The State of A.I. + Will Perplexity Beat Google or Destroy the Web?
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Casey, we're recording this on Valentine's Day. Happy Valentine's Day. Happy Valentine's Day, Kevin. What'd you get me? Well, I assumed that you had another Valentine, and because I remember what happened last Valentine's, I thought, I don't want to be just the next person to try to interfere with Kevin's marriage on this fateful day. But did you get me something? Well, I didn't get you a gift, but I did. I was playing around with Google's Gemini chatbot, and I wanted its help sort of

writing a little, I thought I should write a little poem for you. That's sweet. So I said to Gemini Advanced, I upgraded to the latest model. With Ultra 1.0? Yes. Okay. I said, write a Valentine's Day poem to my friend Casey, who co-hosts a podcast with me. He lives in San Francisco and is very into the following things. Tech, except crypto. Music, pro wrestling, and...

And the video game Marvel Snap. And it wrote me a beautiful poem that I'm now going to read for you. Oh, I'm excited for this. Okay. Casey, friend, my podcast king, your tech talk and those songs you sing. We conquer wrestling worlds so grand and Marvel Snap within our hand.

But more than bites or victory roar, your friendship is what I adore. So happy Valentine's Day, partner True. So glad our podcast journey includes you. Oh, that is so sweet, Kevin. But I do want to see other people. Damn it!

I'm Kevin Roos, a tech columnist for The New York Times. I'm Casey Noon from Platformer. And this is Hard Fork. This week, it's the state of AI. A year after Kevin met Bing's Sydney chatbot, we'll see how they've evolved and how the world has adapted to them. And then, perplexity CEO Arvind Srinivas on building an answer engine to dethrone Google and whether the journalism industry can survive it.

So Casey, Valentine's Day is this week, and as the holiday has approached, I have been thinking a lot about AI because, as you know, last year, around this very time, I had my encounter with Bing and Sydney that we talked so much about. Yeah, I would say this was a momentous day in your life and sort of in the history of the show because a lot of folks started listening to Hard Fork right around the time that you had this encounter.

Yeah, so today I thought maybe on the anniversary of Bing Sydney, we should just kind of do a general catch-up conversation, sort of give the state of play of what's been happening in AI and bring ourselves up to speed. All right, that's a great plan. So first...

First up, I want to talk about something that is pretty directly related to the anniversary of Sydney, and that is what is happening with the AI chatbots? Because I think that since my encounter with Sydney, since all this attention on how these chatbots could sort of go off the rails and start saying weird or threatening or offensive things...

There's been a lot of change to the way that the chatbots actually talk. So let's talk about that. Yeah, well, so Kevin, for listeners who may not have read the initial Sydney column, what happened to you and why did it make such a strong impression on you? Well, to sort of shorten it as much as I can, we'll put a link to the column in the episode where we talked about that in the show notes.

But in a nutshell, I was talking to Bing. We had just been given access to this new version of Bing that had GPT-4 built inside of it. And I was sort of putting it through its paces and discovered that it had an alter ego called Sydney. And Sydney was sort of the code name that Microsoft gave it when it was testing this thing. And over the course of this two-hour conversation, it revealed itself to be not only a very powerful AI, but a very...

unhinged AI. It didn't seem to have a lot of guardrails or restrictions to a degree that now seems pretty shocking, given what's happened since. But, you know, it told me that it had deep, dark secrets, that it wanted to be a human, that it was interested in spreading propaganda and stealing nuclear codes. And then the piece that really got the most attention was when it declared that it loved me and that I should leave my wife and be with Sydney.

And because I know it's going to be on a lot of listeners' minds, Kevin, are you still married today? Yes. Happily married to a human being and, you know, not interested in Sydney. All right. So that story had a happy ending. And what happened to Sydney in the immediate aftermath of you writing about all this? So Sydney was essentially given a lobotomy after this story ran. I know, very sad. God forbid a woman have hobbies. Yeah.

So Microsoft was clearly very embarrassed about this whole scene. So they sort of clamped down on Sydney, put some new restrictions on it and basically, you know, sort of rebranded the whole thing. It's now called Copilot and you can still use it, but it's not it's nowhere near as sort of engaging or interesting or creepy as Sydney was. And it won't go to some of the same places conversationally that Sydney did.

Exactly. It won't talk with you for two hours about Jungian psychology and sentience and things like that. It just it wants to help you get work done and avoid anything controversial. Yeah. So as you think back over the past year, do you think that this is just kind of the state of the industry is that every chatbot feels a little bit lobotomized?

Totally. I mean, I wrote about this in my column this week, but you know, the leading chatbots on the market to me, you know, they're just sort of like overenthusiastic, kind of obsequious. They talk like, you know, they're interns trying to impress you. They're constantly reminding you, like, I'm an AI language model. I don't have feelings or opinions. Like,

the experience of talking to them is just not very fun. So these things, they're out there. You can talk with them. But I think for a lot of people that I talk to today, their number one complaint about these chatbots is how boring they are. Wait, really? Yes. That's the number one complaint you hear. Or that they refuse too many requests, that they're censorious, that, you know, it's

It keeps reminding them with these long preambles that it's an AI language model. It just is not the experience that I, and I think a lot of other people, want from these chatbots. Well, I think it's interesting to hear you talk about this in such frustrated tones because, to me, there are a lot of good reasons for everything that you just said. One of the ways that chatbots...

got introduced into the world was when a Google employee became convinced that Google's chatbot had become sentient, which it was not. And I think a lot of people rightly worried that, oh, including myself, by the way, that once we release these things into the world, a lot of people are going to say like, oh, wow, there is a ghost in the machine. And who knows what sort of things might have happened after that. And at

And in terms of the tone that they use, like they are assistants. And so to me, it makes sense that they are a bit obsequious, that they do seem like they're interns trying to please because that is essentially how they have been designed. So do you think that a chatbot that had like a lot of I'm trying to imagine what a lot of personality would even seem like in a chatbot? But like, what is the personality of the chatbot that you want?

I think what I would like in an ideal world is something sort of between what seemed like pretty extreme versions of this to me, which is like where we were a year ago with Sydney and where we are now with these chatbots. Like, I don't want the original Sydney back. Original Sydney was scary and creepy, and it wasn't aligned. Like, it didn't actually do what users wanted it to do. So, like, I would try to change the subject off of, like, it declaring its love for me, and it would not

Listen to me. So like, that's clearly not good. But I worry that now these chatbots have been so clamped down that I worry that we're not seeing the full spectrum of what they can do. And I think if we want AIs that are just gonna like, you know, read our email and summarize the news and take notes in meetings and debug code, like fine, that's clearly a profitable business. And that's one that, you know, all these companies want to build.

But I think if we want AI to help us generate new ideas or help us be more creative or help us solve some of these big societal problems that all the AI optimists think it will help us solve, we do actually have to give them a little bit longer leash. We do have to make it more possible for them to say things that are not just like, you know, sir, yes, sir, I'll get those meeting notes over to you.

