Lawfare Daily: Grokipedia’s Deafening Silence with Renée DiResta

So, this is the end of the series and the new "Bundestliga" series, again, for the time being, "Mutigreach-mental" and "Sport Directors". Because all the games are available, then, it's even more like the kick-based series. And if there is a kick-based series, what does that mean?

The kick-based is the fourth fantasy "Fußball Manager", and that's the first one.

It would be easy, you are surrounded by an opponent, you can just put your own rules, and then the real "Bundestliga" profile in your card. So, this time, you will know that you know, and the content of "Lolved" the "Bundestliga" series is on the top. It's an August, a dream of being surrounded by a single league, and it's been a month since the kick-based series, just download and leave. Good kick and fun!

What you're seeing is this encyclopedia product is not doing that sort of rapid frequent updates, and it is, I think the problem here was really just that they didn't declare it, and yet it continued to be treated as reputable by other entities. It's LaFaire, a sub-stack live. I'm Kate Kahnick, Senior Editor at LaFaire, with Renee Doresta, Associate Research Professor at Georgetown's McCourt School of Public Policy, and a contributing editor here at LaFaire.

I mean, all these different things kind of feed into each other, and the question is,

“what matters in the very short term when people are forming opinions about things,”

or on the flip side, is this entering into training data or search engine data,

and that's, I think, the growth of PDF problem at that halt. Today, we're going to be talking about the piece that she published yesterday with Ronald Robertson, a case study in what happens when an AI generated in psychopedia, not that there's a ton of them, as far as I know there's only one. Just stops generating itself, stops governing itself, stops updating itself, and doesn't tell anyone.

So, you know, I saw that you were working on this piece, like, in the editorial queue, and everything else, and I know you've been digging into this for a while. And this is really about, for listeners who haven't, for, like, looked at Broccapedia closely, and aren't super familiar with, like, all of the ins and outs of Broccapedia, what it is, and what you did, and what Ronald actually found, can you just kind of walk us through the headline finding,

and kind of when Broccapedia got its start, and how it was kind of initially packaged, coming out of X. So, Broccapedia is an AI generated in psychopedia. It was started kind of late last year. Elon started it as very explicitly as a counter to Wikipedia.

The argument being that Wikipedia's human editors are biased. He was calling it woke a PDF for a while. There was a lot of drama about that. The reason it mattered to him then is that a lot of AI models train on Wikipedia, right? Or they use it as retrieval search engines use it for retrieval.

Also, because they see it as, like, a human created content place. Human created place for content, right? And so, Elon decided that, uh, GROC, right, X.A.I.s product, GROC, was going to make GROCapedia, and the difference between GROCapedia and Wikipedia is, uh, there's sort of two big ones.

“One, GROC can edit and GROC essentially creates an edits, GROCapedia, right?”

And so, what that means is, it is interesting. It would be the same for people that kind of understand it. It would be the same as a chatapedia. Yeah, exactly. Yeah, exactly.

Like, like, existed. And chatGPT would be the model that just kind of self created and edited it. Copyulated it, that it is. So, it was launched with about, um,

350,000 pages or so, um, I paid attention because I was one of the first, uh, I got a bio.

And, um, and I started paying attention to it. I wrote about it for the Atlantic back at the, back last year. Um, because I was very curious about it.

“I actually don't think it's a terrible product, right?”

Like, it, my bio on it was very interesting for me because it was, like, two thirds fantastic and one-third insane. And I was like, and this gets to the second reason why Elon did it, which was basically, uh, again, the belief that sources really matter, and that Wikipedia, which has a very, um, kind of community-designed,

curated source list that they consider to be reputable sources. It's very transparent. Anyone can go look at it. But, you know, they wouldn't consider info wars to be a reputable source. Whereas, grocapedia will and does actually cite info wars in some places.

So, this led to a lot of people looking at, um, what is this AI think is a good source? What is it? How does it create bios or create articles? How does it frame things based on the sort of Elon of a,

Um, and what that means is there is stuff in there where, you know, groca considers it to be reality, even though, uh, most other AIs would tell you at a minimum that it is, like, highly disputed. Some of the pages about vaccines are a little bit wild, you know, that kind of thing. So, that was the, that was the, that was the big difference of the reason why he did it.

