An AI like ChatGPT struggles to provide tech support for its own features because the product changes too rapidly. The web content and documentation it's trained on lag significantly behind the current software version, creating a knowledge gap that doesn't exist for more stable products.

Connecting to a design system is insufficient. AI design tools gain true power by using the entire production codebase as context. This leverages years of embedded decisions, patterns, and "tribal knowledge" that design systems alone cannot capture.

Developers fall into the "agentic trap" by building complex, fully-automated AI coding systems. These systems fail to create good products because they lack human taste and the iterative feedback loop where a creator's vision evolves through interaction with the software being built.

People overestimate AI's 'out-of-the-box' capability. Successful AI products require extensive work on data pipelines, context tuning, and continuous model training based on output. It's not a plug-and-play solution that magically produces correct responses.

While AI can "polish" work, it cannot be used well by someone who doesn't already know what good looks like. For students who have only ever used AI, they lack the foundational judgment to guide the tool or recognize its flaws, leading to superficially polished but poor quality output.

While AI tools excel at generating initial drafts of code or designs, their editing capabilities are poor. The difficulty of making specific changes often forces creators to discard the AI output and start over, as editing is where the "magic" breaks down.

Many AI projects become expensive experiments because companies treat AI as a trendy add-on to existing systems rather than fundamentally re-evaluating the underlying business processes and organizational readiness. This leads to issues like hallucinations and incomplete tasks, turning potential assets into costly failures.

Since current AI is imperfect, building for novices is risky because they get stuck when the tool fails. The strategic sweet spot is building for experts who can use AI as a powerful but flawed assistant, correcting its mistakes and leveraging its strengths to achieve their goals.

AI scales output based on the user's existing knowledge. For professionals lacking deep domain expertise, AI will simply generate a larger volume of uninformed content, creating "AI slop." It exponentially multiplies ignorance rather than fixing it.