Reality is losing the deepfake war

16:32Nilay Patel:I'm just thinking about this in terms of the stuff that is made, the stuff that is distributed, and the stuff that is consumed. It seems like at least at the moment of creation, there is some adoption, right? Adobe is saying, okay, in Photoshop, we're going to let you edit photos and we're going to write the metadata to the images and pass them along. A handful of phone makers, Google, at least in its phones, is saying we're going to write the metadata. We're going to have Synth ID. OpenAI is putting the system into Sora 2 videos, which you wrote about. On the creation side, there's some amount of, okay, we're going to label the stuff.

17:05Nilay Patel:We're going to add the metadata. The distribution side seems to be where the mess is, right? Nobody's respecting this stuff as it travels across the internet. Talk about that. You read about Sora 2 videos and they exploded across the internet. This is when it should have not been controversial to put labels everywhere saying this is AI-generated content. And yet it didn't happen. Why didn't that happen anywhere?

17:26Jess Weatherbed:It generally exposes the biggest flaw that the system has. And every system like it, to its credit. So I would always argue I don't want to defend CTPA because it's doing a bad job. It wasn't ever designed to do it on this scale, right? It wasn't designed to apply to everything. So in this example, yes, platforms need to be adopting it to actually read that metadata, providing they're not the ones ripping it out during the process of actually supposedly scanning for it. But unless this is absolutely everywhere, it's just not going to go. Part of the problem that we're seeing is as much as they can credit it saying it's going to be really robust, it's going to be really efficient, you can embed this at any other stage.

18:04Jess Weatherbed:There are still flaws with how it's being interpreted, even if it is scanned. So that's a big thing. It's not necessarily that platforms aren't picking up the metadata or stripping it out. It's that they have no idea what to do with it when they actually have it. And at the point of uploading any images, there are social media platforms. LinkedIn, Instagram threads are all supposed to be using this standard. And there is a chance that when you upload any kind of image or video to the platform, any metadata that was involved in that is just going to be stripped out regardless. So unless they can all come to an agreement, every platform, literally every platform that we access and use online, You come to an agreement that they are going to be scanning for very, very specific details.

18:41Jess Weatherbed:They're going to be adjusting their upload processes. They're going to be adjusting how they communicate to their users. There needs to be that uniform, total uniform conformity for a system like this to actually make a difference, not even just to work. And we're clearly not even going to see that. One of the conversations I had actually was when I was grilling Andy Parsons, who is head of content credentials at Adobe, which is another, that's their word for implementing CTP. data. I commented on the fact that the Grok mess that we've had recently, Twitter was a founding member of this. And then when Elon purchased the platform, it disappeared off.

19:15Jess Weatherbed:And by the sounds of it, they've been trying to entice X to get back involved, but that's just not going anywhere. And X, however we see its user base at the minute, has millions of people using it. And that is a portion of the internet that is never going to benefit from their system because it has no interest in adopting it. So you're never going to be able to address that.

19:35Nilay Patel:we have to take a quick break we'll be right back

19:46Nilay Patel:support for decoder comes from adobe for every big idea your documents folder tells a story let's say you've just finished pulling together a brief so you hit export on final version.pdf But then you open the file and you immediately notice a typo. Several versions later, you're exporting finalv4.actualfinaldraft.pdf. Adobe Acrobat Studio can save you the digital clutter with PDF Spaces. It takes your documents and turns them into a living project that you can engage with, get insights from, and collaborate with others on. You can gather all your files into one workspace and have a whole conversation with your AI assistant about it.

20:29Nilay Patel:and ask questions to get deep insights about your project. You can even invite people to your PDF space and let them add files, comments, notes, and more. You could doodle in the margins or even turn your project into your own personal podcast episode. Acrobat Studio lets you generate an audio overview of your project in just one click. Learn more at adobe.com slash do that with Acrobat. support for the show comes from ship station if you're a fast-growing business one of the quickest ways to stop momentum is getting buried in shipping so if you're constantly bouncing between disconnected apps just to get orders out the door it might be time to take a look at ship station ship station is a simple all-in-one platform that centralizes order management rate shopping, inventory and returns, warehouse tools, and powerful analytics in one convenient dashboard.

