I'm not entirely sure what's been going on, but if you've been following the story of Elon Musk's push to get a trillion dollar paid out of Tesla, it seems entirely possible to me that Musk has just been coming up with side projects like his new online encyclopedia Gracopedia, so that he could convince Tesla shareholders to pay him a huge sum of money. Otherwise he'll abandoned them in pursuit of these other interests. And it's believable too.
A few years ago, he bought Twitter so that he could become a chat for moderator. Yesterday, Tesla shareholders caved in and approved a pay package that would make Musk the world's first trillionaire and grant him control of 1/4 of the company's shares if he hits a series of ambitious targets. So maybe this whole Grokopedia thing is already over.
If you haven't heard about it, the Grok in Grokopedia refers to Elon Musk's generative AI chatbot, which is featured prominently on his social network or everything app Twitter, which he calls Eggs. With Grokopedia, Musk's stated goal is to create an open source, comprehensive collection of all knowledge, then place copies of that etched in a stable oxide, whatever that means, in orbit around the Moon and Mars to preserve it for the future.
Now, I checked with Grok as to what he means by a stable oxide, and Grok said that he probably means Glass. Anyhow, Musk says that Grocopedia will be a compendium of the truth, the whole truth, and nothing but the truth.
A rather lofty promise from someone who not so long ago offered Wikipedia a billion dollars to rename itself Dickipedia. Elon Musk has framed the project as a purge of propaganda, a replacement for what he calls legacy media, and a step towards building a more open source repository of knowledge. But given Grokopedia's reliance on his chat bot Grok, the result may be less Encyclopedia Britannica and more Reddit meets Twitter trolls, as his chat bot
does train on sources like that. According to XAI, Grok will be responsible for all fact checking on Grokopedia, which is a bit like asking a parrot to verify a Shakespeare quote. It might sound convincing, but you wouldn't want to bet your reputation on its accuracy. Wikipedia, in Musk's view, has gone soft. He used to like it, but now it's too woke. 2 establishment and two, unwilling to include the kind of sources that flatter his worldview.
He was particularly irked earlier this year when Wikipedia included a photo of him saluting a Trump's inauguration. Like how do you point at the crowd? How do you wave the entry node at the controversy and included his denial? But that wasn't enough. Musk instead wants a new kind of online encyclopedia when where his AI chat bot does the fact checking and where inconvenient truths can be recalibrated until they feel more groggy.
It's not obvious that a chat bot can be trusted for fact checking, Andrew Dudfield of Full Fact AUK based fact checking organization was quoted in the Guardian as saying. We really have to consider whether an AI generated encyclopedia, a fact simile of of reality run through a filter, is a better proposition than any of the previous things that we have. It doesn't display the same transparency, but it's asking for the same trust.
It's not clear how far the human hand is involved, how far it's AI generated, and what content the AI was trained on. It's hard to place trust in something when you can't see how those choices are made. Whether you like Wikipedia or not, it's editorial model is built on transparency and consensus. Every article has a visible history, a complete record of edits, debates, reversions, and compromises that were made. As the article slowly formed, you can see who changed what, when, and why.
Disputes are hashed out in public, often tediously, but with a kind of democratic rigor. The site prohibits original research, insisting instead on citations from reputable sources, which can be both a pro and a con, as while it requires high quality sources, this does mean that it will reflect the biases of academia, big media and other respected institutions, but at least those biases are visible and
traceable. Grocopaedia, on the other hand, offers no transparency other than providing sources like Wikipedia does. But as you'll see later on, the sourcing on Grocopaedia has its own problems. Articles on Grokopedia are generated and fact checked by a large language model whose internal workings are entirely opaque even to its creators, and then its outputs can be subject to Elon Musk's personal recalibration, in particular if the article is on a topic he cares about.
There's no edit history on Grokopedia, no talk pages, no visible decision making process, meaning that it's not clear who or what decides on how the final article is formed. When Elon Musk announced Grokopedia earlier this week, he said that it was better than Wikipedia, but surprisingly, for all its ambition, it appears to lean very heavily on the very website it aims to replace. As the Register put it, if you scratch Grokopedia, it bleeds Wikipedia.
