Chapter 1: What is the main topic discussed in this episode?
Hello and welcome to Decoder. I'm Nilay Patel, editor-in-chief of The Verge, and Decoder is my show about big ideas and other problems. Today, we're going to talk about reality and whether we can label photos and videos to protect our shared understanding of the world around us. No, really, we're going to go there. It's a deep one.
To do this, I'm going to bring on Verge reporter Jess Weatherbed, who covers creative tools like Photoshop and Canva for us. It's a space that's been totally upended by generative AI in a huge variety of ways, with an equally huge number of responses from artists, creatives, and the people who consume all of that art and creative out in the world.
Now, if you've been listening to Decoder or my other show, The Verge Cast, or even just reading The Verge over these past few years, you'll know that we've been talking about how the photos and videos taken by our phones are getting more and more processed and AI generated for years now.
Chapter 2: How has generative AI affected the perception of reality?
And now, in 2026, we're in the middle of a full-on reality crisis, as fake and manipulated, ultra-believable images and videos flood onto social platforms at scale and without regard for responsibility or norms or even basic decency. The White House is sharing AI-manipulated images of people getting arrested and defiantly saying it simply won't stop when asked about it.
We are just totally off the deep end now. Whenever we cover this stuff, I get the same question from a lot of different parts of our audience. Why isn't there a system to help people tell the real photos and videos apart from the fake ones? Some people even propose systems to us. And as it happens, Jess has actually spent a lot of time covering a few of these systems that exist in the real world.
The most promising is something called C2PA. And her view is that so far, these systems have been almost entirely failures. In this episode, we're going to focus on C2PA, since it's the one that has the most momentum. It's a labeling initiative spearheaded by Adobe, with buy-in from some of the biggest players in the industry, including Meta, Microsoft, and OpenAI.
But C2PA, which is also sometimes referred to as Content Credentials, has some pretty serious flaws. First, it was designed as more of a photography metadata standard, not an AI detection system. And second, it's really been only half-heartedly adopted by a handful, but not nearly all, of the players you would need to make it work across the internet ecosystem.
We're at the point now where Adam Masseri, who runs Instagram, is publicly posting that the default should shift and that you should not trust images or videos the way that you maybe could before. Think about that for one second. That's a huge, pivotal shift in how society evaluates photos and videos. And it's an idea I'm sure we're going to come back to a lot this year.
But we have to start with the idea that we can solve this problem with metadata and labels, that we can label our way into a shared reality, and why that idea might simply never work. Okay, Verge reporter Jess Weatherbed on C2PA and the effort to label our way into reality. Here we go. Jess Weatherbed, welcome to Decoder. Hi. I want to just set the stage.
Several years ago, I said to Jess, boy, these creator tools are criminally undercover. Adobe as a company is criminally undercover. Go figure out what's going on with Photoshop and Premiere and the creator economy because there's something there that's interesting. And fast forward, here you are in Decoder today, and we're going to talk about whether you can label your way into consensus reality.
I just think it's important to say that's a weird turn of events.
Yeah, I keep likening the situation to the Jurassic Park memo, whereas people thought so long about whether they could, they didn't actually stop to think about whether they should be doing this. And now we're in the mess that we're in.
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Chapter 3: What are the limitations of AI labeling standards like C2PA?
You can see where the logic lies. It was supposed to be that everywhere a little bit of content goes online, this embedded metadata would follow. So what C2PA does is at the point that you take a picture on a camera, you upload that image into Photoshop.
All of these instances would be recorded in the metadata of that file to say exactly when it was taken, what has happened to it, what tools were used to manipulate it. And then as a two part process, all of that information could then hypothetically be read by online platforms where you would see that information. So as consumers, as Internet users, we wouldn't have to do anything.
We would be able to in this way. imaginary reality, go on Instagram or X and look at a photo and there'd be a lovely little button there that just says this is AI generated or this is real or some sort of authentication. That has obviously proven a lot more difficult in reality than on paper.
