OK, after two months of technical posts (and kudos to those who read them) let's go for a classic LinkedIn approach. What did debugging Linux configuration teach me about evolving paradigms of learning in the age of AI?
Jokes aside, I do think there are some interesting threads to pull here. I had a series of technical problems and I used AI assistance to address them. By the end, I had a product (they were fixed) but did I have any understanding? Did I learn anything?
Yes, I did. I learned a lot about Linux debugging tools. I remembered a lot about Linux config. While I did copy/paste a lot of commands, I wasn't simply executing Claude's suggestions. I was constantly asking why - why this command, why this tool, why this next step, and using this to inform my own thinking for moving forward. I made sure I understood what I was doing and slotted that information into my existing mental model, so Claude and ChatGPT were my companions on this journey, and they both helped and accelerated the trip.
However.
This is my experience. I am a professional technologist, with 20-odd years in the industry and more than that digging through the nuts and bolts of computers. I have a lifetime of context with which to understand the suggested steps, and keep a light hand on the tiller in case the AI wanders off in an odd direction. Fundamentally, I could have done this without AI assistance. More importantly, I could learn from the process because I already had enough context to understand and question what it was suggesting. AI simply made that process much quicker.
It's the same as training at my Tai Chi club. It is half an hour a week and many folk there struggle to memorise the forms we work through. I don't, because I've studied Tai Chi since my teens and the basics are ingrained. I am only learning the higher level motions, not the theory. I've also spent many years learning how to learn these patterns. It's a different experience for me because of that significant time investment. Those years of practice haven't just taught me Tai Chi, they've taught me how I learn Tai Chi.
Now, you could certainly make the argument that the particular knowledge gained debugging this problem doesn't matter. I don't need to know it, I just needed the problem to go away. This wouldn't be wrong in this instance. However, if something had gone wrong actually understanding the steps would have been important in fixing it. If I need to do something like this in future I've got more options for starting points.
So why does this matter? Stepping back, we all know that AI adoption across the Tech industry is not just happening, in many places it is mandated from on high. For some, tools and process changes are enhancements - increasing speed and freeing capacity, as my experience here. For others, they are increasingly just doing what the computer tells them.
The people new to the industry are those that concern me most. It is generally accepted in education that simply telling people the answer doesn't help them learn, yet this is the behaviour using an AI tool encourages. Ask a direct question, get an answer, use it and move on without further thought. In Tech, new developers using AI tools to generate code for them aren't gaining the experience needed to become what we currently consider Senior or Lead engineers. This is the difference between AI enhancing your output and AI driving it, and must be countered if we want people to gain the experience necessary to make safe use of these tools.
For those coming into Tech, this is actually similar (if more acute) to using Stack Overflow. Let's be honest, that site was used as training data so using an AI prompt is not far off an interface to the knowledge base there. There is nothing wrong with using Stack Overflow, as long as you read the posts and try to understand them. If you're copy/pasting lines of code without engaging with them, sooner or later you're going to come across someone who posted rm -rf / (DO NOT paste that!) and most people reading this will know what happens if you stick that in a root terminal.
There are certainly ways to actively learn while using AI. I often ask AI questions, then make changes myself - treating it like an editor / coding buddy - but the act of typing it out and questioning what I'm doing makes me learn. One of the reasons I write blog posts is that re-synthesising information in this format helps me turn it over in my mind and ensure I understand it properly. As long as we remain engaged with the process, as long as we value the knowledge and don't just copy/paste, then there is learning.
I find it very interesting to consider what this shift in technology means for the development of Tech (and indeed other industries). Learning passively (on the job, by doing) is likely to become harder, which means actually learning things needs to become much more deliberate - both individually, and at an organisation level - and we need to look again at how we place value on the learning process. The need to learn isn't going to disappear, and we need to be aware that AI does change the learning environment. It creates many opportunities, but if we aren't careful it can also encourage problematic behaviours. We will need to become more intentional about learning - through technique, through creating time, and through asking "why?" a lot. AI can provide explanations. Curiosity still has to come from us.
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