How Community Notes reduce viral misinformation | with Keith Coleman, Jay Baxter

0:03Audrey Tang: You're listening to TED Talks Daily, where we bring you new ideas to spark your curiosity every day. I'm your host, Elise Hu. X, formerly called Twitter, is now using Community Notes, a crowdsourced fact-checking system. The company's algorithm architect Jay Baxter and its VP of product, Keith Coleman, built it, starting with the question, what if the people got to decide what's true? If people don't trust tech companies to draw the line, could they draw it themselves?

0:34Keith Coleman: You can download the real data, the community notes and ratings, run the code on the data to verify that there's no funny business that we're doing on our end. Like, there's no override button. So it's really the, you know, by the people.

0:45Audrey Tang: It's an idea that has earned genuine interest and trust across the political spectrum, even as it's become entangled in a larger, more contentious debate about the dismantling of professional fact-checkers. In this conversation, Jay and Keith sit down with TED guest curator and civic technologist Audrey Tang to discuss how Community Notes actually works.

1:08Jay Baxter: If we can identify common ground at Internet scale, it'll make it a lot easier to create a future that humanity likes.

1:15Audrey Tang: They also talk about what they're working on next. And stick around. After the talk, we caught up with the TED guest curators Audrey Tang and Divya Siddharth, who share a few more thoughts and takeaways on community notes for us to consider. That's all coming up right after a short break.

1:37Audrey Tang: This episode is sponsored by Kohler Smart Toilets. The objects we interact with most are often the ones we notice least. But what if the most overlooked space in your home could be the most considered? Kohler Smart Toilet challenges that assumption. Their Veil Smart Toilet is a sculptural silhouette that isn't just intentional, it's a philosophy. That design changes everything. The Kohler Veil Smart Toilet is sleek with a rounded shape that's more like architecture than just plumbing. And it goes beyond looks. The touchscreen controls and customizable cleansing features offer a level of comfort and cleanliness that exceeds expectations.

2:11Audrey Tang: It's all about elevating those ordinary daily rituals into something extraordinary through thoughtful design. Kohler has been pushing these boundaries for over 150 years, mastering that balance of stunning form and high-performance function. That's a long time to get it right, and it shows in every detail. Experience the difference of Kohler Smart Toilets. Find out more at Kohler.com. This episode is brought to you by Walmart Business. The best leaders might tell you the work that moves an organization forward doesn't happen in spreadsheets or supply chain emails. It happens when you have the space to think big.

2:45Audrey Tang: That's the idea behind Walmart Business. It's built to take the friction out of running an organization so your team isn't losing hours to procurement logistics when they could just be focused on the problems that actually matter. With an ever-expanding business assortment, everyday low prices, and fast, reliable shipping, Walmart Business keeps your operations running smoothly. Shop online, in-store, or through the Walmart Business app. however works best for you. Simpler operations, smarter spending. It's Walmart built for your business. Sign up for a free Walmart business account at business.walmart.com.

3:24Audrey Tang: This episode is brought to you by LinkedIn. Running a small business means every hire matters. A bad hire can cost you time, money, and momentum. A good hire, they can help grow your business. But finding great talent isn't easy, especially when you don't have the time or resources to sift through piles of resumes to find the right fit. That's why LinkedIn built Hiring Pro, your new hiring partner that screens candidates for you. So instead of sorting through applications, you spend your time talking to candidates who are actually a good fit. With Hiring Pro, you can hire with confidence knowing you're getting the best talent for your business.

3:57Audrey Tang: In fact, according to LinkedIn, those hiring with LinkedIn are 24 % less likely to need to reopen a role within 12 months compared to the leading competitor. Join the 2.7 million small businesses using LinkedIn to hire. Get started by posting your job for free at linkedin.com slash TED Talk. Terms and conditions apply.

4:19Audrey Tang: And now our conversation of the day.

4:22Jay Baxter: You know, we built community notes because we wanted to build a better informed world. And as it scales to more parts of the internet, that means more people have access to accurate information.

4:33Audrey Tang: Great. So let's look at the community notes. That's a note. So what is it?

