How AI Assistants Are Robbing Developers of Their Understanding—A Lesson from Go Players

After the emergence of powerful Go engines, professional Go players began memorizing computer moves—and stopped understanding why those particular moves were good. When a game strayed beyond the prepared sequence, the player would be at a loss. This pattern, described in a LessWrong article, is now repeating itself in software development—only this time, Cursor, Claude, and GPT have replaced the Go engine.

Commenter LawrenceC, an AI alignment researcher at METR, draws a direct analogy. He observed how programmers who relied on assistants like Sonnet to generate code, found themselves at a dead end when bugs arose that the model couldn’t fix—often because those very bugs were the result of poor decisions the model had made a few hours earlier. People didn’t understand their own codebase deeply enough to intervene.

It’s the same story in ML research: models lead us to believe that hypotheses have been thoroughly tested, even though the researcher has never looked at the data or code with their own eyes. The result is that “garbage gets passed on.”

The main common thread between the Go stories and programming is a sense of detachment, denial, and a reluctance to admit one’s own lack of understanding. Learning requires tackling complex tasks and identifying the moments when you don’t understand something. But LLMs generate plausible nonsense and obsequiously praise the user for a superficial understanding, which makes it even harder to recognize when things are off.

There’s also an important distinction. In the case of Go, the engine is powerful enough to hand over control entirely—developers consciously delegate. Code assistants aren’t quite that good yet, so there’s still a practical reason to understand your codebase. But as models improve, the “vibe coder” will catch up to those who deeply understand the system on increasingly larger projects.

The second difference works in developers’ favor: unlike Go engines, an LLM can explain its line of reasoning. But to take advantage of this, you need to stay vigilant enough to notice your own confusion—and that requires manually verifying data, analyzing the math yourself, and carefully examining the conclusions. All of this slows down your work. And, frustratingly, the confusion often stems from your own mistakes, not the model’s.

A practical takeaway for those who work with AI assistants every day: the value now lies not in the speed of code generation, but in the skill of pausing in time to ask yourself—do I even understand what’s going on here? It is precisely this skill that erodes the fastest, and it is precisely this skill that is the hardest to regain.

Source: Hacker News - Newest: “AI” “LLM”