this post was submitted on 22 Feb 2026
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What you've given is an example of a problem where an LLM is, by design, the wrong tool.
See, variation is built into LLMs. They're programmed to evaluate probable responses and select from them on the basis of probability - to simplify ridiculously, if a particular word follows another 90% of a time, in 90% of the content it generates the LLM will have that word follow the other, and in the other 10% it won't.
If you give an LLM the exact same prompt multiple times, you will get multiple different responses. They'll all be similar responses, but they won't be exactly the same, because how LLMs generate language is probabilistic and contains randomness.
(And that is why hallucination is an inherent feature of LLMs and can't be trained out.)
But math isn't language. Math problems have correct answers. When you use a software tool to answer a math problem, you don't want variation. You want the correct answer every time.
To solve a math problem, you need to find the appropriate formula, which will be the same every time. Then you use a calculator, which always gives the correct result. You plug the numbers into the formula and calculate the result.
What I'm getting at is, if you use a calculator to do the math problem yourself, and you put in the correct formula, you'll always get the correct result. If you use a LLM to generate the answer to a math problem, there is always a non-zero chance it will give you the wrong answer.
If you haven't looked up the formula yourself, you have no way to check the LLM's work, which means you can't trust the answer for anything important - so you have to look up the formula and check it with a calculator.
And if you have looked up the formula yourself, it's just as easy to use a calculator the first time and skip the LLM.
Right? This is what I'm getting at. An LLM can do some of the same things a human does, but it's always going to be worse at it than a human, because it's not conscious, it's not reasoning its way to a correct answer, it's just generating a string of linguistic tokens based on probabilities. And math problems might be the clearest possible example of this.
Thats well put, I'm under no naive assumption that LLMs are AI. Though I do think youre discounting the usefulness, as it did give the right answer, which is a fine use for average people doing basic math or whatever project theyre working on. I'm under no delusion that its replacing workers, unless someones job is writing fancy emails or building spreadsheets, and I do still think its a massive bubble.
Yeah, I get that it seems like a fine use for average people doing basic math. The nonzero chance of error could end up not mattering. But it could matter very much, depending on the use case. If you're asking an LLM the volume of a bucket, it's not a big deal. If you're asking an LLM "how many milligrams of this drug is the correct dose for a 80 kg man", that's a big fucking deal.
If people don't know LLMs can't be trusted to give the corect answer, they're not going to realize they need to do the math themselves in important use cases. And that is certainly not something Microsoft and Google are encouraging people to learn.
Then there's the efficiency issue - Big Tech spent trillions of dollars to develop and train machine learning processors, which perform quadrillions of energy-intensive processes per second, and they're being marketed to do a job that a 99 cent solar powered calculator from the 1980s can do better.
God, I just realized tax season is coming up. And after all the layoffs and political firings and general dogebaggery at the American IRS, they're going to have to deal with people using AI to do their taxes 😆