You probably read something AI-generated today and never noticed. The industry's fix for that — watermarking — is far shakier than the headlines make it sound, and understanding why tells you a lot about what AI can and can't control.
"Just watermark AI content" sounds obvious. In practice it splits into two very different problems, one nearly solved and one that may never be.
Images are the easy case
For AI-generated images, watermarking is real and working. Techniques like Google DeepMind's SynthID embed a signal directly into the pixels — invisible to you, detectable by a machine, and robust enough to survive cropping, compression, and mild edits. Because an image has millions of pixels to hide a signal across, you can perturb them imperceptibly and still recover the mark later.
Alongside that, provenance standards like C2PA ("content credentials") attach cryptographically-signed metadata describing how a file was made. Together, embedded watermarks plus signed provenance give images a fighting chance at "was this AI-made?"
Text is the hard case — maybe the impossible one
Now try to watermark a paragraph of AI-generated text. The trick is to subtly bias which words the model picks — nudging it toward a secret pattern that a detector can later spot. Clever, and it works on untouched output.
Then a human changes three words. Or runs it through another model to paraphrase. Or just deletes a sentence. The fragile statistical pattern evaporates, because text has almost no redundant "space" to hide a signal in the way pixels do. A short passage carries too little signal to mark reliably at all. Text watermarking isn't fake — it's just brittle in exactly the situations where you'd want it most. This gap between "works in the lab" and "survives the real world" is something I think about constantly across the systems I build.
Provenance beats detection
The quiet shift among people who work on this seriously: stop trying to detect AI after the fact, and start proving origin at creation time. Instead of asking "does this text look AI-written?" — an unwinnable guessing game — you ask "is this content cryptographically signed by a source I trust?" It flips the problem from detection (adversarial, probabilistic, losing) to authentication (verifiable, cryptographic, winnable). The future isn't a magic AI-detector; it's a chain of trust attached to content from the moment it's made.
Why this matters
Watermarking is really a question about trust in a world where generating convincing content is free. And the honest state of it is uneven: solvable for images, shaky for text, and only meaningful if platforms actually adopt the provenance standards. Anyone selling you a reliable "AI text detector" is selling confidence that the underlying science doesn't support — a theme worth understanding before you trust any such tool. I dig into more of these AI-reliability questions at www.divyakush.com.
The takeaway: don't ask whether content looks AI-made. Ask whether it can prove where it came from. That's the only version of this problem that has an answer.
Related reading
- Why LLMs hallucinate — and the patterns that actually stop it — the deeper trust problem underneath all of this.