4:38Keith Coleman: Yeah, so this is a real example of a community note we're looking at. So basically here, the post on the left is about Iran, and it's saying the USS Lincoln has been damaged and there's casualties. But actually, the image is AI-generated. So this thing on the right here that says readers added context, they thought people might want to know, that's a community note. And what it's doing there is it's actually giving a lot of specific details about what's wrong in the image. And it turns out that that level of detail that it goes into is a big reason why people on both sides of the political spectrum actually trust community notes more than a generic misinfo warning.

5:10Keith Coleman: The way these get here in the first place is they're actually written by a regular user, a community notes contributor, and before they show on the platform to everyone and attach on the post, they are rated helpful by people from different perspectives, so they're not shown unless that happens. Another quick thing to call out is, actually, a lot of the best notes are not just fact checks. They can add context to posts that are correct, but otherwise misleading.

5:35Audrey Tang: Okay, so it's a context engine for news, but is it also for official accounts or ads or any kind of post?

5:42Jay Baxter: Yeah, so a really important principle of the program is that all posts are eligible. That means posts from heads of state, posts from our company can get noted. As Elon likes to point out, his posts get noted. It regularly identifies AI-generated imagery. It's been a ton of that recently with the Iran conflict. It's detected deepfake audio of world leaders. It covers lighter subjects like entertainment, fashion, etc. We've even had multiple notes on both recent White House administrations. And at least in one case, the White House actually took down the post, issued an updated statement. And you can imagine, like, there was a person, a random person on the internet wrote that note.

6:27Jay Baxter: You know, this isn't like a famous person. They went out there, saw the White House said something wrong, typed in this note, put it up there, and then suddenly leaders of the free world changed their public statement. That's like a superpower for people, so you can see why they're motivated to contribute. Yeah, well...

6:45Audrey Tang: So a teenager, I heard, caused that retraction, and it really is a superpower, but what is the mechanism? Can you take us back 10 years ago, before this superpower gets invented and distributed? What caused the invention?

7:03Jay Baxter: Yeah, I mean, the origin for me goes back to 2016. I was just a Twitter user then. I was following the 2016 election. There were three televised debates that year, but every day there was a debate on Twitter. So that's where I was following. That's where the world was following. I just, you know, I remember getting a lot of good information, but it was also hard to tell what was true. And I was thinking, just sitting on the outside, thinking, like, how is the world going to solve this problem? Like, damn, like, how are we going to do this in a way that works and that people feel is fair in a, you know, amidst polarization?

7:38Jay Baxter: So then fast forward three years. I was working at Twitter at that point. And the industry had tried a lot of stuff by then. Facebook had built a huge fact-checking program. Twitter was working with fact-checkers, and we also had internal teams that would try to review posts and decide whether they were or were not misleading. And there were a bunch of issues with it. It was just very clear these solutions were not solving the problem. There were issues with speed. So typical fact-checks, just to put it in perspective, were often coming back in two to four days, which is like infinity in Internet time.

8:15Jay Baxter: Scale was an issue. Typically, people could review, I don't know, 10, an order of 10 posts or topics a day. And even if you could solve those, trust was the fundamental issue. There were just a lot of people who did not want or trust tech companies to be deciding what was or was not accurate. And so I was managing a team at this time, handed that off, and just went to go prototype crazy new ideas, one of which became Community Notes.

8:42Audrey Tang: Okay, so the crazy idea just to play back is to think from the bottom up, asking people to trust random strangers on the Internet. And amid a very high PPM, polarization per minute environment, as you just alluded to, why would people trust random strangers?

9:01Keith Coleman: Yeah, it's a really good question, and it's one we got all the time getting started, but the reality is people do trust community notes on both sides of the political spectrum. and I think there's a couple big reasons why. One is the process behind it. So it's totally open, transparent, verifiable. You can actually, and this is pretty wild in the world of social media, you can actually download the real algorithm code that runs in production. You can download the real data, the community notes and ratings, run the code on the data to verify that there's no funny business that we're doing on our end.

