Despite their impressive linguistic capabilities, Large Language Models (LLMs) lack true mathematical proficiency, logical consistency, and a grasp of the physical world, which can lead to critical errors in important tasks.
Imagine this: You are busy writing an important report today. You tell your smart AI assistant sitting next to you, “Take the figures from yesterday’s meeting and give me the total.” The AI immediately provides an answer in fluent sentences. But what if the calculation is subtly wrong? Or what if you ask the exact same question a minute later, and it gives you a completely different number?
We often say we are living in the age of “smart AI.” But when you look under the hood, these Large Language Models (LLMs)—AI trained on massive amounts of text to generate sentences—do not possess the kind of “intelligence” we imagine. Sometimes, they cannot even grasp very simple logic and end up going down completely wrong paths.
Why does this matter?
We are in a world where AI designs school curricula, writes corporate reports, and even does our coding. Mind Matters warns that the education sector is rapidly moving toward an environment where both teachers and students communicate primarily with AI chatbots.
The problem is that AI is just too good at “pretending to know.” According to Hackernoon, when a user asked about hardware performance, the AI answered with a very professional and convincing argument, but provided information that was technically completely backwards. Handling work this way can lead to a “complexity crisis,” lowering the quality of decision-making and destabilizing business operations. Hacker News blindly trusting AI answers is like blindly trusting the words of an unverified expert.
Simply put, what is the essence of AI?
Why do AI models that seem so smart collapse on basic math or logic?
To use an analogy, AI is like an “impersonating actor” who is very good at taking photos. This actor has memorized countless scenarios, so when given a situation, they recite a very plausible-sounding script. However, this actor does not actually know how to solve math problems, nor do they understand what the position or magnitude of numbers actually means. DEV Community
Looking closer at how LLMs operate, they do not understand numbers as the 1, 2, 3 we see, but learn by breaking them into countless pieces of words (tokens). Nate Silver In this process, the position or logical hierarchy between numbers gets jumbled. As a result, the AI is not actually “calculating” anything; it is just probabilistically listing words that seem most plausible in context. DEV Community There is a wide gap between the “intelligence” we expect from AI and the “probability-based word prediction” AI actually performs.
Where we stand: How much can we trust?
Current AI models have the following critical limitations:
- Hallucination: They generate information that is not factual, yet do so with extreme confidence as if it were the truth. Educative
- Lack of Consistency: When asked the same question again just seconds apart, they may provide completely contradictory answers. Mind Matters
- Absence of Understanding of the Physical World: They simply follow text patterns and do not understand the physical laws or logical structures of the reality we live in, leading to absurd errors. Hackernoon
- Basic Logic Failure: They are weak at solving problems that require repeated interactions or complex constraints. Strange Loop Canon
The Builder Society forum is filled with ongoing criticism that while AI is good at basic tasks like writing, it fails to perform fundamental work requiring logical thinking, such as deduplicating text or combining data sets. This suggests we should view AI as a “tool,” but never sit it in the chair of the “judge.”
How will the future change?
Experts urge us to break free from the fantasy that LLMs will be a panacea. Hacker News Instead of trying to solve everything itself, future AI is expected to evolve into a model that directly calls external tools (calculators, code executors, etc.) when needed to solve problems. Hacker News
Imagine this: When complex calculation is required, the AI turns on a calculator itself, derives an accurate figure, and writes sentences based on those results. This kind of “collaborative evolution” will be the future of technology.
Ultimately, instead of thinking “AI is a perfect oracle,” we must adopt the mindset that we are managing a “very capable assistant who sometimes lies and lacks logic.” Even as technology advances, the habit of humans meticulously verifying the results generated by AI and making final judgments will not disappear anytime soon. Hacker News
References
- What can LLMs never do? - by Rohit Krishnan
- AI Leaderboard 2026: Compare & Rank 300+ Top AI Models by…
- Why LLMs Are Bad at Math, Explained Simply - DEV Community
- Three Things LLMs Aren’t Great At (Yet) With Examples!
- ChatGPT is shockingly bad at poker - by Nate Silver
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[LLMs Are Bad at Good Things, Good at Bad Things Mind Matters](https://mindmatters.ai/2025/05/llms-are-bad-at-good-things-good-at-bad-things/) -
[LLMs are still surprisingly bad at some simple tasks Hacker News](https://news.ycombinator.com/item?id=45321983) - What are LLMs Bad At? And Why? - InfernoRed Technology Blog
- A Simple Hardware Question Exposes the Limits of Today’s LLMs
- LLMs - What aren’t they good for? - manhattanmetric.com
- What are the limitations of large language models (LLMs)?
- Limitations of LLMs: Bias, Hallucinations, and More
- Ask HN: Are LLMs slowly making companies dysfunctional …
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[Large Language Models (LLMs) Are Inherently Frail and Unreliable Mind Matters](https://mindmatters.ai/2026/01/large-language-models-llms-are-inherently-frail-and-unreliable/) -
[This is one of the least interesting questions to ask LLMs. I wish it wasn’t so … Hacker News](https://news.ycombinator.com/item?id=41699457) -
[Ask HN: Anyone struggling to get value out of coding LLMs? Hacker News](https://news.ycombinator.com/item?id=44095189) -
[Two things LLM coding agents are still bad at Hacker News](https://news.ycombinator.com/item?id=45523537) -
[2025: The Year in LLMs Hacker News](https://news.ycombinator.com/item?id=46449643) -
[Current AI LLMs are so terrible. Basic task failure beyond writing, is everywhere. Builder Society](https://www.buildersociety.com/threads/current-ai-llms-are-so-terrible-basic-task-failure-beyond-writing-is-everywhere.9062/) -
[What can LLMs never do? Hacker News](https://news.ycombinator.com/item?id=40179232)
- They lack sufficient computer processing power
- They only predict plausible-sounding text rather than performing actual calculations
- They lack sufficient training data
- The phenomenon where AI stops learning
- The generation of plausible-sounding but factually incorrect information
- A feature that reads human emotions
- Blindly trusting the results provided by AI
- Leaving all decision-making to the AI
- Ensuring humans verify the results