# Why we keep using LLMs for O(1) problems > Source: > Published: 2026-07-23 11:45:35+00:00 # Why we keep using LLMs for O(1) problems ## The "Shiny Object" Tax We've all seen it: a dev needs to validate an email address, and instead of a regex, they implement a model call. Suddenly, you've got API latency, loading spinners, and a monthly bill for something that should take a microsecond. You've essentially traded a local CPU cycle for a round-trip to a data center. ## Determinism vs. Probabilism The core difference is simple: an `if` statement is deterministic. The same input always yields the same output. An LLM is probabilistic—it can give you a different answer based on the temperature setting or a model update. **If-statement:** Zero cost, millisecond execution, 100% predictable.**LLM call:** Per-token cost, seconds of latency, variable output. ## When to actually use each If you're building an AI workflow, you need to be ruthless about where the model actually adds value. **Stick to standard code (if/else, regex, lookups) when:** - The rule is binary. (e.g., "Is the cart total over $50?") - You need absolute consistency. (e.g., Billing logic, permission checks). - Performance is critical and there's no ambiguity. **Deploy an LLM agent when:** - The task requires nuance or judgment. (e.g., Summarizing a transcript). - The input is unpredictable free text. (e.g., "What is this customer actually upset about?"). - Variability is a feature, not a bug. (e.g., Creative copywriting). ## Real-world examples **Password strength:** "8 characters and a symbol" is a rule. Don't use AI for this.**Urgency detection:** Checking if a ticket contains the word "urgent" is a keyword search. Understanding that a customer is subtly furious based on their tone is a language problem. Use AI here.**Discount codes:** Checking if a code is expired is a database lookup. Stop asking models to check your DB.**Meeting notes:** Condensing 200 pages of rambling into three bullet points? That's exactly what LLMs are for. Choosing the wrong tool doesn't just look like over-engineering; it kills your UX with unnecessary latency and burns your budget on tasks that a junior dev could solve with three lines of JavaScript. [Next Vibe Coding: Why AI-Assisted Projects Fail →](/en/threads/2240/)