There are two ways to build skills using AI tools—Opt for this method

The rapid integration of artificial intelligence into the professional landscape has created a paradoxical promise: the ability to do more while knowing less. As tools like large language models become ubiquitous in fields ranging from software engineering to data analysis, a fundamental question emerges regarding the long-term cost of our new-found efficiency. A recent study from researchers at Anthropic, titled ‘How AI Impacts Skill Formation,’ provides a rigorous look into this dilemma, revealing that the way we interact with these tools creates two distinct paths for professional development based on how one uses AI tools. The researchers studied a group of coders, dividing them into two groups—one with access to AI tools and another without—to complete a coding challenge. At the end of a 35-minute-long coding challenge, all participants were asked to take a test to check their python programming proficiency. Upon evaluation, the team found those in the control group scored higher than those in the treatment pool, suggesting a stark divide between high-scoring and low-scoring interaction patterns. It shows that while AI can accelerate the completion of a task, it can simultaneously decelerate the mind if used as a substitute rather than a supplement—an idea bordering on my earlier column on building careers in the age of AI. The treatment group path, identified as the low-scoring interaction pattern, is characterised by what researchers call cognitive offloading. In this scenario, the user treats the AI as a primary agent of execution rather than a collaborator. When faced with a complex task—such as learning a new programming library—the low-scoring participant focuses almost exclusively on the output. They delegate the heavy lifting of code generation and debugging to the AI, moving through the assignment with deceptive speed. This group often finishes tasks fast, yet their comprehension of the underlying mechanics remains remarkably shallow. Not just that, the researchers also pointed out that many in this group tended to spend more time interacting with the AI assistant, which could’ve ideally been utilised to learn a new skill. By bypassing the iterative, often frustrating process of trial and error, they inadvertently skip the very neurological “struggle” required for deep learning. For these individuals, the AI tool serves as a high-tech crutch; they reach the finish line, but their internal “muscle memory” for the skill is never built, resulting in quiz scores that plummet when the tool is removed. This contrasts sharply with the high-scoring group whose philosophical approach to AI was fundamentally different. They didn’t see AI as a replacement for their own logic but as a peer or senior. Instead of asking the AI to “write the code,” they asked conceptual questions. They sought explanations for why a particular function is used or request that the AI break down a generated snippet into its component parts. This group demonst