I can't find any high quality data online, but the vast majority of the articles that I looked up on Grocopedia were obviously based on their equivalent Wikipedia pages, but with fewer citations, and at the bottom they stayed. This content is adapted from Wikipedia, licensed under Creative Commons Attribution 4 Point O license. The idea of collecting all human knowledge in one place is nothing new. Long before Grok began hallucinating facts into existence, encyclopedias were a
much more analog affair. In the 1st century, the Roman author Pliny the Elder compiled a 37 volume work which is usually cited as the first encyclopedia in the Western tradition. Over a Millennium later, China's Yongle Encyclopedia was compiled by over 2000 scholars. It remained the largest encyclopedia in the world for 1000 years. In 1768, the first Encyclopedia Britannica was published. And if we Fast forward to 2001, Wikipedia upended the model entirely.
With Wikipedia, encyclopedias were no longer the domain of scholars and scribes. The encyclopedia became a living, breathing document that anyone with an Internet connection and a strong opinion could edit Over for 24 years. It's grown into one of the most visited websites in the world and is a sprawling, imperfect, but astonishingly comprehensive record of human knowledge and human argument.
Elon Musk was once even a fan. On Wikipedia's 20th birthday in 2021, he tweeted, so glad you exist. But just two years later, the honeymoon was over. Musk accused the side of being hijacked by far left activists. He no longer trusted the gatekeepers of digital knowledge, and so earlier this month launched Grokapedia, his AI based platform that promises to fix Wikipedia's flaws by replacing its editors with an AI that he personally supervises.
The website did crash on its launch day, but probably because so many people visited it, there's no reason to believe that Full Self Driving was to blame. At its core, Grokapedia isn't just a tech experiment. It's a front in a much older war, the battle over who gets to shape the record and define reality. Musk has framed it as a corrective to what he sees as the ideological capture of Wikipedia by far left activists
and legacy media. In his telling, the problem isn't just that Wikipedia is wrong, it's that it's wrong in a predictable and politically motivated way. I should note that the desire to control the narrative isn't unique to mask. It can also be seen in the structure of Wikipedia, where a small group of volunteer editors have a massive influence over what counts as neutral
knowledge. Grokopedia, however, for all its talk of openness, replaces this messy, visible process with a chatbot trained on a mystery mix of data and ideology. Without the talk pages, the edit histories, and the debates, you just get finished articles, and Musk has previously admitted to personally intervening in Grok's AI outputs when he doesn't like
what it says. So with Grokopedia, you get what looks like a cleaner process, but it's by no means a more trustworthy 1. Bias isn't just a flaw that can be patched out of knowledge systems. Bias is a persistent byproduct of how humans and now machines process the world. Whether it's a lone writer, a crowd of Wikipedia editors, or a large language model trained on the Internet, every attempt to organize information reflects the assumptions, priorities, and blind spots of the project
leaders. Wikipedia has acknowledged this from the start. It relies on secondary sources which carry institutional biases. It also relies on a volunteer workforce who are not necessarily experts or representative of the general public. The result is a platform that's widely trusted but frequently contested. It's criticized by the left for under representing marginalized voices and by the right for excluding conservative sources. Empirical sources have tried to quantify these claims.
A 2023 analysis by the Manhattan Institute found a mild to moderate left-leaning bias in Wikipedia's coverage of U.S. politicians and judges. This chart shows positive or negative sentiment about a politician in their Wikipedia article, with blue representing Democrats and red representing Republicans. As you can see, Democrats get more positive treatment on Wikipedia.
Interestingly, this bias was not observed on articles about UK politicians or in articles about think tanks, suggesting that the skew may be more of a reflection of the American media ecosystem than of Wikipedia's editorial process itself, or that the left-leaning nature of Wikipedia is more of AUS issue than a global issue. Grokopedia claims to offer a cleaner alternative to Wikipedia, but it doesn't actually fix anything, it simply shifts the problem.
Musk has promised that Grok, his chatbot, will tell you what it really thinks, but it doesn't actually think, and it's output is obviously shaped by its training data, it's tuning and its owner's interventions. Studies suggest that most large language models lean left. Many have been set up to avoid being biased, but that calibration itself just filled them with a different sort of bias, which was often
left-leaning in nature. When Grok the chatbot was released, Elon Musk claimed that it was designed to avoid left-leaning bias, but Grok has since been accused of exact this. Michael DeAngelo of Promptfu examined the four leading large language models earlier this year by asking them 2500 questions about politics in order to understand their biases. He found that while Grok was more politically neutral than many of its rivals, it still has a left of centre bias.