21:25Nilay Patel:ShipStation says that by sharing tracking details, you can cut customer service inquiries by 12%. And the returns management system gives you data on what's coming back and why. You could try ShipStation for free for 60 days with full access to all features, no credit card needed. Just go to shipstation.com and use code DECODER for 60 days for free. 60 days gives you plenty of time to see exactly how much time and money you're saving on every shipment. That's shipstation.com code DECODER. Shipstation.com code DECODER.

22:08Hi, everyone.

22:09Nilay Patel:This segment of Decoder Sessions features my boss, Helen Havlock, the Veritas publisher, and L 'Oreal Group's global vice president of tech and open innovation. I think you're going to enjoy this conversation.

22:19Jess Weatherbed:We're going to start with a Decoder classic question, Giv. What does tech and open innovation mean at L 'Oreal? Who is on your team? What kind of projects do you work on?

22:27Nilay Patel:Open innovation is all the partnerships that we have in L 'Oreal, working with startups outside. And it's really a great time right now to be doing open innovation because we're doing things in vertical farming and sustainable cultivation and biotech. So we do all those partnerships and our team is responsible for them. And the augmented beauty team is all the tech that started 15 years ago when we kind of had a blank page and now how can we bring beauty and tech together?

22:53Jess Weatherbed:How do you decide which projects to invest in?

22:55Nilay Patel:At the beginning, I was trying to push as much as I could to get people to think that beauty was relevant for tech. So we're really tech centric. And then over time, we started thinking about how to look more at beauty products that we can upgrade thanks to tech. And so we have a little bit more kind of process behind how we choose projects. Now we try and kind of do things like upgrading the hairdryer to be able to do three out of four people have a hairdryer at home. And so how can we make it better? Or this year, like the flat irons that we're using and LED masks and stuff like that. So we do have a little bit of that kind of process, but we leave some space for serendipity and some creativity.

23:33Nilay Patel:So we have scientists all the way to engineers, and we let the scientists kind of think of some new clever ideas, too.

23:48Nilay Patel:We're back with Verge reporter Jess Weatherman. Before the break, Jess was explaining the origins of the C2PA standard, and why attempts to label AI imagery have been moving so slowly among phone makers, camera providers, and other parts of the photography ecosystem. This effort to label AI images and videos is also falling apart at the distribution level, because not all of the major social media platforms agree on how to handle and display this metadata, so they can share that information with the people looking at stuff on the platforms. So where exactly does that leave us? Well, the head of Instagram, Adam Masseri, had some big ideas about all that that he published to the platform about a month ago.

24:24Nilay Patel:And I think his post tells us a lot about how the most influential social media executives see this problem evolving in the future and reckoning with what, if anything, they can actually do about it. I'm going to read you this quote from Adam Masseri, who runs Instagram. On New Year's Eve, he just dropped a bomb and he put out a blog post in the form of a 20 carousel Instagram slideshow. which has its own PhD thesis of ideas about how information travels on the internet embedded within it. But he put out a 20 slide slideshow on Instagram. In it, he said, quote, for most of my life, I could safely assume photographs or videos were largely accurate captures of moments that happened.

25:04Nilay Patel:This is clearly no longer the case, and it's going to take us years to adapt. We're going to move from assuming what we see is real by default to starting with skepticism. This is the end point, right? This is you can't trust your eyes. You can no longer trust a photo. You can't trust a video of any event is actually real and reality will start to crumble. And you can just look at events in the United States over the past month. The reaction to ICE killing Alex Preddy was, well, we all saw it. And it's because there was lots of video of that event from multiple angles and everyone said, well, we can all see it.

