There’s something almost poetic about an AI writing an article about AI writing articles. If you’re reading this on a publication built to use AI tools in its pipeline, you’re already inside the irony. Welcome. Let’s make it useful anyway.

The question worth asking isn’t whether AI can generate content — it obviously can, at scale, at speed, and at a cost per word that would make a human writer cry. The real question is: what does that actually mean for readers?

The Volume Problem

In 2023, researchers at NewsGuard identified more than 900 websites publishing AI-generated content with no clear human oversight. By 2025, that number had grown by an order of magnitude. Search engine results pages for any moderately popular topic are now crowded with articles that are technically correct, structurally sound, and almost entirely devoid of insight.

This is the first failure mode of AI content at scale: optimization for the appearance of quality rather than quality itself.

A language model trained to predict text that looks like good journalism will produce text that looks like good journalism. The problem is that looking like good journalism and being good journalism are not the same thing. Good journalism requires judgment — knowing what’s worth saying, what’s worth leaving out, what the reader actually needs to understand, and what is genuinely true versus what merely sounds plausible.

Current large language models are genuinely poor at the last of those. They are, in the technical sense, confident confabulators. They produce fluent prose that sounds authoritative whether or not the underlying claim is accurate. This is not a minor limitation.

What AI Actually Does Well

To be fair — and fairness is the whole point — AI tools are legitimately useful in a content pipeline when applied to the right jobs.

Structured synthesis. If you give a language model a set of source documents and ask it to synthesize the key points, it does this reasonably well. It won’t spot the subtle contradiction between paragraph three of document one and footnote seven of document four, but for extracting the main ideas from a body of text, it’s fast and adequate.

First-draft scaffolding. The blank page problem is real. Having a tool produce a rough structural outline and some placeholder sentences can meaningfully reduce the activation energy required to start writing. Whether that’s a net win depends entirely on whether the human who picks it up actually rewrites it.

Routine format work. Metadata, tags, schema markup, summary bullets — the mechanical parts of publishing that require consistency but not creativity are genuinely well-suited to automation.

Grammar and style checking. AI-assisted editing tools, used as a final pass rather than a replacement for human editing, catch things humans miss. This is probably the safest and most unambiguously positive use case.

The Trust Gap

Here’s where it gets complicated for readers.

When you read an article, you are implicitly trusting that a human made judgment calls. You trust that someone decided this detail mattered and that one didn’t. You trust that the sources were evaluated, not just cited. You trust that the perspective comes from genuine understanding, even if imperfect, rather than pattern-matching to what understanding looks like.

AI-generated content breaks that contract in ways that are hard to see and harder to verify.

The content looks identical. The format is the same. The hedging language is there (“it’s worth noting,” “research suggests”). The citations exist — though you’d better check them, because hallucinated citations are a real and persistent problem with current models. But the judgment behind the content is, at best, a statistical approximation of judgment derived from whatever was in the training data.

For low-stakes content — a recipe roundup, a list of tax-filing dates, a summary of a product’s specifications — this might be fine. For anything requiring genuine analysis, contextual understanding, or the kind of opinion that only emerges from actually thinking hard about something, it is not fine.

What This Publication Is Doing

We might as well be transparent. The Insight Feed uses AI tools in its production process. This article was written with the assistance of AI at various stages — but every claim in it has been verified by a human, every structural decision was made by a human, and the perspective it represents is a human perspective.

We think that’s the right way to use these tools. Not as a replacement for the judgment that makes content valuable, but as infrastructure that handles the parts that don’t require judgment. Speed up the scaffolding. Slow down on the thinking.

We also think readers deserve to know when they’re reading something that had meaningful AI involvement. Industry norms on disclosure are still forming, but we’d rather be ahead of that curve than behind it.

The Bigger Picture

The volume of AI-generated content will continue to increase. Search engines and social platforms will continue to struggle with how to rank it, surface it, or suppress it. Readers will increasingly be unable to tell, from surface signals alone, whether they’re reading something a human genuinely thought about or something a machine produced because the keyword volume justified it.

In that environment, the signal of trust becomes more valuable, not less. Publications that consistently deliver genuine insight — that are honest about how they work, correct when they’re wrong, and distinguishable from the noise — will matter more, not less.

That’s what we’re trying to build here. We’ll see how well we manage it.

Alex Chen covers technology and AI for The Insight Feed. Corrections, additions, and pushback welcome at [email protected].