Who Owns the Argument?
AI, fluency, and the difference between writing and thinking
This is post is part of the ongoing series, Exploring AI and Its Intersection with Human Decision Making. It’s also Part 2 of a 3-part series on Writing, Thinking, and AI.
You can hear AI Matt’s summary of the piece below.
In a prior post, I argued that AI has made fluent writing a terrible proxy for serious thinking. That observation matters for how we read and evaluate work—but it also forces a harder question. If polished prose is now cheap, when does AI actually improve thinking, and when does it simply stand in for it?
That question gets muddled quickly because we tend to talk about “using AI for writing” as if it’s a single activity. It isn’t. The issue isn’t whether AI is involved, but what kind of cognitive work it’s being asked to do. Some uses extend thinking. Others replace it. From the outside, the results can look identical: coherent paragraphs, sensible caveats, arguments that sound about right. Cognitively, they’re not the same thing at all (Sloman & Fernbach, 2017).
The difference shows up if you separate offloading substance from offloading translation. Offloading substance means letting AI supply the ideas themselves—the argument, the structure of the reasoning, the conclusions. The prose may look polished, but the thinking largely belongs to the system, not the writer. The user has ceded ownership of the substance, even if they provided some initial direction.
Offloading translation works differently. Here, the ideas and judgments already make sense—at least internally—but they don’t yet exist in a form that can be examined, revised, or tested. AI is used to help translate those internal models into legible prose. The output isn’t evidence that the thinking is finished; it’s a draft translation the user can now work with.
So the issue going forward isn’t whether to use AI in the writing process. That ship has largely sailed. The real issue is where the line sits between substance and translation—and what happens when we stop paying attention to it.
Why Offloading Substance Can Be Tempting
Offloading the substance of writing or argumentation happens when AI starts determining what the argument is—which claims are made, how they’re supported, and where the reasoning ends up going. This is tempting precisely because the output arrives already organized, already confident, already shaped like an argument.
What’s really happening in those moments is that something still vague or only half-formed gets handed over for shaping. There may be a general sense of the topic, maybe even a preferred conclusion, but little clarity about what actually matters or how the pieces should fit together. AI is then asked to do more than help with expression—it’s asked to impose form, direction, and coherence on ideas that don’t yet have them.
At its worst, this looks like providing only marginal direction about where the argument should land and letting the system do the rest. The claims, the structure, even the framing of what counts as a good reason all emerge from the output itself. By the time the user starts reacting, they’re no longer deciding what the argument should be so much as evaluating whether they like the version they’ve been given.
I’ve written before about how AI can sound as though it knows what it’s talking about even when it doesn’t. The same dynamic shows up here and can affect both non-experts and experts. What changes isn’t the illusion itself, but how it plays out.
For non-experts, offloading substance often starts with genuine uncertainty. There’s a topic of interest and perhaps a vague sense of where things should land, but little clarity about what actually matters or how the argument ought to be built. When AI supplies a coherent structure in that moment, it can feel like an authoritative argument. At that point, the user—lacking the background needed to meaningfully refine or redirect what’s been generated—may be tempted to simply accept the argument largely as given. The substance of the argument is effectively decided with little contribution from the user. That’s what people mean when they talk about “AI slop”—writing where fluency substitutes for judgment.
For experts, the handoff tends to begin differently but can still end in the same place. Rather than uncertainty, the starting point is often confidence—about the conclusion, the framing, or the general thrust of the argument. That confidence then gets translated into a prompt, and AI is asked to generate a coherent case that aligns with it.
The point at which substance gets offloaded isn’t the initial direction, but what happens next. When the output sounds good enough—internally coherent, well-structured, and broadly consistent with the expert’s intent—it becomes easy to accept it largely as is. Critical engagement drops off. Decisions about which reasons deserve emphasis, which objections warrant attention, and how the argument should ultimately be organized are left embedded in the generated text rather than actively revisited by the user.
This dynamic has become more likely as LLMs have improved. Earlier versions of these tools struggled to produce sustained, coherent arguments without extensive refinement, which naturally pulled users back into the process. As the outputs have become more polished, the stopping point has moved. Each new generation makes it easier for experts to disengage earlier—ceding substantive decisions because the output no longer obviously demands it.
In both cases, the problem isn’t that users lack ideas or judgment. It’s that those judgments stop being exercised at precisely the point where they still matter. For non-experts, that point comes early, before there’s enough leverage to reshape what’s been generated. For experts, it comes later, once the output sounds good enough to accept. The result is the same: substantive decisions end up embedded in the text rather than actively owned by the person presenting the argument.
This also helps explain why these outputs so often feel superficial or oddly un-nuanced. When substantive decisions are handed off early or accepted wholesale, the result isn’t necessarily incoherent—it’s often a flattened version of an argument. Tradeoffs go unexplored, tensions get smoothed over, and qualifications appear generic. It may still possess polish, but that polish largely lacks the true substance that defines most complex arguments.
Translation Is Still Thinking (Just a Different Kind)
Offloading translation works differently. Here, the substance of the argument is already doing real work internally—the claims make sense, the judgment calls have been made, and the direction is intentional—even if the ideas aren’t yet expressible in clean prose. The difficulty isn’t deciding what to say; it’s getting what already makes sense out of one’s head and onto the page.
