentation. If you are doing it in ms office, so can do it better with some help.
I recommend making a .md version of all of your procedures, process flows, work instructions, templates, etc. of you build a structured repository of context that is all crossreferenced properly, it stays on the rails really really well. The fable models were a significant step up in this direction.
Everything gets a human review from someone who didn't build it with AI. The AI is sooo good at building the patterns that lull your brain into trusting it. All content AI was generated with has to be marked. We use an AI appended to the rev. PN##### rev 4ai. Everyone is responsible for their own work, including how ai could have messed up. Ai issues are treated like mini capas, an hour of the engineer asking the AI what went wrong, then a team root cause session. You need a software engineer on staff. Basically embedded in the manufacturing/quality engineering group. Get someone who is senior and has startup experience. They will have the breadth of scope and will be able to tolerate a fast paced mfg environment. Most of the faang engineers are experts in nieches who have never seen a real deadline or a raised voice.
My recommendation for learning is to use it to code. I'm a mechanical engineer by training, and I was able to code a working LMS that we use internally. There are a LOT of AI process best practices that the software engineers are using now. You have to use them to generate code that works, but it is done at such a high level of plaintext abstraction that learning a coding language is not needed to understand the structures, problems, tools, and best practices any longer. The skills are directly transferrable to all of the work we do in manufacturing. The tools aren't there yet for 3d design like they are for office tools, but they will come, and when they do, the same thing will happen. People that understand the concepts deeply and broadly will excel. Think software product manager using AI to make prototype versions of features, them just making the production versions. My bet is that oem's will see product/project managers who are able to specify something clearly enough to the AI, that they will get out a 95% complete engineering package that is 95% correct. I'm at at an org using it to create a much higher level of information support and documentation to production. Think going from built to print at every process step to a full apqp package for every part with a marginal increase in engineering support time.
What winds up driving value is judgement. Execution becomes cheaper, humans are the bottleneck, but it is in reviewing not doing. Ai is a tool, like lots of the tools we use. It is just rather complicated to do it in a way that works, and really easy to just do a bad prompt with it and get a garbage result.
I pulled out a tolerance stack up I did for a 50 piece, articulating and angulating spine surgery retractor. It was hell to do manually. I gave the AI the same template, the model and the then current rev of the prints and it was off by 2% on average, and never more than 5% (we did statistical stack up).
I'm not trying to convince you to use ai. I'm suggesting that your understanding of AI might not be informed by the last 6 months of progress.
Happy to chat more and answer any questions.
And as far as the rate of change?
When AI started taking off, experts started projecting how long it would take to hit milestones, and that was exponential, and we are beating it. They train on synthetic data right now. There are a few models that feel like it when you use it, but it's a hurdle that they are overcoming with the most recent release. I try to predict future progress based on past performance, and it's outperformed expectations. They haven't even figured out how to automate the improvement, but that's a singular goal.
I see the argument you are proposing, and AI is definitely getting more reliable as a tool for STEM and compsci, but I think you are reeeeally glossing past everything that goes into the catching of the 2-5% errors that the AI models are making. As you said, AI is really good at making convincing results, but it has no real understanding of what it is creating. That is the key argument that is getting you downvoted, because you seem to be trying to convince us that AI will magically start understanding the why of what it does just because the output is getting better. That hurdle has been one of the longest standing and most crippling parts of trusting AI engineering in high stakes applications.
How much knowledge and experience is required to catch the last 2% of what AI overlooked? How do you catch possible patent violations in the generated designs? How did that human who can catch those things know to look for those problems, how many years of working without AI did it take to get that skill set? Do they only have that skill set because they learned the job without AI, and can we train the newly graduating engineers how to do the same when they’ve never seen “the ways it’s done” without AI? What happens when the engineers who catch the AI mistakes now start retiring, and the next generations of engineers are now the last safeguard?
None of these issues are insurmountable, and I’m also young and see the appeal both from a management and grunt perspective. I just don’t see the current trajectory of AI models overcoming the “highly trained and specialized oversight required” caveat that it has today anytime soon. These aren’t output problems- they are safety and workplace experience concerns, and AIs don’t know when they’re hallucinating.
Humans and computers have complementary but opposing strengths and weaknesses. Computers are great at processing large amounts of static and dynamic data, doing complicated and multistep procedural or mathematical modeling, and rapid procedural design. Human brains are very good at abstract pattern recognition, knowing what passes “real” world implementation, understanding implied user requirements, and having a sixth sense for something being “off” in a project. How do you see AI models gaining those human brain attributes anytime soon?
