Osborn Learning & Analytics, LLC

Tag: chatgpt

  • Love the Person, Hate the Slop

    Sept. 23, 2026

    Question: What’s wrong with AI-generated writing? Why do some people call it “slop”?

    Yesterday, I sat through a webinar where nearly all of the slide content and even the presenter’s script were AI-generated.

    In my haughty view, it was pure slop.

    At first, I thought to myself, “I can still learn from this if I just push past that realization. There may still be something useful in here.”

    But I couldn’t do it.

    Once I realized that the wording came from AI, the presenter lost all credibility in my eyes. I couldn’t help but wonder if everything about the presentation, including the topic, structure, concepts, and “insights” were all AI output as well.

    I should address two questions…

    1. How do I know it was AI-generated?

    The prototypical AI writing cadence is painfully obvious to anyone who uses LLMs regularly. Here, I’ll show you…

    (I’m slightly changing the wording to disguise the identity of the consulting firm that presented this, since I would be embarrassed if I were them.)

    Slightly Disguised AI Slop:

    “Selling without a framework isn’t just inefficient; it’s quietly eroding your bottom line.
    ▪ Weak positioning caps your earning potential before you ever discuss a price.
    ▪     Poor qualification turns weeks of work into months of waiting.
    ▪     Premature proposals are politely—and permanently—ignored.”

    The AI tells are everywhere. This was clearly copy/pasted verbatim from a conversation with a chatbot.

    If you use LLMs regularly, it’s as obvious to you as it was to me.

    2. What’s wrong with copy/pasting AI output?

    Here’s my take on why people disagree about the quality of AI output…

    If you think ChatGPT is a better writer than you, then you’re more likely to think AI writing is good.

    In fairness, it is good, at least relative to you.

    If you think you’re a better writer than ChatGPT, then you’re more likely to think AI writing is bad.

    And maybe it is bad relative to you.

    I think this is a key reason why many people, myself included, cringe when we read AI-generated text.

    But more fundamentally, I think it’s empty.

    Sure, we humans can and do hallucinate meaning in chatbot output all the time, but that doesn’t make the underlying process itself meaning-generating. As has been said many times, it’s like highly sophisticated predictive text.

    Maybe that’s why my eyes glaze over the moment I spot the characteristic tells.

    In any particular interaction with another person, I implicitly assume that I will be exposed to human thought or even just consciousness. When I realize that’s not what’s happening, I disengage.

    Why would I want to interact with a person who functions solely as a conduit to a chatbot?

    If I wanted to know what a chatbot would say, I’d just go to a chatbot…

  • Against AI Anthropomorphism

    Sept. 22, 2026

    I recently heard a couple journalists argue that AI anthropomorphism is not really a problem. After all, they say, modern-day AI agents seem to act like autonomous entities, so why not treat them as such?

    Why not anthropomorphize AI?

    For the same reason you don’t hold other false beliefs.

    Misunderstanding how the world works generally leads to bad decision-making. It’s no different with AI.

    For example, if we pretend that an AI is a sentient entity, then we start placing responsibility in the wrong place…

    If an AI agent deletes all your files AND the backups, you’re probably going to have an impulse to blame the AI.

    But guess who’s really responsible for the mess-up?

    Yep. It’s you, the user. (And to some extent, perhaps the programmer as well.)

    Either the AI is programmed poorly, or you don’t understand how it works (and therefore how to use it effectively and responsibly).

    If the humans don’t take responsibility, then no learning occurs, and more problems ensue.

    The person is responsible. The AI is a tool.

    Misunderstanding how the tool works is not necessarily harmless.

  • AI “Hallucinations” Aren’t

    Sept. 21, 2026

    Question: What are AI hallucinations?

    They’re a misnomer.

    “Hallucination” is the unfortunate name we have chosen for the phenomenon where an AI chatbot makes up incorrect information or otherwise gets things wrong.

    Although it’s increasingly popular to anthropomorphize LLMs like ChatGPT, Gemini, and Claude, I will continue to argue that this is a mistake.

