10 Use of AI

As of this writing in 2025, AI is the new hotness⁴². Everyone is using it, and no one can stop talking about it.

The question no one seems to have an answer for is what’s going to happen. Sam Altman⁴³ and others will tell you that AI is any second now going to take over the world and solve everything and humans will be rendered obsolete.

I like to think that will not happen, and that even if AI starts solving everything, people will still want to use their ingenuity to push the envelope farther than AI is able to.

What does all that mean for you as a computer science student (as of this writing)?

Let’s talk about how you should use an AI as a student and at work, because those are two different things.

But before that, let’s talk about what you’re not supposed to do.

10.1 How Not to Use AI as a Student

I’ve mentioned elsewhere that I like to study the book SICP⁴⁴ to improve my skills. And how I give myself a six-hour time limit to solve the problems presented.

Now, these problems aren’t real-world problems at all. They’re contrived training problems. And—get this—the answers are all freely available on the Internet in a wide variety of places.

So why do I spend up to six hours? What a waste, right? Why not just clone someone’s repo and declare that it’s implemented so I’m done?

Or, if not that, why not just copy what my friend made?

Or, if not that, why not hire someone to write the answers for me?

Of course, you know the answer. I don’t do that because if I do, I haven’t learned anything. More specifically, I haven’t struggled with the problems so the learning that would come from that is lost.

I think you see where this is going: just asking AI to solve the problem isn’t going to increase your skills at all. It’s just asking someone else to do the hard work.

It’s like I went to the gym and asked someone else to lift the weights for me. Sure, I can walk out saying the weights had been lifted successfully, but I gained nothing from the experience.

I want you to name an activity that, aside from being at the gym, involves lifting weights all day. Answer: there are none (generally speaking for the majority of the population). Then why do we spend time lifting dumbbells at the gym if there are zero other activities that involve it?

The weights, of course, are just tools we use toward the greater goal of being generally stronger.

School is exactly like this. The programming problems you get in school are dumbbells. They’re not real. They’re designed to give you a workout so that when you get to the job, you have the strength to do the work.

And because the problems aren’t real, AI can solve them all really easily. There’s tons of training material out there for them to learn from.

But don’t be fooled. Just because AI can solve your school problems doesn’t mean it can solve the real-world problems you’re going to face in your work. (As of now, it can’t.)

(And if it could solve all those problems, how much do you think devs would earn? There’s a reason being a dev pays well, and it’s because the work is hard. If it was as easy as typing in an AI prompt, it would pay minimum wage. That should clue you in to the fact that if all you can do is prompt AI, you’re not getting a high-paying gig.)

But that’s not to say you shouldn’t get good at using AI; it’s just that while you’re a student, you have to use it the right way to maximize your skills development.

The TLDR of this section is this: never ask AI to solve your entire programming project. It probably can do that, but you’ll learn nothing. The goal of the project is not to complete the problem; it’s to get a workout while you complete it.

10.2 How to Use AI as a Student

First things first: if your school or instructor has banned AI, that’s the rule. And you have to ignore what I’ve written here. Sorry. I’m going to assume it hasn’t done that foolish thing, and proceed.

So how should you use it? Use it like you’re working with a decent tutor who knows a lot of things kinda well. The AI tutor definitely makes mistakes and gives poor advice from time to time, so you should cast a critical eye on everything you learn from it.

When you’re stuck on a small part of a project, ask about that. When you can’t remember syntax, ask about that. Ask about the idiomatic way to write a loop that removes elements from the array it’s iterating over. Ask about what a particular operator does or how to use it. When you have questions about the language or library, ask those. Little bite-sized pieces are fine to ask it about. It can be way faster than a standard Internet search.

When you’ve completed the project (and it works completely), then you can feel free to ask AI for a solution that you can use for comparison. Or, better still, feed your solution into the AI and ask for improvements.

Keep in mind that some of the “improvements” aren’t going to be improvements at all, and you’ll want to ignore them. Cast that critical eye. Be opinionated and have a rationale for which advice you’re accepting and which you’re rejecting.

Lastly, there’s another time it’s fine to use AI to solve entire projects: when your instructor says you can. This happens when they’re giving you more real-world practice as opposed to lifting weights. Both are valuable uses of your study time.

