AI Makes You Boring: How Large Language Models Are Reshaping Creative Thinking

The programming community is experiencing a quiet but profound transformation. What was once a vibrant ecosystem of deeply considered projects and thoughtful discussions has become increasingly populated by what one observer calls "boring people with boring projects who don't have anything interesting to say about programming." This isn't merely a complaint about declining quality on forums like Hacker News' Show HN section—it's an observation about how AI tools are fundamentally reshaping the creative process itself.

The phenomenon is visible everywhere: a surge in volume accompanied by a decline in substance. Projects that once represented months or years of careful consideration now appear as if conjured overnight, complete with slick interfaces and working features but lacking the depth that comes from wrestling with a problem space. The author of this critique points to a fundamental shift in what makes programming discussions valuable. Pre-AI Show HN submissions offered something precious: the opportunity to engage with someone who had spent far more time thinking about a problem than you had. These interactions were learning opportunities, chances to gain entirely different perspectives on challenges you might not have even known existed.

What's particularly striking is how this pattern extends beyond any single platform or community. While some of the increase in shallow projects can be attributed to newcomers drawn to the apparent ease of AI-assisted development, the critique goes deeper. The argument is that AI doesn't just enable boring people to produce boring work—it actively makes people boring.

This claim rests on a crucial insight about how original thinking actually works. Large language models, despite their impressive capabilities, are fundamentally conservative in their approach to knowledge. They excel at synthesizing existing information, at finding patterns in what humans have already thought and written. But they are "extremely bad at original thinking." When we offload our ideation to these systems, we're not just getting help—we're getting a filter that systematically eliminates novelty.

The human-in-the-loop argument, often advanced as a defense of AI-assisted creativity, contains a fatal flaw. The premise suggests that humans can maintain their creative agency while delegating the heavy lifting to machines. But this misunderstands where original ideas come from. They emerge from the very work that AI tools are designed to eliminate: the long, immersive engagement with a problem space that forces us to confront contradictions, explore dead ends, and gradually build a unique understanding.

Consider the traditional educational practices that AI tools threaten to replace. We make students write essays not because we need more text in the world, but because the act of writing is itself a form of thinking. The struggle to articulate an idea, to find the right words and structure, is what transforms vague intuitions into coherent understanding. Similarly, professors teach undergraduates not just to transmit knowledge, but because explaining concepts to beginners forces experts to examine their own assumptions and discover new connections.

Prompting an AI model is not equivalent to this process. When you ask an AI to generate content, you receive an output, but that output is discardable. The valuable work—the thinking, the struggle, the gradual refinement of understanding—never happens. It's like trying to build muscle by having someone else lift weights for you. The result might look similar, but the underlying development never occurs.

This creates a troubling feedback loop. As more people rely on AI for ideation and creation, the overall pool of original thinking diminishes. The AI models themselves are trained on human-generated content, so as that content becomes more derivative, the models have less original material to learn from. The result is a kind of intellectual monoculture, where ideas become increasingly similar not because people are thinking the same thoughts, but because they're all using the same tools to avoid thinking at all.

The implications extend far beyond programming forums. Any field that depends on original thinking—writing, design, research, entrepreneurship—faces the same challenge. The temptation to use AI as a cognitive shortcut is powerful, especially when deadlines loom and the blank page feels intimidating. But each time we choose the shortcut, we lose an opportunity for genuine intellectual growth.

There's a certain irony in using AI to write about the dangers of AI to creative thinking. But perhaps that irony itself illustrates the point: even when we recognize the problem, we often lack the discipline to resist the very tools that make our work easier but less meaningful. The question isn't whether AI tools have a place in creative work—they clearly do, and can be valuable aids when used appropriately. The question is whether we can develop the wisdom to use them as tools rather than crutches, to enhance rather than replace our own thinking.

For now, the evidence suggests we're failing that test. The programming community, once known for its deep dives and passionate debates, increasingly resembles a factory for polished but shallow imitations of innovation. The challenge ahead isn't technological—it's philosophical. It requires us to decide what kind of thinkers we want to be, and whether the convenience of AI-assisted creation is worth the cost to our intellectual depth.