Um, and I followed it as more and more pages rolled out,

“because what you started to see was that, um, other AIs were picking it up, right?”

So chat, GPT would occasionally return grocapedia as a source, Google would occasionally have a grocapedia link in its, uh, in its sources. And it's very interesting, right? Because it's an AI deciding that another AI, which is, you know, kind of creating the entire site itself, is a reputable source. And so that is very interesting because then you get, again,

if you go one level down, you're getting to the info wars aspects of grocapedia, I wish you're then all of a sudden being returned by chat, GPT. So this leads to a lot of interesting questions around what do we consider to be reputable sources, uh, good information. How should an AI generate in cyclapedia be treated?

But also, like I said, it wasn't terrible. I mean, there were a lot of entries that were quite solid. Um, the two thirds of my bio that was good was like, way deeper than anything that everyone showed up on Wikipedia. And it's an interesting question, right? And what should we think about, um,

about AI as a, as a, as a creator of reference information? Yeah, totally. And so tell me a little bit kind of about, um, some of what, uh, how you actually did kind of the methodology of some of this, because I actually find this super nerdy and fascinating. But it's hot, like, I want to, like, kind of point out

you're the first person until, like, go and see that Broccapedia has kind of been abandoned

as a project. First of all, I want to kind of hear you say,

“why was this abandoned? Do you have any hypothesis?”

And then we'll get into kind of how you figured out all the ways that it was abandoned. So no, I don't, um, I actually don't, you know, Elon is, you know, kind of notorious for starting things and then changing them, or somebody actually pointed out just, I do want to clarify, when I started noticing this myself, I did go searching the internet to see if other people

noticed this, right? And there are one or two people who are, um, and I think I mentioned this in the article. I think we put this in there. Uh, we did an analysis on, like, who was editing it? And there are some, like, real super users in there who are very, very frequent, uh, creators of Groccapedia edits, because even though the encyclopedia

primarily writes and edits itself, humans can go and you, there's a little drop down up at the kind of top right where you can say, suggest an edit or suggest a change. Uh, and then you can actually submit a change request, and the AI decides if it is going to incorporate your change request.

I think I wrote this in the Atlantic piece because, of course,

“I tried to edit some of the crazier stuff in mind, right?”

Or I was like, I didn't censor 20 million tweets. Like, that's bullshit.

Let's get that changed, right? Um, so there was some of the, some of the other people, though, who were in there as just diehard contributors across a range of topics, much like you see on Wikipedia, people who are just very passionate editors about a topic. Um, we're in there trying to make changes.

And some of them started actually saying this on Twitter, when they noticed that their edits were frozen in queue for a couple of weeks. And it was very, uh, pronounced for them. And also for me, because it used to accept or reject changes within a couple of minutes, right, you knew maybe within three to five minutes,

if you're at it was amenable to the sort of AI overlord. Uh, so these other people were maybe two or three of them. We're saying on X, and then a couple on the grapidious subreddit. We're also talking about this, but nobody had really done any kind of systemic audit.

And, um, I think that was where Ronald and I, uh, you know, I started it and then I reached out to him. And I was like, okay, I'm on someone else to look at this with me, make sure I'm not wrong, you know. Uh, totally. So tell us, so you didn't have kind of a leaderboard to work with.

The grapidia kind of shows per page views counts. Um, but there's no, like, kind of say, uh, wide ranking. And I thought your methodology around this was super interesting, because you ended up where you can struct and re basically reconstructing a leaderboard. Um, using like the search type, like the search autofill feature.

Yeah, the type ahead feature across the 10,000 kind of most common words in page titles. Why go to kind of that length to do this? Like, why was that the methodology that you picked? What do you think that shows us? Why was it the right way to look at this?

Yeah, so there's a couple reasons why. Um, first Ronald gets credit for that.

He does amazing work on search quality.

