Osborn Learning & Analytics, LLC

Tag: decision-making

  • AI Means You Have To Know MORE, Not Less (Part 2)

    Oct. 10, 2026

    In the last post, I criticized overreliance on AI, especially as a replacement for learning, critical thinking, and/or decision-making.

    To recap…

    • Argument 1: Offloading too much thinking to AI will make you…um, let’s say less bright.
    • Argument 2: AI does not think, so it cannot think for you.

    And no, I’m not a Luddite, by the way. I use various forms of AI daily. To me, it’s all about how you are using the tool and for what.

    In my view, the right way to use AI is to automate things you already know how to do well (or that you never needed to know how to do anyway).

    But I also have a third claim: The increasing ubiquity of AI means you will have to know, understand, and do MORE, not less.

    Some people seem to be imagining a world like the Jetsons, where George complains about having to push the button five times that day.

    But that’s not what’s going to be expected of employees, at least not in any capitalistic society.

    If you use AI effectively, it will multiply your efficiency. Then, eventually, you’ll be expected to complete more and higher-level work.

    (We’ve already had different versions of this for a long time. Think back to stories of employees who knew how to automate cumbersome spreadsheet tasks with a bit of coding, eliminating the majority of their workload and allowing them to goof off. What kinds of things happened after the manager found out about it?)

    I’m not saying it should or should not be this way; I’m just describing what I think is inevitable.

    Efficiency multipliers can only make you lazier and more sedentary at work if no one else knows about them.

    (Warning: Everybody knows about AI.)

    Consider an example from literature. One of my favorite books is called The Diamond Age: Or, A Young Lady’s Illustrated Primer, by Neal Stephenson.

    In the book, an impoverished girl discovers a tablet that is basically a genius personalized AI tutor. Without spoiling the story, let’s just say that she goes on to do far more incredible things than she would have done without this technology.

    Now, in the real world, imagine that the next generation of children grows up with AI as an individualized tutor. We will obviously expect them to understand more about the world, not less.

  • AI Means You Have To Know MORE, Not Less (Part 1)

    Oct. 9, 2026

    A common perception these days seems to be that new AI technologies allow us to know and understand less.

    This comes from the general idea that newer technologies always relieve us of some burden. Consider this progression…

    • Machines can lift heavy objects for us, meaning that we no longer need to be able to lift heavy objects.
    • The Internet and Google can recall facts for us, meaning that we no longer need to be able to memorize facts.
    • AI can think and make decisions for us, meaning that we no longer need to be able to think critically or understand how things work.

    There’s a problem with this list in general and a serious problem with the last item in particular…

    First, the fact that a technology can replace some human function does not mean that it always ought to.

    • If I never lift heavy things (or engage in any form of physical exertion), I become weak and unhealthy.
    • If I never bother to memorize any facts about the world, I become dull and helpless.
    • If I never engage in critical thinking or seek to genuinely understand, I become irrational and foolish.

    For example, consider how some students have offloaded educational tasks to LLMs. By having the AI write the essay for them, they bypass the process of actually struggling with the concepts, and they effectively avoid learning.

    The summary of the thing is NOT the thing.

    Removing all active ingredients from the learning process transforms formal education into a shockingly expensive waste of time. In what other context do we pay exorbitant rates (not to mention dedicate years of our lives) for something…only to dump it into the garbage?

    Second, I would argue that the last item’s claim, “AI can think and make decisions for us,” is not actually true in the first place.

    Previously, I’ve argued against AI anthropomorphism. LLMs only appear sentient to people who don’t understand the basics of how they work.

    There’s no thinking involved in an algorithm, at least not the kinds of algorithms we currently have. (Not everyone agrees, and that’s fine.)

    Does my calculator understand math? I would say No.

    I know that’s not a fair comparison, but consider this: If you dial down your LLM’s temperature setting to 0, then it will give identical (or virtually identical) responses to the same prompt every time.

    This kind of direct input–output process, which underlies every LLM’s programming, does not fit with what I mean by terms like thinking or sentience.

    LLMs boil down to shockingly persuasive imitation of human speech. Incredible and useful in many ways, but not sentient.

    (Of course, this isn’t to say that AI could never become sentient. I’m just arguing that people seem to be falling into the common trap of likening our brains to computers, which is a poor comparison in so many ways.)

    More on the implications of this next time…

  • Advice and Feedback Are Neither Right Nor Wrong

    Sept. 26, 2026

    I often hear people say things like, “That was great advice,” or “That was a terrible suggestion.”

    Whether the advice is judged to be right or wrong is usually based on one of two factors:

    1. Whether it sounds intuitively plausible in the moment
    2. Whether it later produces a desirable outcome

    The second approach may appear sensible, but it turns out that both reasoning approaches are irrational.

    (For example, even if you happen to win the lottery, that positive outcome doesn’t mean the original decision was rational. Almost literally everyone who plays the lottery loses, either completely or on average.)

    Relatedly, I often hear employees characterize feedback they’ve received as accurate or inaccurate.

    The other day, I heard someone say, “I think his feedback was 80% right but 20% wrong.”

    But things like advice and feedback are (usually) not the kinds of things that can be right or wrong.

    Both are types of guidance regarding what actions one should take under uncertain conditions. As such, their success rates are inescapably probabilistic.

    • It may be situational: some guidance applies well under certain conditions and poorly under others.
    • It may depend on the person: perhaps what works for you doesn’t work as well for someone else.
    • It may be generally unpredictable: sometimes an action is effective in one instance but ineffective in another, for reasons we don’t understand.

    Consequently, the key to effectively applying advice and feedback is to treat them only as descriptions of how things may work, at least in certain situations.

    They’re just more tools in your toolkit, examples of particular options among many others.

    You don’t need to verify if a particular piece of advice is “true” or “right.” Even if you do, you’ll inevitably run into situations that contradict your conclusion, sending you back to the drawing board.

    Instead, you simply need to discover the situations in which a piece of advice does or does not apply (for you). This can be done through critical thinking, reflecting on your own experiences, and of course experimentation.

    Caveat: The only exception is when there is no actual uncertainty. When a piece of guidance overwhelmingly leads to positive or negative results, across all people and situations, then it can reasonably be described as right or wrong.

    Of course, in those cases everyone tends to agree, so we rarely think of such guidance as “advice” anyway.