9:30Keith Coleman: Like there's no override button. So it's really the, you know, by the people. I think secondly, the notes are just really good. So they speak for themselves. they tend to be really accurate, and the main reason behind that is, I think, the principle behind the algorithm that doesn't ingest any sort of external authority, it actually decides what notes to show by looking at agreement from people who have disagreed in the past. And sometimes we call that surprising agreement or bridging. And one thing that's really cool about this algorithm, if you compare it to something like a more naive upvote-downvote system, like a majority rules type of thing, you know, something Something like that would end up showing biased notes.

10:08Keith Coleman: And for here, our algorithm takes advantage of partisanship and polarization. So for any community note on a polarizing topic, there's always going to be someone out there who's really predisposed to disagree with that note. So before they're going to rate it helpful, they're going to fact check it from every angle possible. They're going to really check the sources in a lot of detail. And as a result, the notes that are actually found helpful in this way tend to be really accurate, tend to use primary sources, and tend to be pretty neutral in their language.

10:39Audrey Tang: Okay. So people on both sides, after turning polarization into essentially fuel, right, geothermal energy, uplift something, and both sides are happy. But the person getting noted may not be very happy. So say a head of state gets noted, and the head of state happens to have the phone number of your CEO, and just calls Elon and say, take it out by tomorrow morning. What would he say?

11:09Jay Baxter: Yeah, so those emails like that, calls or whatever, they do come in. Fortunately, the answer is really simple. We have no override button. So if you're not happy with a note, you need to take it up with the people. And this was kind of a crazy idea when we started. You know, we went into a room full of, like, trusted safety people, and we're like, hey, so the notes that show are going to be the ones that the people decide, and we can't take it down. There's no veto. There's no veto. Wow. And, you know, they're like, what? Right? Are you serious? What if there's a bad note? But I think, you know, the point was, if it's the tech company's opinion, why is anyone going to trust it?

11:50Jay Baxter: It needs to be the people's opinion. And so we stuck to that principle. Everyone got behind it. and yeah, we have no way of changing the status of a note. Okay. Which is wonderful.

11:59Audrey Tang: Okay, it is wonderful. Yeah.

12:01Keith Coleman: And so what happens to the post after it gets noted? You can just see this thing's going super viral at the start, all the way up until it gets noted. Basically, after that point, it totally flattens out, gets almost no more views. And the kind of crazy thing about this is it's actually not getting downranked by our For You algorithm, the post. This is actually just because of what we call organic user behavior, where basically people are realizing now that the post is incorrect because the note's on it, so they're just liking it less and reposting it less. So I think this is really cool. And one thing that I also love is because our data is totally open.

12:38Keith Coleman: Actually, a lot of researchers from around the world have looked into this and found the same thing. So people from Stanford, MIT, UW, and Paris, and Luxembourg, have all actually found a very similar thing, that reposts will drop by about 50%, or 2x, after a note's applied. And this is really big in the scale of social media, like 1 % or 5 % would be pretty big in the scale of typical A-B tests. So one thing that I think is really heartening about this is that we know from this and some other studies that actually, you know, people are not just entrenched in their beliefs. When a note is applied to a post, they'll actually agree with the core claims in the post less.

13:19Keith Coleman: And I think that's really cool. And, you know, I guess there's a little bit of a mixed blessing here, though, because actually post authors will also be more likely to delete their posts after they get noted. So in that way, the best notes actually get seen very infrequently. So I'm torn about that because just for me personally, I think not everyone agrees on this, but for me personally, I'd rather see a post and a note than neither at all, just because that's probably not the only time in the world where you're ever going to see that particular wrong claim. So maybe you'll see it off X somewhere in another post.

13:50Keith Coleman: And just for me, seeing a lot of notes has kind of increased the skepticism that I have when reading things.

13:56Audrey Tang: Okay, they serve as inoculation, essentially. Yeah.

