Should You Still Study Engineering in the Age of AI?

0:38 Hi and welcome back to the Neil Ashton podcast. Today I wanted to talk about a topic that is probably on many people's minds. This is the topic of how will AI affect my career? How much does it affect the choice that you may be making? If you're an undergraduate or postgraduate wondering how is this gonna affect my job prospects, what career should I go in? If you're in a sector that you believe is potentially gonna change because of AI, should you retrain? I believe there is tremendous positivity and promise with AI, but I am also sensitive to the fact that it can cause anxiety and uncertainty, particularly for younger people who are early on in their careers.

1:29 And I wanted to at least give my opinion and it's just an opinion and it's based on limited knowledge that I have and could be completely wrong. But I wanted to go through and also really focus probably a little bit more on people who are earlier on in their careers and the things that I think are good to focus on. The idea is really: should you still be, you know, specializing in engineering or sciences or maths in the era of AI. So, why ask this now? Well, you know, it's clear to see that AI is getting better and better and by AI in some sense, I mean, large language models. So their ability to write code and analyze data is just tremendous.

2:22 I struggle to find anybody nowadays who isn't using some sort of coding agent to develop code. More and more, I speak to friends of mine who are not at all in the technology industry. And they are, you know, really on board and love the desktop and mobile applications, you know, whether it's Gemini or ChatGPT or any others. And so it really is now, I think today in August of 2026, gone past any sense of, this is just hype. You know, I think people are seeing genuine usefulness out of AI. But if you look at those various reports from like the World Economic

3:13 Forum, I picked out some stat here, around 39% of workers' skills will change by 2030. And so the question is really how to prepare for that change. Now, when it comes to engineering, and maths and sciences, I cannot see a short or medium term, and longer term it's science fiction to think about longer term. But in the short to medium term, as in years and decades, there's only an increased need for engineers. If we look across energy, for nuclear, for wind, even still for fossil fuels, there is a huge need to build that

4:04 out. Of course, for AI, there's a massive focus on energy. There's still a lot of amazing research going on on nuclear fusion. And that sector needs people. Transport, buildings, electronics, manufacturing. There's a buzz now in the semiconductor industry. There's a huge focus in defence, whether we like it or not. And there's a shortage of engineers. Anybody who speaks to a company will know that it's difficult to hire, it's difficult to find good people. Now that may sound odd to graduates who are struggling to find a job, but there is still a need for it. And it is hard to predict what tasks will

4:54 change, but I would say routine coding, you know, simulation setup, writing documentation, that sort of thing is going to become much more of an AI-driven thing. That doesn't mean that we're hiring less developers, but it does mean that the skills that you need are changing. And in some ways I see this as a positive thing. And I think I've told this story before, but Like many other people who did a more classical engineering degree, I was not a hardcore programmer where when I did it, 20 years ago whenever it was, we didn't do as much on programming,

5:47 we didn't do as much on that, it was more about engineering. And probably 10 years ago or five years ago, I did sometimes feel that that held me back a little bit when I wanted to, you know, test an idea out, write a code, do some more sophisticated analysis. I kind of realized I couldn't do that. And I actually felt that was a limitation. And I kept saying to myself, I should really go and, you know, do some immersive course in Python or, you know, do something to really become amazing at programming as if that would help me. Or, for things that I've mentioned before like, computer-aided design (CAD), I always said,

6:35 if someone today said, Neil, design a, you know, front wing of a Formula One car, I would conceptually, well, more than conceptually, I would know what I need to do, but I just don't have the ability to do it in CATIA or NX or whatever CAD package. And so again, I would probably therefore think, well, I can't do it because I don't have those skills. So now with AI, I know that I can code pretty much anything up. Now I'm not talking about building an enterprise-quality thing and we can get to that in a minute, which still requires people with the skills of how to create a good program, how to organize, how to have a team of people working on it.

