So... what's going on here?
How can a system so advanced stumble on something a 9-year-old would ace in seconds?
Let’s talk about why large language models (LLMs) are still really bad at math—and why that matters for teams relying on AI for decision-making.
1. Numbers Aren’t Numbers to LLMs
LLMs don’t see numbers the way we do. They see them as tokens—individual text chunks that happen to include digits.
So when you write "437 x 892," the model breaks it apart into separate bits like "437," "x," and "892"—but doesn’t treat it as a cohesive mathematical problem.
Imagine trying to do math by treating each digit like a separate word. Yeah. Not ideal.
2. They’re Built for Patterns, Not Precision
LLMs like GPT-4 are trained to predict the next word. That’s it. They’re not calculators, they’re word guessers.
They’ve seen enough examples to know that "2 + 2 =" is probably followed by "4," but they don’t actually understand math. It’s like your friend who memorized all the answers to the math test without learning any of the formulas.
As researchers from Anthropic and OpenAI have pointed out, LLMs are exceptionally good at mimicking human-like responses. But mimicry isn't mastery.
3. No Memory = No Multi-Step Math
When we solve a complex problem, we carry the middle steps in our head or jot them down. LLMs? They don’t have working memory.
Once they generate part of a response, they can’t reference it the way you might track a running total on paper or in your head. Which means they often forget what they were doing halfway through a multi-step equation.
It’s like trying to bake a cake without remembering what ingredients you’ve already used. Chaos.
4. There’s No Internal Error-Checking
Humans know when a math result looks wrong. We check. LLMs don’t.
There’s no internal alarm that says, "Hey, 437 x 892 is definitely not 200."
LLMs don’t cross-check with mathematical rules. They just keep going, confidently wrong.
This is one reason why companies like OpenAI are building in external calculators—because these models need outside help for precise tasks.
5. The Internet Isn’t a Math Textbook
The data used to train these models? Mostly the internet. And the internet is not overflowing with accurate examples of complex math.
So while LLMs might see thousands of examples of basic addition, there’s less exposure to advanced calculations or edge-case math. This results in uneven performance, especially for less common formats or larger numbers.
As noted in this paper from Google DeepMind, even the best LLMs show sharp drop-offs in accuracy as problems grow more complex.
Why This Matters for Ops Leaders
You might be thinking, "Okay, cool, but I’m not trying to build a robot accountant." Fair.
But if you’re relying on AI to help make business decisions—especially involving revenue projections, forecasting, or even pipeline health—you need to know when not to trust the machine.
This doesn’t mean ditching AI. It means knowing its blind spots.
Where Scoop Comes In
At Scoop, we’ve built a platform that understands those blind spots—and fills them in.
Our approach? Combine the narrative power of AI with the mathematical rigor of real data systems. Scoop doesn’t ask you to trust a chatbot to do arithmetic. It uses your structured data to build real insights—with presentation-ready outputs to back it up.
Tools like Instant Recipes take the guesswork out of analysis. Instead of relying on the model to "know" what to calculate, you define what matters—and Scoop does the rest, integrating directly with your CRM, financial systems, and marketing data.
It’s AI-powered, but human-approved.
So... What Do We Do With This?
Understanding AI’s limitations doesn’t make it less powerful. It makes you more powerful.
AI can brainstorm, write, categorize, summarize. But unless you pair it with systems built for accuracy, you risk making decisions off math that couldn’t pass a pop quiz.
The future isn’t LLM-only. It’s LLM + structured systems + smart workflows.
So next time your model flubs a math problem? Laugh a little. Then ask yourself:
"How is my team combining the best of human logic, machine intelligence, and structured data?"
And if you’re not sure? Let’s talk.
Because getting the R's in "strawberry" right shouldn’t be harder than forecasting your revenue.