I'm sorry. He couldn't join us for this. He had a conflict, but, um, but he is just amazing when it comes to auditing, auditing search engines and search results and such. So that was why I reached out to him. Um, so I noticed that controversial pages were not being updated, right?

And that was just sort of a qualitative observation. Um, I said, okay, maybe it is stepping back from certain types of pages.

Let me start looking at extremely common things that should go through. And then I submitted a correction request to SpaceX saying, um, you know, I made an account to do this submitted with the SpaceX. And, um, and then it, um, it just like was in queue there. And this was something where all I said was SpaceX has IPOed.

Just as basic and neutral and boring, a fact is possible on a page that Elon ostensibly would care quite a bit about, right? Um, and it, and it held it in queue. And I looked at the queue because you can see that you can see about 20 of them. You can pull that from the API.

Um, and, you know, I use cloud code a lot to do these things at this point. Uh, so pull down. I started pulling down from very, very popular pages. Things, you know, things that would naturally occur to you as likely to be popular. And then I said, let me do this a little bit more rigorously.

So the way that this search token methodology works.

So, um, we started with the, there's about 5.9 million pages listed on

grakipedia's site map, right? And then, um, but Ronald did was he sort of broke those into individual words, which are called search tokens. And then found the 10,000 most frequently occurring title words. And then entered it into essentially the autocomplete the type of head search tool.

So one of the things that happens with, um, a lot of, a lot of websites say it's very common for search is that it will suggest things that you are likely to want when you type in a word. So if you type in the, you might get the Beatles, Alexander, the great. Those would put two of the examples that we gave.

You can kind of envision how, um, you know, typing in, uh, like a famous person's first name will likely autocomplete with their sort of second name. You'll get a small list of them as opposed to just alphabetically every single person whose name might be Alex, for example. Um, so that from that, we started to pull a key, started to pull, um,

the most commonly returned popular pages and then, uh, kind of combined and deducated them and that got us a sample of about 300,000 pages. And again, the intent with this was to find the popular ones. Because you can use grakipedia's API to pull the page count. But it's a, it's a single shot thing.

Like you can say, I want the page count for this entry. But you can't make that leaderboard, right? So that was what we wanted to find the most commonly viewed most commonly edited pages. The assumption being that those popular ones would be the place where we would see if edits were happening.

“You should expect to see edits on like Elon Musk or SpaceX, for example, Barack Obama.”

Um, and that was how we sort of validated that, um, the sort of initial sampling that I did. And I should also say the initial sampling I did was based on the Tao Center had made a, um, they did, they wrote an article they published in January and they had collected edits. Because grakipedia.com/live used to show you a live feed of all the changes and that had halted. Um, so I went to the way back machine.

I started pulling way back.

I scraped basically the, you know, through way back machine API basically pulled everything I could get.

Um, and looked at when the, when the, you know, what changed logs I was getting there. And then also we wanted to see if maybe it was just the search. Kind of cue that had halted, but they were still making changes. So that was where I also did like a site comparison for popular pages. Um, on their Wikipedia on their way back machine timestamps.

I know that possibly sounds very complicated. I think we explained it a little bit more clearly in the. No, I mean, so there's, I mean, essentially kind of, I guess what, like, what I wanted. My, my follow-up question to this was like, there was, and like, as you mentioned in the article and you mentioned just now,

“the Tao Center had been like kind of chronicling this edit lock, right?”

There is this nice kind of, I mean, in terms of transparency, this was actually quite useful. Um, but a broke. It just stopped doing that. And so kind of an instead of which is wondering that left leave someone with a question, right? Is the edit log broken or is no editing happening?

Yes, and that's right. And so those are two very different things. And you had to go to these like extreme lengths basically answer that question, right? Yes. Um, and like, so what is that?

Where does that kind of leave us with, like, the idea of edit log as an accountability mechanism? I guess it raised this because I feel like transparency and accountability are things that we are terms that we throw around all of the time as solutions. Um, but they're really just like, transparency is only as good as like the check of, like, of what it can tell you to ask. Right?

“Like that's the only thing that makes transparency super worthwhile.”

