AI services and cloud providers have the same business model: they have a direct financial incentive for you to be inefficient. The more calls you make and the more processing you consume, the more they profit. This creates a hidden tax on inexperience.
While experts know where to optimize to keep costs low, "vibe coders" and those following basic tutorials rarely receive this level of training. They are taught how to get a result, not how to get it sustainably and reliably. While at the surface level, AI-generated code can be cheap to create, running it can quickly negate those initial savings.
A Tale of Two Implementations: The AI Tax Calculator
Let's consider a practical example. A developer is asked to build a function that uses AI to approximate taxes based on a user's location and income.
The Naive, "It Works" Approach
The most direct approach, and the one often generated by a simple prompt, is to have the application call the AI service every time the user changes a number. The user types in their income, an API call is made. They adjust their deductions, another API call is made.
While this works, it's a disaster in practice for three reasons:
- It's extremely slow. The user has to wait for a network round-trip for every single change, creating a sluggish and frustrating experience.
- It's wildly expensive. This method racks up API charges for every number the user tries, turning a simple calculator into a money pit.
- It's unreliable. You have no validation that the math was done correctly. You are trusting a probabilistic model to perform a deterministic calculation, which is a significant and unnecessary risk.
The Expert Refactor
An experienced engineer will immediately recognize these flaws. They understand that the AI's real value isn't in doing basic math; it's in providing information that the application doesn't have. Their thought process is different:
- Isolate the AI's Core Task: The only unique data the AI needs to provide are the location-based tax rules. The actual calculations are just arithmetic that can and should be done locally.
- Identify the Trigger for New Data: The rules only vary by location (e.g., state). Therefore, a new API call is only needed when the location changes, not when the numbers do.
- Implement Caching: If the user toggles between two locations they've already checked, there's no need to re-request the same data. The rules can be cached locally.
With this knowledge, the application is refactored to only fetch the location rules from the AI when the location changes for the first time. All calculations are then done instantly and deterministically within the app itself.
The result? The expert approach is 100x faster for the user, 100x cheaper for the business, and infinitely more reliable.
This is the difference between simply using a tool and wielding it effectively. As we integrate AI more deeply into our products, the ability to architect for efficiency and reliability will be the defining characteristic of a great engineer.