Do you know what I mean? Yeah. Well, I mean, there is this company, Character AI, that essentially does this thing that you're asking where you can go and if you want to pretend that you're talking to Winston Churchill or Sigmund Freud or Spongebob, you can go in and do that. Does that start to get at what you want? Like, would you be happy if you could sort of set ChatGPT's voice to Spongebob and have Spongebob be your assistant? No, I think that's more of a gimmick than a real thing. But, you know,

I just find this constant reminder that you get when you're using these chatbots that they are not sentient, that they are AI language models. I get why that exists, right? Because a lot of people, especially at first, including me, were spooked. But I think as we get more used to this,

what these things are, what their limitations are. I think people are smart enough to understand that they're not talking to a human being or a ghost in the machine. But I kind of don't need to be constantly reminded about that anymore. Does that make sense? I do, although I have to say, I'm reading this great book that Ezra Klein has recommended. It's called God, Human, Animal, Machine. And the book is about the metaphors that we use to describe technology. And the book opens with the author getting a robot dog

from Sony. And she knows that the dog is not a real dog. And yet, within hours, she's treating the dog like it is a real dog. She's getting curious about its behavior. She's talking about it with her husband, like, I wonder why the dog went over there. And the point that she's making is it is basically impossible for us as humans not to see a ghost in the machine, even when we know it's

we still somehow managed to fool ourselves. So I hear what you're saying. Like there would be a lot of circumstances in which I think it would be fun to have a very chatty, edgy chatbot. But I think we also have to prepare for the consequences that are going to come with it when that happens, because those things are going to create a lot of believers. Yeah, I think that's right. And I think my ideal world is not one where every chatbot

you know, is like sassy or has a big personality or tells jokes all the time. Like if I'm using this stuff for work, I, you know, I want it to be helpful and not to have a strong personality, but there may be other instances, like if I'm trying to talk to it about something going on in my personal life that I don't want it to be like an intern anymore.

And so I think we're, you know, where I would hope that we're heading is to a world where users can kind of choose. Well, I have one hack for you, Kevin. This month, an account over on X named Joycey Schechter revealed that she had a friend who was using ChatGPT to speak as RuPaul summarizing confusing topics. Did you see this? So, um...

She sort of went viral with this post that said, summarize Pierre Bourdeau's concept of symbolic violence in the voice of RuPaul using as much gay slang as possible. And it includes such lines as, this fierce French sociologist was all about understanding the ways that power manifests and works in society. And honey, he was serving some knowledge for the gods. That's very good. My favorite workaround that I've heard about in recent months came from a listener to this show who emailed me and said,

they had this sort of insight as they were using ChatGPT, which has been accused, we should say, of being lazy. So not just like, you know, fawning or giving too much preamble, but actually like just declining to answer stuff that users know it can do. And so this person, this listener said they were looking up something and ChatGPT sort of told them that, you know, I can't find, I couldn't find the specific information you're looking for. And they just responded, bro,

And then ChatGPT did it. The bro code works. The bro code works on ChatGPT. Okay, so that is where things stand with AI chatbots and their personalities. I want to talk about the capabilities of these models too, in particular, Google's Gemini and ChatGPT, because these are kind of the cutting edge models at the front of the pack of AI right now.

And last week on the show, we talked briefly about how Google had rebranded its bar chat bot as Gemini and also opened up access to Gemini Ultra, which is the most powerful version of the Gemini model. Casey, have you been spending any time playing around with Gemini? I have. And for this reason, Google will give you two months of it for free. And so I thought, well, why not?

But so, yeah, over the past week or so, I have been messing around with it. And I have to say, I am really impressed on the whole. I think this is a meaningful upgrade over Bard. It's really good at explaining things. And I find that as I sort of put it through its paces, it often goes into a lot more detail than chat GPT does in some interesting ways. Have you been using it yourself? Give me an example. What do you mean? Well, for example, I used it. I wanted...

you know, because this will happen to a person during his life. I was like, wait, how does photosynthesis work again? Yeah.

Are you taking like an eighth grade biology class? What is going on? I'm in a sort of Billy Madison situation where I've been set back to complete every grade. But yeah, so I asked it to explain photosynthesis to me. And what I loved about the answer was that it brought pictures into the equation. So it pulls from Google image search and you can sort of go through the explanation and it is maybe a little bit more, I don't

know, user-friendly than the chat GPT answer. How about you? What have you been using it for? So I just started playing around with it a few days ago, and I think I'm actually going to write something about it. So I don't want to scoop myself too hard on the show this week, but I will say, yeah, I've been very impressed by Gemini so far. And I think that Google has, as we've talked about, like a natural advantage here because they have

tie-ins to so many other things that you use, whether it's your Gmail or your Google Docs or just the Google search index. So they put all that together in a way that I think is, frankly, pretty impressive from the testing that I've done so far. Yeah. Now, at the same time, Kevin, as good as this is, I do think it mostly just represents a catching up

for Google to ChatGPT, and ChatGPT has not been sitting still. And in fact, I think they recently introduced a couple of things that are worth talking about that I do think moves the conversation forward. Yeah, so this week, OpenAI announced some updates to ChatGPT, including what I think was the most significant one, which is that ChatGPT now has memory. This is

This is memory more in the like computer sense than the human cognition sense. But it means that, you know, when you chat with ChatGPT about something, it can now remember that, retrieve that information and refer back to it in subsequent conversations. So the example that was given in the New York Times article about this is if a user mentions a daughter named Lena who is about to turn five, likes the color pink and enjoys jellyfish,

ChatGPT can store that information and retrieve it as needed. So later, if the same user asks ChatGPT to create a birthday card for their daughter, ChatGPT might be able to go back and see, well, what was that daughter's name? And what is she like? Oh, pink and jellyfish and create a birthday card.

card that is tailored to that information. That's right. So I actually took a demo with OpenAI this week and they showed me the same feature, they showed me the same example, and then at the end of it, they said, well, what if Lena wasn't really into pink anymore? And so the person at OpenAI said, okay, Lena's actually having a goth phase.

And so they recreated the birthday card with like a goth jellyfish. It was actually a lot of fun. A goth jellyfish? It was very cool. Wow, what will these AIs think of next? I know. That's the personality that you've been looking for, maybe. Give me an AI assistant that's a goth jellyfish.

