How AI Software Development Changes The Premature Optimization Paradigm | by Zachary Ernst | Jun, 2026 | MediumSitemapOpen in appSign up<br>Sign in

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How AI Software Development Changes The Premature Optimization Paradigm

Zachary Ernst

10 min read·<br>13 hours ago

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Why Wasn’t It Built Right the First Time?<br>Every developer experiences this situation eventually. They join a project well after it has been built, notice some expanding tech debt, notice how obvious the resolution is and wonder: “why wasn’t it built the right way the first time?” I found myself there a few years into my career; a heavy read/write json document, thousands of lines in length, stored directly in a cell in a SQL database. At the time, I quickly caught on to how this architecture was doomed to scale at a certain point, and that is exactly what happened. This required an entire rewrite of that architecture, retroactively resolving what, I thought, should have been built right in the first place. This is one of a few examples, and I thought that very same question, why wasn’t it built right the first time?<br>What I was too naive at the time to realize is, deadlines, especially with real stakeholders and paying customers, push a heavy weight on “just getting things done.” In retrospect, many of these decisions I thought were obvious, were results of these very pressures on the dev team. The architecture didn’t represent “the right way”, it represented “good enough to meet the requirements and ship on time.” This was not a flaw in any of these decisions, on the contrary, these were deliberate tradeoffs that were carefully calculated at the time. When doing things “the right way” costs time and cognitive expense, alternatives don’t quite look like “the wrong way.” This was the very premise of Donald Knuth’s statement: “We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.”<br>However, what I believe is, this was the old paradigm, and AI-native Software Development argues entirely against this premise.<br>When Optimization Was Expensive<br>So, to Knuth, why was this the case, and what were the actual costs? When he made this statement in 1974, computing was still a far newer field than it is now; microprocessors were only a few years old by this point. So what were these costs? Complexity and time. Because computing was new and exciting, people were eager to ship software and solve real-world problems with these exciting new machines. “Just getting code to work” made sense; at the time, even “high level languages” were still complex by modern standards, such as C.<br>Premature optimization meant that the cost of optimizing was real human time and effort, wasted on what would otherwise be a shippable product. And this was the paradigm that has existed until very recently. In fact, the purpose of even higher level languages, like Python and JavaScript, was to expand this premise, accepting inefficient interpreted languages for the sake of easier development and shipping speeds. No one would dispute that hand-writing a C web API is far more complex and time consuming for a person to write than a Flask API in Python.<br>Beyond just code efficiencies, optimization of the “robustness” of the application also was subject to this paradigm; unit tests, SQL query efficiency, infrastructure as code over hand-creating resources, everything revolved around the question “do we need this?” To be clear, Knuth’s quote does not necessarily apply to these, as they are not the micro-optimizations he was referring to, even code built under tradeoffs usually includes these basics. But all of this folds into the broader challenge, that “good enough to ship” can trend towards ignoring whole classes of “do it right” work.<br>The crux of all software engineering, beyond just writing code, is that it is a profession of weighing tradeoffs, and time to ship vs. optimization is one of the most prominent of these tradeoffs any engineering team faces.<br>What AI Changed<br>The past few years has shown us something that upends a significant amount of this paradigm; premature optimization is now more possible and more accessible than ever. The plain reason why this can be done is AI agentic coding. The act of handing off the writing of the code to an AI has collapsed the time constraints to an extreme degree. It is no secret that AI can write actual code at lightning speeds now. After realizing this and seeing this first hand, I shifted my entire perspective on what it meant to write and ship software. If the “difficulty” of writing good, robust and optimized code is now nearly the same as writing bad, slow and sloppy code, why should I choose the latter approach? If writing highly optimized software is now far less costly than it used to be, “doing it right is...