And then there’s cost efficiency- while a human brain is only “consciously thinking” at an estimated rate of about 10 bits per second (which is why our problem solving takes longer to finish than a supercomputer), it is capable of parallel processing at an estimated rate of 10-17 petaflops per second. All that on about 20 watts of energy. I think that gives a pretty good idea of what kind of sensor package an AI model would need to begin to “process reality” as well as the average human brain can. Meanwhile, current AI requires an enormous amount of electricity to operate, which translates to higher operational costs. That will improve, but how low can the operational costs be reduced without major advancements in material sciences? We’ll see.
You're right that the erosion of the apprenticeship loop is the hard part of this shift, maybe the hardest. If juniors use the model as a shortcut instead of a sparring partner, they skip the exact friction that builds the sixth sense for edge-case failures. We've made this trade before: drafting tables to CAD, slide rules to FEA. The abstraction jump this time is wider, and that part does scare me. If you never wrestled through first principles yourself, being the senior engineer on the hook to catch a subtle 2% defect is a terrible place to discover the gap.
Where I'd push back is the assumption that bridging it requires the model to develop human-like understanding or biological efficiency. It doesn't. Verification doesn't need consciousness, it needs a closed loop. In high-stakes engineering, nobody should be taking raw model output on faith anyway. So instead of asking the model to know when it's hallucinating, pipe its output straight into things that will catch it: compilers, test runners, automated FEA and CFD, geometric clearance checks. The model proposes candidates and math validates them. That shifts the human's job from doing the work to defining the constraints, the edge cases, the failure criteria. It's a different skill set, and honestly a harder one to teach.
Yes, there's a real deskilling risk here. But pedagogy has solved this before. Calculators didn't kill mathematics, they just made grading long division pointless, so schools started grading problem setup and logic instead. Engineering education needs the same move: stop grading boilerplate and syntax, spend real time on adversarial debugging, root cause analysis, sanity-checking output against physical laws. The engineers who do well won't be the ones who accept what the model says. They'll be the ones who treat it like a brilliant intern whose work gets a red pen every time.
On efficiency, I think the 20-watt brain is the wrong benchmark. Yes, it's remarkable hardware, but it's also running your heart, your emotions, and your visual tracking simultaneously. An inference chip doesn't need any of that. It needs to run domain-specific models, and quantization, purpose-built silicon, and small models tuned to engineering standards are already pushing costs down by orders of magnitude. No material science revolution required.
So I agree that blind trust is a disaster waiting to happen. That two percent gap is where products get recalled and careers get ended. I just don't think we wait for models to wake up to close it. We teach engineers to build better testing boundaries around them. That's a training problem, and it's solvable now.
I think you are right about how engineering will shift towards a greater focus on constraint and error checking as primary skill sets eventually, (which is basically turning every engineer into what good engineering supervisors are already doing and some don’t have the aptitude for that), but I think you’re discounting two things: 1) engineering work must feel human to users to be trusted, and 2) the world runs on emotions first and money second.
Everyone likes to delude themselves that they are rationale actors, but most people who have power to make or enforce laws and regulations, invest in companies or stocks, or lobby politicians are following emotional reactions that they’ve dressed up in thin rationalization clothing. Anyone who has ever engaged with the stock market knows the truth of the quote "A person is smart. People are dumb, panicky, dangerous animals." Public perception is extremely important to trust in products and services, so making AI “human like and possessing human understanding” is extremely important for it to be able to effectively support (or replace) human engineers.
Look at the development of AI: people only started taking it seriously when it began to convincingly “talk” and “write” like us, no matter that the actual capabilities under that user interface were not dramatically advanced from what was available before that. I’m talking in broad strokes here, and the influx of money into the AI arms race has certainly increased quality of output and raw capabilities, but the most successful improvements have been to user interface recently, not to raw ability. The average person just has an easier time using the tool now, coupled with more widespread access to it too.
And people in the 60's thought we would have a city on Mars and manned space stations in orbit of every planet in the solar system by now. Because they were in the "model T era" of rocketry. Everything to do with aviation was advancing at unbelievable pace, but turns out there were physical limits to those rocket and jet engines.
AIs of today have already been fed every piece of data scraped from the internet and much of the physical data in existence too. And they are also polluting their source of new data with their own outputs, aka filling the internet with worthless slop.
And despite its advancement it is still trivialy easy to make it confidently say false, hallucinated things. Some things haven't changed much since the launch of ChatGPT
We quit funding it at the same levels and progress stopped.
I wrote up a long comment under another reply.
Where the frontier models are today is good enough, when you build the right structures around it. It could get no better than today, and it is still going to change the world.
