· Newsletter #90 ·

Is the AI Watermark
Our Panda/Penguin Moment?

Greg Heilers Industry News September 2026

Happy Tuesday!

A couple of weeks ago I dropped a Facebook thread into our team Slack with one question: "What do we think about watermarking?"

  • I'm a little late to this party; that x-Pacific move wore me out!

But, the more I read, the more I felt like you all needed to hear about this:

On August 11, Anthropic announced that text from newer Claude models now carries an invisible watermark. And a group of long-time marketers I respect started asking whether that's the beginning of a Panda/Penguin moment for AI content.

· THE NEWS ·

What Actually Happened

Stripping out the hype, here's what's on the record:

  • Regulation. Anthropic signed the Code of Practice from the EU AI Act's transparency rules (Article 50).
  • The watermark lives in the words. Per Anthropic's own help center, it's embedded "directly into the text itself," travels with copy and paste, and "may persist through some editing." Files also get C2PA signed metadata.
  • Only models launched on or after August 2, 2026. Older models are on a transition period.
  • There is no public "Is this AI?" button… yet. Detection is in private preview for regulators, law enforcement, and researchers. A file checker exists. Broader API access is "planned."
  • Everyone else signed the same code. Google, Meta, Microsoft, and OpenAI are on the list. Google's SynthID for text has existed since 2024.

So today, nothing on your site is being flagged as “AI made this”. But, what about tomorrow? Next year?

· HOW IT WORKS ·

The Best Plain-English Explanation
I Found (on Reddit, Naturally)

I don’t know what your mental model of "watermarking" is, but mine was wrong.

Reddit commenter “CircumspectCapybara” updated my understanding better than any press release:

Reddit comment explaining that AI watermarking is not hidden characters but the word choices themselves, via Google's SynthID keyed hash approach
Not hidden characters. The watermark is the word choices themselves.

Stay with me here… this follow-up answer made everything click for me:

Reddit comment walking through the 'I love fruit. My favorite dessert is ____' example of a keyed hash choosing among top candidate words
The "favorite dessert" walkthrough. Read this twice… it's worth it.

We don’t have transparency into Anthropic’s method yet. Here is how Google’s SynthID works:

  • Still an LLM, but different. At every word choice, models have several good candidates. Normally, they pick one at random.
  • The text reads fine. With a watermark, a secret key plus the previous few words decides which candidate wins. The text reads exactly as well, because every option was a good option.
  • Impossible coincidence. Repeat that a thousand times and you get a statistical fingerprint that can't happen by accident. The commenter's math: one in 16… to the power of 1,000.
  • Light edits don't erase it. You'd have to rewrite nearly every three-word window in the piece.
· THE HOT TAKE ·

Chrome Plugin That Highlights
“AI Generated” Content

Nathan Collier runs The Content Marketing Lounge, a Facebook group full of people who were doing this job before "SEO" was a LinkedIn headline. He posted a thought exercise that made me pause to think: what if all AI-generated content is visibly watermarked?

Nathan Collier's Facebook post in The Content Marketing Lounge: the AI watermark might be the biggest disruption to marketing since Panda/Penguin, and a Chrome plugin could color-code every post by the model that wrote it
The Chrome-plugin scenario: every post color-coded by the model that wrote it.

Interesting idea that this scenario doesn't require Google to do anything. A browser extension calling the AI companies' detection APIs is enough to color every LinkedIn post orange or blue.

· PANDA / PENGUIN ·

Is the Comparison Fair?

If you weren't around for it: Panda landed in February 2011 and Google said it touched about 12% of queries. Penguin landed in April 2012, aimed squarely at manipulative links.

The businesses that got hurt weren't the ones dabbling. They were the ones that went all in on the tactic and had nothing else.

Here's what I wrote to the team, lightly cleaned up:

  • For companies going "all in" on LLM-generated content, there could be a day when years of marketing effort (2026, 2027, 2028...) get wiped out with a single deploy of a "watermarking reveal" update to our favorite platforms: YouTube, LinkedIn, Google, ChatGPT, Perplexity.
  • That reveal could be legislated, as in the EU, or self-motivated by an entity larger than us: Google, OpenAI, Anthropic.
  • I'm not fear-mongering. I can't speculate on the probability. I just want to think about an event beyond our current horizon, before it's on the horizon.

Panda didn't punish a page, it punished a pattern. A watermark reveal would do the same: everything you shipped that way, all at once, retroactively.

But, again, the sky isn’t falling here.

Panda needed just one company to act. A watermarking reveal would need three things lined up: watermarks in the text, a detector someone will ship, and a platform willing to show it. Today we have the first of the three.

One more lens before we get to the comments. Jimmy Park has a fun alternative theory on why the labs would even bother:

  • Watermarking is the "stick" in the frontier labs' carrot-and-stick approach to making sure humans keep producing content for future training runs.

Think about it from the model's side. If every AI paragraph is marked, human-written text stays the scarce, valuable ingredient, and the next training run still has something fresh to learn from.

Under that theory, the watermark isn't aimed at marketers at all. We're just standing between the labs and their next dataset.

· THE PUSHBACK ·

The Peanut Gallery Had the Best Insights

Per usual, the comments were even better than the OP (no offense, Nathan!), which is why I saved them for you:

Facebook comment thread: Adam J Thompson suggests open source models will strip watermarks; Colin Theriot replies the only reason to do that is to deceive; Nathan Collier says the pattern sounds hard to erase without erasing the substance
Adam J Thompson: a GitHub repo to strip AI watermarks got 4,700 stars in two days, comparing it to article spinners dodging duplicate content filters
A watermark-stripping repo with 4,700 stars in two days. Article spinners, 2026 edition.

The article-spinner comparison is the one I keep coming back to. Spinners "worked" right up until they were the thing that got you caught.

Colin Theriot: AI boosters claim customers don't care but resist self-reporting AI use and are mad about tattle-ware being built in. Nathan Collier: Big same.

And the counterpoint, in nine words:

Drew Kutnick: If everything is highlighted, nothing is highlighted.
If 80% of the feed turns orange, does anyone still scroll past orange?
· WHAT I'D DO ·

The Takeaway

I'm not a purist. I use Claude most workdays.

So this isn't "stop using AI." It's "know your exposure," the same way you'd want to know your link profile before a Penguin refresh:

  • Take inventory. Which published assets are pasted straight from a model, and on which platforms? If a reveal happened tomorrow, that list is your blast radius.
  • Keep the parts that matter human. Opinions, first-hand data, client stories, and the voice. That's what AI can't copy anyway, and it's the part a highlighter can't cheapen.
  • Treat pure AI text as a short-term loan, not an asset. Fine for a first draft. Risky as the thing you're known for.
  • Look hardest at your AI visibility assets. The Reddit threads, LinkedIn posts, and YouTube videos that get you recommended are the exact surfaces a platform would highlight first. Ours are made by people on our team, and that used to be a quality argument. It might turn into an insurance policy.

What do you think about watermarking? Hit reply and let’s party like it’s 2011!

Until next week,
Greg

Never Miss an Issue

Join fellow SEOs and marketers who read the Jolly Newsletter every Tuesday.