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Message: <blockquote data-quote="Jacob Lewis" data-source="post: 9698241" data-attributes="member: 6667921">I appreciate the range of perspectives here, though I do think we’ve drifted a bit from the main thread—how LLMs are being used as game masters in practice. That said, I think it’s worth addressing some of the broader perceptions, since misconceptions can easily shape public opinion before people have a chance to explore and form their own views.Most people don’t have clear, reliable guidance on how to use LLMs. They’re handed the tool with little to no instruction, and their experience is shaped almost entirely by whatever assumptions, narratives, or hype they've already absorbed. Some get good results. Others don’t. And then those mixed results become the new input for public discourse—articles, forum posts, videos—all reinforcing a kind of consensus about what these tools are supposed to be.What keeps standing out to me is that most of the time, the results we get from these tools reflect more about the user than the system itself. The way we prompt, the structure we give, the assumptions we carry in—those all shape the output. And yet, the public conversation often treats the output as if it reveals something essential about the model, not about the interaction.What’s ironic is that this human feedback loop—the way narratives form, get repeated, and reinforce themselves—mirrors many of the concerns people raise about AI: that it recycles dominant patterns, amplifies bias, resists nuance, and flattens complexity. We talk about LLMs doing this, but public discourse does it just as reliably. And without realizing it, that cycle ends up training expectations more than the systems do.So I’m less interested in debating autonomy or intelligence, and more focused on how people are actually using these tools—with intent, structure, and awareness—and what’s been learned from those experiences.</blockquote>

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