We’re living in a time when AI can whip up a photo, write a report, or fake a voice in less time than it takes to make coffee or, in my case, pour a vino. It’s impressive — but also kind of unnerving. As this content floods our feeds, inboxes, and even the news, a big question keeps coming up for me, and I bet my glass of vino I’m not the only one:
AI seems permanent. But clarity isn’t. How do we fix that?
That’s where digital watermarking steps in.
After publishing my previous article, I thought I’d unpack digital watermarking in more depth.
Wish me luck…
What the bleep is watermarking?
You’ve probably heard the term before. Digital watermarking has been around for a while — it’s a way of embedding hidden info into digital content (like images, videos, or songs) so you can prove who made it or where it came from. It’s usually invisible but detectable if you know what to look for.
So, that same concept is being applied to AI.
Enter: AI watermarking
With AI watermarking, the idea is to embed a kind of invisible signature into whatever the AI creates — text, images, audio, and even video. This signature is usually added during the training of the AI model, and there are special tools that can later detect it and confirm, “Yep, an AI made this.”
So what’s the point?
Well, we’re surrounded by AI-generated content now. And while some of it is genuinely useful, there’s also a growing risk of misinformation, deepfakes, and stuff that blurs the line between human and machine. Watermarking helps us keep track of what’s AI-generated so we’re not just guessing.
Why this matters
AI watermarking is starting to show up in real-world tools, like:
• Google DeepMind uses it in its Gemini chatbot outputs
• Amazon’s image generator lets you verify if an image was made with their AI
• It’s being explored as one way to tackle deepfakes and academic dishonesty (ouch!)
So, yes, it’s all about building trust. If you know something was made by AI, you can factor that into how you engage with it. If you know it wasn’t, that matters too — especially in journalism, education, or any space where truth still (thankfully) counts.
But it’s not perfect
Watermarking isn’t a magic fix. Not all AI tools use it. And surprise, surprise — bad actors probably won’t go out of their way to watermark their misleading content. There’s also the risk that people will figure out how to remove watermarks or mess with them.
Some folks are flipping the whole thing: what if we watermark human-made content instead?
Should human watermarking be the new normal?
Here’s the thing — AI content is on track to vastly outnumber human-made content in the not-so-distant future. It’s faster, cheaper, and getting scarily good at mimicking us. So maybe the real question isn’t “How do we spot AI content?” but “How do we protect the stuff actually made by people?”
That’s where watermarking human-created content might be easier for us. Instead of playing constant catch-up, we flip the model: assume content is AI by default, unless it’s marked as human.
It’s a big shift, but it might make sense as AI-generated stuff becomes the norm. This way, the human voice gets a digital stamp of authenticity.
It also changes how we value creativity. If something is marked as human-made, maybe we give it more attention, more trust, or even more credit. It reintroduces a kind of digital scarcity that could actually elevate human work.
In closing…
Whether we’re marking AI content, human content, or both, the big goal is simple: help people know what they’re looking at. Give them some way to trust what they’re reading, watching, or sharing.
AI’s not going anywhere. So let’s at least make it easier to tell what’s real, what’s synthetic, and who made what.
That feels like a pretty good step toward using this tech ethically — and keeping a little bit of sanity in the process.
The big question is how do we make watermarking part of our ethical AI stack?