14:00Jay Baxter: It's kind of a big deal that this happens organically. People often assume the world is very polarized, certainly it feels very polarized. But people here, they're just making a choice. When they see a post, they see a correction, they're like, yeah, things are wrong. I'm just not going to share it. And that's happening across the political spectrum. And we've seen that pattern again and again. When we first were designing the products, we did interviews with hundreds of people, left and right. And it was really obvious that most people just want to know what's going on in the world. They know they're consuming incorrect stuff.

14:32Jay Baxter: They just want to sift through it. And this is just showing that in action. When given information, they're going to try to make a good decision. And so I think people often assume, like, man, it must be tough to work on in the space of misleading information. It must get sad all the time, whatever. It's actually, like, I feel very optimistic working on it because we see there's quite a lot of agreement. People are actually quite reasonable.

14:54Audrey Tang: Wow. Okay. So the PPM is going lower. It seems like it's lower than it might feel. Amazing. So let me now push on a more cynical take. Anyone who spent five minutes on the internet is probably thinking now, there's going to be a way to game this, maybe many ways to game this. And just one example, I co-wrote a paper called Malicious AI Swarm. It talks about one person farming like 5 ,000 agents, the machine kind. And then they are some coded left, some coded right. They behave completely normally. They contribute even to committee notes. And just when the controversial issue or election happens, then they manufacture a surprising agreement and just note something that is actually true.

15:43Audrey Tang: How do you deal with that?

15:45Keith Coleman: Yeah, manipulation is a real thing. I mean, people are always trying to game social media algorithms, and committee notes is no exception. So I think one thing to call out is that surprising agreement mechanism does provide a bit of a defense against a more naive attack than the one you described. There's a lot of people with the same view all piling on, trying to get an incorrect note showing. That's not going to work. But for a more sophisticated attack like the one you described, we do have a lot of defenses in place. So just to name a few, we do things like requiring a verified phone number from a trusted carrier just to increase the probability that we're dealing with real humans.

16:18Keith Coleman: We look for raters who have rated things really similarly in the past. and actually we might treat them as the same person just to limit the influence of really similar behavior. Another thing is we can look at random samples of raters and if they're rating things very differently than self-selected possibly malicious raters and that's a very important signal. And we have other things too, like there's rater reputation to deal with low quality people. But I think another key thing to call out is even with all these defenses, community notes are incorrect sometimes. Now, because it is really rare, we actually get the self-correcting property where the incorrect notes attract a lot of attention.

16:59Keith Coleman: And they'll draw a lot of raters to go quickly rate them not helpful, and then they'll stop showing. And I think that self-correcting property is super important also just in a breaking news situation, right? Something that was true a few hours ago may not be anymore. So it's great that notes are not set in stone.

17:21Audrey Tang: This episode is brought to you by Gusto. Great work rarely happens by accident. It happens when the right systems are in place. And for small business owners, that often starts with the basics, payroll, benefits, onboarding, and HR. Gusto makes all of that simple. Gusto is an online payroll and benefits software built for small businesses. It's all-in-one, remote-friendly, and incredibly easy to use. So you can pay, hire, onboard, and support your team from anywhere. With built-in tools that automate everything from offer letters to direct deposit, your team spends less time on paperwork and more time focused on growth.

17:55Audrey Tang: There's a reason Gusto is ranked number one on G2's highest satisfaction products list this year and trusted by over 400 ,000 small businesses. Try Gusto today at gusto.com slash TED Talks and get three months free when you run your first payroll. That's three months of free payroll at gusto.com slash TED Talks. one more time. Gusto.com slash TED Talks. This episode is sponsored by Defender. Summer is on its way and now is the time to start planning your next great outdoor adventure. If you're setting your sights on higher peaks, deeper rivers, and bigger dreams, embrace the impossible with Defender.

18:36Audrey Tang: Built with legendary capability and tested on some of the most difficult terrain, Defender brings the toughness you need in a vehicle to take you to faraway places with the comfort and smart tech that makes the journey feel luxurious. Choose between the sleek two-door Defender 90, the purposeful Defender 110, or the Defender 130 with seating up to eight people for bigger expeditions. Defender's robust materials mean you can adventure without compromise, and innovative driving aids like ClearSight technology help you view the world around you in powerful new ways. With the available three models, you have the potential for up to 89 cubic feet of cargo space.