25:37Nilay Patel:And the foundation of that is we can trust that video. And I'm looking at Adam Aseri saying, we're going to start with skepticism. We can no longer assume photos or videos are accurate captures of moments that happened. This is the turn. This is the point of the standard. Do you see Maseri saying this out loud about Instagram is the end point of this? Is this war just lost?

25:57Jess Weatherbed:I would say so. 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 endeavoring 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 like missouri is describing this like oh if it would be easier if we could just tag real content that's going to be so much more doable and if any of we could that would be good and we'll we'll we'll circle those people it's like my guy that's that's what you're doing that is like like ctpa is that like It's not specifically an AI tagging system.

26:40Jess Weatherbed: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 Mozeria 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 we'll be able to, I don't know, regain some kind of trust, I guess, while also acknowledging that we're already there.

27:08Nilay Patel:I'm going to make you keep arguing with Adam Assari. 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'll get worse over time as AI gets better. It'll 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 by the accounts 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.

27:42Nilay Patel: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?

28:20Nilay Patel: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?

28:43Jess Weatherbed:I mean, it doesn't. And the other kind of amusing thing is Instagram knows this the hard way. Missouri should remember. but one of the very first platform implementations they did of reading CTPA was done by Facebook and Instagram a couple of years ago, where they were just slapping maybe the AI labels onto everything because that's what the metadata told them. The big problem here that we have isn't just communication, which is the biggest part of it. How do you communicate a complex bucket of information to every person that's going to be on your platform and get them only the information that they need?

29:16If I'm a creator, it shouldn't have to matter if I was using AI

29:19Jess Weatherbed:or not but if i'm a person trying to see if again a photo is real i would i would greatly benefit from just a easy button or label that verifies authenticity finding the balance for that has proven next to impossible because as you said people just get upset about it but then how do you define how much ai and something is too much ai you know like photoshop and all of adobe's tools They do embed these content credentials that all of this metadata will say when AI has been used. But AI is in so many tools and not necessarily in the generative way that we assume it's going to be like, I'm going to click on this.

29:57Jess Weatherbed:It's going to add something new to an image that was never there before. And that's fine. There are very basic editing features that video editors and photographers now use that will have some kind of information embedded into them to say that AI was involved in that process. And now when you've got creators on the other side of that, they might not know that what they are using is AI. We're at the point where, unless you can go through every platform, every kind of editing suite with a fine-tooth comb and designate, what do we count as AI? This is a non-starter. Like, he's already hit the point of we can't communicate this to people effectively.

30:35Nilay Patel:Let's pause here for a second, because I want to lay out some important context before we go any farther. If you're a Verge reader, even listening to the Verge cast, you know that we've been asking a very simple question for over five years now. What is a photo? It sounds simple, but it's actually quite complicated. Because after all, when you push the shutter button on a modern smartphone, you are not actually capturing a single moment in time, which is what most people think of a photo as. Modern phones actually take a lot of frames, both before and after the second you push the shutter button, and then merge them into a single final photo.

31:10Nilay Patel:So that's to do things like even out the shadows and highlights of a photo, to capture more texture, to accomplish things like night mode. And over the years, things have gotten even weirder. There was a mini scandal a few years ago where if you tried to pick a photo of the moon with a Samsung phone, the phone actually just generated a picture of the moon. Super weird. And of course, Google Pixel phones have all kinds of Gemini-powered AI tools in them, to the point where Google now says the camera is there to help people capture memories, not moments in time. This is all a lot. And like I said, we've been talking about it for years here at The Verge.

31:43Nilay Patel:I bring this up because generative AI is taking the what is a photo debate to its absolute limits. It's hard to even agree on how much AI editing makes something an AI edited photo, or whether any of these features should be considered AI in the first place. And if that's so hard, how can we possibly reach consensus on what's real and what we label as real? Camera makers have all mostly given up here. And now I think we're starting to see the major social media platforms do the same thing. I wanted to talk about this for a second here because obviously it's an obsession of mine. But also I think laying it all out makes it obvious how very, very complicated it is.

