According to a blog post by developer Sydorets, artificial intelligence is transforming software development much like modern cooking equipment has simplified steak preparation—but with a critical caveat: speed and accessibility don’t guarantee quality.
The post uses the steak metaphor to illustrate a fundamental gap. Anyone can technically cook a steak by placing it in a hot pan and flipping it, just as anyone can now generate code using AI. But producing consistently excellent results—a properly seared, evenly cooked steak—requires knowledge, judgment, and practice that tools alone cannot provide.
Sydorets describes the current state of AI-assisted development: teams build continuously, creating agents, tools, and workflows, then “throw everything at a model and hope it gives us what we imagined, without ever having to understand how any of it actually works.” The results are inconsistent. Sometimes the output is surprisingly good; other times it’s fundamentally broken while appearing confident.
When AI-generated code falls short, developers often seek external solutions—premium AI products, agencies, new frameworks—hoping someone else has already solved their problem. But this pattern mirrors paying for a restaurant meal only to receive the same disappointing result, particularly when that restaurant also uses the same AI systems everyone else does.
The post argues that AI functions best as a tool for automation and rapid prototyping, not as a replacement for human understanding. It can follow recipes, maintain processes at scale, and provide starting points. What it cannot do is understand what you actually want unless you translate that vision into detailed requirements, constraints, examples, and tests. Even with explicit instructions, AI remains “boxed in by its own capabilities, its context window, the quality of the system wrapped around it.”
For developers seeking genuinely good software, the path is clear but demanding: understand the fundamentals. Learn what choices matter and why. Know when something is technically correct but fundamentally wrong. Build repeatedly, fail, and iterate until you can produce desired results reliably rather than hoping to stumble into them.
The conclusion is pragmatic: AI can accelerate development and handle repetitive work, but it cannot replace the judgment required to define quality, evaluate tradeoffs, or catch errors that are technically valid but conceptually flawed. To build software you care about, you still have to understand software.
Key facts
- AI-assisted development can produce inconsistent results ranging from surprisingly good to completely broken
- Developers cannot rely solely on AI tools; they must understand software fundamentals to judge output quality
- AI cannot replace human judgment in defining quality standards or deciding acceptable tradeoffs
- The tools work best when developers understand what they’re asking for and can evaluate what comes back