Cooking a perfect steak requires skill, yet many approach AI in software development similarly to a novice cook: hoping for excellent results with minimal understanding. We build complex AI tools and workflows, feeding them into models and expecting ideal software without grasping the underlying mechanisms. The desire is for polished, consistent software, much like a perfectly cooked steak, but the reality is often inconsistent output, ranging from surprisingly good to unusable.

This leads to seeking 'premium AI products' or agencies, hoping others have solved the problem. However, the article argues that AI, at best, is a 'steak machine' that can follow instructions but lacks true understanding of user intent. To achieve desired software quality, one must translate abstract desires into concrete requirements, constraints, and feedback. Simply standing by and correcting the AI won't transform it into an expert.

Ultimately, the path to quality lies in learning the craft, much like learning to cook. AI can automate repetitive tasks and provide starting points, but it cannot replace human judgment, define quality, or make critical trade-offs. To build good software with AI, developers must understand software principles, articulate their needs effectively, and critically evaluate AI-generated output. Continuous learning, building, and failing are essential to move from hoping for good results to consistently producing them.