Reddit is fighting AI spam with AI. Here's why that's not the finish line it sounds like

In 1973, the evolutionary biologist Leigh Van Valen borrowed a line from Lewis Carroll to explain something odd he'd noticed in the fossil record: species don't seem to get safer over time, even as they keep adapting. He called it the Red Queen hypothesis, after the character in Through the Looking-Glass who tells Alice that in her world, it takes all the running you can do just to stay in the same place. A faster gazelle doesn't end the arms race with the cheetah — it just forces the cheetah to get faster too. Neither side wins. The race just continues at a higher speed.

Anyone watching the last few years of the internet's spam problem will recognize the pattern.

Reddit recently detailed the tools it's built to fight spam, and the headline is a little ironic: the company is now using large language models to catch content that was very often generated by large language models in the first place. According to Reddit, the platform blocks roughly 23 million spam views a day and catches about 25,000 new spam posts and comments daily. The company says its updated systems are catching a subtler class of problem than older filters could — coordinated inauthentic behavior and manufactured hype that's designed to look organic — and that user exposure to spam dropped 20% from January to March compared with the previous quarter.

Reddit isn't alone in wrestling with this. YouTube, Meta, and Instagram now require disclosure when content is AI-generated, and TikTok has gone further, letting users dial up or down how much AI-generated content they want to see in their feed at all. The logic is the same everywhere: platforms can't out-write bad actors, so they're trying to out-detect them instead.

We think this is a useful case study for any business thinking seriously about where AI fits into their own content, marketing, or customer-facing systems. A few things stood out to us.

Detection and generation are locked in the same race, and neither one "wins." Every improvement in spam detection creates pressure for spam generation to get more subtle, and every improvement in generation raises the bar detection has to clear next. There's no future state where this problem gets solved once and stays solved — the realistic goal is staying ahead, not finishing the race. Any business relying on AI-generated or AI-assisted content, at any volume, should plan for that as an ongoing cost of doing business, not a one-time setup.

The win Reddit is reporting is a reduction, not an elimination. A 20% drop in exposure is a genuinely good result. It is not zero. Platforms with far more resources than most companies will ever have are managing this problem down, not out. That's a useful reset for expectations around any AI content-quality or moderation tooling — the metric that matters is the trend line, not a claim of having "solved" spam or fraud.

Disclosure rules are inconsistent, and that inconsistency is now a business risk. Some platforms require creators to label AI-generated content; others simply try to filter it out algorithmically without asking anyone to disclose anything; TikTok now lets viewers choose their own exposure level entirely. For any organization publishing content across multiple platforms, that patchwork of rules is worth tracking closely — what counts as compliant disclosure on one platform can be silent non-disclosure on another.

Machines are catching the pattern; people are still needed to judge the exception. Researchers and platform-trust experts have been consistent on this point: automated moderation systems perform best paired with human review, not left to run alone. The pattern-matching that LLMs are good at — spotting coordinated behavior, unnatural engagement spikes, manufactured hype — is exactly the layer that scales. The judgment calls about edge cases, context, and intent are exactly the layer that doesn't, at least not yet.

The Red Queen framing isn't a pessimistic one, even if it sounds like it. It just means the goal isn't to build a filter and walk away — it's to build a system, and a team, that keeps pace as both the spam and the detection tools keep evolving. That's a very different kind of project than "install the AI tool and you're done," and it's the one most businesses actually need.

If you're trying to figure out where automated detection should end and human judgment should pick up in your own content or customer-facing systems, that's exactly the kind of workflow we help teams build.