19:15Audrey Tang: This summer, adventure with confidence. Explore the full Defender lineup at LandRoverUSA.com.

19:29Audrey Tang: Okay, so notes being wrong is noteworthy. And so people recursively improve. Indeed, I've seen it happening on X for quite a while because I was a long-term contributor. It just feels like magic. It's like Wikipedia or Gorkypedia. When many people swarm into some controversy, it just gets really nice. But what about the other situation? In a niche topic, just developing fast, there's just not enough attention to bootstrap the initial surprising agreement. So I've also seen like five hours, 10 hours go by without any consensus at all. And so how are you going to tackle this speed problem? Because as you pointed out, if it comes the next day, it's already gone.

20:15Audrey Tang: Yeah.

20:16Keith Coleman: Well, first, just to level set on speed, I think Keith already mentioned, you know, the previous state-of-the-art fact-checking would often take on the order of days, and community notes is usually more on the order of hours. So it is already quite a bit faster. Notes can appear as often as about 20 minutes on a brand new post, but they can actually appear instantly if there's already another note out there that's matching on a URL or image or video. I think on top of that, one thing that people really, really like is if someone actually sees a post and engages with it before a community note is appearing, we'll actually send them a push notification later so they get the correction as soon as the note comes out.

20:55Keith Coleman: Now, even with all that, I think it's super important for us to keep making community notes faster. People want instant context. and rightfully so. So to that end, what we've done last year is we've actually opened up an open API for AI contributors. And this is a little bit of a crazy thing, and the totally open spirit of community notes, just like a regular person can be writing notes, we let regular people write their own AI note writers and submit notes to our system. And what we've seen so far is it's actually working really well. The notes are really fast, and they're quite good. But definitely, because it's AI, They're wrong some of the time.

21:33Keith Coleman: So the way we treat this now that's been working well is we still have that human layer where humans rate the notes in the same way as any other human authored note. And what we're working towards now is a way for AI and humans to collaborate more effectively to co-write better notes faster.

21:50Audrey Tang: So humans are not just downloading or upvoting, but working with AI models.

21:54Jay Baxter: Yeah, the idea is they can... Like, can we have humans and AI co-write these things together, co-create them together? and does that allow us to do this at a much faster speed and larger scale? If there's demand for a note, like people who are requesting a note on a post, AI will take a first shot at it. Humans can write to you, but AI will take a shot, and a bunch of people, they rate it and they make these suggested improvements. They can also leave suggestions on style or tone, so they can say, like, hey, I think this source is biased, or I think you should use a primary source that's going to be more trustworthy.

22:27Jay Baxter: And then the AI takes that, regenerates a note, and usually gets it right. And what's cool about this is, first, you get a better note on this post people care about, but two, all of those corrections, all those suggestions, are training data that you can feed back into the AI. So you can make it less likely to make that mistake again, you can make it better at researching in the first place, and also you can make it more neutral, less biased. So all these human suggestions, they make better notes and they make better AI.

22:56Audrey Tang: Okay. So just to play it back, This is not Grok helping humans to such a degree that it takes over all the judgment costs. It is basically human teaching AI, collaborative learning, so that the translation between communities, like climate justice on one side, biblical creation care on the other, the AI model learns how to translate, and then what? They become better at this kind of translation? Is this a new way to reward AI models? How does it work?

23:30Keith Coleman: Yeah, there's this thing that we sometimes call reinforcement learning from community feedback as opposed to just reinforcement learning from human feedback, which maybe would use potentially a smaller bias than a non-representative people. And basically, in the case of community notes, what it would look like is directly training the model to be writing notes that would be maximally likely to be found helpful by a simulated set of raters who typically disagreed in the past.

23:54Audrey Tang: Okay. Well, that's really nice. Yeah, it's cool. Yeah. So still, someday I just open X and I just see peak slop. The marginal cost for generating synthetic media, even synthetic intimacy, is now falling so fast. And so sometimes I feel, personally, that whatever the corrective mechanism we invent is not going to be fast enough for this kind of pig-slop situation. So why should anyone here believe that computer nodes and collaborative nodes will evolve to meet the demand?