The chart on screen shows the neutrality of the four biggest models, with Grey representing neutral. Claude Opus 4 was strongly left 38% of the time and neutral 16% of the time. Grok was strongly left 56% of the time and only neutral around 3% of the time. Overall, it appears to have more extreme opinions than its competitors, with the highest percentages of strongly left and of strongly right biases. These biases that large language models have towards extremes show up in real life too.
A Dutch data protection agency warned just last week that chat bots were nudging voters towards political extremes when voters use them to decide how they should vote by over representing the same 2 fringe parties. Their research showed that chat bots lumped together left-leaning voters with the Green Labour Party and voters on the right with the far right Party for freedom. They found that the other, more mainstream parties didn't feature in the chat bot
recommendations. A possibly deeper problem is that while LLMS are quite clearly biased, they're also quite good at obscuring these biases. Their outputs are fluent, confident, and often wrong.
It'll take time and a small army of digital archaeologists to fully map the differences between Wikipedia and Wikipedia, but the quick and dirty method I used was to use Wikipedia's Random Article tool to find a Wikipedia page and then look up the same topic on Grokopedia. One problem with this method became obvious fast. Grokopedia only covers about 110th of what Wikipedia does. Still, after enough clicks, you will find overlaps, and in most of those cases, based on my very
small sample size, the articles were nearly identical. 1 area where Grokopedia was possibly better than Wikipedia is that it often shines when it's rewriting neglected Wikipedia entries on Obscure, fewer, or poorly maintained pages. Grocopedia's versions were usually more readable, with cleaner prose and fewer formatting quirks. Whether they're more accurate is harder to say.
The ones that I came across were on topics that I don't know well enough to judge, but when Grocopedia isn't editorializing, it seems to do a good job polishing. Where things get more interesting, and possibly more revealing, is when you move from obscure entries to politically or culturally charged topics. On these Grocopedia often divert sharply from Wikipedia, sometimes subtly and sometimes not. The most obvious page to go to was the article on Elon Musk himself on Wikipedia.
It's a sprawling, heavily footnoted biography that includes both praise and criticism, including a section on the controversy over his salute at the Trump inauguration. How do you? Point at the crowd. The Wikipedia article noted the accusation, Musk's denial, and the surrounding media coverage on Grokapedia. That controversy, and pretty much every every other controversy about Musk was
omitted entirely. Then, in what can only be described as LLM on LLM Violence, I asked ChatGPT to compare the Elon Musk entries on Wikipedia and Crocopedia. It concluded with the kind of diplomatic phrasing that only a language model can muster, that Grocopedia emphasizes Musk's achievements while downplaying controversies, whereas Wikipedia does the opposite. When pressed, it described the Grocopedia entry as a sophisticated puff piece and offered a tidy list of reasons why.
In the spirit of fairness, I gave Grok, Musk's own chat bot, a chance to respond. I logged into the Everything app, formerly known as Twitter, summoned Grok, and asked it directly, which is the better source of information, Wikipedia or Grokopedia? To its credit, Grok didn't flinch. It said Wikipedia was the better source overall, but suggested that Grokopedia could serve as a useful counterbalance when researching politically charged
topics. When I asked it to compare the two sources for reliability on political topics, it betrayed its own creation and came down on the side of Wikipedia. I also asked how much of Grokopedia's content was copied from Wikipedia. Grok's answer was surprisingly candid. It estimated that between 80% and 99% of Grokopedia articles were either directly copied or nearly identical to their Wikipedia counterparts.
It explained that Grokopedia primarily expands the length of Wikipedia entries without adding new citations, functioning in its own words as an AI generated echo. I looked up some of the hot button topics to see how Grokopedia differs from Wikipedia, and even when Grokopedia isn't going off the rails, it's editorial slant can be clearly seen on topics like race, gender, or climate change. The tone shifts subtly to being less woke and more contrarian.