10.3 How to Use AI at Work

First, a caveat: the last time I was working in production (and I did so for 20 years), AI as we know it today did not exist. That said, I do use it to get things done more quickly today. So that’s my level of “expertise”.

I’d mentioned earlier that new devs solve problems with logical reasoning, but experts, in addition, recognize patterns. As a more-experienced dev, you have a better understanding of which building blocks make up a problem. You recognize the pieces that you need, and you logically reason about how to assemble them.

In other words, experienced devs are better at Understand and Plan. (They’re better at all phases, but recall that Understand and Plan is where the battle is.)

As such, they can leverage AI to help them write the code for those building blocks. They can say things like, “I need to filter those results for anything that matches this regular expression”—and then they ask an AI to code up that building block, they expertly verify that the code is correct and modify it to suit their needs, and then move on.

Even with technologies they aren’t familiar with, this can help them get the job done. But they still need to rely on their expertise to know when they need to learn more. That is, experienced devs have a nose for dangerous code and know when they need to proceed carefully and gather more knowledge.

In this regard, AI can be really useful for doing proof-of-concept work and rapid prototyping where the code is often throwaway.

“Let us hurry! There is nothing to fear here!”
“That’s what scares me.”

—Satipo and Indiana Jones, Raiders of the Lost Ark

Again, as a student, you can’t bring that experience (that you haven’t yet acquired) to bear, and if you just try to use AI like a seasoned dev, you’re going to have buggy, fragile code that you don’t know how to fix. And worst of all, you won’t be developing the skills you need.

But as you gather more experience, you can definitely rely on AI to write a large amount of boilerplate code for you that you already know the logic behind, anyway.

10.4 AI and the Jobs Market

When you read this, I want you to know that I, the author, thought Yahoo!⁴⁵ was a dumb idea when it first launched. I kinda still do think it was, but it made a bazillion dollars since then, so I was wrong. At least capitalistically.

One thing old devs have been hearing their entire careers is that we’re on the verge of the “no code” revolution, and that this tool or that tool will finally put coders out of a job because everyone everywhere will be able to produce software.

Every one of these predictions has something in common in that they were all fantastically wrong.

But things are only wrong until they aren’t, and LLMs like ChatGPT are definitely novel beasts that don’t play by the previous rules.

I do recognize the stock pumping, though. All those “no code” companies were talking a big game trying to get big returns for their investors. OpenAI and other AI players also talk that big game.

That’s not to say they won’t realize those gains; but it is to say it smells familiar.

And LLMs are showing incredibly coding expertise. I am perpetually amazed by what they can do. But can they do it all?

I have a thought experiment for you. Let’s say there’s an AI so good that I can tell it, “AI, design and implement a corporation that will crush all my competitors and make me the richest person on Earth,” and it will actually successfully do it.

But the catch is that everyone has access to the same AI and they can all make the same request. Where does that land us? We’re back to square one where we’re on equal footing.

As a capitalist, though, I don’t like equal footing. I want to get an edge on my competition. So I start thinking, “What can we do that’s slightly different than what the AI is telling my competitors?”

And just like that, humans are back in the game!

I think that trend’s going to continue, maybe forever.

What will happen, I predict, is that the easy boilerplate jobs that exist now will experience a massive tightening. AI can solve lots of those easy problems, and you don’t need a big team of engineers behind them. The more novel problems will still need a lot of human work.

But maybe not as much work as before. AI can help in a variety of ways, outlined above, so it helps speed things up.

Going back in time, consider when most programs were written in assembly language⁴⁶ and it took a lot of specialized knowledge to get these error-prone programs written. And then compilers became popular and now no one⁴⁷ writes in assembly any longer; those jobs are toast, destroyed by the faster, easier coding that compilers afford.

In short, I think we’re going to keep pushing it, and AI will become a very useful tool, but only with humans at the helm. I think. We shall see.

“And… Always look on the bright side of life…”

—Lead Singer Crucifee, Monty Python’s Life of Brian

10.5 Chapter Reflection

• Contrast the useful and non-useful ways students can use AI.

• What goes wrong if you use AI badly as a student?

• How do things differ with AI at work versus at school?

• What are some of the hazards of using AI at work?