ChatGPT, Cursor and Claude, Real Learning, and the Risk of Skipping the Hard Stuff

TLDR;

AI tools are incredible accelerators, but they can lull us into a false sense of mastery. Without deliberate reflection and exposure to underlying principles, we risk creating teams that are fast but fragile. The most resilient and innovative organizations invest in both speed and substance, combining the best of automation with the wisdom that comes from struggling through the hard work.

If your team is moving fast but feels like it’s skating on thin ice, don’t wait for a crisis. Start weaving that mesh of understanding today—or bring in someone who can help you build it.

Suppose you spend any time around software engineers these days, especially those in the education space. In that case, you’ll notice a recurring, low-grade anxiety in the air: Are we actually learning anything, or just getting really good at asking machines to do our thinking for us?

This isn’t just old-school grumbling. It’s a real, growing concern as Large Language Model (LLM) coding assistants become an everyday fixture. The recent blog post, “Pitfalls of Premature Closure with LLM-Assisted Coding”, hits the nail on the head: while AI tools have supercharged our productivity, they might be quietly eroding the foundations of genuine expertise.

Let’s break down why this matters, and what we can do about it, especially if you’re a CTO or technical leader responsible for your team’s long-term capability, not just their short-term velocity.

The Difference Between Doing and Learning

Real learning isn’t just repetition. Cognitive science tells us it’s all about cognitive effort: reflecting, interleaving concepts, building and rebuilding mental models, and accumulating experience. This is the foundation that Piaget, the father of developmental psychology, identified that kids (and adults!) learn by working through challenges and discussing problems, not just memorizing answers.

AI coding assistants—like Copilot, ChatGPT, and their competitors—optimize for speed, not depth. They can churn out code by the ton, but rarely nudge you to pause, think, or connect new knowledge with old. If you want those layers of understanding, the “mesh” of concepts that makes an expert, you’ll have to put in extra effort to reflect and connect. Without that, you’re left with lots of output, little insight.

A Red Corner Fractional CTO’s Take:

I’ve watched teams crank out features faster than ever with AI tools… and I’ve also watched them get stuck on basic troubleshooting because they’ve never had to dig deep. Red Corner’s Fractional CTO services have often stepped in to diagnose problems that a tool couldn’t, because we invest in building true expertise, not just pushing buttons faster.

Abstraction: Blessing and Curse

Abstraction isn’t new in software. Decades ago, most developers “skipped” learning about hardware; operating systems and high-level languages shielded them from the gritty details, freeing them to build more complex systems. But, as one commenter put it, the more you know, the easier it is to learn new things, because you have more hooks to hang new knowledge on.

The risk with LLMs is that we’re abstracting away not just complexity, but also the opportunity to build those hooks. If you’re plugging in prompts and copy-pasting code, you’re skipping the struggle that forms the mental muscle. The foundation gets weaker, and future learning slows down.

Should everyone be an expert in compilers, hardware, or quantum mechanics? Of course not. But the problem is when nobody knows how things work under the hood, or worse, nobody realizes they don’t.

Expertise: Mesh, Not Stack

There’s a helpful metaphor here: building expertise is not just stacking blocks higher, but weaving a mesh of understanding. You don’t just learn “how” to do something—you learn why it works, when it doesn’t, and what to try when things go sideways. True experts can travel up and down the abstraction pyramid, connecting the surface-level tools with the deep mechanics underneath.

LLMs, used passively, encourage “shallow” practice of lots of solutions, few principles. Reflection is the missing link. Without it, it’s like running a thousand machine learning experiments without ever looking at the training data. You might get lucky, but you’ll never know why.

Is Abstraction Always Bad?

Not necessarily. Sometimes, abstraction saves time and energy that can be better spent elsewhere. We don’t make new devs build with punch cards or solder wires anymore, thankfully! But we do want them to know why things work the way they do. The best abstractions teach as they shield, giving us the core concepts without all the outdated cruft.

The real danger is in hiding the foundations entirely, producing a generation of “knowledge workers” who can’t troubleshoot when the abstraction leaks. As Matt Gioe pointed out in the discussion, we haven’t yet seen what happens when whole generations skip learning how compilers work, or what to do when a tool fails silently. That day is coming.

What Should Teams and Leaders Do?

• Encourage reflection, not just repetition. Make code reviews and design sessions about why decisions are made, not just what code was generated.

• Expose the layers. Occasionally walk through the lower-level details—even if just for a day or two. Use real bugs, in-depth architecture dives, or post-mortems as teaching moments.

• Balance productivity and learning. Yes, velocity matters. But make space for mentorship and guided practice, where engineers build and reinforce their mesh of concepts.

• Leverage experts. If you don’t have that depth in-house, a Fractional CTO from Red Corner can fill the gap, bringing in not just architectural guidance but also a commitment to growing real expertise on your team.

Source:

• Pitfalls of Premature Closure with LLM-Assisted Coding

If you’re curious how to balance speed, learning, and resilience, let’s talk about what a Fractional CTO can do for your team.