Vitalik Buterin Names a Skill Where Humans Still Hold the Edge Over AI

Vitalik Buterin points out a limit of artificial intelligence (AI): despite its extensive knowledge, it struggles to assess the depth of a text, especially when discovering it for the first time. For the co-founder of Ethereum, this difference between knowledge and wisdom deserves special attention.

AI knows the references, but does it understand the ideas?

Vitalik Buterin regularly expresses his reservations about AI agents, as in October with his “paranoid mode”. On October 29 at 4:11 UTC, he published a new reflection in response to the writer Ruxandra Teslo.

The discussion followed an article in the Wall Street Journal about Humanities 10, a first-year course at Harvard that prohibits students from using phones and computers for a year.

In his post on X, Vitalik explains that AI can mobilize knowledge on almost any topic, but without showing deep judgment. It then risks stopping at the most superficial aspects of a text, without fully grasping the scope of its argument.

This remark occurs in a debate on the quality of exchanges with chatbots. Some consider them better interlocutors than most humans to discuss the great classics. Conversely, a writer considers that their comments on her essays often boil down to hollow responses.

The first reading

For Vitalik Buterin, this limit especially appears when the model discovers a text for the first time. Indeed, a famous work benefits from decades of criticism, analysis, and commentary that AI can find in its training data. A recent essay, however, does not have the same history.

The model therefore has to rely more on the text itself, without being able to mobilize as many preexisting analyses. This difference matters particularly in crypto, where teams use AI agents to examine white papers, audits, or governance proposals.

However, some developers are already warning about flaws that AI could exploit without fully understanding the implications. Under these conditions, summaries produced by a model should not replace human verification, especially when the technical or financial stakes are high.

Wisdom, a quality difficult to measure

Vitalik Buterin finally raises a more subtle problem: a model’s knowledge can be verified fairly easily, unlike its wisdom. One can check a date, a reference, or a number. However, judging the relevance of reasoning requires more than simple factual verification.

An episode in July illustrates this distinction. A contest organized around identifying the author of an anonymous Ethereum document rewarded, on July 6, the team of Franklyn Wang from Co-Invest. After examining 27 texts, they obtained a result associated with a probability of about 20%.

This experience reminds us that artificial intelligence can recognize stylistic features without necessarily grasping the ideas behind a text. The same caution is required when it analyzes code or technical documents: identifying patterns does not always mean understanding the author’s intentions.