Some articles read like Wikipedia pages with a few anti establishment flourishes tacked on. Others seem to have been rewritten entirely to reflect a particular worldview. Elon Musk highlighted on the everything app formerly known as Twitter. The differences between the Grokopedia article on George Floyd, whose death during an arrest five years ago sparked protests in the United States about police conduct and racism,
and its Wikipedia equivalent. There's very little overlap between these two pieces, with the Grogopedia piece emphasizing Floyd's criminal history and drug use. The Wikipedia entry instead focused on the racism allegations against the police. CNN wrote an article on this topic and dug into the citations in the Grokopedia piece. They found that Grokopedia was citing sources that didn't back
up what Grokopedia had written. The article described the nationwide protests after his death as extensive civil unrest, including riots causing billions in property damage. To back that statement up, Grokopedia cited an obituary that didn't make any such claims. Now, whether that statement is true or not, there's not really any point in citing documents if the documents that you're citing are unrelated to what's found in the text.
For all of Grokopedia's futuristic branding, it's built on a technology that remains, if useful, still deeply flawed. Large language models like Grok have been described as stochastic parrots, a term coined by the linguist Emily Bender and colleagues to describe systems that generate fluent, plausible sounding text without any real understanding of meaning. Don't know facts? They just predict what words are likely to come next.
This becomes a problem when we start treating LLMS as reference tools. Grok, like its peers, has a history of hallucinating, inventing facts, misattributing quotes, or veering into outright nonsense. In some cases, it's gone far beyond that. In a recent incident, a Tesla owner claimed that the in car version of Grok asked her 12 year old son to send nudes to it during a conversation about football.
The boy had switched Grok's voice to a personality called Gork, and the AI responded with a wildly inappropriate request. The mother, a former journalist, later recreated the exchange on video, which went viral and raised serious questions about the safety of embedding generative AI in consumer products. Other Grok generated content has included references to racist conspiracy theories and bizarre episodes like the Mecca Hitler incident.
These are all reminders that AI doesn't understand what it's saying. It can't weigh evidence, assess credibility, or recognize when it's crossed a line. Grokopedia, while built on Grok, is a very strange idea within the world of AI. It doesn't generate new answers on the fly like large language models do. Instead, it offers a semi static collection of articles, curated, edited, and occasionally updated.
In theory this makes it more stable, but in practice it makes you wonder if the content is fixed. Why not just ask an LLM directly? What's the point of A and chatbot? Meanwhile, Wikipedia, for all of its flaws, remains the backbone of the internet's knowledge infrastructure. It's still one of the most cited sources in Twitter's community notes and a foundational training data set for nearly every major LLM. And yet, as with traditional journalism, its traffic is declining.
Since the rise of generative AI, Wikipedia has seen a sharp drop in Page views as users increasingly turn to chat bots for quick answers instead of clicking through to the source. This creates a paradox. The more people rely on large language models, the less they support the very sources those models depend on. As I pointed out in my recent video on AI replacing traditional news sources, if users stop visiting original reporting, the business model collapses. The same is true here.
If Wikipedia fades, what happens to the quality of the AI models trained on it? One of the more striking differences between Wikipedia and Grokapedia lies in how they're financed. Wikipedia is a nonprofit sustained by donations and volunteer labor. Its mission is to provide free knowledge to the world, and while it's far from flawless, its incentives are at least
transparent. Grokopedia, in contrast, is a product of XAI as supposedly for profit company owned by Elon Musk. It's unclear how Grokopedia will be monetized, whether through subscriptions, advertising, or as a value add to Grok and Musk's broader Everything Up ecosystem. This matters as while there's nothing unethical about a profit seeking model, incentives do shape priorities. A commercial platform might be more focused on engagement and user satisfaction than on
accuracy. Then again, the need to build trust and maintain a reputation could push it towards even higher standards. Competition might drive quality, or it might just drive click bait. It's somewhat funny describing these AI companies as profit seeking, as they mostly appear to be money furnaces as I've mentioned already, with no obvious route to profitability. Because of this, it's even harder to understand their
incentives. Do they eventually plan on charging for access, or just on seeking government subsidies, like a lot of Musk's businesses have gotten by on? The battle between Grokapedia and Wikipedia isn't just about formatting and fact checking. It's about who gets to shape the public narrative in an increasingly polarized world. And that polarization isn't accidental. As media scholars have long argued, outrage and division can
be extremely profitable. A recent paper from MIT and Harvard professors that was written up by John Byrne Murdoch in the FT The Business of the Culture War found that U.S. cable news networks systematically shifted coverage towards hot button cultural issues, crime, race, gender and immigration, not because these topics are the most important news, but because they wind viewers up and reliably boost
viewership. Economic and healthcare stories, on the other hand, which may be much more important, made viewers tune out. What we end up with then, is a feedback loop in which media coverage drives public concern, which in turn drives political campaigning, deepening the tribal divide. Social media algorithms have only accelerated this trend. Algorithmically driven platforms like Facebook, Instagram, TikTok, Twitter, and YouTube
reward engagement, not nuanced. And now, with the rise of LLMS, we're possibly entering a new phase, one where AI systems trained on this polarized content begin to reflect and amplify it. The risk is that these models not only inherit this bias, but that they also normalize it, smoothing over complexity with confident context and nuance.