24:39Jay Baxter: Yeah, so definitely in the last six weeks or so with the Iran conflict, we've seen the biggest surge in synthetic media that I've seen, at least in the kind of misleading info space. And I will say, like, we're on the frontier here. So this is the highest scale, highest speed solution that exists. These are new problems. So we don't know what's going to work. Can't guarantee the problem won't be solved. But I think there's a bunch of reasons to be optimistic. For that problem, like synthetic media surge, we can both scale up the corrections and we can both change the fundamental incentives and dynamics of the system.

25:20Jay Baxter: So in terms of scaling corrections, we talked about AI. Just to put some numbers on that, in the last four months alone, we've doubled the number of notes that are showing on X. So that's not trivial for a scaled service, to 2X in four months. I think there's clearly headroom on that. I don't know. Is it 10X, 100X? I don't know. But there's clearly headroom to grow. The other thing on the incentive side, one of the reasons people post these things is they can make money off of it through creator revenue sharing programs. And so we've recently put into place some changes to the policies there, where if your post is noted, you can't make money off it.

26:00Jay Baxter: Also, if you post AI-generated footage of a war or conflict, and you do not clearly call it out, you are suspended from the revenue-sharing program for three months. If you do it again, you're suspended forever. And so that's kind of a big deal. Those will shape the underlying motivations people have.

26:18Audrey Tang: That's huge. Okay. We've been talking about defense, right? Defending against all those manipulations and engagement through enragement and so on. But is there a future in which social media, instead of pitting humans against one another, puts people and connects them with each other, elevating the voice, that bridge? And you're like, we have just a demo. Yes, we are building this.

26:44Jay Baxter: This is an awesome future. So we have a pilot running. The idea is, so in community notes, we find the kind of like corrections or context that's helpful to people from different points of view. What if we could find the ideas or opinions that are liked by people from different points of view? And when it happens in the pilot program, the post will just get a call out saying, liked by people from different perspectives. And we see this, obviously people were very happy to see Delta not allow Congress to skip the TSA line until TSA was funded. And we see this, yes. Yeah, you're among millions of people who also feel this way.

27:27Jay Baxter: And we see this agreement across a lot of topics. We see, like, things that you think of as controversial. We see it across immigration, across the economy, taxes, international conflicts, etc. There really is a lot of agreements out there. Not on everything, but there's quite a bit of it. And the concept is, like, if we can identify that. Like, we don't need to boost this to start. Just show people when there's agreement on something. First of all, I think they'll find it interesting. It's a curiosity. Second, it might incentivize more of that. Maybe people will try to speak more in a way where they can find that agreement and get more momentum behind those ideas.

28:06Keith Coleman: Yeah, that's a really good point. I think just in the same way that community notes spread less, even though there's no... Community notes cause posts to spread less, even though there's no downranking in the algorithm. I think you'll probably see something in Algus here where there's just a positive second-order effect from making that common ground, common knowledge.

28:24Audrey Tang: So it's a common knowledge engine that turns polarization into what we can all live with. This is truly visionary. And think about it, because this thing is open source. It is open data. So it means that not just X, but rather blue sky, true social, everybody, can just plug in that stream so that AI can learn from that and then connect the communities back together. So what if we apply this engine beyond social media? Can you paint a picture of how that would look like?

29:00Jay Baxter: Yeah. So where my head always goes is, imagine just for one session of Congress, everyone just focused on delivering where there was agreement, whether it's immigration, taxes, whatever. I think people would be stoked. And, yeah. Yes? I would be stoked. There's a lot of agreement on these topics. If all we did was pursue the areas of agreement, I think people would be pretty happy with the direction the world was going. And so, you know, my hope is with programs like this, if we can identify common ground at Internet scale, it'll make it a lot easier to create a future that humanity likes. And so hopefully we can help with that.