7:27 But just in terms of developing prototypes or just automating things in a typical engineering company. When I was in Formula One, I was doing a lot of the methodology. I think those teams exist basically in every company, which is, you know, you have a bunch of designers or aerodynamicists and this is across all of CAE now. But you had a core group of people who would actually decide what methods would be used. How would you script it? How would you automate it? How would you make it the most efficient? And that's what I was working on. And a lot of what we were doing was scripting and automating things. Now, I would imagine if you go into a Formula One team,

8:15 it would be totally transformative compared to when I was there in terms of there's no barrier to the sophistication because you have probably the world's best coding agent in some of these AI tools now. So why am I saying this? I'm saying that in some ways AI is a great enabler and democratizes knowledge. It is a level setter. It actually in some ways makes more people able to do the jobs that they couldn't do before. I strongly believe that. I think AI can help you; you could apply for a job and AI could help you to do that job. So in that sense, rather than it being a negative thing,

9:08 I would say I would now feel more comfortable going into a job that I wouldn't probably have applied for before because I know AI is gonna be there to help me. So that is a positive thing in my mind. Now, if you think about it, what really matters are still the fundamentals. So another example I give is if someone said to me now, go and work in a bio company looking at drug discovery. I haven't got a biology degree. I have no idea how drug discovery works. I have no chemistry background, no biology background, no medical background. For sure, I could, you know, get a really good AI model.

9:56 And I'm sure it could do many of the things that someone would ask me to do. But I would have no idea if it was right or not. I could be smart and I could maybe get a couple of different models and get the model to check the other model. But I really would be just, you know, a dumb user of it. I really wouldn't have any sense. I have no background in that. So in that way, until AI models become so unbelievably good. And that, I think in a broad sense, will still take a long time. There will always need to be the people there to orchestrate and guide and check and sort of, yeah, orchestrate is the word I like to use.

10:45 So in that sense, does the fact that AI is great at drug discovery mean that you shouldn't study biology and chemistry and go into that? No, of course you need to, you need people with that background. If we go to engineering, the reason that I find AI so useful to me is I have an engineering degree and a good knowledge of the area. So if I ask the AI to help me to do a task, I'm able to steer it to point it and tell it and validate it and check it. I'm on all those levels. That's where the fundamentals still matter. You still need to sort of understand physical principles. I'm talking about engineering now. You still need the domain knowledge.

11:30 You still need to know what codes should I be using. You still need to have a sense of economics, of team working. So in that sense, if you want to go into industry, I still think an engineering, computer science, maths or physics degree is the right choice, even though AI is great at programming and statistics and data, you still need to know it. At this point, I would say the biggest opportunities available now are for people who know AI. So if you look at the job market, the best-paying jobs today, the greatest need is for people

12:20 who can help the company to use AI in the best way. Because AI is not cheap. And so if you're gonna be spending, you know, huge amount of money on tokens and subscriptions, you wanna extract the most out of it. So actually at the moment, you need people who know how to use AI. That's probably the skill. And to be fair, this is actually where younger graduates are in a better position. I've spoken to several people and they have genuinely agreed that I think there was three scenarios. And this is probably gonna be a bit negative for older people, but I'll say it anyway. So if you were gonna hire an engineer today,

13:03 would you hire a 50-year-old with 30 years of experience, but who has no AI experience and actually has some AI negativity? And I do see that a lot, unfortunately. Would I hire an AI person that has no background at all in engineering? Or would I find somebody who's sort of early career, but extremely proficient with AI and has the right mentality? It used to be the case that a lot of companies would want the extreme expert. And it wouldn't matter if they know AI because that's somebody what IT will do or something. When I spoke to most people, they would actually prefer option three to one

13:53 or two. It's becoming essential to have those AI skills. To some, they would rather sacrifice a little bit of that domain knowledge because right now what matters more is someone who can really take advantage of AI. And I don't mean just take it and use it and blame it but be the person who wants to improve it. In a larger company that's maybe helping to negotiate which AI tool they should use, whether they should be building things internally, understanding all the different players where it's good, where it's bad. So in that sense, if you're not an expert in AI right now, at least in today's job market, I would say you're at a massive disadvantage

14:38 and you should learn very quickly. And by being an expert in AI, I don't mean just using the chat interface of ChatGPT. I don't mean just copy and pasting stuff. I mean actually getting in and understanding how it works, understanding how you could build out some agents. You can still be using some of these commercial tools. I don't mean write it all from scratch, but somebody who knows how to do it, knows how MCP servers work, how to connect different tools together, has an inquisitive mind, is looking at where are things going. That is so valuable. If I interview somebody now for a job, I want someone who is passionate about it.