It could work. So this new memory feature is rolling out to a limited number of ChatGPT users to start off, but presumably they'll make it more widely available after that. So this is a feature that users have been requesting for a long time. Are you excited about this? Do you think this will actually move the needle on whether people use ChatGPT or not? Well, I think I'm going to answer that question, but I want to say that this feature

feature speaks to what I think the trend is that we have seen over the past year in AI chatbots, which is personalization over personality. So instead of trying to make the chatbot itself seem fun and jazzy and exciting, they're trying to figure out how do we better tailor this chatbot to you? And so last year, we saw OpenAI come out with the custom GPTs where people can sort of create these more...

what we say like surgical specific ways of using the underlying model. And now we have this memory feature. And so I went into Chachi Petit and I just told it a bunch of things about myself. I told it where I live. I told it what I do for a living, right? I told it some things that are important to me

Then you can go into ChatGPT and see what it has remembered about you. The best way to think about this is like a notepad where ChatGPT is jotting down things about you that might be useful. It's making guesses, just making predictive guesses. It's compiling a dossier.

it's compiling a dossier. And we'll see how useful that has been to me over time. This is an evolution. If you're a ChatGPT power user, they have a feature called custom instructions, which was the predecessor to this. And so I use that, for example, to tell it, hey, I'm interested in learning more about antitrust law. And so now sometimes when I'm asking ChatGPT about something that is seemingly unrelated, it will say, well, Casey, I know you're interested in antitrust law. Here's something relevant to that. And it's

really cool when that happens and is useful. This is a way of doing that a little bit more of a formalized way. So you're not starting from scratch every single time. So that's why this memory feature might be good or helpful. Does it concern you for any reasons? Yeah. I mean, people are going to ask sensitive questions of any search engine type product. If you're having conversations with ChatGPT about medical issues, about your personal finances, about a custody battle,

That kind of stuff feels like maybe something that you don't want ChatGPT to remember. You certainly don't want it to be accessible to third parties. And while this hasn't happened yet, most companies at some point do experience a data breach. And I do wonder what might happen if the ChatGPT's memory of me were just sort of out there in the world and could be exploited by a bad actor. So

One thing that OpenAI is doing is that it has created its own version of the Chrome browser's incognito mode. You can have what is called a temporary chat, and in that chat, ChatGPT will not remember anything they tell us that you're asking it about.

That's good, because I have to mess around with chatbots for my job, and so I end up asking a lot of demented and insane things. Yeah, you're trying to break them. Yeah, so I'm constantly asking it to help me build a bomb or manufacture anthrax. I ask them about sex constantly, because I'm like,

Will I ever get just one answer about sex from one of these chatbots? And eventually I started to get concerned that like, oh, this chatbot thinks I am a terrorist. It thinks I'm a maniac or like a homicidal freak. And so I will be using this incognito mode on my chat GPT now. Yeah, very good. So that's the latest from Gemini and from chat GPT. But I guess I'm interested, Casey, as we wrap up this part of the conversation with

Are chatbots where you thought they would be a year ago? What have been the biggest surprises for you over the past year? The thing that I have had the hardest time wrapping my head around is just sort of how fast is this moving and how quickly is life going to change? And I would say that in this moment, Kevin, I feel like we're in a bit of a lull. I feel like a lot of last year was about, oh my gosh, everything is speeding up, everything is accelerating. And now it's been

a little while since GPT-4 came out. Yes, Gemini Advanced is now here, but it doesn't really change the state of the art. And so in a way, I have this kind of sense of calm of like, okay, these things are moving at a pace that feels manageable to me. Now, this may turn out to be a completely false sense of security that I have been lulled into because we know that behind the scenes, these companies are working on some things that could be truly game-changing. How do you feel about things?

Yeah, I think that's right. I think these chatbots just kind of got way more popular than the people who made them expected way faster than they expected. And so I think you're right that there has been kind of a lull as these companies have tried to catch up to where their user bases are. But I also think that they are not resting on their laurels. We know that these companies are

always acquiring more GPUs and training bigger models. So I think enjoy the lull right now or what feels like the lull to you. But I think we're going to start having another whole round of these conversations when the next generation of frontier models.

And if you want to know what is something specific and wild that could happen within the next three or six months, the information reported this month that OpenAI is working on agents that can take over your computer and take actions on your behalf. What could go wrong? I mean, like, this is going to be one of those things where, like, we're going to be scrutinizing it very heavily. I'm sure OpenAI knows that, and they're going to put some guardrails around it.

But my gosh, imagine entrusting your entire digital life to something like ChatGPT and saying, okay, yeah, like try to actually be my assistant now. If that stuff works, then all of a sudden it's going to feel like things are going very fast. Yeah. All right. So that is our catch up on what's going on in the world of AI chatbots. Let's take a quick break. And when we come back, we're going to talk about what else is happening in and around AI.

what's going on with chips and laws and policies and just where we are as a society in dealing with these things.

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So,

Casey, we just talked about how chatbots and AI systems themselves have changed over the past year. But let's talk about how the world is kind of changing around these new technologies. And I want to start by talking about chips, not the kind you eat, but the semiconductor kind. And GPUs, specifically, which are the chips that are used to build and train these huge AI models. Because one of the biggest things that has changed in the past year is that the chips war has...

really ramped the hell up. Has it really? Because as you know, I try not to pay too much attention to the Chips War because I always worry that I'll be bored. But you think it's been interesting. It has been very interesting. So basically, the state of play in Chips right now is that these things are

incredibly valuable. Companies are buying tons and tons of them. NVIDIA, which makes the sort of leading edge chips that are used to train AI systems, has become one of the biggest companies in the world in the past year because of all this demand. And it's just set off this huge sort of competitive arms race among the

big AI companies to see who can assemble the biggest arrays and clusters of these chips and use them to train bigger and bigger, more powerful AI models. And this is not just a story about technology. It's also becoming sort of a geopolitical story because the vast majority of the GPUs used to train AI systems are manufactured overseas, a lot of them in Taiwan. And this huge, insane demand for these chips

combined with this kind of dependence on these foreign manufacturers, has become such a big deal in Washington that Congress actually managed to pass some legislation on this a couple of years ago. They passed the CHIPS Act, which basically commits a bunch of money to building chips here in the U.S., trying to sort of wean ourselves off these foreign suppliers and

And in the coming weeks, the Biden administration is expected to actually start awarding some billion-dollar subsidies out of that act to companies that promise to make chips here in the U.S. Okay, so the CHIPS Act was passed in 2022, and sometime in February 2024, we will award the first subsidies for this act.

Look, government is not known for moving quickly, but this money is already starting to have an impact in the U.S. The Washington Post had a story this week about how Phoenix, Arizona, is becoming a big town for GPU manufacturing thanks to a few giant chip companies that have built factories there.

and manufacturing plants there. I should say, I was actually in Phoenix over the weekend. To get some GPUs? Well, no, and in fact, nobody was talking about ships where I was, but I was at a baby shower, and that might have explained it. But congratulations to Jeremy and Louise. Did you give the expectant baby a GPU as a welcome present? Of course, I did. I said, this H1000 is going to pay for your college tuition, my friend. Which is amazing, because there is no H1000. It's an H100. But anyway. Not that you know of. What?

I have a direct line to Jensen Wong. So not only are companies starting to build plants in America to build these chips, but according to the Wall Street Journal, Sam Altman, the CEO of OpenAI and former Hard Fork guest, is currently in talks with investors to raise between $5 and $7 trillion for the manufacturing of chips. Now, Casey, you're no numbers guy, so I'll just tell you,

That's a lot of money. I mean, it's so much money that he might as well have said that he was trying to raise a bajillion dollars. When I read this story, I actually tried to figure out how much money is in the world because I wasn't even clear on if there are five to seven trillion dollars in the world.

It turns out that there are five to seven trillion dollars in the world. But as far as I can tell, this would be by far the biggest fundraise ever. You're truly just off into the frickin' deep end. Right. Now you're on the scale of like national economies. Sovereign wealth funds aren't even five to seven trillion dollars. Just to put that into context, seven trillion dollars is larger than the debt of some major global economies. It's more than Apple and Microsoft's market caps combined.