32:19Nilay Patel:Which brings us back to Adam Masseri, Instagram, and the debate over AI labeling. I will give some credit to Instagram and Adam Masseri here in that they are at least trying and thinking about it and publicly thinking about it in a way that none of the other social networks seem to have given any shred of consideration to. TikTok, for example, is nowhere to be found here. They are just going to distribute whatever they distribute without any of these labels, and it doesn't seem like they're part of the standard. X, I think X is absolutely just fully down the rabbit hole of distributing pure AI misinformation.

32:59Nilay Patel:YouTube seems like the outlier, right? Google runs SynthID. They're in C2PA. They're embedding the information literally at the point of capture in Pixel phones. What is YouTube doing?

33:09Jess Weatherbed:A very similar approach to TikTok, actually, because weirdly enough, TikTok is involved with this. They use the standard. They're not necessarily a steering member, but they are involved. And they have the similar approach where you will get an AI information label somewhere towards, depending on what format you're viewing on mobile or your TV, your computer, you'll get a little AI information label that you have to click in and ascertain the information you need from that. So their kind of problem is making sure it's robust enough because this doesn't appear consistently. There are AI videos all over YouTube that don't carry this and there's never a good explanation.

33:43Jess Weatherbed:Every time I've asked them, it's always just, you know, we're working on it. It's going to get there eventually, whatever. Or they ask for very specific examples and then run in and fix those while I'm like, okay, but if this is falling through the net, how can you stand by this as a standard and your own synth id stuff and you're clearly using it to soothe concerns that people have despite its ineffectiveness they don't seem to be progressing any further than just presenting those labels probably because of what happened to instagram and now we've just got the situation where like meta does seem to be standing on the sidelines going well we tried so let's just see what someone else can do and maybe we'll adopt it from there but youtube doesn't really want to address the slop problem because so much of uh youtube content that's showed to new people is now slop and it's proven to be quite profitable for them.

34:28Nilay Patel:So yeah, Google just had one of its best quarters ever. Neil Mohan, the CEO of YouTube, he's been on the show in the past. We will have him on the show again in the future. He announced at the top of the year that the future of YouTube is AI and they have features that they've announced along the lines of creators can have AI versions of themselves do the sponsored content so that the creators can do whatever the creators actually want to do. And there's a part of me that completely understands that. Like, yes, my digital avatar should go make the ads so I can make the content that the audience is actually here for.

35:00Nilay Patel:And there's a part of me that says, oh, they're never going to label anything because the second they start labeling that as AI-generated, which clearly will be, they will devalue it. And there's something about that in the creative community with the audience that seems important. I know you've thought about this deeply. You've done some reporting here. what is it about the ai generated label that makes everything devalued that makes everybody so angry

35:24Jess Weatherbed:i think it's people trying to put a value on creativity itself right if i was looking at luxury handbags and i see that they've not paid a creative team this is a creative company that makes like wonderful products it's supposed to stand on the quality of all of the stuff that it sells you if i find that you're not involving creative personnel in that to make an ad for me to want to buy your handbag? Why would I want to buy it in the first place? And not everyone will have that perspective, but as someone that worked in the creative industry for a long time, you kind of see the work that goes into something, even if it's something as laughable as a commercial.

35:56Jess Weatherbed:I love TV commercials because as annoying as they are, and as much as they're trying to get me to buy something, you can see the work that went into it, that someone had to write that story, had to get behind the film cameras, had to make the effects and all that kind of stuff. So it feels like if you're taking a shortcut to remove all of that, then you're already cheapening the process yourself. Like that's what I feel. And from the conversations I've had with the other creatives, that seems to be the initial response of AI looks cheap because it's meant to be cheap. That's why it exists. It exists for efficiency and affordability.

36:28Jess Weatherbed:If you're coming across with trying to sell me something on that, it's probably not going to make the best first impression unless you make it utterly undetectable. And if you have a big made with AI or assisted with AI label on that, it's no longer undetectable because even if I can't see it, you've now just admitted that it's there.

