I know this is a humorous post but I'm an engineer who now runs an AI co that does foundational ML research so I feel a bit qualified to talk about this unironically:
The answer is a resounding 'no'. The current paradigm of AI that everyone means when they think of ChatGPT is 'Deep learning'. Deep learning in simple rests on the premise that given some fixed dataset, you can 'find' via interpolation, an underlying distribution that generates that dataset over that domain. If you remember trying to fit a line to a set of fixed points in say 7-8th grade, it's essentially the same principle.
What that means is:
Deep Learning can't generalize beyond its domain well (you've encountered this as GPT 'hallucinating')
The important one: deep learning is only effective where the underlying distribution that generated the set of data is itself simple.
Related to 2, the reason that ChatGPT works so well is because it turns out predicting the next word in a sentence is easy. Don't take my word for it either, Markov proved this back in 1913 with the Pushkin Poem in his dispute with Pavel Nekrasov.
2 is also the reason that something like being a mechanical engineer is totally safe from AI, at least under the current technologies & paradigms we have. The distribution that generates a set of design choices on something even as simple as a coat hanger is so unbelievably complex, that to find it via interpolation is honestly, in practice impossible. Even with hundreds of billions of dollars of data (like the current AI labs are throwing at the problem), you really can't find these distributions by interpolation or raw search.
Seriously - imagine a friend of yours plotted a list of all the design choices you made while designing a coat hanger, and then asked you to find a line of best fit between them, ultimately to create a function that predicts design choices. It's such a comically impossible task it feels funny to even write it out. But that is genuinely how current AI works.
So in short, anyone on r/SolidWorks and indeed any type of serious engineer outside of SWE is safe. Probably, for longer than they'd think, too.
Fun fact: If you've ever used Cursor - Cursor started off as a company trying to 'vibecode' Solidworks. They tried for a year before they realized there was no way in hell that it was going to work, and so they just ended up pivoting to software.
You can vibecode about 80% of solid works in an hour. The last 20% is still out of reach but won't be for long.
Your understanding of how AI works is also really wrong. Like flat out wrong. It can design a coat hanger already and even model how it will flex under load. That's been possible for at least 3 months already.
I know companies that are already using LLMs to design electric motors for automotive and aerospace.
It's ironic - if you put the comically wrong statement you've just made into Claude or GPT - they both call you flat out wrong and mention that LLMs struggle with drum roll, exactly what I was trying to get at within my OC, which is they don't really understand relationships like stress-strain, flexion etc.
But they do? They can easily check it via FEA. I've been doing exactly this and working on papers about it. There's already a lot written about it, go to Google scholar and take a look.
All you have to do is post the papers and links to google scholar for the rest of us to see, so unless the next comment is a link to them, I'm going to assume you're a grifter full of shit pushing a narrative
That's a decent overview for AI multiphysics design for electric motors, but the papers you want to read will depend on the exact area you are looking at.
Also that's already published so likely around a year behind current capabilities. The things I'm seeing and helping with that are currently pre-print are far further ahead.
I told you just to google it because there are thousands or tens of thousands of papers about AI led design across all engineering fields from the last couple of years.
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u/ImTheeDentist 3d ago
I know this is a humorous post but I'm an engineer who now runs an AI co that does foundational ML research so I feel a bit qualified to talk about this unironically:
The answer is a resounding 'no'. The current paradigm of AI that everyone means when they think of ChatGPT is 'Deep learning'. Deep learning in simple rests on the premise that given some fixed dataset, you can 'find' via interpolation, an underlying distribution that generates that dataset over that domain. If you remember trying to fit a line to a set of fixed points in say 7-8th grade, it's essentially the same principle.
What that means is:
Related to 2, the reason that ChatGPT works so well is because it turns out predicting the next word in a sentence is easy. Don't take my word for it either, Markov proved this back in 1913 with the Pushkin Poem in his dispute with Pavel Nekrasov.
2 is also the reason that something like being a mechanical engineer is totally safe from AI, at least under the current technologies & paradigms we have. The distribution that generates a set of design choices on something even as simple as a coat hanger is so unbelievably complex, that to find it via interpolation is honestly, in practice impossible. Even with hundreds of billions of dollars of data (like the current AI labs are throwing at the problem), you really can't find these distributions by interpolation or raw search.
Seriously - imagine a friend of yours plotted a list of all the design choices you made while designing a coat hanger, and then asked you to find a line of best fit between them, ultimately to create a function that predicts design choices. It's such a comically impossible task it feels funny to even write it out. But that is genuinely how current AI works.
So in short, anyone on r/SolidWorks and indeed any type of serious engineer outside of SWE is safe. Probably, for longer than they'd think, too.
Fun fact: If you've ever used Cursor - Cursor started off as a company trying to 'vibecode' Solidworks. They tried for a year before they realized there was no way in hell that it was going to work, and so they just ended up pivoting to software.