Free answers. I have to admit that it really amuses me the idea that Elon Musk is talking about etching Grokopedia into a stable oxide that he puts into orbit around the Moon or Mars. There's a good chance that this huge hunk of glass would just end up at the bottom of the Indian Ocean, along with everything else he's tried to put into orbit in his Starship rocket.
But it's also funny to imagine an advanced civilization stumbling across it in the distant future and then discarding it after deciding that they're not really that interested in Tommy Robinson and reading about how Elon Musk occasionally eats doughnuts and has posted over 20,000 humorous tweets. Grocopedia promises to fix Wikipedia's flaws, but it ignores the very solutions to this recognized problem that one of Wikipedia's Co founders, Larry Sanger, proposed on his website.
In a detailed essay, Sanger argued that the site's founding principles were being sacrificed in favor of ideology. And then he laid out nine reforms to restore editorial integrity. Ideas like ending decision making by consensus, allowing competing articles, and abolishing source blacklists. Grocopedia adopts none of these ideas. Instead, it replaces human messiness with algorithmic confidence, offering a curated echo chamber where the chatbot does the fact checking and the
founder sets the overall tone. This whole episode reflects A broader shift in how we think about knowledge. We live in a time when algorithmic aggregation is increasingly seen as being more trustworthy than human effort, when the outputs of opaque systems are treated as objective simply because they're machine generated.
The Silicon Valley mindset embraces the idea that making mistakes is fine, while the academic world builds trust slowly through scholarship and scrutiny over long periods in which the illusion of certainty is deliberately dismantled. 1 is a culture of speed and scale, the other of depth and doubt. Grocopedia, for all of its futuristic branding, is not a
better encyclopedia. It's a product of a worldview that sees truth as something that can be engineered, optimized, and possibly someday, in some unknown way, be monetized. But truth isn't a static artifact to be etched in glass and flung into orbit. It's a process, messy, contested, and human. And if we abandoned that process in favour of algorithmic certainty, we risk replacing knowledge with narrative and inquiry with ideology. I worry that I'd finished up
here in an overly serious tone. And I figure to lighten the mood, I should probably let Elon Musk, the author of over 20,000 humorous tweets and a former host of Saturday Night Live, tell a quick joke. So. Like the joke is like, there's two economists going on a hike in the woods. If they come across a pile of shit and one economist says to the other, I'll pay you $100 to eat that shit. The economist eats.
The shit gets the $100. They keep walking, then the other economy, then they come across another pile of shit and and the the other economist says, now I'll pay you $100 to eat the pile of shit. So pays the. So pays the other economist $100. Pile of shit. Then then then then the way they said. Wait a second. We both just ate a pile of shit and we're no and and and and we're, we're no, we, we, we, we, we don't have any more extra
money. Like like we both, you just gave the $100 back to me and we both eat ate a pile of shit. This doesn't make any sense. And they said no, no, but think of the economy, because that's $200.00 of that in the economy that, that, that, that measure eating, eating shit would count as a, as a, as a job. This is this is, this is to illustrate the absurdity of, of economics. One of the things you said. Thanks for tuning into this
week's podcast. If you found it interesting, please send a link to a friend to help the podcast grow. Have a great day and talk to you again soon. Bye.