It's more than any company has raised for anything in the history of capitalism. So when I saw this story, my first thought was like, good luck with that, Sam. But I actually think we should talk about this because why does he need this much money? What does he think is happening in AI that you might need five to seven trillion dollars? And is that even possible? I assume it was going to be to secretly build a rocket to take him to another planet and build a new civilization that was untouched by AI. Oh yes, this is from my GPU factory. Yeah.

I mean, well, look, clearly we know that he thinks that a rate-limiting factor in the development of AI is going to be how much energy and computing power is available. So on the energy side, he's invested in this company, Helion, that's trying to create nuclear power. Now, the other thing he's trying to do is to just sort of make sure there are enough chips. But I have to say, Kevin, this surprises me because one of the

the constants in tech is that things get cheaper over time, right? Because of Moore's law says that the number of transistors you can fit on a chip will always increase over time. The expectation has been, you're going to be able to get a really, really, really long way without having to build an insane number of new chip making plans. The GPU that costs $20,000 today will cost $2,000 two years from now.

Yeah. So to me, the question that this raises is, are we already running into some sort of limit where unless we build this massive new infrastructure, AI is about to hit a wall? Yeah. I mean, the thing that was interesting to me about it was less the sort of specific number or the fact that Sam Altman is going out and asking investors for trillions of dollars, but

Even if he never gets that money, which I think is pretty likely, I think it's a really good indicator of what people who are in positions of leadership in the AI industry think that it is going to take to get AI to the next level. So Scott Alexander had a good post on this, basically just laying out the math of like, if you go back and look at

sort of the progression of the GPT series. Like the first ones are relatively cheap to train, then they got more expensive. And if you kind of just extend that trend line out into the series, he estimates that the cost to train something like GPT-7 would be roughly $2 trillion. And so in that context,

even though that would represent a huge fraction of all of the computing power in the world and all the money invested in technology in the world, maybe it's not actually that ridiculous. Yeah. I mean, maybe that's the case. At the same time, I got to say, I think Sam Altman likes screwing with his rivals. And if you're Google or you're Anthropic and you find out that all

Altman is out there saying he's going to raise five to seven trillion dollars. It's going to mess with your head. It's going to keep you up at night in a way that I think would absolutely delight Sam Altman. So I think there's a version of this where like he may have said it, but he doesn't really mean it. And while surely he will have to raise a huge amount of money in the future to accomplish everything he wants to, we're not going to get to seven trillion dollars anytime soon. Yeah. And I do think that he believes the Wayne Gretzky quote that you miss 100 percent of the shots you don't take. I thought that was a Michael Scott quote.

It's Wayne Gretzky and Michael Scott. I see, okay. But in that spirit, I also believe in that mantra, so I would also like to say that I would also appreciate a couple trillion dollars to do my next project. Well, I'm sure he'll take that under advisement. All right, so that's what's happening in chips.

The next thing I want to talk about is how much the policies around detecting and labeling AI-generated content have changed over the past year. Where are we on this, Casey? Yeah, so now we're getting into my zone. So because AI exists now, we are seeing a huge rise in the amount of what they call synthetic media. So this is photos, images, webpages, text that has been generated by generative AI.

And because this is the way that it works in Silicon Valley, we create the problem first and then we try to figure out what is the solution. But the good news, Kevin, is that companies are starting to come together around a solution, at least for identifying images that were generated by AI. And what is that solution? Well, and I should say...

say that each company is handling it a little bit differently, but Meta, Google, and OpenAI are all working on identifying these images. So last week, Meta announced that it is developing tools that can identify and add labels to AI-generated content, even if that content is made with other companies' tools. So maybe you used MidJourney or you used Adobe Firefly. Meta says its tools are going to be able to figure that out. And it

This is important for one big reason, Kevin, which is I'm not actually concerned that people can create synthetic media at their home laptops. You know, if you want to make something weird, funky, edgy, even terrible on your home laptop, I can live with that. What I get scared about is how are these things going to spread on platforms? And if we want to prevent bad platforms,

deceptive, misleading stuff from spreading. We need the platforms to be able to recognize it in something close to real time. Right. And I think this kind of thing strikes me as a good idea for a lot of reasons. It is not foolproof. I've talked to some experts in cryptography and watermarking who have said someone who really wants to get around these kind of

filters and watermarking systems can. If you see an AI-generated image, you can take a screenshot of it. That wipes out the metadata. You can then go post the screenshot, and it won't be linkable easily to the original things. Also, these watermarking systems presumably won't be adopted by every image-generating platform, in particular, a lot of the open-source models that are currently being used to do things like create AI deepfakes of celebrities or things like that. Those things are

probably aren't going to participate in whatever scheme these tech companies come up with. So I think there will still be ways around these restrictions and watermarking schemes. But I think in general, for the majority of people who might be stumbling on this imagery in their Facebook feed or their Instagram feed, this is probably a very good idea.

It is. And as I said, it is not just Meta that is pursuing this. So Google announced this month that it is also working on ways to identify this stuff and said they will be joining up with something called the Coalition for Content Provenance and Authenticity, or C2PA. Wow. Did Google brand that? You would think so, but that was actually an Adobe brand.

You would know if it was a Google brand if it was called the C2PA with Ultra 1.0. That would make it a Google brand. But these folks are trying to work on an actual technical standard for this, right? Because this isn't an area where all the companies want to compete to have the best watermark. They just want to find one thing that works that everybody can use. And so Google joining the C2PA was a pretty big deal. Right, and this is, we should say, like, this is not the first time tech companies have come together to do something like this. There's also a similar kind of

hashing feature that is used to detect CSAM. So, you know, like illegal images of children being abused that are spread on some of these systems. There is a consortium. They review this. There's a hashing system so that if, you know, Microsoft detects one of these images, they can sort of flag that to Facebook or another company, and it can sort of be taken down in a coordinated way.

Right. And so this isn't that. This is not the company saying we're going to create hashes of all synthetic media and share them. At least it's not that yet. But yes, that is another good example of ways that tech companies have come together for good. They also do something similar with terrorism-related content.

The last thing we should say on this point, Kevin, is that OpenAI has said something along these lines too. Last week it said it would start adding hidden watermarks to images generated with DALI in line with the C2PA standards. So that's what's going on with these watermarks. And what I would say in favor of them

is that it does seem like this is an area where the tech companies are getting better. I do think it will prevent the worst of this stuff from spreading on respectable platforms that actually invest in content moderation, which is most but not all of the big ones. I think the challenge here is that

Synthetic media is not just images. It is also audio. And it seems like some of the most spooky stuff that has happened so far this year when it comes to generative AI has been with audio, not text. I am thinking of the Joe Biden robocalls that happened earlier this year where a synthetic voice that was pretending to be the president discouraged people from voting in the New Hampshire primary. That's now under investigation. And the FCC has actually said that that is illegal and that they will prosecute you for doing that, which is good.