15:25 Because like it or not, that is the technology of today. It's a bit like hiring somebody who is, well, I don't really like to use computers, 40 years ago. Yeah, I prefer to do everything by hand. At that time, maybe computers were seen as like, we don't really need it. But for sure, the company that did have it, or the same with the internet. If you hired somebody who was a whiz with building websites during the time of the dot com, that was really valuable for your company. Having that mindset might have helped you shift to an online company instead of just physical stores, which as many people know, there's many examples of companies that failed because they didn't anticipate where

16:09 things were going. So that's a long-winded way of saying that today I would say it really matters to know AI itself, not just background in engineering or science, which is still super important, but AI itself. And the great thing is now there's so many YouTube courses, there's so much free content, many companies are desperate to give you free training so that you use their products essentially. And this is true if you're working in the software side, in the area I'm in, in more CAE and EDA, absolutely massive opportunities there. So let's look at what practical skills should you develop? And maybe we can look across undergraduate,

16:56 postgraduate, and then early and late stage careers. If you're an undergraduate, I would not be tempted to use AI to do all of the coursework for you. Even though it's very tempting to do it, you're sort of ultimately gonna cheat yourself out of learning some of these fundamentals that do matter later. And even I, there's certain things now I kind of wish that I'd got even better grounding on when I was at university. You know, some of these like fundamental understanding of mathematical theory or engineering principles, they really do give you that intuition and physical understanding and logic that helps. But at the same time, I would absolutely be using AI as a tutor.

17:48 I think I've said this before, I think AI's greatest role at the moment is to help you to study. So if you get a piece of coursework, instead of sort of copying and pasting it in and saying, you know, give me the answer, essentially, I would do it to like create me a podcast that will teach me this area. Or I quite often have it where I will ask it to create me a presentation that will teach me the area. And you can ask it to help you to do things as long as it's legally allowed in whatever study you're doing. But I would then be like, explain to me in a high level how this works and then dive deeper. It's like having the ultimate tutor.

18:31 So in that sense at the undergraduate level I would use AI as a teacher not as something that does it for you or you just you're ultimately just cheating yourself and you're not going to learn the things. In the PhD, wow, if you do do a PhD or research thing, if I had AI when I was doing my PhD, my God, that would have been so useful, unbelievably useful. I mean, just incredible productivity. I just think how much I could have done, how much more studies I could have investigated, and the way that it would have helped me to understand new areas, just mind blowing. So I would fully embrace it as the ultimate companion.

19:17 At a sort of research level, whether it's masters or PhD, I think the AI should be the bit where you really hone those skills, help it to be your own PhD student a little bit, to help you to do things. And that's the mentality that I think most companies want. They want you to use AI as your helper, not just a lazy way that you just sort of do this for me, but rather something where you almost have your own team. And that's why a lot of companies are now talking about a sort of agentic engineer or an AI engineer, and they don't really mean it to replace you as an engineer, but it's almost to give you extra help. And so you're sat there farming off things to go and do.

20:04 So that's how I would do it. Now, of course, there's a debate of what would you actually study. In that sense, it's very dependent on your area of study. But at least if you were doing it now, then I would still focus very much on, let's say in engineering, how can AI work for engineering in a more holistic way? That's clearly one topic. But it doesn't have to be just on AI itself. If you're coming up with new theories, or studying fluid mechanics, AI can just help you to do the post-processing, it can help you to understand what's going on. You don't have to be studying AI as the topic,

20:53 it's just AI can help you essentially. And I know probably as I'm saying this, because I've said the word AI so many times, some people may be skeptical and think, but yeah, it would just be unwise for you not to be looking into this. The good thing is if you set that sort of strong basis, a good strong maths background, engineering background, computational science, I would understand how chips work, semiconductors work. That's a big area now. If you think about it, if AI does become the revolutionary technology, you need to understand the methods, how AI works, how it's trained, how inference works, and all the stuff around fine-tuning and