Um, what it tells you to go and get receipts for, what it tells you to go and, like, you know, like, ask people about letter and power about. And so I'm kind of curious, like, do you think that this is very useful? Um, well, I think the read.

So there are a couple reasons why I wanted to do a million different types of checks.

You don't want to get it wrong when you say something like this.

So it's always been, you know, Ronald and I worked together and we were at SIO.

And just having, like, as many different types of ways to verify that what we are saying is accurate before putting it out there. So the combination of corroboration from anecdotal comments from frequent, you know, contributors. Um, I also should say I looked at the edits that were kind of in queue or that had that had been in the, the town center snapshot. Um, and then I had clawed just kind of go and pull all of those basically through the identifiers to see, um, what of those. Again, had the, and that was how we found actually that things that had been marked accepted in queue were then retroactively changed to rejected.

And it looked like there had been a major site rewrite some time around March. Um, so this actually made me wonder, you know, are they halting because they're going to do another major rewrite? The transparency point though, um, they didn't tell anybody about this. I think that's actually the bigger problem. So if you believe that you are reading accurate up to date information or more importantly if other machines believe that this is a site with accurate up to date information.

The combination of those two things is why it actually matters that they say,

Hey, we've temporarily halted the site as temporarily down for a couple of months while we rebuild the model. Actually, so I'd rock address this on, back to day because I did go and search to see if, you know, Um, what the reception had been and if anybody from XA, I had responded. It's not to me then to use publicly like, hey, we saw this article. We just want to clarify. Um, I didn't send them a note because they sent me poop emojis back the last three times that emailed them.

So I was like, no, we're not going to bother with that. Um, if that's how they want to be.

“That's how they get to be, but then they can make, I'll make my statement.”

They'll make their statement. That's how we do this. Um, so with that, it was, uh, I didn't see any, any official responses from humans at the company, but grock said something. There was an ad grock comment, um, to the effect of, uh, yes, uh, you know, everything is halted at the moment.

No edits have been accepted since April. I actually wondered if it was just ingesting what we had, you know what, what we had published but law fair had published, uh, but it said something to the effect of, um, as the model is, you know, is reworked or something along those lines. So maybe again, that they're planning to put out a new version of it. I don't know.

Um, but they did grock did actually confirm, um, that this was, in fact, the case. Yeah. So I think that like, let's put the, I want to get back to kind of their comment on this

“and how it ingested the law fair article in like one second because I think it absolutely”

either came from that or the birch cover of Jabra or something else. Yeah, it's like a couple of different things. But in the very least, it came from you. Like, that was the forcing function that kind of has made it update because it hasn't released anything like, right? Right? Like, I think that we can, we don't need like, I mean, we can't prove the, the null.

But like, I do think that this is, I think that that's pretty strong evidence. But I just before we get to that, let's put this in context. Like,

similar web has grockipedia at 6.7 million visits in June alone.

You cite eros analysis basically saying it's been cited about 356,000 times inside other AI systems, mostly chapiti, cheapiti and Google's AI mode. Um, so they're, so like, it's basically like this frozen in time, not updated. Inact quietly an accurate kind of reference source. That is be like being fed into this model. And this isn't just, I mean,

we could, I could have like, we could have a whole conversation about the concept of grockipedia and whether like a cell friend friend, or model that builds off of like an increasingly AI populated web is ever going to be able to be accurate, et cetera, et cetera. But setting that aside, this just hasn't been updated and is putting

itself out there as a valid source and it's being ingested by the LLMs. What do you think about that?

“So I think grock is actually incredibly important in the information”

environment today. I joke around about how I write for the LLMs now, but I really do mean it in a lot of ways in that, um, people on the back, some particular really trust grock as an authoritative source. And the phenomenon of like, at grock is this true is a very common way that people try to fact check over on X today. These are people who are largely

distrustful of what you might call mainstream fact checkers, the little fact AP, that kind of thing. Um, I do a lot of looking at like election narratives or vaccine narratives, public health narratives. Um, and at the end, it is actually not that bad. I know that this really does surprise people because there are occasionally the times that you'll get the info

words response. Um, but it is often actually not bad, which is why I think it's actually important to see it as a trusted voice so to speak in the