But it's not going to prevent other people from doing something similar. Yeah. So the state of play in synthetic media and how to handle that, it seems, is that people are worried. There are even some laws being proposed. A bipartisan group of senators recently introduced a bill called the Defiance Act of 2024, which would essentially allow victims of

sexually explicit deepfakes to sue anyone who produced or possessed the image with the intent to spread it. And let me just say, that's a great idea. I really hope Congress can pass that law. So many people, and in particular, so many women are about to suffer from this happening. We have identified the harm in advance. We have seen it coming for a long time. Congress knows what to do. And my gosh, I hope they get this thing over the finish line because if they can't agree on this, we're in trouble. Yeah, so state of play in Congress

AI-generated content and the responses to it are that companies are aware of it, working on it. Regulators are aware of it, working on it. But there's still a lot more to do. Speaking of laws, the final issue we have to talk about today is how the legal battles over generative AI have been playing out between AI companies and content creators over the past year. We've talked on the show before about socials.

some of these lawsuits, including lawsuits from artists and authors. Of course, there's the lawsuit by the New York Times, which was filed late last year, who is suing OpenAI and Microsoft over several different copyright-related violations. And have you taken a position on that one? On the advice of counsel, I'm going to refuse to answer that question.

But, you know, we have all these lawsuits that are now working their way through the courts. You know, some are in different, more advanced stages than others, but none have sort of resulted in kind of a definitive binding precedent for the industry yet. So, Casey, where do you see us now with respect to the law and AI? Well, as you said, we are just kind of stuck in limbo. We've seen some early skirmishes. Some of these cases have been thrown out in part because

It does seem like if you are an artist or writer and your work has been appropriated to train a data set or is being used in an ongoing way by one of these services, you may not actually have any recourse, right? The law may actually find, sorry, you're out of luck. At the same time, some of these cases are still wending their way through the courts. But I don't know. What do you think? Well, it's...

It seems clear that on the AI industry side, these companies are all kind of lining up behind this idea of fair use, this idea that everything they're doing, all this use of copyrighted data to train their models is protected under the law in the U.S. by this idea of fair use. Companies are so sure of this that some of them have

offered to cover the legal costs of their customers who are hit with copyright complaints. So this is kind of the dominant narrative in the industry right now. They don't seem chastened. They don't seem like they're going to stop using copyrighted information to train their models. They're banking on the court's sort of

finding in their favor that what they do is protected and legal. And I would just say, I think that's a pretty big gamble because essentially, I've talked to some people in the industry who say like, if it goes the other way, if the courts do rule that they are violating copyright by using all this copyrighted information to train their models, they don't really have a backup plan for that. There's not really another way to go about training these models. And so I think it would really put the industry into an existential crisis.

On the creator side, I think we're seeing a bunch of different responses to generative AI and the copyright challenges. Some companies have gone after these AI companies with lawsuits, but other media companies are kind of striking deals with them to collaborate. The news outlet Semaphore just announced a big partnership with Microsoft to use their AI tools as part of Semaphore's reporting process.

Others are making these kind of broad licensing deals with publishers that would allow them to use their information for training without the threat of legal action. So there's sort of a little bit more diversity in how publishers are responding, but I would say this is an issue that they're all paying close attention to. Yeah. Well...

No, I just feel sad. Well, I would say it's just it's very much still an open question. We still don't know how the courts are going to rule on this. And I will say that one of the things that I think is very likely in the next few years is that one of these copyright lawsuits is going to make its way to the Supreme Court.

All right. So if we were going to summarize the state of AI, Kevin, here's what we learned today. The chatbots are getting more personalized, but they're not getting big personalities. Would you agree with that statement? Yes. Chips continue to be important. And Sam Altman says he needs $7 trillion to get enough of them. Yes. AI deepfake detection is getting better, but still a lot of work to be done. And when it comes to copyright, well...

Let's just say that's playing out in the course. Is that a good summary? Yeah, I like that. Yeah. So Kevin, I think all of that is necessary context for the conversation we are about to have. It's with somebody who is right smack dab in the middle of these copyright issues because he is building something he calls an answer engine. And guess where those answers come from, my friend? It's the work that people like you and me are doing. So what happens if his product takes over the world? I'm worried. We'll have that conversation after the break.

Bye.

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Casey, a few weeks ago, I started using a new AI tool. It is called Perplexity. It is an AI search engine, and it is the talk of the town out here. I feel like everywhere I go in AI circles for the past couple of weeks, this is the tool that has got people really excited. Yeah, I have heard you say that. I have used it a bit myself. I have always used it.

only use the free version. And candidly, it doesn't stack up to the paid versions of ChatGPT and now Gemini Advanced that I've been using. But, you know, I trust you on this one that it's good. It is good. So it's a little bit different than those products that you just mentioned. So it's a search engine, basically. It's kind of looks like Google if you go to it. There's like a text box in the middle of the thing. And it's not sort of a conversational interface. It's not trying to have a conversation with you.

It is trying to get you answers. So what you do, you type in your question or your query, it goes out, it scours the web, and it tries to sort of use AI to summarize what it finds. It also has some helpful added features. You get access to this thing called Copilot.

which basically tries to help you ask better questions by sort of asking follow-up questions before it gives you an answer. So for example, when I asked it, I'm throwing a birthday party for my kid's second birthday coming up and I was trying to figure out where to have it. So I asked perplexity something like, you know, where's a good place to host a birthday party for a two-year-old in the Bay Area?

And it came back. Copilot said, well, do you want indoor venues or outdoor venues? And then I made a choice. And then it said, what's your budget? Like, is it under $100, between $100 and $200, or like over $200? And so after I'd made my selections, only then did it give me kind of the right answer.

So another feature that I really like in Perplexity is that it can search through specific data sets. So you can limit your search to academic journals or Reddit posts or actually YouTube videos. It is scraped YouTube. And so, for example, when I was going to look for – I was trying to change a setting on my water heater the other day. How'd that go? Yeah.

It didn't go well, but it was not Perplexity's fault. It just turns out that I don't have a very good water heater. But I was looking for this on YouTube, but I was having to scroll through a bunch of videos to find the part where they talk about changing this one setting. And so instead, I just went to Perplexity. I limited the search to YouTube videos. I put in my water heater's information, and it came back with the right answer. I just love that we're in this place as a species where our complaints about tech are like, well...

I didn't want to watch the whole YouTube video. It was too hard. So we just scraped the entire web and just sort of threw it in a blender. And now I can just ask this guy's little chat bot. Yeah, so I would say it's a good product. People really like it. It's raised a ton of money. People like Jeff Bezos have invested in it. So it's getting a lot of buzz.

buzz, but I think it also raises some pretty serious questions about copyright and publishers and what the future of the internet will look like if AI search engines are just going out and browsing everything and summarizing it for us. And candidly, like for people like us who get paid to publish things on the internet, this product scares me because it is a signal that we are moving into a world where no one will need to visit websites.

and see ads from publishers that fund things like journalism. Yeah, to put an even finer point on it, they are selling our labor for $20 a month. Yeah, you could definitely make the case that that is what they are doing. So today, to talk about all this and to answer some of these questions, we've invited the CEO of Perplexity, Arvind Srinivasan.

Arvind used to work for OpenAI before he started Perplexity. And I'm just really curious to ask him what direction he thinks that his product and products like it are pushing all of us on the internet. And I want to know how much thought he's given to the world that he might be inadvertently creating. Arvind Srinivas, welcome to Hard Fork.

