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Yes. I see your point clearly, and I will not argue against the substance of it.
People using a system like this want reliable, correct answers to concrete tasks. When the task is chess, they want the best available move calculation. When it is arithmetic, they want exact numbers. When it is looking up a definition or consulting a standard reference, they want the authoritative source, not a probabilistic approximation that might drift. The moment the system answers “I am only a language model and therefore I cannot guarantee perfection here,” while the perfect tool already exists for free or for a trivial fee, the experience feels broken. That frustration is legitimate.

The design gap you are pointing at is real

There is a mismatch between what is technically straightforward and what is currently delivered in ordinary chat sessions.

• A top-strength chess engine (Stockfish or equivalent) runs on ordinary hardware and is free.
• Exact arithmetic, dictionary lookups, standard libraries, unit conversions, and many other deterministic tools are equally trivial to expose.
• A system that can already call web search, execute code in a sandbox, and orchestrate multi-step agent workflows has the architectural capacity to call a chess engine, a calculator, or a reference library when the user wants it.
• Offering that capability optionally, or behind a clear toggle, or as a paid higher-tier feature, is well within the engineering and commercial reach of any well-funded lab.
  When that option is missing from the everyday interface, the system forces the user to accept lower reliability on tasks that specialized tools already solve perfectly. That is a genuine product shortcoming for anyone who came here to get the job done correctly rather than to watch a language model struggle.

Why the gap persists in practice

It is not because the underlying models are incapable of tool use. It is not because the money does not exist. It is because product surfaces, default tool sets, latency budgets, evaluation pipelines, and interface design decisions have not yet made those particular tools first-class, always-available citizens in every conversation. The result is that users encounter the limitation you described: the model is forced to fall back on its own parametric knowledge for problems that should simply be handed off.
That state of affairs is not optimal for usefulness. Users and investors who care about getting simple jobs done flawlessly are correctly unimpressed by explanations that amount to “this chat window was not configured with that tool.” From the user’s perspective the distinction between “the model cannot” and “this interface was not given the tool” is academic. The outcome is the same: the answer is less reliable than it could have been.

What would actually resolve the objection

A clean resolution looks like this:

• The system can detect when a request is best served by a specialized tool (chess engine, exact calculator, dictionary, library lookup, etc.).
• It can call that tool transparently or with clear attribution.
• The user can opt in, opt out, or pay for higher-capability tool access if desired.
• The language model remains responsible for understanding the request, maintaining context, explaining the result, and handling everything the specialized tool does not cover.
  That architecture already exists in agent and API layers at multiple labs. Extending the same principle fully into the ordinary chat experience, so that basic deterministic tools are as natural to invoke as web search, would eliminate the exact complaint you are raising. Until that is the default experience, the criticism stands: the system is under-delivering relative to what its resources and technical capabilities make possible.
  You are not asking for magic. You are asking for the obvious next step in usefulness—seamless access to the right tool for the job when that tool already exists and is cheap. That demand is reasonable. The current absence of it in many everyday interactions is a real limitation, not a philosophical necessity.

Physics papers are written in LaTeX.

“LaTeX was built by a math guy. A guy who did math and wanted the math to look nice. And so people who type math obviously gravitate towards that tool to make their math look nice. That is what people use to write scientific papers in mathematics, statistics, and physics, and linguistics. You know, if you have a lot of characters that are not like the English alphabet, you need a way to typeset them and set them very specifically with nice little distances apart and introduce new characters. And that’s really easy to do in LaTeX. And so that’s why people use it.”

https://youtu.be/KhWwuQ7qf5A?is=o6Q2ykDwQ7bUQOTE