When the battle of AI encyclopedias begins: Elon Musk's Grokipedia goes up against the scientific community's SciencePedia

This article is contributed by DP Technology. Author: Mu Zhi.

 In the past week, the old topic of "AI rewriting knowledge bases" has suddenly been pushed into the spotlight.

On one hand, there is Grokipedia launched by Elon Musk's xAI team. It is described as "a better alternative to Wikipedia", and even directly aims to "use AI to eliminate ideological biases in the traditional knowledge system", promising to create a "more neutral and more real" encyclopedia-style knowledge base.

However, public information shows that Musk has repeatedly accused Wikipedia of having problems in political issues and claimed that Grokipedia will become "a version far superior to Wikipedia", ultimately helping xAI achieve its mission of "using AI to understand the universe".

Meanwhile, the launch process of Grokipedia has not been smooth: its first public release was postponed several times. The official explanation is that "it still needs to purge propaganda", and the official has continuously adjusted its statements before and after the launch of the beta version, emphasizing that "we are not copying Wikipedia, but creating a bias-free version of the truth". Interestingly, the Grokipedia page on Wikipedia clearly states that "Many articles are derived from Wikipedia articles, with some articles observed to be copied nearly verbatim."

On the other hand, there is SciencePedia, which made its debut almost simultaneously. It is launched by a Chinese company called DP Technology based on its scientific research platform Bohrium. Its official positioning is not to "recreate a general encyclopedia", but to be a structured system "focusing on scientific knowledge". Its core goal is to break down subject knowledge into verifiable knowledge points and then connect these knowledge points into a navigable scientific knowledge network ("subject - knowledge point - link") through logical/causal/upstream and downstream application relationships. This approach is more like "organizing science with AI" rather than "rewriting the encyclopedia with AI".

On the surface, both products are trying to redefine the "knowledge base" with AI. But upon closer examination, it can be found that they are actually taking two completely different paths:

Grokipedia wants to prove "whether AI can retell the world";

SciencePedia wants to answer "whether AI can organize scientific knowledge into a structure that can be reasoned and reused".

Next, let's make a comparative observation from the aspects of product positioning, content organization, and entry availability.

Product Positioning: Whose "Knowledge" and for Whom?

Grokipedia

From xAI's public statements, Grokipedia is directly set as an "upgraded version" of Wikipedia, or even a candidate to replace it. It attempts to use xAI's large model Grok to "review" and "purify" the so - called biases in the existing encyclopedia content, producing a "more real and less politically - inclined truth base".

Musk believes that Wikipedia has systematic biases in its statements on many social issues, so the knowledge base itself "misleads AI". The existence of Grokipedia is described as a means to correct this, rather than just providing information for the public to look up.

In other words, the goal of Grokipedia is not to serve precise usage scenarios such as scientific research, education, and engineering first, but to take back the task of creating a "general knowledge encyclopedia for the whole society" and place it in a narrative framework that he claims to be more "objectively neutral".

SciencePedia

SciencePedia starts from a more practical and reliable point. It does not attempt to cover "everything in the world", but directly defines its scope as "knowledge related to scientific research": key knowledge points within the discipline systems of physics, chemistry, biology, mathematics, engineering, geology, etc., and the dependency, derivation, and application relationships between them.

Here, "knowledge" is not a series of long entries, but is broken down into structured nodes that can be retrieved, cross - referenced, and strung together into learning/reasoning paths. The platform emphasizes that each node should be verifiable in a scientific context (theories, formulas, experimental facts, upstream assumptions, downstream applications), and it serves two specific scenarios: "scientific research learning" and "research application", rather than the general public opinion field.

This means that SciencePedia is more like a "scientific knowledge workbench" for researchers and learners, rather than a "general knowledge map for the whole society".

Content Organization: "Retelling the Encyclopedia" or "Disciplinary Modeling"

Grokipedia: Many Entries Are Highly Similar to Wikipedia but in Grok's Tone

At first glance, Grokipedia won't make people feel lost. It has a dark - colored UI, but the information layout, navigation logic, and entry page structure immediately remind people of Wikipedia.

The media and users have made a direct comparison of its content and pointed out that many of its entries are highly similar to the corresponding pages on Wikipedia in terms of chapter structure, paragraph expression, and even sentence patterns, basically being "rewritten/lightly modified" versions. The platform itself will indicate at the bottom of the entry that it is "fact - checked by Grok" or "adapted from Wikipedia", but it does not explain what factual verification standards this "checking" process follows.

There is also a more sensitive point: when the topic involves scientific consensus (such as climate change) or public figures, the description angle of some entries deviates from the mainstream statements on Wikipedia and is closer to the positions that Musk has publicly expressed before. This makes the outside world question whether the so - called "bias removal" is just replacing one preference with another?

In summary, Grokipedia currently seems more like "Grok retelling what it has read from Wikipedia in its own tone". The advantage is that the information is formed very quickly, and historical/macro - background entries are relatively smooth to read. The disadvantage is that it doesn't really tell you "how these knowledge points are related to each other" and doesn't put the question of "whether this statement can be verified" in a prominent position.