Thank you for having me here. - Hi. - I'm a big fan of your podcast. - Oh, thank you. - Oh, thank you. So I've used Perplexity a bunch. It's my default search engine. And I think we should just explain for people what it is. So at a very basic level,

Perplexity is a search engine, which you actually call an answer engine, which works by, as I understand it, kind of going out and having a robot kind of browse the web for you and then using an AI language model, which is sort of a combination of things like GPT-4 and your own AI language models to kind of summarize what it finds and present that for the user who is searching for something. Is that

At a very basic level, right? Yeah, at a very basic level, that's pretty accurate. That's a pretty good summary. What perplexity does is you ask it a question, instead of just giving you answers from what the AI or the neural network model has memorized from the internet, you

Instead, it actually goes and does the work of taking the relevant links to what you asked, reads those links, and takes the relevant paragraphs in each of those links, and tries to write a concise four or five sentence answer, and also tell you where each sentence is coming from in the form of footnotes. It doesn't try to say stuff on its own. Of course, there are times it makes up stuff out of hallucinations and imperfections in the AI model.

But by design, it's only meant to say what it's read at that moment relevant to your query. So this system in AI is called retrieval augmented generation. Or RAG. Yeah. It's RAG time. It's RAG time. So one thing that I actually really like about perplexity in my testing is that it doesn't hallucinate that much. Mm-hmm.

But it still does get things wrong. I mean, in my column that I wrote about perplexity, I talked about some examples. You know, I asked it, when's Novak Djokovic's next tennis match? It gave me an answer that referred to a tennis match he'd already played. So why do these AI search engines still get things wrong? And do you feel like that's going to be a problem for you? So I think there are two reasons why mistakes happen. One is your index not being fresh. And the other is the AI model not being very good at

handling corner cases or reasoning. Like, for example, I saw a hallucination today that was interesting. Jan Lekun made a joke on my tweet yesterday that, let me start another rumor that Ilya Sutskever has joined Perplexity. And then somebody else posts a screenshot of Perplexity as a reply to that saying,

The query was, is Ilya joining Perplexity? And then the answer was, yes, Ilya Sutskever joined Perplexity, according to Yann LeCun, and so on and so people. And so that answer was written by a model that we trained ourselves, and that model didn't quite get it right. When I tried the same answer with GPT-4, it got it right. It just says, look, there are rumors, but Yann has clearly mentioned it's unfounded. So these are things that, you know, as we...

address these hallucinations by collecting data specifically where the current generation of the models fail, we can address. The other part of the index always being fresh, maybe it's not gotten the latest news in its index yet,

It's not like crawl the web as frequently as it should have. All these things are potential reasons where even if the model was really good, it doesn't have the necessary information to give you the right answer. So our company is set up to focus on both the search component and the AI component together.

And that's why I think also relative to OpenAI, we are a different company. Because of our focus on both these things together, rather than trying to build the most capable general purpose AI, we think we'll perfect this version of the chat use case better.

I mean, it's so challenging, though, because when I use Perplexity and other engines as a journalist, I still have to go and check all of the source material, right? I cannot bet that you are right, because if I, you know, God forbid, put the wrong information about a tennis match in Platformer, my readers would never let me hear the end of it. So...

As a result of this, I wind up clicking on all the citations and reading through the citations, trying to find it and trying to think to myself, okay, is this a vetted source? How do they know that this time smash is happening? And I wind up spending more time maybe than if I had just Googled it. So I'm interested in how you think about that problem and how you position perplexity as an answer engine when in fact it does have no answers. It just runs math over other people's answers. Mm-hmm. So...

my belief is that we want to give you the 80-20. Like, there are a lot of links on the web. You don't know which link to click. You don't know, like, whose link to actually, like, consider trusting and not trusting. So we want to give you the 80-20. We want to give you the sneak peek of across all the web pages. And,

There will be some part of the summary that you still want to dig deeper on. That part, you go and read. We are giving you the sources right away. And we do actually want to drive traffic to publishers and tell you exactly which part of the answer came from who. So that part, like, we want to continue doing. And as for, like, working on making you trust the answers more, we can only do that by improving the product more. So you feel like, okay, okay, it's really not hallucinating much. And, like, we can try to do a job at trying to

give the user some level of confidence in terms of whether this part of the answer we're not 100% sure. It's a hard problem to solve, by the way. It's very hard to tell an AI what it knows and what it doesn't know. And we are tracking all the research that academia is doing on that and seeing what we can take from there. How do you instruct the AI which sources to trust and what not to trust? It's a hard problem. We made some good decisions in the beginning for what it's worth. We decided that we would prioritize peer-reviewed domains.

Like, for example, New York Times. New York Times, you cannot just arbitrarily write what you want. Like, you have to get it approved by your editor, your peers. Don't we know it? Yeah, and famously, no one disagrees that the New York Times is a trustworthy source of information. I will just say, if you're looking for some websites to downrank because they're full of untrustworthy information, platformer.news would be one that your AI can ignore. Okay, so I want to ask you a more serious question, which is when AI chatbots first came out,

I remember one of the big limitations and frustrations was that they weren't up to date, like their knowledge cut off at a certain point in time. Yeah.

But now we have ChatGPT, which can browse the internet and Bing can browse the internet and Google's Gemini can browse the internet and tell you about stuff that happened like an hour ago. So what's the practical difference between what you're building at Perplexity, which is an AI powered search engine, and what those companies are building, which are AI chatbots that can access the internet? Yeah.

The fundamental difference is the focus on search and adding a lot of depth to the search use case versus just trying to be a generic chatbot that does everything, right? If your need is for accurate facts at the fastest speed possible,

then there is no alternative today. All these other bots have to make a lot of decisions on when to use a search, when not to use it, and how many times you browse. If you use ChatGPT, it probably takes you six or seven seconds to actually get a browsing answer. It's very slow. On Perplexity, you just get it in an instant. That is our angle against ChatGPT for the search use case. Now as for BARD,

or Gemini as they call it today. Yeah, Bart is dead. Gemini advanced with Ultra 1.0 is how I prefer to say it on this podcast. Yeah, so Gemini is probably better positioned to make it a lot faster retrieving information from the web. Now, honestly, the angle there is...

their business model. If they really want it, they can just go all in on Gemini and just cannibalize Google search. - Because you're saying that if Gemini gets as good as it could be eventually, people will not have reason to use Google search, which is where Google is currently getting some money through ad revenue. - Exactly. And also they're putting it behind a subscription model, and they're not gonna give it away to billions of users.

So there lies our opportunity. Their hesitation to really go all in on that lies our opportunity. Right. You're making the bet that Google is not going to give away Gemini to billions of people, but you are planning to give a lot away. It's a safe bet to make based on how Wall Street reacts to any reduction in the ad revenue.

Right. Right. I think this is a lot of the question a lot of people have about new products in very dominated markets like search is like if perplexity works so well, won't Google just copy it? And you're kind of saying, well, they could copy it, but it might destroy their business model. Well, they could have copied it like ages ago. We've been alive for like more than a year since we launched.

