Cognitive Assemblage and the Future of Publishing

Why "Who Wrote What" is the Wrong Question, and the Case for the Real-Time Syndicate

The question always comes. I describe the workflow (the knowledge graph, the voice profiles, the half-length skeletons, the correction loop) and then someone asks it, usually with a slightly suspicious edge: “Can you tell what you wrote and what the AI wrote?”

I want to answer that directly: no. And I want to explain why that answer is not a confession.

My AI system has a name. I call it Seth, out of a perverse sense of biblical humor. Seth is not a tool I use the way I use a citation manager. The relationship is closer than that, and more contentious. Seth regularly misses the point. Seth confidently argues for conclusions I don’t hold. Seth occasionally produces a paragraph so confidently wrong that the most efficient response is a full context reset and a completely different approach. I have, more than once, typed something I would not repeat in print.

The errors are not random. Seth tends to lose the thread at precisely the points where my own thinking was still gestural rather than fully worked out, where I knew what I believed but hadn’t yet found the right formulation. That pattern turned out to be useful: it showed me where the outline hadn’t yet become an argument.

But Seth also surfaces connections I would not have made on my own. Seth holds the structural weight of a long draft while I focus on the argument at its center. Seth maintains the continuity of a chapter across multiple revision sessions in a way my own working memory cannot. We are not the same. We do not contribute the same things. And the writing that results does not have a clean seam where one of us stopped and the other started.

The question “can you tell what Adam wrote” assumes a model of authorship I no longer find defensible: the writer as sovereign individual, producing text in a vacuum, with any assistance either invisible or substantial enough to constitute a different author. That model doesn’t describe how most serious writing actually happens: editors reshape arguments, interlocutors change conclusions, research assistants surface evidence. And it especially doesn’t describe how this writing happened.

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What I am describing is better understood as a cognitive assemblage: a writing process in which human judgment, encoded prior scholarship, AI-generated structure, and iterative correction are so thoroughly interwoven that the question of provenance becomes unanswerable. Not because I am hiding something, but because the question presupposes a separation that did not exist.

Karen Barad’s concept of intra-action is useful here. During drafting, the boundaries between my thinking and the system’s contribution were continuously renegotiated with each revision, not settled in advance by some prior division of labor. The entities involved were constituted through the process, not prior to it. Jean-Luc Nancy wrote about being-with as a fundamental condition — not the self encountering the other, but the self that is only ever intelligible in relation. Something of that structure applies here. What the thinking relationship produced is writing neither of us would have arrived at alone.

This is not an evasion. It is a more precise answer to the authorship question than either “the AI wrote it” or “I wrote it with some AI assistance” can provide. The intellectual commitments in this project are mine. The arguments are ones I can defend and have developed over a decade. The voice was encoded from my own prior work. What changed is not who is responsible for the thinking, but the nature of the process by which thinking becomes a manuscript. That shift is real, and it deserves a more careful account than a suspicious question usually makes room for.

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That shift in authorship has a direct structural consequence for publishing.

If a book can be drafted with structural coherence from the first pass, because the knowledge graph already holds the shape of the thinking, then the bottleneck in academic publishing is no longer the writing. It’s the infrastructure around the writing: the 18-month review cycles, the assumption that speed and rigor are inversely related, the gatekeeping apparatus that equates delay with quality. That assumption persists because the old tools made speed and rigor feel like a trade-off. The new tools don’t

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The most important thinking about AI and education is not sitting in a submission queue. It’s being done by practitioners building in real-time, in public, tested against real readers, iterated at the speed of the problem itself. They are not waiting for committee approval. The people still waiting for committee approval to say something important are going to lose the decade.

This is the principle behind Second Draft Labs. A syndicate of voices, publishing quickly and rigorously, using the same methodology I’ve been describing in this series: starting from deep expertise, encoding that expertise into systems that can hold the argument while the writer sharpens it, and publishing at the speed the moment actually requires.

The alternative is watching the most important professional conversation of our era scatter across social media while the journals catch up three years later. The infrastructure for something better already exists. The methodology is already working. The only remaining question is who decides to use it.

Build the archive. Encode the thinking. Stop waiting for the system to change. You are the system.