[PRIMO Tech-a-Break]: Why Do "Genius" AI Models Still Fail Basic Math?

[PRIMO Tech-a-Break]: Why Do "Genius" AI Models Still Fail Basic Math?

It’s a strange paradox, isn't it? Generative AI can write code, compose poetry, and pass bar exams, yet it often stumbles over a simple addition of 10 rows of numbers.

Recently, I tested an AI by asking it to sum up values from an image. Even though the OCR (Optical Character Recognition) identified every digit perfectly, the final "Total" was way off.

It’s not that the AI is "unitelligent." Rather, it’s because the fundamental architecture of Large Language Models (LLMs) is vastly different from a calculator. Here are 4 reasons why your AI might be failing its math test:

1. AI Sees "Tokens," Not Quantities

This is the most critical hurdle. Models like ChatGPT or Gemini don't perceive "1234" as a single value of one thousand two hundred thirty-four. Instead, they use a process called Tokenization.

The AI breaks numbers into chunks (tokens), such as "12" and "34," to process language faster. Imagine trying to solve an addition problem when you can only see the end of one number and the beginning of another, without grasping the whole value. This makes carrying over digits and maintaining place value (ones, tens, hundreds) incredibly difficult.

2. It’s a "Predictor," Not a "Calculator"

We must remember that LLMs are built for Next-Token Prediction, whereas calculators are built on 100% mathematical logic.

  • Calculators: Use rigid, deterministic algorithms.

  • AI: Uses Probability.

When you ask for "1 + 1," the AI isn't "calculating" in the traditional sense. It knows from its massive training data that the most statistically likely word to follow "1 + 1 =" is "2." However, when numbers become complex or unique, they don't appear often in the training data, and the AI begins to "hallucinate" a probable-sounding (but wrong) answer.

3. The Lack of a "Mental Scratchpad"

When humans solve complex math, we use a scratchpad to keep track of intermediate steps. Traditional AI models operate on a "Feed-forward" basis—they generate the final answer in one go without "stopping to think" mid-sentence.

💡 The Fix: This is why Chain of Thought (CoT) prompting is a game-changer. By forcing the AI to "show its work" (e.g., "First, let's add the units column..."), we provide it with a digital scratchpad, which boosts calculation accuracy significantly.

4. The "9.11 vs 9.9" Trap

Why does AI sometimes claim 9.11 is greater than 9.9? It’s because it confuses mathematical decimals with Software Versioning. In the tech world, v17.11 comes after v17.9. The AI prioritizes "textual familiarity" from its database over actual quantitative comparison.

How to Make AI Better at Math (Pro Tips)

Modern AI is getting better with features like Code Execution (where ChatGPT writes a hidden Python script to calculate for you), but for the best results, follow these steps:

  1. For Simple Tasks: Verify the output; never take it at face value.

  2. For Complex Math: Always use the prompt "Show your work step-by-step." This triggers the Chain of Thought process.

  3. For Developers: When building an AI Agent, don't let the LLM do the math. Use Tool Calling to send the numbers to a dedicated calculator API or Python environment.

  4. For Critical Finances: If it’s about your taxes or a bank balance—stick to Excel or a Calculator. Precision is non-negotiable!