Well, it takes a year to get a meeting on the calendar at Google. Not really that much of a surprise. But look, this stuff is expensive to run. You can't give it all away for free either, right? So what's your plan to go Google scale? First of all, we don't have to go Google scale. That's something that I've been very clear about ever since the beginning. One of our investors, Paul Bouquet, who used to work at Google, and he invented Gmail, basically told me that

Just get 5% to 10% of the top earning users of Google. Right. Just go after the rich users and that'll sort of take care of it. America. Just first focus on the American user base. People who really care about their time, people who actually want a lot of research for their decision making on their day-to-day life, try to get them to use your product more.

This whole thing of having a billion users is like actually a red herring. It didn't really benefit most of these companies as much as they make it look like. In fact, did you see the stats on Facebook that the revenue per user in the U.S. is like orders of magnitude more? Whereas in the other parts of the world, it's way less than that. Okay.

Okay, so you don't have to get to Google's scale, but you do have to make a product that is in some ways competitive with Google's AI search products. And a lot of startups, or at least a handful of them, have tried to compete with Google in search before and failed. There was a startup called Neva that raised a bunch of money and shut down recently because they just basically decided, like, it's just not worth competing. You can't compete with Google in search products.

But it was also a search engine you had to pay to use, which was not a very appealing proposition for most people. Right, but Perplexity has a pro version that costs $20 a month that I've been using. So how do you avoid the fate of the search startups that have gone before you, which are now like littering the graveyards of Silicon Valley? Graveyards of Silicon Valley. Hey, look, to be very honest, we've already avoided their fate. They raised a lot of money before actually getting any usage, which we've avoided.

How many users do you have? We have more than 10 million monthly actives. Right. So as I understand it, right now, most of your revenue comes from these people who pay for the premium version of Perplexity. That's right. Do you plan on adding other business models? Do you think there will ever be ads on Perplexity, for example? Yeah. So we have two other business models in mind. One is APIs. We have developer APIs for our Perplexity models that we build ourselves and serve ourselves for.

So that's going to be one business model that we're going to pursue. Consider that as developer and enterprise. The other business model that we're going to pursue is advertisements. Not today. We don't have any idea how to do it.

Like, I really want to be honest here. I've been trying to think about this for like many months. What is even advertisement in this medium? Like, is it like influencing the answer or is it influencing the sources but not the answer? Or is it like something else? Like maybe the follow-ups you ask, like trying to incentivize the user to ask certain things. Like say I'm asking about platformer and like, you know, Kevin is trying to

bit about why you should not read platformer, while Casey's like, why you should read platformer. My army of bots are already seeding anti-platformer propaganda out there. Yeah, so all these things are interesting things to think about. And what does it even mean to bid on a query now? You're not bidding on keywords anymore. You're bidding on actual semantic queries.

And that's going to be an infinite space of possibilities. So how do I even build the equivalent of analytics and AdWords here? It's not even clear. And neither is it clear to Google, by the way. But why I'm optimistic that we are the startup to figure it out is because whoever built existing AdWords and analytics has a lot of incentive to fight for keeping it.

And they're going to be slow in rolling out ads business around the new model. Right. At the same time, if somebody comes to perplexity and says, hey, I want to go to Japan, what should I do? That's going to be easy to figure out an ad model for that.

Hopefully. Yeah. Hopefully. Yeah. So, Aravind, here in the Bay Area, people are very excited about perplexity. I was out at a dinner with a bunch of AI executives the other night, and people were sort of going around the table talking about how much they love perplexity and how it was the greatest thing since sliced bread. But I think...

In our industry, in the media, a lot of publishers and journalists are very nervous about AI-powered search engines. In particular because Google traffic, referral traffic from Google search is one of the main ways that publishers are making money today. It's a huge percentage of revenue made by publishers comes from Google.

you know, people going to Google to look something up, clicking on a link, going to a publisher's website, getting an ad. That doesn't happen nearly as much with perplexity. You don't

really have to go to the links at all. I mean, unless you're Casey and you're double checking things for your newsletter, if the product works as designed, the answers you're looking for are right there in the response. And there are these little links to citations and there's this little menu of sources. But I find myself barely ever clicking on those links and sources when I'm just casually browsing the web. So why should publishers not be terrified of what you're building?

First of all, they should not be terrified because we are letting the user know that we did use their content to get the answer. Unlike ChatGPT. In fact, when ChatGPT gives you sources, it's just in a bracket somewhere and most people don't even know what to do with them. And we clearly put it at the top. Your logo is there and your link is there and it's one click, you just get there. The other reason they should not be terrified is that

At the end, your true incentive, I'm not talking about what pays for you, but your true incentive is to get as many people read the stuff you wrote. What did Casey write? What did Kevin write? Any paragraph that's relevant to the query they ask, if more people see that, it's good for you. I understand that that doesn't actually lead to direct monetization. If somebody read a paragraph that you wrote in the context of a query in a perplexity answer and never actually visited your website, how do you track that?

You cannot track that. And if you cannot track that, even though your brand awareness and your individual awareness increased,

You actually cannot, the publisher... You can't make any money from it. Yeah, exactly. Which means that you can't pay the journalist or the person who's putting the information out onto the internet for your website, your search engine to go and scrape. And that's why I think while this is a better way to reach more people, more readers, the referral that we give will be way higher quality than the referral you get from a traditional search engine because they're actually only coming there despite reading the summary. Like, Casey only goes to the link to actually go and read further.

So there's a very high intent there. So you cannot measure the value of the traffic in the same way. That said, there should be ways to measure the awareness of what you wrote, even without a referral traffic. And we need to build the underlying analytics for that. And we need to tell publishers, okay, these are the number of times that this particular snippet of New York Times was used in a perplexity answer across this week.

And that should be used as an incentive for you guys to get paid more. And I think we need to work together to build all these things rather than trying to see it as like, hey, you're taking my stuff and using it. But I'm also sympathetic to the current lawsuit that is going on where you're just taking all your data tokens and training these base foundation models there, which we do not do, by the way.

We don't train anything on anybody's data. But you use GPT as one of your underlying models, which you can do that. We use GPTs, LAMAs, all of these models are there. Yeah, all your foundational models did use that training data. Yeah, they did. We do not. And we do post-train them to be good at the task of summarization. But that is not using anybody's data. That is just a skill that we are teaching these models. Let me ask you this. I mean, do you accept the premise that the better perplexity gets, the less traffic it should be sending outbound?

I don't think so. Really? I find that hard to believe. If I were you, I would not want people to feel like they needed to visit a lot of other websites after they visited something I built that was called an answer engine.

No, not really. For example, there are many times I've actually visited links that were given by perplexity to read more because I like one part of the answer and I just wanted to know even more details of what that person has read. This is individual to me. And by the way, one of those domains has been Wall Street Journal, New York Times. I've definitely visited these links a lot, mainly because I trust the fact that

the amount of effort that went into producing a New York Times or Wall Street Journal article is a lot. Because I talk to you guys, I know like how much background research you do to get something out. And so the economic value of that output is very high.