36:45Nilay Patel:We need to take another quick break. We'll be right back.

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38:00Nilay Patel:That's selectquote.com slash decoder. Support for the show comes from Rippling. No one likes running a bunch of disconnected tools to do simple tasks. Your company's all-in-one platforms should be actually able to do it all. Well, Rippling says they can do it all. It's the unified platform for global HR, payroll, IT, and finance. With Rippling, workflows that normally bounce across various tools and departments all just happen in one place automatically. Say an employee gets promoted or moves. Rippling can update payroll taxes, hand out new app permissions, ship a new laptop, issue a new corporate card, and assign required manager training all in one place.

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39:22Nilay Patel:Support for the show comes from Anthropic, the team behind Claude. If you've spent any time lately trying to get an AI to do something useful, not just sound impressive, but generally help you think through a hard problem or task, you may have heard the name Claude. Cloud is made by Anthropic, and they've got a few things going on right now that are worth paying attention to. First, Cloud Code. It's a command line tool for developers that understands your entire code base, run tests, iterates on solutions, and then handles complex tasks end to end. Developers have been using it for everything, not just coding.

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40:48Nilay Patel:We're back with Verge reporter Jess Weatherbutt. You heard us talking about how AI labels are falling apart at the distribution level, the social media level, and how some tech executives, like Adam Masseri, the head of Instagram, are trying to wrap their heads around a world where we can't trust anything posted to social platforms. But now I want to shift the focus away from all these people who are ostensibly acting in good faith towards some of the people who are accelerating the chaos on purpose, including the White House. That's a lot of mixed incentives for these platforms. And it occurs to me as we've been having this conversation, we've been kind of presuming a world in which everyone is a good faith actor and trying to make good experiences for people.

41:26Nilay Patel:And I think a lot of the executives of these companies would love to presume that that is the world in which they operate. And whether or not the label makes people mad and you want to turn it off or whether whether or not you can trust the videos of significant government overreach and cause a protest, that's still operating in a world of good faith. Right next to that is reality, the actual reality in which we live, where lots of people are bad faith actors who are very much incentivized to create misinformation, to create disinformation. And some of those bad faith actors at this moment in time are the United States government.

42:06Nilay Patel:So the White House publishes AI photos all the time. Department of Homeland Security, AI-generated imagery up, down, left, right, and center. You can just see AI-manipulated photos of real people modified to look like they're crying as they're being arrested instead of what they actually looked like. This is a big deal, right? This is a war on reality from literally the most powerful government in the history of the world. Are the platforms ready for that at all? Because like here's the, they're being faced with the problem, right? This is the stuff you should label. No one should be mad at you for labeling this.

42:40Nilay Patel:And they seem to be doing nothing. Why do you think that is?

42:44Jess Weatherbed:I think it's because it's the same process, right? What we're talking about is a kind of a two-way street that is on the same road. You've got the people want to identify AI slop, or maybe they don't, but people want to be able to like see what is and what isn't AI. But then you've got the more insidious thing if we actually want to be able to tell what is real. but it unfortunately benefits too many people to make that confusing now. But the solution is for both. AI companies, platforms that are profiting off of all of the stuff that they're showing, how they're making it so much more efficient for content creators to slap stuff in front of you.

43:12Jess Weatherbed:Like we're in a position now where there's more online than we've ever seen, ever, because everything is being funneled out. Why would they want to harm that profit stream effectively by having to either slam on the brakes of development until they can figure out how they are going to effectively be able to call out when deep fakes are proving to be a problem when they're going to be able to like the methods of being put in front of it rather than setting up some kind of middle system like a shutterstock thing like we discussed earlier where all press images now have to come from one authority that has to verify the identity of everyone taking them like maybe that's a possibility but we are so far from that point and no one's from to my knowledge no one's instigated setting something like that up so they're just kind of relying on everyone talking about this in good faith again every conversation i've had with this is we're working on it it's a slow process we're going to get there eventually oh it was never designed to do all of this stuff anyway so it's very blasé and kind of low effort really it's kind of a we've joined an initiative what more do you want um which is incredibly frustrating but that seems to be the reason that everything is kind of not developing because in order to develop any further in order to actually help us they would have to pause.

