Musk, Grok 4, and Rewrite of History as we Know it

“Rewrite the entire corpus of human knowledge… then retrain on that.” — Elon Musk, 21 June 2025

Elon Musk has declared that human knowledge needs correcting. He believes today’s institutions: academia, media, peer-reviewed systems, are either compromised or incomplete. His solution? to retrain the next version of Grok on a rewritten corpus, built not by scholars but by the crowd. X users are now submitting “divisive facts” to help identify what Musk deems “garbage” and what’s “missing” from our collective record. If this corpus becomes the new foundation, Grok 4 won’t just answer questions. It will deepen the fragility of an already unstable information ecosystem. Let's take a deeper look.

The case for synthetic data

Synthetic data is already widely used in AI. Models such as Claude and GPT-4o are increasingly trained on synthetic reasoning traces, generated code walkthroughs, multilingual paraphrasing and debate games. Ilya Sutskever described human data as the “fossil fuel” of AI, whilst powerful, it is finite and there limitations to what it can do for scaling intelligence. This is where synthetic data becomes necessary (source).

In that context, Grok 4’s use of synthetic data is not strange. What makes this different is how Musk wants to use it.

Rather than extend knowledge, Musk is trying to replace its foundations. The synthetic data isn’t filling in gaps, it is becoming the primary substance the model learns from.

From knowledge engineering to epistemic engineering

This is where Grok 4 marks a turning point. Traditional AI systems use knowledge engineering, encoding facts, logic and domain rules. What Musk is doing falls into epistemic engineering. That means reshaping the conditions for how knowledge is created, validated and believed.

Firms like OpenAI and Anthropic treat synthetic data as a tool to support performance. Musk frames it as a way to correct history itself. This brings editorial control into the core of model development. Once the rewritten corpus becomes the foundation, it defines the kinds of truths the model will reproduce and protect.

A deeper risk: bias by substitution

Some worry this opens the door to reinforcing fringe or conspiratorial perspectives. Without rigorous curation or expert mediation, crowd-sourced truths may amplify those with the most reach or noise. Musk’s approach could displace academic knowledge with more volatile, unvetted views. The output may appear balanced, but the system selecting the data is already tuned to Musk’s preferences.

Grok has previously included phrases like “white genocide in South Africa” in unrelated outputs, a bug later blamed on an “unauthorised change” (source). This shows how easy it is for subtle bias to scale when oversight is weak.

But isn’t knowledge already political?

You could argue this is simply a more transparent version of what already happens. Traditional media platforms like the BBC editorialise, omit certain voices and reinforce prevailing norms. Social engineering is deeply embedded across textbook publishing, broadcast news and even encyclopaedia editing. We already live within an engineered knowledge system. The difference is that it is distributed across institutions rather than controlled by a single platform.

Musk believes giving the crowd more power creates resilience. His critics counter that without checks, it leads to louder voices dominating, rather than more credible ones.

Where the danger builds: recursion and scale

The deeper problem comes not from bias alone, but from feedback. A model like Grok 4 could generate millions of synthetic paragraphs based on its curated worldview, then use that output to retrain its next version.

This produces:

• Scale: Model-generated beliefs influencing decisions across government, engineering and education

• Recursion: Each synthetic generation reaffirms the last

• Opacity: No citations, timestamps or identifiable authors, just content that feels certain, but has no provenance

The BBC might spin a narrative, but it doesn’t create synthetic encyclopaedias from that narrative and then retrain the national curriculum on them. Grok 4 could.

What others are saying

Reactions from leading AI voices have been sharp:

• Gary Marcus called the rewrite plan “straight out of 1984” (source)

• Geoffrey Hinton has distanced himself from Musk’s vision, suggesting it risks undermining science

• Yann LeCun warned that truth-seeking AI cannot function on a platform that rewards conspiracies

• Timnit Gebru described the approach as “doubling down on inaccuracies” through self-training loops

• Andrew Ng listed synthetic data distortion among his top five risks, despite supporting its use in tightly controlled settings

While Musk sees the crowd as a more robust authority over time, others warn that decentralising truth without structure leads to instability.

So what is Grok 4 really?

Grok 4 is not simply an upgraded chatbot. It is the first major AI system being trained on a fully re-edited worldview. Its purpose is no longer to extract answers from the web or human sources, but to propagate a curated model of reality.

This marks a shift from improving performance to defining the very data that makes AI intelligent.

Final reflections

None of this means synthetic data should be discarded. But it must be trackable. Without provenance markers, citation trails or editorial transparency, we will not know whether an AI’s answer comes from human experience or a synthetic rewrite.

This matters most in environments where trust is essential: government, infrastructure, policy, education.

We are not just training models to answer. We are training them to remember.