AI Won’t Fix Bad Products—It Just Builds Them Faster

The author embraces AI as leverage that speeds thinking and craft—after investing in training it on voice, context, and intent. The real problem is people building unneeded products with generic AI outputs, leading to wrong things built poorly. Designers and engineers who pair taste, curiosity, and judgment with AI will thrive; those who ignore or over-trust AI will struggle.

Key Points

AI is powerful leverage when guided by context, brand training, and intentional direction; it is not a replacement for thinking.
The real issue is a surge of products built without validated needs, compounded by AI’s tendency to generate generic, unpolished defaults.
Copycat AI outputs (e.g., the Molly Baz case) reflect laziness, not an AI flaw; creating original work with AI is both possible and easier with intention.
Core future skills: taste, curation, clear prompts, and iterative refinement; engineers must understand generated code, and designers must avoid both rejecting and over-trusting AI.
AI amplifies existing strengths and weaknesses; combining human insight with AI is how you build polished solutions to real problems.

Sentiment

The overall sentiment of the Hacker News discussion is strongly in agreement with the article's nuanced perspective. Commenters reinforce the idea that AI is a powerful tool for amplification and leverage, but its utility is entirely dependent on strong human judgment, deep domain knowledge, and continuous guidance. There are no significant opposing viewpoints; rather, some comments clarify or elaborate on specific points, further supporting the article's core arguments about the necessity of human intentionality.

In Agreement

AI functions as a powerful leverage tool, accelerating tasks like debugging, documentation lookup, and code generation, thus freeing up human attention for higher-level thinking.
Human knowledge and fundamental understanding are indispensable; AI cannot replace knowing what needs to be done and will confidently lead to wrong paths if not guided.
AI requires active human guidance, akin to supervising a junior developer, with constant oversight, iteration, and refinement.
The 'craft' of using AI successfully hinges on applying domain-specific knowledge, taste, and curation to guide the tools effectively.
AI-generated code, if left unguided or given vague instructions, tends towards 'gold-plating' – over-engineered or overly complex solutions for simple problems.
With sufficient human care, many iterations, and integration of good UX/code practices, AI-generated outputs can be indistinguishable from human-developed work, supporting the idea that human judgment elevates AI capability.
Skills like prompting, refining, and iterating are crucial to harness AI effectively and prevent it from producing generic or misguided results.