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Scientific publishing is entering a turbulent transition rather than a terminal collapse, a dynamic captured in The Atlantic’s January 2026 article “Science Is Drowning in AI Slop” https://www.theatlantic.com/science/2026/01/ai-slop-science-publishing/685704/ The piece highlights how AI lowers the cost of producing fluent but low-quality or misleading research, overwhelming peer review and eroding trust. This feels new, but it is better understood as AI amplifying long-standing weaknesses: narrative plausibility, citation density, and prestige signals standing in for verification.

In the near term, this creates a noisy and uncomfortable phase. AI not only launders weak or outcome-engineered studies by polishing them, it also exposes how much questionable work already existed. Retractions, skepticism, and institutional anxiety increase. This phase delays reform because institutions instinctively defend existing processes—but it also makes their failure undeniable at scale.

The likely future is a shift from narrative-centric publishing to verification-centric science. As AI makes large-scale checking, replication, statistical forensics, and cross-paper analysis cheaper than traditional review, authority migrates from prose to data, code, and reproducible methods. Single papers matter less; replicated clusters matter more. What looks like breakdown today is better seen as a phase transition: the slow collapse of narrative authority and the emergence of evidence-first norms shaped as much for machine readers as for human ones.

The Sound of a System Changing

Articles like “The AI-Powered Web Is Eating Itself” https://www.noemamag.com/the-ai-powered-web-is-eating-itself/ frame AI as a breaking point for the internet — a moment where incentives collapse, creators are erased, and the web begins consuming its own foundations. The tone is familiar: something vital is being lost, and the damage may be irreversible.

But much of what’s being described isn’t destruction. It’s noise.

Specifically, it’s phase-transition noise — the turbulence a complex system makes while reorganizing into a new equilibrium.

The pre-AI web was already brittle. Discovery was winner-take-most, SEO drowned out originality, traffic was a proxy for value rather than a measure of it, and most content was effectively invisible. AI didn’t break this system; it stripped away the friction that concealed its weaknesses. Compression replaced browsing, summaries replaced scavenger hunts, and the redundancy of the web suddenly became obvious.

From inside the transition, this feels like collapse. Interfaces change faster than institutions. Old metrics stop working. Revenue models tied to clicks unravel. That local entropy is real — some sites will vanish, some careers will shrink, some forms of writing will no longer be economically viable. But local disorder is not global decay. In complex systems, it’s often the precondition for higher-order structure.

Crucially, user intent hasn’t disappeared. People who want brief answers get them faster now. People who need depth — journalists, analysts, researchers, obsessives — can still find primary sources, often more efficiently, aided by tools that surface clusters of links, perspectives, and provenance on demand. AI doesn’t block seriousness; it lowers the cost of reaching it when it’s actually needed.

What many of these essays mourn is not the loss of knowledge, but the loss of a business model and a familiar status hierarchy. They mistake the erosion of traffic for the erosion of truth, and interface change for epistemic failure. Yet knowledge doesn’t die when it’s summarized. It dies when discovery goes unfunded — a problem that long predates AI and won’t be solved by preserving artificial friction.

Every major leap in information technology has sounded like this while it was happening. The printing press, broadcast media, the web itself — all produced a chorus of warnings about collapse that, in hindsight, were the soundtrack of emergence. Optimization always sounds destructive before new structure stabilizes.

What we’re hearing now is not the web eating itself. It’s the noise of a new information metabolism forming.

What follows was written by ChatGPT 5.2 Instant and it reflects my conversation with it about the two articles linked below. Enjoy.

Ego plays a complicated role in periods of technological change. It sharpens skill during stable eras, but it often hinders adaptation during inflection points.

Two recent essays on AI and software engineering illustrate this tension clearly.

Emir Ribic’s “From Craftsmen to Operators” https://dev.ribic.ba/the-rapid-evolution-of-software-engineer-s-role

Ribic frames the rise of AI-assisted development as a loss of craft. He mourns the disappearance of deep, line-by-line problem solving and the sense of authorship that came with it. Engineers, in his telling, are becoming operators—prompting, reviewing, and assembling—rather than builders. The piece captures something real: pride in difficulty, satisfaction in mastery, and the joy of personally solving hard problems.

But that pride is also where ego enters. What’s being lost isn’t just a way of working—it’s a form of status. Manual coding was scarce, hard-won, and socially rewarded. When AI erodes that scarcity, it threatens identity as much as technique. The article largely dwells on that loss, while giving little attention to the exhilaration many engineers feel when modern tools collapse days of work into minutes.

P.C. Maffey’s “AInxiety” https://pcmaffey.com/ainxiety-1/

Maffey takes a more balanced view. He openly acknowledges discomfort, ethical concerns, and the risk of over-reliance on AI, but he also accepts the productivity gains and uses AI extensively in his work. Rather than framing the shift as a fall from craft, he reframes the role: less time spent on syntax, more on design, planning, judgment, and responsibility. Where Ribic sees loss, Maffey sees trade-offs.

Taken together, the contrast suggests a broader pattern we’ve seen before.

During the Industrial Revolution, skilled artisans resisted mechanization not only because of economic threat, but because identity and status were tied to difficulty and exclusivity. The same thing happened when typewriters gave way to personal computers in offices—typing lost its prestige once everyone could do it. In each case, ego slowed adaptation, but also reflected a real commitment to excellence that had previously raised standards.

That leads to a fair synthesis:

Ego hones skill in stable environments. It motivates mastery, pride, and depth. But during technological inflection points, the same ego can become friction—blinding people to leverage, speed, and new forms of craftsmanship.

Progress doesn’t eliminate craft; it relocates it. The challenge isn’t to abandon pride in skill, but to recognize when clinging to old expressions of that skill prevents adaptation to more powerful tools.

The winners in these transitions aren’t ego-free. They’re ego-aware.