Are First Principles Becoming a Distraction?
I’d like to explore the uncertainty many are grappling with in balancing fundamental understanding against the immediate results enabled by AI tools. For clarity, I use “first principles” and “fundamentals” interchangeably here.
Lifelong learning is essential for a lasting infosec career. For the past year or so, as I’ve worked to expand my knowledge and skill set, I’ve dealt with a persistent internal dilemma.
I like to know the bare truth behind systems — how individual first principles come together to make a larger whole. That doesn’t seem like a problem on its face; the fundamentals are the most important thing, no? At least that’s what my teachers did their best to hammer home. Lately, that lesson keeps getting called into question.
In cybersecurity — and similarly in many IT fields — practical necessity often forces us to learn tools rather than the underlying first principles. The industry is results-oriented: knowing how to quickly secure systems, respond to incidents, or manage infrastructure often matters more immediately than grasping the theory behind every practice. That pragmatism isn’t inherently flawed — it’s effective — but it creates tension around long-term retention and adaptability. As AI tooling grows more sophisticated, it becomes even easier to focus purely on immediate outcomes at the expense of deeper understanding.
First principles remain of extreme importance — you need some understanding of a system before you can truly validate its safety. That said, there’s no such thing as a “perfect” security attestation. Security is fluid, and ensuring it requires ongoing vigilance. So we rely on our tools to get the job done, because we often have little time for much else.
More and more, we’re outsourcing our understanding of a domain to the LLM. What use is there in cognitively front-loading all the fundamentals when the model can worry about them? And if it doesn’t have the capability, can’t you just fill the gaps as needed?
Students and knowledge workers in IT-oriented fields face this dilemma as the age of AI progresses. The value of putting in the effort to learn first principles versus relying on outsourced intelligence is genuinely difficult to calculate, and it’s always in flux as model capability continues to enable more sophisticated agentic behavior.
On one hand, AI tooling can show you the bigger picture — it can augment your ability to understand fundamentals; it’s an amazing learning tool. On the other, it gives you an intelligent cudgel to smash problems with: tell it to do Z without knowing X and Y yourself, because it already has the information it needs — usually.
It’s hard to avoid the sentiment online that young and future generations could be at a career disadvantage as AI capability outpaces the rate at which humans acquire similar skills. Optimists claim AI will merely automate the boring parts, freeing cognitive space for what only humans are good at. There’s no telling whether that will hold. And there lies the dilemma — at this point, it’s impossible to know.
I don’t mean to portray AI tools negatively; they’ve been of great use to me. Still, whenever I’m studying on my own — the fundamentals of some IT domain, a programming language — I find myself asking: are first principles becoming distractions? Am I wasting time mastering them while others leap into AI tools and find rapid success? Would diving straight into results-oriented projects offer a faster, equally robust understanding? And how does relying on these models square with personal privacy, given how often they handle sensitive data behind closed doors without clear guarantees of trust?
It’s a real rabbit hole. I remain resolved that an understanding of first principles will always matter, and that some measure of human intervention in our systems will always be necessary — but I grow uncertain how practical and feasible they’ll remain as time passes.
Are these tools changing how you learn, or how you approach problems? Do you feel over-reliant at all? What are your feelings on the bigger picture?