very important to the fact that it is so integrated into a platform like X

“means that when people want an immediate response, that's what they go to. I”

published on Law Fair a couple months back. Um, a study of some semi-on attributed US government propaganda sites, right? And when I did that work, what I found was that as these unattributed government propaganda accounts were putting their content out, people were asking grock is this true, right? In all sorts of languages too, I was mostly

looking at the Latin American stuff, but people were asking grock and Spanish to essentially check the propaganda of the this US government site. So you really do see, I think, why it matters what information we put out there in ways that LLMs are, you know, understand, right? So it's sort of curating information in ways that they can

very clearly get a snapshot. In return though, what you're seeing is this encyclopedia product is not doing that sort of rapid frequent updates and it is, I think the problem here was really just that they didn't declare it to be treated as reputable by other entities. Yeah, so so that's one part of it. And but I kind of want to actually also loop

us back around to why in the first place we're covering stuff like this

at Law Fair where it has kind of a national security band, it has the, you know, has a rule of law, uh, focus.

“I think the one of the main things that I see something like this is that”

these are highly exploitable systems. Yeah. And so, and they and they change people's information ecosystems entirely and they're doing it before our very eyes and they're getting incredibly sophisticated and the cat and mouse game is getting harder and harder. And so I'm just kind of curious

what you think about this, how this story, how this particular thing that you're researching, how that kind of doubles down on that thesis. Yeah, so I am very interested in what rockapedia considers to be a reputable source, what it considers to be a legitimate edit, right?

That was sort of what got me paying attention to it. I've been paying attention to it since it launched, which I guess is, I think it's maybe right around a year now, maybe I'm trying to remember if it was August or October. But the, the thing that, because per your point,

there's a, uh, term that gets tossed around a little bit, but like data poisoning or model poisoning, right, where you are actively intentionally trying to manipulate an AI system by creating a perception of a reputable source with, you know, accurate information. And one thing that we see is that

they do, in fact, ingest and regurgitate a material from the open web that they think is accurate, right? And so that question of how much of this stuff makes it in is actually why the sort of source wars really do matter. We see Russia doing this with content farms, we see Iran doing it with content farms.

I'm sure China is doing it though, they're content farms are usually not as good. So you just have this phenomenon of, um, in, in the days of just plain old SEO, it was called a data void search engine optimization. Sorry. I mean, like, I don't know, like, I'm just like, so like, in the days where you kind of would like write an article and fill it full of keywords

so that it would get picked up by Google search. So you were writing for pickup by the search engine. Now we're in this age of like AI.

“Yeah, or AI optimization. AIO. Sorry. Yeah. I think the people call it, like,”

geogenative engine optimization. I use AIO, answer, and just sorry, AIO, answer engine optimization is the one that I went with. But, you know, it'll, it'll become a term at some point, um, one of them will win. But the point about, if you can make a machine think that you have created something accurate or if you do create something accurate and you get it out there

as fast as possible, optimize for them. Then that is going to influence how they see the world, what they synthesize, you know, what a model synthesizes as reputable information, and that in turn is what is returned to someone who is searching for it. And I see this all the time because there are the thing that, um, that gets me about the data void problem a lot of the time

is that there are, uh, and a data void is just a thin search term. Basically,

when you look for it, there's not a lot there, or there's a lot of stale stuff and not a lot of recent stuff. So if a court decision comes down and media doesn't report on it, they actually don't really pick it up for a while because they're not just there scraping PDFs out of databases, right? And so you have this moment where the machine doesn't realize that the

world has changed, and so that provides a really a prime opportunity to put out a whole pile of press releases and shape public opinion about that event. And that's where I'm like, okay, that really irritates me. And I do think that there are actually information integrity issues with that and so I'm very curious. From a research standpoint, and, you know, just as a

almost 10 or 12 years now. What is essentially the playing field

that is created and how do people engage with it? Yeah, and so one of the things that I'm curious about that you've kind of mentioned a few times is, like, the static mess at the page, or they're not going into a database and pulling out a PDF, they don't know that the world has changed around it. One of the things that kind of, I know that you did at the Stanford

Internet Observatory and other types of things, and I know this is also just how a lot of platforms do things like spam removal or bought or

“removal or cybersecurity kinds of things is actually behavior based, right?”

