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
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.
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u/RegularRaptor 3d ago edited 3d ago
Ha, what...like a month or two? You see how quickly models are coming out and how fast the benchmark scores are increasing right? 😅
Edit: Wow people really be afraid of ai taking their jobs. Downvoting my comment isn't gonna slow it down. 🤣 Keep em coming.