21:40 agents, etc.—harnesses and scaffolding, blah, blah, blah. But arguably what powers it and what powers it are chips. So I would argue that actually learning more about semiconductors and EDA is probably a very useful thing. Learning about, you know, the engineering, the power, the economics, that whole stack will be very useful to understand. But I think you're in a great position if you have that engineering background, science background, and you're very proficient with AI, I think you could turn to many jobs. But I guess the elephant in the room is how do you stand out? If in some ways AI is the great enabler, how do you make sure

22:32 you get the job versus somebody else? It used to be that you would just be the best specialist, the best university, the best degree. And that is to some extent still true, but I would argue that if some of the technical ability is now taken by AI, then the soft skills matter even more. Maybe I, you know, lean too much into this, but I cannot tell you how much I value the soft skills. I want to see someone who can work well in a team, who has the right attitude, who has the right organisational ability, who understands emotional intelligence and sensitivity,

23:22 who maybe has skills in languages and understands different cultures—just someone who is, you know, good with people. More and more, anybody who goes into leadership roles knows this: how well a company does is really down to the people and the vibe and that is a lot about how you recruit people and the people you have. The technical ability matters a huge amount, but if we argue that AI is gonna solve some of that technical ability, the dynamic of the team and the people is gonna matter more. So I don't believe you can be the sort of super brainy but a bit awkward person that won't stand out as well. So I think the sort of soft skills and I don't mean everyone needs

24:10 to be an extrovert, but someone who can do well in the team is probably gonna matter even more. Showing demonstrable proof is important. I always find that if someone just sends a CV, if someone actually shows me a presentation of something they've built, something they've done, something—that's important. Internships are super important. That is ultimately the way that a lot of companies hire now. They get somebody as an intern, it's a bit of an extended job interview, you know, and then they hire. So actually, because there's so much open-source stuff out there now, because AI is so great, you should be able to build things.

24:56 You should be able to demonstrate your ability much easier than before. So if you wanna go into a job, and it's about doing data centre design, you could probably pick up some research yourself, you could build some simulations yourself, you could do so much just to prove that you want and are able to do it, all with relatively open-source and free tooling. Now of course the actual token cost is another thing, and that's where there's a lot of student programs, but frankly, it's probably worth it for you to pay, and I think most people do, for a decent subscription to an AI thing, because it's gonna help you out.

25:41 So, it's hard for me to think, but if I was starting again, I think engineering would still be the right choice. I would still do it. And I think it's even more relevant today as a good degree, regardless of what you go into, just because every industry has been transformed by AI. And I think engineering gives you that grounding in those subjects. Maybe maths, you could argue, it's very specific. An engineering degree, by design, has elements of maths, elements of physics, elements of computer science. I actually think it's a great all-round degree if I'm being honest. I mean, I'm biased obviously, but I think today it's even more relevant for

26:24 you. So I've probably not addressed all of the points, but I really don't think that this is gonna be a matter of a mass wipeout of jobs. Okay, long term if you extrapolate things and you say robots can do everything and AI could do everything. Okay, maybe there's a very uncertain long term future, but frankly, none of us know and it is almost like a sci-fi thing to look at. I'm not even sure it's worth looking at that. I can look at the short to medium term, years or decades and I don't see any mass wipeout of jobs in engineering. I see just it being a different skill set, a different sort of productivity.

27:15 And that is not something where people are just gonna suddenly hire like 50% less. I just don't see that. I think you just need to be flexible in what you will be doing for that company. At least for engineers, I can't really comment about all the other jobs in the job market, but as engineers, I can't see a point where we're gonna suddenly need 50% less engineers. I think they're in such short supply. So I think if you are listening to this and you are doing engineering or want to do engineering, I think that's a very sensible and future-proofed idea. And, yeah, I probably waffled on a bit, but I just wanted to give

28:03 you some of my perspectives on this and if you have questions for me or comments or thoughts that you have then please put them in the chat. I guess YouTube is probably the easiest place to do it. All right, I hope you enjoyed that. The next few episodes will be back to speaking with a guest, I think. So, I hope you enjoyed it. Thanks