Is your tracking certain types of behavior? So one of the things that I'm interested in, and I wonder if you've seen any of this or if Gorkapedia is like exemplary of this, is how much is that do we have any idea if the staticness of Gorkapedia

tanked? Actually, it's like decreased, it's role in the LLM,

like maybe somebody posted about that. There's a guy on ex who's, I unfortunately didn't like jot his name down and notes or anything, I feel like it started with G. He's been posting about this. He actually posted about it two days ago, just as we were getting ready to publish, he said, look at the tanking of Gorkapedia in search results and something that he put out

and the speculation, because I saw him comment on the law fair article today and the speculation was that it had realized that this had essentially stalled, but nothing new was happening. And there is a, and this again, this is sort of people speaking in hypotheticals because you know, it's not like being has come out and said it and it's not like xa, i has come out and said anything.

“But a lot of times you will hear that your performance in search”

ranking is highly dependent upon your site looking fresh,

right? This is true in social media recommender systems too. They are looking for,

you know, if you don't post a lot and then all of a sudden you post ones, you're posting going to get seen by very many people. It's the same phenomenon on site and you updated very very sporadically. Maybe it's not going to get indexed as quickly. Maybe it's not going to show up. Maybe it's, you know, you're going to be playing a little bit of a different,

um, in a different league as far as, uh, your search rankings. So that, it was the speculation about this, was that that was what had happened. And I should say, I did also reach out to the way back machine folks. And I was like, hey, you know, I'm about to put this out. If you see something different, like, this is what I did. I used your API called this. I did that.

Um, if you see something different, please let me know. But, uh, but they sort of saw the same thing I did. So, you know, that question of just, um, no updates.

“What does some of this mean for the weaponization of these systems?”

So, uh, and so I'm going to, specifically, you kind of, you've said one thing, which is like maybe keeping your site fresh. However, that is my, I mean, you could just put gobbledy, go back onto the page and white text. And that was, yeah, so I fractured, right? Like, you don't need to actually be like, you know,

as it need to be human eye legible, um, at least not initially. But also one of the ways you could gamify this, it's occurring to me as you were kind of talking about how people use grock as this true, is like, I wonder how much X-A-I is training grock on the material, like the volume of material,

like, and that how often people ask X-A-I is true. And whether or not that type of information that gets lots of questions around that, gets a, like, gets a disproportionate amount of, uh, uptake into, into grock. And so it probably does, right? And so like, what's stopping now to kind of like, to play this out?

What's stopping the Russian, Chinese, like kind of interference from hiring a bunch of bots to spam X, and so, "Grock is this true?" 30,000 times every time, like, something comes out. This is something where, um, I'm going to make a one transparency comment here, which is just that, like, we used to have research access to Twitter,

and we used to be able to actually, um, look at stuff like that. And now, if I want to know, you know, who's saying what with that grock is this true, you know, you're either, like, trying to scrape from somewhere. And then, you know, you're probably going to be able to do that. And then, you know, you're going to be able to do that.

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And then, you're going to do that. And then, you're going to do that. And then, you're going to do that. Because I think it is ingesting from X. And you see companies will drop their latest stats on X.

They'll put a press release on X. And so, some of the other models don't fetch a very, very, very recent stuff quite as well. And it's for very niche things like startup stats and stuff. But it is an interesting way that he has designed this particular AI to have that. And they're obviously, there's significant other challenges that come with being very heavily trained on Twitter.

At this point.

“But it is, there's different ways that I think my hope is that there's somebody in there who's thinking in adversarial abuse terms.”

But you could either be flooding the zone with, you know, a million different bots talking about a particular topic in a particular way, saying something happened when it didn't.