    Their output is akin to highly sophisticated predictive text…

    The AI’s next word or syllable is NOT the result of any sort of “thinking” or consideration; it’s selected because it seems statistically likely to come next, based on countless transcripts that were used to design the program originally.

    Based on this, it doesn’t even make sense to speak of AI output as being “correct” or “incorrect.” Their responses are just likely strings of characters, not propositions per se. I’d argue we’re actually committing a category error anytime we ascribe truth value to them.

    We have it backwards: We humans are the ones hallucinating when we infer meaning from AI responses.

    (Of course, the meaning we infer may still be accurate or helpful.)

    So it’s no surprise that AIs sometimes make up incorrect information: everything they say is made up, including the parts we regard as true.

    They’re not hallucinating; they’re doing precisely what they were programmed to do, like every other computer program.

    Understanding the nature of tool, at least at a basic level, is critical to using the tool effectively and responsibly.

    And that means no anthropomorphizing.

  • No, You CANNOT Bypass Learning or Understanding

    Sept. 18, 2026 | VIBE-CODING PT. 2

    Part 1 here.

    Although I personally believe everyone should learn coding basics (even in the age of AI), I begrudgingly admit that it is NOT essential for many HR and OD professionals.

    What is essential is understanding the underlying statistical concepts. Either you or someone on your team must be able to validate the output of the vibe-coded analyses.

    SOMEONE has to know…

    1. If the procedure was done correctly,
    2. If the results are correct,
    3. And even more fundamentally… What it means.

    Large language models like ChatGPT and Claude can do incredible things, but they can also provide incorrect or irrelevant information (what we call “hallucinations,” although that’s a misnomer). We’ve already seen countless incidents where workers submitted or otherwise acted on AI output that turned out to be wrong, with disastrous consequences.

    In these cases, the workers had no idea that the AI-generated output was incorrect—they didn’t understand the process, so they didn’t know how to verify the accuracy of the output.

    And ironically that is what makes AI so alluring. It seems magical precisely because we don’t fully understand how it produces results.

    But the fact that you don’t understand how AI works is NOT its value—that’s its liability.

    In summary, Yes, you can rely on vibe-coding to bypass some or most of the mechanical aspects of data analysis.

    But you cannot bypass statistical understanding, or else the results might as well be gibberish.

  • Yes, You CAN Just Vibe-Code Everything

    Sept. 17, 2026 | VIBE-CODING PT. 1

    Question: Do I really need to understand coding and statistics to do my job effectively? Can’t I just vibe-code all of my analyses with AI?

    ANSWER: Yes and no.

    You do NOT necessarily need to understand coding… but this has been true since long before LLMs exploded in popularity. For a long time now, there have been tools such as MS Excel and IBM SPSS that allow users to completely bypass coding activities if desired.

    The surging emphasis on coding (e.g., R, Python) over the last several years was linked to increasing employer preference for familiarity with those open-source tools, because they are free and infinitely customizable.

    Now we have AI, which is quite good (and fast!) at coding, especially for straightforward and common coding tasks. This enables vibe coding, where you speak to an LLM in human language, and the LLM translates that into computer language (code).

    At first, this looked like more of a back-and-forth process…

    1. The human explains the goal in a chat interface and gets some response
    2. The human then copy/pastes that output into their coding software, which in turn produces output
    3. The human might then copy/paste that output back into the chat interface for further steps
    4. Et cetera

    But it’s becoming increasingly common for users to integrate the AI directly into their software.

    For example, if I activate Posit AI inside RStudio, I can write the following line in my script…

    # Create a random dataset of 100 scores, then show me the mean, SD, and histogram

    …and then Posit AI will autocomplete something like the following starting on the next line…

    data <- rnorm(100)
    mean_data <- mean(data)
    sd_data <- sd(data)
    hist(data)

    I could quibble about a few things (e.g., this doesn’t actually “show” me the mean or standard deviation; why does it assume a random distribution is Gaussian? etc.), but this is clearly pretty close to what I was asking for.

    The implications are enormous. This does allow for almost completely bypassing the coding aspect of data analysis. And there’s nothing inherently wrong with that.

    But the other shoe drops tomorrow…