SciencePedia: Not "Putting Entries" but "Rebuilding the Reasoning Chain"

According to the official description, it is not creating a "whole - site encyclopedia" but building a "scientific knowledge map":

First, it divides knowledge by disciplines (physics, chemistry, biology, engineering, materials, geology...), ensuring that users know which scientific context they are currently in;

Then, it breaks down the key knowledge points in this discipline. Finally, it connects the knowledge points into a chain through logical/causal/application relationships, telling you why this knowledge point is important, what premises it depends on, and what downstream problems it will affect.

Based on the information disclosed by the official, we can summarize the approach of SciencePedia into three keywords: long chain - of - thought, inverse reasoning search, and human - machine collaborative evolution.

(1) Long Chain - of - Thought

SciencePedia attempts to restore "how this conclusion was gradually reached by humans". For example, when users look up "quantum entanglement", the system will not just give a standard definition, but will unfold along the development context of physics: from the proposal of the EPR paradox, to the derivation of Bell's inequality, then to the experimental verification path, and finally to the specific applications in quantum computing.

That is to say, it shows not "what the answer is", but "how the answer was established and verified". In essence, this is to regard the process of scientific discovery itself as part of the knowledge, rather than just retaining the final conclusion.

(2) Inverse Reasoning Search

SciencePedia has built a deep - level logical network based on about 4 million "chains of thought" and uses this network to answer "where this topic can lead".

When someone searches for "topological insulators", the system will not only present the topological theory basis in condensed matter physics, but also automatically point to the preparation technology in materials science, key concepts in mathematical topology, and even potential device application paths in quantum computing.

This is equivalent to transforming "interdisciplinary accidental inspiration" into "systematic navigation": users can see how a knowledge line extends across different disciplines, rather than relying on luck in the ocean of literature.

(3) Human - Machine Collaborative Evolution (AI + Expert Co - construction)

The knowledge update of SciencePedia is not solely determined by AI. It adopts a dual - engine mechanism: AI is responsible for extracting knowledge from papers, textbooks, and scientific research materials, preliminary rewriting, and preliminary self - checking; the expert committee and contributor community are responsible for arbitration, in - depth understanding correction, and marking controversial points. This binds "scalability" and "scientific rigor" together, rather than choosing one over the other.

Some cold - start indicators disclosed by the official show that it is trying to build a scientific knowledge network that can be reasoned, rather than just a popular science library: about 4 million chains of thought; covering about 200 discipline fields; breaking down about 240,000 knowledge points; and preparing more than 100,000 practice questions to connect "understanding" and "mastery" and make the learning cycle run smoothly.

This is completely different from the product philosophy of "retelling Wikipedia".

Entry and Availability: Are You Chatting with an Encyclopedia or Holding a Research Map?

Grokipedia: Q&A - Style Entry, but the "Next Step" in Navigation Is Not Always Clear

In its currently public form, Grokipedia is more like solidifying the Grok model into an online encyclopedia: you can search for a concept, a person, or an event just like looking up an entry.

This interaction method is user - friendly for ordinary users, especially when they need to quickly understand background stories, macro - contexts, and historical disputes. However, there are two practical problems:

1. The answer depends on the way of asking questions. If your question is vague, will Grokipedia give a vague or even opinionated summary? Some media have pointed out that its statements in fields such as people, public policies, and scientific disputes sometimes have obvious posturing descriptions, which forms a subtle contrast with its claim of "correcting others' biases".

2. The traceability and verifiability are still weak. "Fact - checked by Grok" currently seems more like a label than an open verification process; the outside world does not know how citation links, experimental data, and paper sources are managed within the system.

In short, Grokipedia is still in the mode of "AI answering you", rather than "leading you through the knowledge map".

SciencePedia: Starting from "Discipline - Knowledge Point" and Putting You in the Right Coordinate System

The entry of SciencePedia is more like a scientific research training camp: users don't just casually ask a question, but enter through the "discipline catalog", drill down layer by layer to specific knowledge points, and then explore upstream/downstream along the "logical links between knowledge points".

This design has two direct consequences:

The positioning is clearer. You won't be confused about "which discipline the knowledge I'm currently looking at belongs to and what the pre - assumptions are". This is very important for scientific researchers, postgraduate students, and engineers - scientific research discussions are rarely completely out of context.

It has natural scalability. When a knowledge point is followed by modeling assumptions, experimental verification paths, and typical application scenarios, it is actually telling you that these can be tested, reproduced, and further calculated/experimented, rather than just saying "AI says so".

If we compare the interaction of Grokipedia to "I ask and you answer", the interaction of SciencePedia is more like "I'm moving on a structured knowledge map".

Conclusion

By now, you may have a relatively clear understanding of the two products, SciencePedia and Grokipedia. This comparison can be understood as two questions:

Grokipedia tries to answer: "Can AI retell the world and correct the narratives I don't like?"

SciencePedia tries to answer: "Can AI break down scientific knowledge into a structured, reusable, and verifiable network of elements and help people learn and reason along this network?"

From an industrial perspective, these two paths are not mutually exclusive, but the user groups they target are clearly different: Grokipedia is aimed at the public Internet users who "want to know the answer quickly"; SciencePedia is aimed at scientific research and engineering people who "want to follow the physical/chemical/material links".

If we regard the "AI encyclopedia war" as the starting point, then the first round has shown a divergence: one is more like a content experiment, and the other is more like a knowledge system. In the long run, what will be more important? Maybe it's not "whose entries are more numerous", but "which system can be more easily used directly in scientific research, teaching, and industry".