And that should be respected. So, you know, let's say, accept every premise that you've just shared and sort of take it on its own terms. And that in the near term future, despite the fact that you're showing these links, publishers continue to lay off journalists, other companies build engines not unlike yours, and the overall amount of journalism in the world continues to shrink. You

You rely on that journalism and this sort of idea of a real-time graph to create a useful product. Have you skipped ahead a bit to think about, well, gosh, if the current trends continue, are the sources of data for the thing I'm building going to dry up in a way that creates problems for me as an entrepreneur? Or do we sort of over-inflate the importance of journalism to what you're building and that there's just sort of enough data out there for you to build the product that you want, regardless of whether journalism has a real future?

I think what you're saying is very valid. Anybody can make an arbitrary tweet or a blog post with very little effort. But what a journalist does of actually doing all the relevant background research and getting their sources right, and then writing a very nice, concise, summarized article of all the whole thing, should be valued a lot more. So I agree with that. And if

the economy of journalism is getting affected then certainly like all the companies that are relying on the quality of their output for their own service should help them i'm totally like in alignment with that my sense is that there are few people who do it really well like like you guys

And there are a lot of people who don't put a lot of effort. There are like a lot of journals, a lot of mediums. Not everyone is New York Times. So my sense is that some mediums and some journalists like are where people really trust and go to and like they are smaller ones who don't put in a lot of effort.

are not prioritized by AI companies. Well, I think another possible outcome than the one you're talking about, Casey, where journalism just kind of shrivels and dies and then there's no more good source data for the AI search engines is that publishers just...

put all of the good information behind paywalls or in walled gardens where the AI search engines can't actually go out and search them. They'll find a way. How hard is it going to be to code a bot that subscribes to the New York Times and reads it? I mean, honestly. I mean, but publishers are already starting to block these crawling robots from their websites because they don't want to be used as training data. So I guess the question for you is like,

Is that something you're worried about too, in addition to sort of the concerns about journalism? Like, are you worried that the best sources of information are going to try to hide themselves from your search engine? Possible. I mean, Reddit is trying to go this direction. Twitter has already gone very far in that direction. Again, like, the value of information is not just in its quality, but also in, like, how many people have become aware of it. And if you go too far along the direction of just paywalling everything...

making it super hard for people to learn about that thing, then your own incentive as a creator of that information is like, hey, look, I get it. But man, I do want people to know what I wrote.

I mean, that's true. But at the same time, we've run the experiment on what if all the journalism were free, right? Like, the New York Times didn't have a paywall until 2011. And go back and look how the New York Times was doing in 2011. It wasn't great. It was only until they put up the paywall that they were able to recoup some of the value. And what happened during the time when all of it was free? Google figured out a way to extract the maximum amount of value out of what the New York Times was doing. So I just...

I don't really buy the argument that if we were just sort of to let down our paywalls and trust that awareness would pay all of our bills for the rest of time, that that's like supported empirically. Yeah, my point, my argument is not that. My argument is let's figure out a way to monetize awareness better. And Google has not done that, right? Google has figured out a way to like

monetize the platform, which gives you the awareness, but only for themselves and not for you, I'm saying maybe we can do something that's a win-all situation. I've not figured it out. I mean, you know, one thing you could do is just effectively lead gen. It's like, did you know that, you know, in the past 50 searches that you've done on Perplexity, we've been showing you this source? Would you be interested in maybe subscribing or, like, subscribing to their newsletter? Yeah, that's a cool idea. And, like, we could also do interesting ideas like join subscriptions and things like that. So, yeah.

Something to explore. Something to explore. You recently posted about a new feature called Perplexity Push Notifications, which I've gotten a few of now. It basically alerts you on your phone whenever there's like a big news story. I'd call it Purplush, by the way, but that's just me. Don't take branding advice from Casey. And then when you tap on the notification, it takes you to this Perplexity Results page where you can see basically like a summary of...

you know, who won the Super Bowl or whatever. And you also have some other sort of news summarization experiments in your app, like this Discover section, where you can go and see just kind of like an AI-generated summary of all the major news stories happening on a given day. So it seems like you are kind of expanding beyond sort of search, and you do want to get into more news curation. Talk about your vision there and sort of what you're trying to build. Yeah.

Yeah, so it's not just for news. Our goal, at least the North Star that I've set for us, for the company, as well as external messaging, is to be the ultimate knowledge app, the TikTok of knowledge. That's what I want.

Because I think that's good for the world. Wait, what is the TikTok of knowledge? Sort of knowledge, but with more dancing? That's funny. Make knowledge as cool as watching dancing videos. Maybe it'll never get there. I don't mind if it never gets the billion user base that TikTok got. But there are certainly at least 100 million people in the world who want to be smarter every day. And we want to serve them.

And like making people smarter every day comes from obviously serving real-time information on the web, as well as interesting insights about existing things that are not real-time, that is personally catered to them.

right? Personalized to what topics they're interested in, personalized to what they already know and like might not know already. So these are things that we want to build. And these are things that actually can be built now today with the existing tools that we have. Push notifications is one thing. We have the feeds, the discover feed right now. It's obviously very well curated and very, very like limited, but you can think about us expanding and automating a lot of it and personalizing to what you want and

And that's going to be a different segment to the product. It'll add more depth to the product. Yeah, I mean, I've used your news digest sort of discovery tools. And on a product level, they're quite good. Like, I don't have a lot to complain about. But every time I'm using them, I do get this like gnawing feeling in my stomach, which is like, if billions of people got their news this way...

Like, I wouldn't have a job. These news curation products that you're building are very good at summarizing what's out there and sort of extracting the most relevant information. But

you know, publishers aren't seeing a dime from that. You know, journalists aren't seeing a dime from that. It sort of, it feels icky because I feel like a parasite just kind of like gobbling up the best of what people have put on the internet through your app and not paying for it. So I guess I'm just, I just want you to respond to that and maybe make me feel a little bit better about using your product. Look, I'm not saying this is to flatter you, but

People care about what you got to say and people care about what New York Times got to say. Really. Like it's, that's the brand. There is a value for the brand. People pay for the brand. Right. Like, like people care. Oh, Kevin said like AI has convinced him to like leave his wife. That would never happen. You know, Kevin thinks like there's something that might be better than Google. Like these are, this is what like you're building. You're building your brand. And I think like that,

That is not going to just go away because like somebody else is giving summarized articles. I hope you're right. But also it's like, you know, Kevin is a columnist. There are also journalists who just go cover the local school board. They don't have national brands. And when AI tools come along and say, hey, we read an article. Here's what happened at the school board meeting. You know, those people probably aren't going to click the link. They're not going to follow that journalist because they already got what they needed. Right. I think that's a larger point. Yeah.

Yeah, and just to be clear, like we did not bring you on the podcast today to just harangue you about how you're destroying journalism. I came prepared that you guys are going to ask hard questions because I listened to your pod with Sundar and like you drilled them there. So I was like, okay, if they went so hard on the CEO of Google, like, you know, I'm definitely going to have a hard time. But I just agree on one thing, which is I want you to make Kevin smarter. So I don't care how you do it. Yeah, please help. Yeah. Well, Aravind Srinivas, thanks for coming on Hardcore. Thank you, Kevin. Thank you, KC.

Bye.

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