Large language models and agentic systems appear to benefit experienced engineers far more than they help less experienced ones. A useful analogy is that LLMs resemble an exceptionally fast sous-chef rather than a professional head chef, one who has memorized every recipe ever written. In the hands of a skilled chef, such a sous-chef is extraordinarily powerful. The chef understands the cuisine, the balance of flavors, and can immediately recognize when a dish lacks acidity, has too much salt, or clashes with the rest of the menu. The sous-chef accelerates preparation, suggests variations, and supports creative exploration, but responsibility for taste, consistency, and quality remains firmly with the chef.
For someone without cooking experience, however, that same sous-chef can be actively harmful. Recipes appear polished, instructions sound authoritative, and substitutions seem reasonable. Yet without an understanding of fundamentals, heat control, seasoning, timing, even correctly following the instructions can result in an inedible dish. Worse, the cook may not understand why it tastes wrong. The problem is not the recipe generator but the lack of judgment needed to evaluate its output.
This maps closely to how LLMs are used in software development. Experienced engineers treat AI-generated code as a draft: they quickly identify missing error handling, unclear contracts, poor separation of concerns, or long-term maintainability risks. They adjust proportions, replace ingredients, and sometimes discard the result entirely. Less experienced developers, by contrast, may assume the code is “good enough” simply because it compiles and produces output—much like assuming a dish must be correct because the recipe was followed step by step.
This is where Hyrum’s Law becomes relevant. In cooking, undocumented quirks, a pan that runs hot or an oven that browns unevenly, inevitably become part of the recipe. Change the pan, and the dish breaks. In software, the quirks introduced by LLM-generated code are just as likely to become accidental dependencies. Experts compensate for such quirks intentionally; novices unknowingly encode them into future systems.
Agent-based systems extend the analogy to automated kitchen stations. In a well-run restaurant, automation improves throughput without sacrificing quality because the menu is stable, processes are well understood, and chefs remain in control. In an inexperienced kitchen, the same automation produces inconsistent dishes at scale. Errors are no longer isolated, they are multiplied. The same principle applies to technical debt.
Fast food is cheap to produce but expensive to live on. LLMs make “fast-food software” remarkably easy to generate: quick, filling, and immediately satisfying. Experienced teams invest effort to turn that output into something balanced and sustainable. Others accumulate indigestion, systems that are brittle, difficult to reason about, and hard to evolve.
The core truth is simple and increasingly visible: LLMs do not eliminate taste, judgment, or responsibility. They amplify them. Just as good tools do not make a great cook, LLMs do not make a great engineer. They merely reveal the difference sooner and at scale.