AI made content so cheap to produce that this was inevitable. The content-analytics firm Graphite reported last fall that AI-generated articles had pulled even with human-written ones online, briefly edging ahead in late 2024. Human writing still dominates where people actually look, though: Graphite found 86% of articles ranking in Google, and 82% of those cited by ChatGPT and Perplexity, were written by people.
The easy solution is to ban AI content outright, which some have done. Medium barred it from its paid Partner Program, and the sci-fi magazine Clarkesworld had to submissions after a flood of AI-generated spam. The problem is that bans are a blunt instrument. They get rid of the bad stuff, but they throw out the good—people who use AI, paired with human judgment, to enhance and improve their content—along with it.
Bans also depend on reliable filters, and that’s not a given. AI detection is notoriously unreliable and prone to false positives. And sophisticated prompting plus the generational jumps in AI models, which land every few months, turn the whole thing into an arms race. What works today may not work tomorrow.
The better approach is the scalpel, not the hammer: cut away the poor, valueless AI content, but leave intact the AI-enhanced work that audiences appreciate. The way to do that is to target outputs and outcomes, not the mere presence of AI.
In the span of one week, two big platforms made moves that show the contrast between these approaches. YouTube introduced new controls on certain content types, including the all-too-common AI-narrated video padded out with stock B-roll or generative imagery. Those videos, along with a few other cases, are now harder to monetize, which removes the main incentive to make them.
In the world of text, Substack rolled out an AI detector powered by Pangram. It shows up two ways: First, as a button when a writer is about to publish, scans the post, and returns an estimate of how much was written by a human and how much by a machine. Most writers already know whether they used AI, but for larger publications with guest contributors, the feature could work as an extra vetting step. Second, for readers, you can scan any article on the platform for AI writing as long as it was published after July 21, 2026.
Again: no one likes slop, and it should be disincentivized. But whether something is synthetic doesn’t tell you much about whether it’s any good. It’s ultimately up to each reader what to do with that score, but Substack’s scanner quietly nudges everyone toward a simple equation: AI equals bad.
Then there’s the false-positive problem, which dogs AI detectors. A Stanford study on GPT detectors found they misclassified 61% of essays by non-native English writers as AI-generated, and at least one detector flagged 97% of them, while essays by native writers drew just a 5% false-positive rate. The tools mistake unfamiliar rhythm for a machine.
Those error rates get brutal at scale. As one analysis noted, even a 1% false-positive rate would wrongly flag thousands of pieces a year at a single mid-size operation. Now picture that across a platform the size of Substack or Forbes.
The better approach is to look downstream of basic AI detection, which is what YouTube has done. Admittedly YouTube has an easier job, since at its scale distinct abuse patterns show up fast. But the principle travels: bad outcomes reveal themselves in the work. Repetitive formulas, weak engagement, high bounce rates, negative comments, and the rest.
For Substack, the answer is to get specific. The detector treats “made with AI” as the thing worth flagging, when the real target is content that’s valueless to readers. Those aren’t the same thing. Substack would do better to name the behavior it wants gone and target it directly: posts that use a lot of words to say nothing, auto-generated digests with no human judgment behind them, or even whole publications spun up to feed crawlers instead of readers. If a brand stands up a Substack and opens it to bots to flood the zone with narrative-shaped filler, by all means downrank it or clear it out. Detection can help there, as a signal on the back end. But the label a reader sees should be about the quality of the work, not the tools behind it. I’ll cop to my bias here. The Chatbox, the news digest in my Substack newsletter, is built with heavy AI assistance. It’s also prompted against a knowledge base tuned to what media people need, edited by a human, and published because a person decided it was worth your time. The use of AI is also disclosed, by the way. A provenance scan adds nothing and introduces a signal that some readers may use as a blunt filter.
Broadly, all of this is progress. Both YouTube and Substack’s moves point to platforms getting smarter about filtering. That’s good news for anyone who makes or reads things online. The market never cleaned up slop on its own, so having filters in place is welcome.
The open question is how best to calibrate these new filters. Get it right, and the internet a couple of years from now looks better than it does today: people using AI to sharpen what they make, audiences getting more of what they actually value, and the slop filtered out before it clogs the feed and degrades everything. Get it wrong, and we’ve just built a more expensive way to distrust each other.
The filters are here, and that’s mostly a good thing. The harder part is pointing them at the right targets.