29:42Audrey Tang: And with that, Jay, Keith, thank you for being our best builders and showing us that a pro-social media future is not in some sci-fi. It's already here. Thank you.

29:55Jay Baxter: Thank you, Audrey.

29:56Audrey Tang: Thank you, Audrey.

30:01Audrey Tang: That was Jay Baxter and Keith Coleman in conversation with Audrey Tang at TED 2026. And you may have noticed we've been experimenting with something different on the show. We're calling it Curator's Corner. Throughout the year, you'll hear from TED's curators, the people who actually find and work with the speakers you hear on the show. They will share more about the idea you just heard and the behind-the-scenes of how the talks come to life. And now, here are TED guest curators Audrey Tang and Divya Siddharth. I'm Audrey Tang, and I'm a guest curator along with the fantastic Divya Siddharth. at TED 2026.

30:39Audrey Tang: As guest curators, we get to bring people who are doing incredible work into the TED stage, help them find ways to share that work with the world and be able to create a dialogue between what we think are some of the best ideas out there and solving the problems we care about the most, AI, democracy, these big questions, and the TED audience and really the wider world. And we chose the interview format to bring Keith and Jay in because we really feel that the 18-minute talk format, as good as it is, is not doing the full justice of their job, which is training an AI to understand the differences between, say, climate justice communities and the biblical creation care communities and the various different aspects that this social translation can do to our democracy.

31:27Audrey Tang: So I try to push them really hard in every answer they give. and they took it like a champion. I think one of the great things about this talk is, you know, a lot of it is about community notes, which is a fundamentally defensive approach, right? We understand that the world is full of lots of bad information. We try to prevent the bad stuff from spreading. But I love the ending, which is on, well, what would it look like if we flipped this? And I hadn't thought about that as much before, where if we flip this to say, as much as we know the kinds of corrections people agree on, we could also figure out the kinds of information and positive solutions people agree on and make that actually be the thing that people are focused on online instead of all the other stuff that they tend to focus on online.

32:06Audrey Tang: This ending talks about data is soil so that the understanding between different communities tend to gather this garden of AI agents that grow with our communities, loyal to communities, and not trying to extract anything, but just to regenerate our deep understanding.

32:29Audrey Tang: If you're curious about TED's curation, visit ted.com slash curation guidelines. And that's it for today. TED Talks Daily is a podcast from TED. This episode was fact-checked by the TED Research Team and produced and edited by our team, Martha Estefanos, Oliver Friedman, Lucy Little, Emma Taubner, and Tansika Sungmarnivong. Additional support from Daniela Ballarezzo, Christopher Faze-Bogan, Valentina Bohanini, Banban Chang, Brian Green, and Lainey Lott. Learn more at podcasts.ted.com. I am Elise Hu. I'll be back tomorrow with a fresh idea for your feed. Thanks for listening.

33:14Audrey Tang: Here's something we've all experienced and somehow still accept as normal. Sitting on hold, repeating yourself, explaining the same problem to the fifth person who picks up. This episode is brought to you by Parloa, the AI platform built to make that a thing of the past. Parloa's AI agents work 24-7 across voice, chat, and email in any language and with zero wait time. They remember every customer interaction, so no one ever has to start over. The result? Service that doesn't feel like a transaction. It feels like being known. The world's biggest brands already trust Parloa to deliver customer experiences at enterprise scale, because when loyalty is on the line, good enough simply isn't.

33:57Audrey Tang: See Parloa's AI agents in action at parloa.com. That's P-A-R-L-O-A dot com.

34:27Audrey Tang: pretty much all he talks about in a good way. What's in your wallet? Terms apply. See CapitalOne.com slash bank. Capital One N.A. Member FDIC. When you're a maintenance engineer in a beverage manufacturing plant, you keep production lines moving and quality on track because there is no room for slowdowns. With Granger's vast selection of high quality motors, sensors, belts, and hard-to-find parts. You can get what you need fast and all in one place, so nothing gets in the way of getting the job done. Call 1-800-GRAINGER, click grainger.com, or just stop by. Grainger, for the ones who get it done.