Tell me about the actual label. You said it's metadata. I think a lot of people have a lot of experience with metadata. You know, we are all children of the MP3 revolution. Metadata can be stripped. It can be altered. What protects the C2PA metadata from just being changed?
They argue that it's quite tamper-proof, but it's a little bit of an action to speak louder than words kind of situation, unfortunately, because while they say it's tamper-proof, this thing is supposed to be able to resist being screenshot, for example, by the way.
But then OpenAI, who is actually one of the steering community members behind this standard, openly says it's incredibly easy to strip to the point that online platforms might actually do that accidentally. So the theory is there's plenty behind it to make it robust, to make it hard to remove, but in practice that just isn't the case. It can be removed maliciously or not.
Are there competitors to C2PA?
Well... It's a little bit of a confusing landscape because I think one of the few kind of like tech says that I would say there shouldn't actively be competition. And from what I've seen, from what I've spoken to with all these different providers, there isn't competition between them so much as they're all working towards the same goal. Google Synth ID is similar.
It's technically a watermarking system more so than a metadata system, but they work on kind of a similar premise that stuff will be embedded into something you take that you will then be able to assess later to see how genuine it is. Like the technicalities behind that are...
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Chapter 4: Why are social media executives concerned about AI manipulation?
It's always that people are very quick to assure me that things are progressing. There's never any kind of conversation about incentive to motivate other people to do so. So yeah, Apple doesn't sound to really gain anything financially from this other than maybe the reassurance that people know that if they're taking a picture with their iPhone...
it could help to contribute to some sense of establishing what is still real and what isn't. But then that's a whole other can of worms because if iPhones is doing it, then all the platforms that we see those pictures also have to be doing it. Otherwise, I'm just kind of verifying that this is real to my own eyes as me, the person that uses my iPhone. I think it's just Apple may be aware that
all the solutions that we currently have available are inherently flawed. So throwing your lot in as one of the biggest names in this industry and one that could arguably do the most difference, you're kind of almost exacerbating the situation that Google and OpenAI are now in, which is that they keep lauding this as a solution and it doesn't fucking work.
I think Apple needs to be able to stand on its laurels about something and nothing is going to offer them that at the minute.
I want to come back to how specifically it doesn't work in one second. Let me just stay focused on the rest of the players on the sort of content creation side of the ecosystem. There's Apple, there's Google, which uses it in the Pixel phones. It's not an Android proper, right? So if you have a Samsung phone, you don't get C2PA when you take a picture of the Samsung phone.
What about the other camera makers? Do Nikon and Sony and Fuji, are they all using the system?
A lot of them have joined. They've released new camera models that have got the system embedded. The problem that they're having now is in order for this to work, you don't just have to do it on your new cameras because every photographer in the world worth their salt isn't going to go out every year and buy a brand new camera because of this technology.
It would be inherently useful, but that's just not going to happen. So backdating existing cameras are where the problem is going to be. We've spoken to a lot of different companies. As you said, Sony has been involved with this, Leica, all of them, Nikon. The only company willing to speak to us about it was Leica. And even they were very vague on how internally this is progressing.
They just keep saying that it's part of the solution. It's part of the step that they're going to be taking. But these cameras aren't being backdated at the minute. If you have an established model, it's 50-50 as to whether it's even possible to update it with the ability to log these metadata credentials in from that point.
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Chapter 5: What is C2PA and how does it attempt to label content?
I think we've kind of been waiting for tech to basically admit that. I see them using stuff like CTPA almost kind of a meritless badge at this point because they're not endeavouring to push it to its utmost potential, really. Even if it was never going to be the ultimate solution, it could have been at least some kind of benefit. And we know that they're not doing this because in the same message,
Missouri is describing this like, oh, it would be easier if we could just tag real content. That's going to be so much more doable. And if any, that would be good. And we'll circle those people. It's like, my guy, that's what you're doing. CTPA is that. It's not specifically an AI tagging system. It's a, where has this been? And who took this? Who made this? What has happened to it?