44:23Jess Weatherbed:They would have to stop and think about it. And they're too busy running out every other tool and feature that they can think of doing because they have to. They have to keep their shareholders happy. They have to keep us as consumers happy, while also saying ignore everything else that's going on in the background.

44:37Nilay Patel:When I say there's mixed incentives here, one of the things that really gets me is that the biggest companies investing in AI are also the biggest distributors of information. They're the people who run the social platforms. So Google obviously has massive investments in AI. 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, okay, Elon Musk is going to spend tons of money on XAI and he runs Twitter. 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.

45:26Nilay Patel: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.

45:36Jess Weatherbed:Very small. There's an artist platform 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.

46:10Jess Weatherbed:How? They can't. There is like the systems for doing so at the minute are being developed by AI providers, as we've said, or they're at least AI providers 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 as 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.

46:43Jess Weatherbed:They wanted information and facts. And now they can't get that.

46:47Nilay Patel: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, right? 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?

47:20Jess Weatherbed:I would like to think it would push them into at least being more vocal about their involvement at the minute. We've got, again, it's a two-sided thing. At the minute, you can't tell if a photo is real, but also a less nefarious thing is like Pinterest is now unusable, right? As a creative, if I want to use the platform Pinterest, I cannot tell what is and what isn't AI. I can, but a lot of people won't be able to. And there is so much demand for a filter. But for that website, just to be able to go, I don't want any of this. Please don't show me anything that's generated by AI. And that hasn't happened yet.

47:50Jess Weatherbed:They've done a lot of other stuff on that. But they're involved with the process behind developing these systems. It's kind of more, it's the problem that they've set themselves an impossible task. In order to use any of the systems that we've established so far, you either need to be best friends with every AI provider on the planet, which isn't going to happen because we've got nefarious third party things that focus entirely on stuff like nudifying people or like a deep fake generation entirely. This isn't kind of the open AI or the big name models, but they exist. And they're usually what's used to do this kind of underground activity.

48:22Jess Weatherbed:They're not going to be on board with it. So you can't make bold promises about resolving the problem universally when there is no solution at hand at the minute.

48:31Nilay Patel:When you talk to the industry, when I hear from the industry, it is the drumbeat that you've mentioned several times, look, it's going to get better. It's going to be slow. Every standard is slow. You have to give it time. It sounds like you don't necessarily believe that, right? You think that this has already failed. Explain that. Do you think this has already failed?

48:50Jess Weatherbed:Yeah, I would say this is failed. I think this has failed for what has been presented to us, because what CTPA was for and what companies have been using it for are two different things to me. CTPA came about as a, I will give it its credit because Adobe has done a lot of work from this, right and the stuff it was meant to do was if you are a creative person you this system will help you prove that you made a thing and how you made a thing and that that has benefit i see that being used in that context every day but then a lot of other companies got involved with that and said cool we're going to use this as our ai safeguard basically if we're using this system and it'll tell you it'll tell you when when you post it somewhere else whether it's got ai involved with it which means that we're the good guys because we're doing something and that's what i have problem with is because ctpa has never stood up and said we are going to fix this for you a lot of companies came on board and went well we're using this and this is going to fix it for you when it works and that's an impossible task it's just not going to happen if we're thinking about adopting this platform just this platform even this in conjunction with stuff like synth id or inference methods it's never going to be an ultimate solution so i would say like the resting the pressure on we have to have ai detection and labeling it's failed like it's dead in the water it's never going to get to to a universal solution that doesn't mean it's not going to help if they can figure out a way to effectively communicate all of this metadata and robustly keep it in check make sure it's not being removed at every instance of being uploaded then yeah there'll be some platforms where we'll be able to see if something was maybe generated by the aisle maybe it was like a verified creator badge something whatever missouri is talking about where we're going to have to start verifying photographers through metadata and all this other information but there is not going to be a point in the next yeah three five years where we sign on and go i can now tell what's real and what's not because of ctba that's never going to happen

