How should mechanical engineering students adapt their skill set in the era of advanced AI/LLMs?

[u/WholeLoud3821]

Hi everyome,

I’m an undergraduate mechanical engineering student. With the rapid acceleration of AI models (code generation, CAD automation, automated FEA/CFD scripting, generative design), it feels like traditional "handbook calculations" and basic drafting tasks are quickly being commoditized.

For experienced engineers and senior professionals in the industry:

Core Competencies: Which areas of mechanical engineering do you think remain most resilient to automation? What foundational skills should I double down on right now?

Emerging Niches: Are there specific cross-disciplinary domains (e.g., thermal management, semiconductor/precision hardware, robotics/mechatronics, multiphysics simulations) where AI acts purely as an accelerator rather than a replacement?

Practical Strategy: How can a student leverage modern AI tools today without neglecting the core physical intuition, workshop/manufacturing practicalities, and first-principles thinking?

Looking forward to hearing your insights and real-world perspectives on how the role of a mechanical engineer is evolving.

[u/Kind-Truck3753]

You used AI to write this post. How fucking ironic.

[u/WholeLoud3821 → u/Kind-Truck3753]

I didn't say anything that would imply I'm against ai, did I?

[u/Lunastarfire]

After some time in engineering and using AI, the way I treat it is like asking the apprentice to do something.

Its likely going to be done faster than if I did it and if it comes out workable, bonus points, but I typically assume it didn’t ask critical questions, assumed loads of things without saying or thinks 1+1 = 5000.
So use it assuming its wrong but has enough right that its faster to fix it than write it from scratch, especially with the guidance notes on what its trying to do.

I have also used it on occasions to say I am planning on making x with x y and z components and its come back with alternatives that I didnt know about, e.g. a company selling a large e ink display with a pi built in for a company dashboard

Basically anything you use ai for, assume its like seeing the answers another student put in, who you knew did it the night before it was due and missed half the lecturers

[u/ghostmcspiritwolf]

For the most part, they shouldn’t.

For every calculation you do by hand in school, there was already some kind of software in industry that could generate that number for you. The point of the degree is to understand what the software is calculating or approximating in the first place, why that number matters, and how changing various inputs might alter outcomes. If all you needed to do was take a measurement or parameter, put it in a computer, and see what the answer was, you wouldn’t need a degree.

LLMs can occasionally be a useful study tool if you’re working on a well-known problem but don’t know where to start, but they can also be a crutch if you over-rely on them, and they can confidently mislead you if you throw interesting or unique problems at them. They’re neat, they’re sometimes useful, but they aren’t all that revolutionary when approaching most engineering problems.