So if we're going for authenticity... Like, Mozerio is just openly saying, we're using this thing and it doesn't work. But imagine if it did. Wouldn't that be great? It's like, that's deeply unhelpful.
So, yeah, it's his way of kind of like deeply unhelpfully musing into some system that will be able to, I don't know, regain some kind of trust, I guess, while also acknowledging that we're already there.
I'm going to make you keep arguing with Adam Aseri. We've invited Adam on the show. We'll have him on and maybe we can have this debate with him in person. But for now, you're going to keep arguing with his blog post. He says platforms like Instagram will do good work identifying AI content, but it will get worse over time as AI gets better.
It will be more practical to fingerprint real media than fake media. Labeling is only part of the solution, he says. We need to surface much more context about the account's sharing content so people can make informed decisions. So he's saying, look, we'll start to sign all the images and everything, but actually you need to trust individual creators.
And if you trust the creator, then that will solve the problem. And it seems like... you're really skipping over the part where creators are often fooled by AI generated content, like all the time. And I don't mean that to say like creators as a class of people, I mean literally just everyone is fooled by AI content all the time.
And so if you're trusting people to understand it and then share what they think is real, and then you're trusting the consumers to trust the people, that also seems like a whirlwind of chaos. On top of that, and you've written about this as well, there's the notion that these labels make you mad at people, right?
So that if you label a piece of content as AI generated, the creator gets furious because it makes their work seem less important or less valuable. The audiences yell at the creators. And so there's been a real push to get rid of these labels entirely. because they seem to make everyone mad. How does that dynamic work here? Does any of this have a way through?
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Chapter 6: How do different platforms approach AI-generated content?
They run YouTube. Meta has massive investments in AI to what end? Unclear, but massive investments in AI. They run Instagram and Facebook and WhatsApp and the rest. Just down the line, you can see – OK, Elon Musk is going to spend tons of money on XAI and he runs Twitter. And this is a big problem, right?
If your business, your money, your free cash flow is generated by the time people are spending on your platforms and then you're plowing those profits back into AI, you can't undercut the thing you're spending the R&D money on by saying we're going to label it and make it seem bad. Are there any platforms that are doing it that are saying, hey –
We're going to promise you that everything you see here is real because it seems like a competitive opportunity.
Very small. There's an artist platform, right, called Kara, which says that they're so for supporting artists that they're not going to allow any AI-generated artwork on the site. But they haven't really clearly communicated how they are going to do that because saying it is one thing and doing it is another thing entirely.
There are a million reasons why we don't have a reliable detection method at the minute. So if I, in complete good faith, pretend to be an artist that's just feeding AI-generated images onto that platform, there's very little they can really do about it. So anyone that's making those statements saying, yeah, we're going to stand to merit and we're going to keep AI off of the platform. How?
They can't. The systems for doing so at the minute are being developed by AI providers, as we've said, or at least AI providers are deeply involved with a lot of these systems. And there is no guarantee for any of it. So we're still relying on how humans intercept this information to be able to tell people how much of what they can see is trustworthy.
That's still kind of putting the onus on us as people. It's, well, we can give you a mix mash of information and then you decide whether it's reliable or not. And we haven't operated on that way as a society for years. People didn't read newspapers to make their own mind up about stuff. They wanted information and facts. And now... They can't get that.
Is there user demand for this? This does seem like the incentive that will work. If enough people say, hey, I don't know if I can trust what I see. You have to help me out here. Make this better. Would that push the platforms into labeling? Because it seems like the breakdown is at the platform level. The platforms are not doing enough to showcase even the data they have, let alone demand more.
But it also seems like the users could simply say, hey, the comment section of every photo in the world now is just an argument about whether or not this is AI. Can you help us out? Would that push them into improvement?
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