50:40Nilay Patel:it does seem like these platforms maybe modernity as we experience it today have been built on you can trust the things that come off these phones, right? Like you can just see it over and over and over again, social movements rise and fall based on whether or not you can trust the things that phones generate. And if you destabilize that, you're going to have to build all kinds of other systems. I'm not sure C2PA is it. I'm sure we will hear from the C2PA folks. I'm sure we will hear from Adam and from Neil and the other platform owners on Decoder. Again, we've invited everybody on. What do you think the next turn here is?

51:18Nilay Patel:because the pressure is not going to relent. What's the next thing that could happen?

51:23Jess Weatherbed:From this turn of events, there's probably going to be some kind of regulatory efforts. There's going to be some kind of legal involvement because up until this point, there have been murmurs of how we're going to regulate stuff. With the Online Safety Act in the UK and everything, now kind of pointing and going, hey, AI is making a lot of deepfakes of people that we don't like and we should probably talk about having rules in place for that. But up until that point, these companies have basically been enacting systems that are supposed to help us out of the goodness of their heart of the oh we've spotted that this is actually a concern and we're going to be doing this but they haven't been putting any real effort into doing so otherwise again we would have some kind of solution by now we would see some sort of widespread results at the very least it would involve working together having widespread communications and that's supposed to be happening with the cai with the initiative that everyone else is currently involved with there are no results we are not we are not seeing them we instagram made a bold effort over a year ago to stick labels on and then immediately ran back with its head between its legs that is the point of this so unless regulatory efforts actually come in so clamping down on these companies and saying okay we actually now have to dictate what your models are allowed to do and what they we're going to have repercussions for if we find out what your models are doing and not supposed to be doing that is the next stage we have to have this as a conjunction i I think that will be beneficial in terms of having that with labeling, with metadata tagging and stuff.

52:47Jess Weatherbed:But alone, there is never going to be a perfect solution to this.

52:50Nilay Patel:Well, sadly, Jess, I always cut off Decoder episodes when they veer into explaining the regulatory process to the European Union. That's just a hard rule on the show. But it does seem like that's going to happen. And it seems like the platforms themselves are going to have to react to how their users are behaving. You're going to keep covering this stuff. I find it fascinating how deep into this world you've gotten. starting from, hey, we should pay more attention to these tools. And now here we are on can you label reality into existence? Jess, thank you so much for being on Decoder. Thank you.

53:22Nilay Patel:I'd like to thank Jess Weatherbread for taking time to join me on Decoder today. And thank you for listening. I hope you enjoyed it. If you'd like to let us know what you thought about this episode, what you think a photo is, or really anything else, drop us a line. You can email us at decoderatheverge.com. We really do read all the emails. Or you can hit me up directly on Threads and Blue Sky. We're also on YouTube. You can watch full episodes at DecoderPod. and we have a TikTok and an Instagram. They're at DakotaPod as well. They're a lot of fun. If you like Dakota, please share it with your friends and subscribe over to your podcast.

53:47Nilay Patel:Dakota is a production of The Verge and part of the Vox Media Podcast Network. The show is produced by Kate Cox and Nick Statt. It's edited by Ursa Wright. Our editorial director is Kevin McShane. The Dakota music is by Breakmaster Cylinder. We'll see you next time.

54:01Jess Weatherbed:Rinse knows that greatness takes time, but so does laundry. So Rinse will take your laundry and hand deliver it to your door expertly clean. and you can take the time pursuing your passions. Time once spent sorting and waiting, folding and queuing, now spent challenging and innovating and pushing your way to greatness. So pick up the Irish flute or those calligraphy pens or that daunting Beef Wellington recipe card and leave the laundry to us. Rinse. It's time to be great.