I'm trying to remember, I wrote this on my sub stack. There was this very dramatic thing that happened with Spencer Pratt during the primary in Los Angeles, Los Angeles, mayoral primary. And somebody had posted a screenshot of a, of a ballot dump sort of as the AP was updating its numbers. And in this one second of the screenshot, it looked like Spencer Pratt had gotten no new ballots in this drop of, I think it was 24,000 if I'm recalling correctly ballots. Now, that is statistically very weird. That is also not what happened, right? It updated his number like instantly.

But that screenshot went viral like Elon posted about, I mean, all of the big right wing influencers posted about it. And if you asked at Grock is this true, it would answer yes for a period of time. It kept answering yes because what it is saying is that screenshot is accurate. That screenshot is real. It's not forged.

“And that's where you see these like interesting ways in which the, it eventually did update, right?”

As AP and others put out statements and fact checks, then it eventually did understand that know it was not true. But for a while, the screenshot of Grock saying, yes, this is true. Also went viral because it was people saying, look, they cheated and Grock knows it. And that was a very interesting thing to watch happen. And this is where you can see the kind of gaps in the machine. And I think that's an interesting space to be looking at.

Yeah, I think that that's exactly right. Like you were saying that it's really valuable for checking things like numbers that a company puts at its press releases or it's quarterly kind of burnings or whatever.

I mean, yes, but a company could always put that out or something could always someone can always be putting that kind of information into the system.

And that never means that it's necessarily true. And that it exists in a system and maybe it's even true that the company released it. We don't know a lot of these unverified accounts or whatever else is like it's hard to kind of know, although Grock does kind of answer for that type of thing by having paid for verified accounts. But yeah, I know, which has its own problems exactly. So that's exactly right. And so we're just kind of in this in this really strange, this really strange epistemological loop.

“I think essentially and it's there's a delay and the delay as you kind of put it is never is like, you know, the, you know, like can get around the world before the truth can put its boots on.”

And like I see that what you're describing makes that idiom kind of feel real and on a speedrun, like it feels like it's kind of just happening every day and all of these small ways. Yeah, there's some there's a phrase I have to credit Google with this one, I was one, it was there assertive provenance paper, which is about image verification. And but I like the phrase a lot of this is the difference between is this real or is this true, right, and is this real, like that screenshot is real, right, that's a great, you know, I think this is a really great example of that is this real, yes, that image exists that is actually the AP page that did go up that happened at that time is this true.

No Spencer Pratt did get ballots in that drop, right, and that's and that's the gap and the question of how do you handle this now this is where I think community notes comes into play, right, where that is supposed to be the system that adjudicates reality with more human oversight because people are still voting on the notes, but interestingly what you see there is it's very slow and increasingly human. There's so much polarization and humans deciding something is true that those notes actually aren't clearing, which is why the combination of rock being faster and honestly, rock actually being willing to.

This particular case once that AP fact, I came out, rock changed its answer, it updated with, you know, it treated AP as a reputable source even as community notes never got to the point of actually finding enough agreement between diversion, publics on acts to make that note show up.

Enduring into training data or search engine data and that's I think the rock a media problem that that halt.

“I think that that's a great way of thinking about it and I guess we're going to leave it there, but I do really really appreciate this article.”

I hope it gets fed into the ecosystem as we as as we have evidence of from rock itself it already has been and things are kind of at least being updated in. On the X ai universe, if not, if they're not fixing rock a media, then maybe they will, I don't know whether that's good or bad, and so hopefully we'll have you back on to kind of give us at some point yeah an update about whether or not rock a media is just going to be this dinosaur that stops existing or whether it will come back on I really do wonder if it there will be a moment in which it becomes politically expedient.

For for Elon at some point and thus he'd like kind of like forces it to go into reboot and puts the energy and money and again to it or it's not but.

“I think I think that he will like I actually really do expect it to to kind of come back in some capacity, but you know.”

I think that AI assistance in the encyclopedia space we had Jimmy Wells on on law fair on the pod and that was a really great interview that I did that one with him I hosted that one when this book came out. But this question of how can you use it potentially as a tool where there is still more active human involvement so that if there is a halt like this there are still people who are doing something. I'd say difference between. The fragility of machines like the sort of breakage there versus the you know innate biases at all humans have so.

Yeah totally well Renee thanks for coming on.

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