Translation isn’t a cosmetic step that comes after thinking is finished. Forcing ideas into language exposes gaps, tensions, and ambiguities that aren’t always visible internally (Hayes, 1996). Once an argument exists as text, it can be examined, challenged, reorganized, or rejected in ways that aren’t possible when it remains implicit.
Used this way, AI isn’t deciding what the argument should be. It’s helping generate a provisional rendering of ideas the user already owns. The output isn’t treated as “the argument” but as a draft translation—something to interrogate, revise, or discard. The user stays engaged after the text appears—tweaking, reshaping, and revising the prose—rather than treating fluency as a stopping point.
It’s worth contrasting this with substance offloading, where critical thinking largely stops once the text appears. In those cases, AI isn’t just translating ideas into prose—it’s doing the work of deciding what the argument is. Fluency becomes a signal to move on rather than a prompt to think further. The tricky part is that the shift from translation to substance offloading is often subtle. From the outside, both uses can look the same: fluent text, reasonable structure, arguments that sound fine. The difference lies in whether fluency prompts further engagement—or brings it to a halt.
Why the Two Can Get Conflated
One reason offloading substance versus translation can get easily conflated is that, in practice, they aren’t distinct categories. They sit on a continuum. What changes along that continuum isn’t whether AI is involved, but how much of the argument already exists—clearly and deliberately—before it is.
At one end, there’s little more than an inchoate idea: a topic of interest, a vague intuition, perhaps a preferred conclusion. In those cases, most of the substantive work is handed over to the system—what the argument is, how it’s structured, which reasons are foregrounded. At the other end, there’s a fully articulated draft with claims, premises, and tradeoffs already in place, where AI’s role is limited to polishing prose or smoothing expression.
Most real uses fall somewhere in between, and often shift along that continuum depending on context. Users may bring some substance to the table—a rough argument, partial structure, or set of commitments—and ask AI to help refine it. The system then fills in details, tightens phrasing, or extends the reasoning, which in turn prompts the user to revise, tweak, or redirect the text, either through direct editing or additional prompting.
The challenge is maintaining control over the argument as that process unfolds. As long as the user continues to make the substantive decisions—about what matters, what counts as a good reason, and how the case should ultimately be framed—AI remains a tool for translation and refinement. When that involvement drops off, even briefly, collaboration can slide into something else: a subtle handoff where substance is no longer being shaped so much as accepted1.
As AI systems continue to improve, the balance has the potential to shift in a predictable way—and in some cases, this shift if already evident. Producing coherent arguments, plausible reasoning, and polished prose now requires far less effort than it once did. From an ecological rationality perspective, it can start to look rational to offload more of the substantive work to the system and retain less of the difficult cognitive labor oneself.
Earlier versions of these tools often demanded sustained involvement simply because the output wasn’t reliable enough to stand on its own. Gaps were obvious. Errors were frequent. Engagement was necessary. As those rough edges disappear, so does the immediate pressure to stay involved. The work still can be done by the user—but it no longer has to be.
But what looks like sensible efficiency in the moment can subtlely reassign where thinking happens. Decisions that once required active judgment—about emphasis, tradeoffs, or argumentative direction—are increasingly handled upstream by the system. Over time, the user’s role narrows to approving, tweaking, or lightly editing what’s already there.
None of this requires bad intentions or misunderstanding. It’s the predictable result of tools getting better. When fluent, well-structured output is readily available, the path of least resistance increasingly involves relying more and more on these tools.
Who Owns the Argument?
At some point, all of this collapses into a simpler question: can you attest to the argument that’s been produced? Do you actually stand behind its logic, its flow, and its substantive claims? However the text came into being, the argument ultimately carries your name—not Claude’s or ChatGPT’s.
That question is important because fluency can mask ownership in the psychological sense. It’s possible to recognize that an argument sounds reasonable without ever having fully engaged with the judgments that give it shape. When that happens, authorship becomes nominal rather than real.
This is something I see regularly in teaching contexts, where students sometimes submit work that is polished and coherent but clearly unowned—arguments they can’t explain, defend, or revise once the surface fluency is stripped away.
The longer-term risk is developmental. If substantive decisions are routinely handed off to the system, users may begin to exceed their own expertise without realizing it—or never fully develop that expertise in the first place. Judgment doesn’t accumulate simply by approving fluent outputs. It develops through repeated engagement with the hard parts: deciding what matters, weighing tradeoffs, and living with the consequences of those decisions (Ericsson et al., 1993; Klein, 2017)2.
AI doesn’t absolve authors of responsibility for what they put into the world. However helpful these tools become, the argument still belongs to the person who signs it, submits it, or teaches from it. The challenge going forward isn’t avoiding AI, but resisting the temptation to let fluency substitute for ownership—especially when doing so feels efficient, rational, and easy.
It’s tempting to think this can be solved by front-loading enough guidance up front—an intuition that sits behind much of what gets labeled “prompt engineering.” For relatively simple or well-bounded tasks, that can work. But as arguments become longer, more interdependent, and more judgment-laden, it becomes harder to specify all the substance in advance. At that point, what looks like polishing often involves offloading judgments that weren’t made beforehand.
This doesn’t require that all substantive thinking occur prior to AI involvement. Even when some initial substance has been offloaded, ownership can be reasserted through sustained engagement with what the system produces—interrogating its assumptions, tracing implications, weighing consequences, and revising accordingly. What matters is not when judgment is exercised, but whether it ultimately is.