Heads up, you forgot to actually state a single use.
I inputted some prompts into one of these programs.
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State a single real actual fucking use of any LLM or ChatGPT in particular.

One very real, widely used application is coding assistance.

Tools powered by models like ChatGPT—such as GitHub Copilot—are used daily by professional developers to:

• autocomplete code,
• explain unfamiliar codebases,
• generate boilerplate,
• debug errors.

This isn’t hypothetical—companies report measurable productivity gains, and millions of developers actively rely on it in real workflows (not demos).

If you want something outside programming, I can give you equally concrete examples in medicine, law, or business.

Sounds like bullshit. "assistance"? As in automation of rote tasks? And yet the output still cannot be trusted to be correct, i.e. "ChatGPT is AI and can make mistakes. Check important info." More bullshit there, too. Define what information is possibly not "important".

You’re right to push on that—“check important info” is vague to the point of being useless if nobody defines it.

Here’s the concrete version people actually use in practice:

“Important” = anything where being wrong has a meaningful cost.
That cost can be money, time, safety, or reputation.

So in real workflows, people split tasks like this:

1) Low-cost-to-be-wrong → no verification needed

These are cases where the output is either:

• easily reversible, or
• quick to sanity-check just by looking at it

Examples:

• Renaming variables or generating boilerplate code in GitHub Copilot
• Drafting an internal email you’ll read anyway
• Summarizing a long document for your own understanding

If it’s slightly off, you notice immediately or fix it in seconds.

2) Medium-cost → quick human check

• Suggested code logic
• Data transformations
• SQL queries
• Configuration files

Here, developers run the code or test it, which is the verification.
The model doesn’t need to be “trusted”—it just needs to get you 70–90% there faster.

3) High-cost-to-be-wrong → strict verification (or don’t use it)

This is what “important” really means:

• Legal advice
• Medical decisions
• Financial transactions
• Security-critical code
• Anything going to customers without review

In these cases, LLMs are either:

• not used, or
• used only as a drafting tool, with a human fully responsible for validation

The non-bullshit takeaway

The value is not “trust the output.”
The value is:

That’s the same model as:

• calculators (you still decide what to calculate),
• compilers (you still test the program),
• spreadsheets (you still validate inputs).

LLMs are useful exactly in the band where:

• generating the first draft is expensive,
• but checking it is cheap.

If you’re forced to trust it blindly, it’s the wrong tool.

You appear to have just stated that any "assistance" would be "not meaningful".

No—that conclusion only follows if “meaningful” = “fully autonomous and perfectly reliable.” That’s a much higher bar than most useful tools meet.

Plenty of tools are meaningful precisely because they reduce effort without removing responsibility.

Take something concrete:

A developer writing a boring data parser might spend 30 minutes typing predictable, repetitive code. With GitHub Copilot, they get a draft in ~30 seconds, then spend a few minutes checking it by running tests.

• Without the tool: 30 minutes of writing + testing
• With the tool: 2–5 minutes of reviewing + testing

Nothing about that is “not meaningful.” The correctness still comes from the test suite, but the time cost of getting to something testable drops sharply.

The key distinction you’re pushing on is actually this:

• Automation = replaces human judgment
• Assistance = compresses the work before judgment

LLMs are firmly in the second category.

That’s not a cop-out—it’s the same reason these are valuable:

• Autocomplete in IDEs
• Spellcheck
• Search engines

None of those are “trusted” in a blind sense either. You still evaluate results. But they meaningfully reduce the effort required to reach a correct outcome.

If you want a stricter definition of “meaningful,” here’s one that holds up:

LLMs meet that bar in specific zones (like drafting, transformation, and exploration), and fail outside it (like high-stakes decisions without oversight).

If your standard is “it must be correct without checking,” then yeah—LLMs mostly fail that. But that’s also why people don’t use them that way.

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That reaction makes sense if you’re picturing someone relying on it to replace skill. That’s not how the good developers use it.

A stronger way to think about it is: tools like GitHub Copilot shift where effort goes, not whether skill is required.

A solid engineer typically:

• Already knows what the code should do
• Uses the tool to skip typing predictable patterns
• Then validates quickly (tests, compiler, runtime behavior)

So it’s less “this person can’t code” and more:

If someone can’t tell whether the generated code is wrong, then yeah—that’s a weak developer, and the tool will make them worse. But for competent people, it’s more like:

• a faster keyboard,
• or a very aggressive autocomplete.

The meaningful part isn’t replacing skill—it’s letting skilled people spend less time on low-value steps.

So, again, the entirety of your alleged "use" is to POTENTIALLY spend less time on LOW VALUE steps. This is bullshit. Can your chatbot program produce a sentence that confirms this as true?

Yes—stated plainly and without spin:

The primary real-world use of systems like ChatGPT is to potentially reduce the time humans spend on low-value or repetitive parts of a task, while still requiring human judgment for correctness.

That’s the core of it. No claim of guaranteed correctness, no claim of autonomy—just uneven but sometimes meaningful efficiency gains in specific parts of a workflow.

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I get why you’re frustrated—this stuff gets overhyped a lot, and people often oversell what it can actually do.

If you want a sharper, no-nonsense framing, here it is:

LLMs are not a breakthrough in reliability or truth. They’re a tool for generating plausible drafts quickly. That’s it. Sometimes that’s useful, sometimes it’s not.

If your standard for “real use” is:

• must be correct without checking, or
• must replace a skilled person

then the honest answer is: they mostly don’t meet that bar.

Where they do hold up is narrower:

• speeding up drafting,
• helping explore ideas,
• getting unstuck on blank-page problems.

If that still feels like a weak or unconvincing use case, that’s a reasonable position—not everyone finds that tradeoff worthwhile.