AI 의존이 코딩 전문성을 붕괴시킨다

AI Coding will Prevent Expertise The need for ongoing friction in long-term skill formation. "We see a future where intelligence is a utility like electricity or water and people buy it from us on a meter and use it for whatever they want to use it for" - Sam Altman of OpenAI In my previous article, Agentic Coding is a Trap , I discussed the "skilled orchestrator paradox", where the skills required to manage AI agents for coding are the same ones that can be diminished through the continued use of said AI agents. Expertise was largely the differentiator; the more experienced a developer is, the less likely it is that they might experience skill atrophy, as the knowledge has had a chance to ossify after years of experience. If you look around right now, you'll find the vast majority of those that are seeing the most benefits from these models are those that have had years, if not decades, of experience in the field (which predates AI tooling, of course). And any industry veteran will tell you the same: the bedrock of this knowledge comes from doing the work. Developers who've entered the field around the time of LLMs are placed in a position where they don't have the benefit of longevity, but they are being guided (and sometimes mandated) to accelerate their efforts using coding assistants that require a history of expertise to wield effectively and responsibly . It's an awkward place to be for that demographic, as it creates a scenario where a novice needs expert-level skills to leverage the tools and keep pace in the industry. The "Expert Novice" We're currently sending very mixed signals to people across the industry. We're hammering in that if you're not using AI tools, you will be "left behind" by your peers who are using them. "AI won't replace you, someone using AI will" has been on repeat since 2023. And in the same breath, it's also said that the way to get the best results from these models is to apply higher-order thinking ; "vibe coding" is a dead end; you need to "move up the stack" and create robust specs, architect with good design patterns, and always review the outputs diligently so you never ship something you don't understand. The skills to do so, however, are a function of someone who has experienced the friction and challenges over time that culminate in "good taste" . This leads to another situational paradox: If these tools demand expertise , yet the tools can actively circumvent the friction that cultivates expertise , then what is the path for one to become an expert so they can effectively use these tools? Confidence without Comprehension One hope is that these models will end up accelerating learning as they are used for code generation. Junior developers can work with the same gravitas and confidence as industry veterans with their "personal AI tutor". Knowing syntax is increasingly less important, and any knowledge or ambiguity gaps are filled by the AI tool. The deeper mechanics of the code stay abstracted away, since the developer sits higher in the stack. JetBrains , a major player in developer tools, recently completed a study of junior and novice developers by painstakingly analyzing their individual behavior in live coding sessions, and testing their ability to learn coding with AI tooling in varying degrees of assistance. Their main takeaway was stark and counterintuitive: "Participants thought it was like having a personal tutor. From the data in our study ... we observed that they did not , in fact, use GenAI tools like a personal tutor. In fact, it was quite the opposite ." The participants that leaned into heavier AI assistance: " Often skipped crucial planning stages , finding that because they hadn’t reasoned themselves into this position, Copilot had." "Finished with an 'illusion of competence' rather than true understanding. " Counter to that, the participants that mitigated their usage of AI: "Succeeded because they had developed 'negative expertise'—which is 'the ability to ignore incorrect or unhelpful GenAI suggestions '—allowing them to focus on writing their own solutions rather than being led astray." "Were able to use GenAI to accelerate, creating code they already intended to make. " The novice developers who were the most unrestricted and confident in their AI usage "had skipped crucial steps in the programming problem-solving process, and were now lost." Perhaps unsurprisingly, the novice developers who performed the best were the ones that greatly mitigated or outright ignored the AI coding assistance. Inverted Learning Due to the self-directed nature of LLMs, the more experience you have, the more benefit they provide since you can accurately steer, audit, and verify the outputs. The less knowledge you have, the more they can mislead you . Interacting with LLMs for learning new skills takes the shape of an "inverted learning" model, a role reversal where the student is initially guiding the mentor , the mentor responds, and then the student, again, steers the mentor. The process is precarious; LLMs are incredibly sensitive to the shape of the prompt. When you're exploring new domains, you don't know what you don't know , and the malleable and accommodating design of an LLM can lead you to believe you know more than you actually do . If you're exploring territory that is even somewhat unfamiliar, you often don't even know the questions that you need to ask that could properly guide the model to providing the best answers. It begins to feel like a compass that always points north, wherever you suggest north might be. From the same JetBrains study , even the most prepared students were derailed by the AI assistance due to this type of learning model: One participant demonstrated good fundamental planning and habits, but suddenly "skipped crucial problem-solving planning stages, jumping directly to coding and was enticed by Copilot into quickly producing code" and had to rely on the LLM to fix the error that the LLM introduced in the first place . AI models lack judgment, empathy, and pedagogical intent, and the solutions provided are not rooted in experience but rather in patterns in the training data (LLMs are, at their core, incredibly complex pattern interpolators) . The infinite answer machine is tempting, and known to be addictive . It can unwind rather quickly, especially for inexperienced developers. Once you get deep enough into a generated solution, you are often beholden to the AI tool to also finish the job, circumventing the problem-solving friction that is required for the formation of a mental model (and to be fair, senior developers are prone to this phenomenon, as well). The Friction is a Feature Expertise and mastery don't happen purely through observation and dialogue, but through experience, repetition, and trial and error; you have to fail to succeed. If I wanted to learn how to cook, I could watch a Master Chef work and make endless inquiries. After a month, I would be able to describe the perfectly medium-rare ribeye but never know what it's like to cook one, and I'd almost certainly overcook it on my first attempt. Coding has endless moments of tracing obscure errors with no log file to help, experiencing the subtle performance differences of certain methods, or having to rewrite an approach when it's clear it won't going to scale. This applied friction is directly what builds "developer intuition" (or "taste"). The Germans have a great word for this: Fingerspitzengefühl (fingertip feeling). It’s the muscle memory that triggers when a developer looks at something and thinks, “yeah...this is probably going to cause problems.” By avoiding the mechanics of the struggle, this intuition is never built. In UPenn's large-scale 2025 study Generative AI without guardrails can harm learning , they followed 1,000 students using an LLM to learn mathematics and found students used AI as a crutch and ended up performing 17% worse than students with just a textbook (and just as with the J