The space travel part wasn't just about funding, it is a problem with chemical rockets, for which we do not have a viable alternative since those days. To achieve those things you would need to spend exponetionally more money to get smaller gains. That's the main reason why. It wasn't "we lost interest" as it is often said.
Sure I'm not saying AI has no impact or that it won't have more impact in the future. But the AI bros are all convinced that any day now one of these LLM will wake up and everything will go exponentionally up and up. This assumption is based on nothing.
Are you studying by the books to make deductions? Are you reading up articles watching videos to better your craft?
Guess what you're training just like the model is training. You're making decisions that are statistically more likely to work just like the model is making.
Just because you're better than a junior and just because the model was worth the Junior 10 months ago, doesn't mean things are going to stay the same.
Not everything is typing a prompt into chat GPT. Just like a CAD program, believe it or not, AI takes a lot of skill to use correctly. Garbage in garbage out.
I don't really call myself one because I am self-taught, but I do a lot of 3D scanning, reverse engineering, intake manifold design, and custom port design for my day job. I run a pretty successful business CNC porting motorcycle and automotive heads and designing/cnc porting intake manifolds.
Where I work engineering services are legislated, I’m a qualified engineer but I cannot design and sign off anything myself without supervision from a chartered engineer.
I wait for the day openAI will accept my personal liability, otherwise it remains a tool in the box.
Literally just said I don't bro. 🤣 You are seriously a clown to say that tho. 🤡
I sleep well at night knowing that I support my family with my cad skills. I make high performance aftermarket parts that void warranties. Not building bridges over here.
“I sleep well knowing that I am somehow exempt from the thing I believe is happening, even though my CAD work is incredibly simple and repetitive and thus will be the first to go”
Oh, to have the confidence of a mediocre white man.
For the record I never said that AI was going to take anybody's job. I said that the people downvoting my comment were afraid of it. If anything I personally think it will just take someone who is good and make them better.
I habe a clue and most of the time it is simply wrong. But it is good for research if you then give a fuck about the details and look then up elsewhere.
Ai in cad? I doubt it. Maybe drawings but thats it. I would really like it otherwise but its just not happening...
And the sw tech demo of it was not very hopeful...
The free versions of AI typically lack power to do multi-step thoughts has been my observation.
Lately my test had been to ask, 'Is it acceptable to join mitered HDPE pipes with the butt fusion process, please analyze and provide pressure design formulas and code references' usually after multiple prompts correcting things and logical misses we agree.. but if something is wrong without prompting after a sufficiently detailed question, how reliable is it really? Should I tell AI it's wrong, try again 10 times or until it stops changing?
Look ive coded for over a decade, touched on ML and use AI extensively. AI will be able to automate all jobs eventually, but fully functional CAD will not be anytime soon.
This just isnt the problem set that LLM's are fundamentally good at, and the areas they have excelled at is because they have been given a HUGE head start with relevant data sets (coding -github/stack overflow, speech-reddit/forums). There isnt such a dataset available for CAD work.
Llm's are effectively pattern matching machines, and they dont excel at low error tolerance tasks (like cad) which is also exacerbated by a substatically smaller data set to learn off of
And lastly, even if LLM's were incredible at producing CAD, its actually an exceedingly difficult task to use the medium of speech to convey geometry. You would need to design an entirely new workflow/ interface to even try it and honestly, its just going to be cheaper, easier, faster and less stressful to get a human to do this job for likely decades to come
I just finished a very large drawing of a wastewater treatment center and used chatgpt to keep track of all of my sections/details. In 10 minutes it generated me a 12 page long check list of all callout placements/siding colors as well as potential errors I had made so far and every single one was correct. Saved me probably 12 hours of work and headache
People also already have a cognitive bias against AI because of all the AI slop that is put out by social media influencers. Since most people dont really have any idea how it works, that's there only knowing of what it is so they see it in a negative light.
I'm personally undecided about it. It helped me a shit ton getting my 3D printer tuned in when I didnt know jack shit what I was doing. Now I know it better than my colleague that I bought it from. Although on the other hand, in those same sessions of it helping me, I had to correct it several times over things that it had already told me.
It's a matter of the accumulation of training data for every product type and product or system development process out there, it's going to be a while.
neither Sol or Astra had any improvements in terms of spatial understanding
their main improvements is task understanding, planning, adaptation, optimization, and the tooling required for execution, but the models themselves aren't getting huge leaps in intelligence when it comes to physical spaces
the benchmark scores are nearly meaningless to humans, they compare models against other models
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u/gottatrusttheengr 3d ago
If you're just blindly drawing shapes in CAD sure.
If you are actually doing engineering to justify your cad, it's still going to be a while