Stop burning GPU cycles to check if a number is even. A guide to regaining our technical sanity.
It is an old adage in the toolbox of life: “When all you have is a hammer, everything looks like a nail.” In 2026, that hammer is the Large Language Model (LLM), and we are currently smashing it against problems that were solved fifty years ago with a few lines of C or a simple shell script.
We are witnessing a strange trend where “innovation” is being confused with “inefficiency.” Developers and organizations are rushing to integrate AI into workflows where simple automation or standard library functions would suffice. We have stopped asking “Should AI do this?” and are only asking “Can AI do this?” The answer is usually yes—but at what cost?
Case Study: The $0.01 Date Check
Let’s look at a concrete example. Imagine you need to display the current year in the footer of a website or timestamp a log file. A rational engineer might reach for the system clock. But the “modern” approach often looks disturbingly like this:
The “Over-Engineered” Way (JavaScript)
In this scenario, we spin up an entire SDK, authenticate against a cloud provider, and incur network latency just to ask a supercomputer a question that a wristwatch could answer.
javascriptimport { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({ apiKey: "YOUR_API_KEY" });
async function getYearFromGemini() {
try {
// We are firing up a massive model just for 4 digits
const response = await ai.models.generateContent({
model: "gemini-3-pro-preview",
contents: "What is the current year? Answer with just the number.",
config: { thinkingConfig: { thinkingLevel: "low" } }
});
const year = parseInt(response.text.trim());
console.log("Year:", year);
return year;
} catch (error) {
console.error("Error:", error);
}
}
This code works, but it introduces network dependency, latency (200ms+), API costs, and environmental impact for a static integer.
The Engineer’s Way (TypeScript)
Now, compare that to the native solution. Zero latency. Zero cost. Zero dependencies.
typescript// The "Engineer" Way
const getCurrentYear = (): number => {
return new Date().getFullYear();
};
console.log(`Current Year: ${getCurrentYear()}`);
The difference is stark. One solution requires an internet connection and a credit card; the other runs on a toaster. Yet, I see pull requests like the former increasingly often.
The “IsEven” Phenomenon & Cognitive Atrophy
We are seeing a resurgence of the “IsEven” problem—developers relying on external logic for basic arithmetic. There are real examples of code where AI is asked to validate if a number is even or to format a string.
This isn’t just inefficient; it is a sign of cognitive atrophy. We are becoming lazy. Instead of thinking through logic or reading documentation, we use AI for confirmation bias. We implement the AI’s solution not because it’s the best, but because the AI “confirmed” that our vague idea was plausible.
If we don’t understand the underlying principles—like how modulo operators work—we cannot judge if the AI’s complex solution is actually necessary. We are raising a generation of developers who know how to prompt, but not how to parse.
The Human Layer: HR and the Skill Gap
This leads us to a structural problem. Companies are frantically hiring “AI Specialists” when they often just need solid Full-Stack Developers.
HR departments need to look beyond the buzzwords. Don’t just hire someone because they can use Copilot; hire someone who knows when to turn Copilot off. Instead of buying enterprise AI seats for everyone to fix bad processes, invest in training your team on the fundamentals. A human who understands Linux, RegEx, and basic algorithms is often cheaper and faster than an AI agent trying to guess its way through a chaotic system.
Sometimes, the best “AI strategy” is actually a “Human Strategy”: hiring people who can build robust systems that don’t require probabilistic guessing to function.
The Strategy: The “Anti-AI” Prompt
You don’t have to ban AI, but you should change how you query it. If you want to save your organization money and technical debt, make this your standard prompt:
“I need to solve [Problem X]. Please propose a plan of action. I want the cheapest, simplest, and most robust approach, preferably using standard libraries or simple automation scripts without external AI dependencies.”
This forces the LLM to dig into its training data for actual engineering solutions—Bash scripts, SQL queries, Excel formulas—rather than hallucinating a complex RAG pipeline you don’t need.
Conclusion
AI is a powerful tool, like a jet engine. But you don’t strap a jet engine to a bicycle just to go to the corner store. It’s dangerous, expensive, and ridiculous.
Save your organization money and save yourself the headache. Before you import @google/genai, check if new Date()can do the job. Let’s make engineering boring again. Boring is stable. Boring is fast. Boring works.