Crossposted from Substack, originally published on September 17, 2025.

This post aims to contextualize the discussion around "If Anyone Builds It, Everyone Dies". It surveys the book’s arguments, the range of responses they have elicited, and the controversies that have emerged.

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For more than two decades, Eliezer Yudkowsky has been one of the most insistent voices on the existential risk associated with advanced artificial intelligence. With the September 2025 publication of If Anyone Builds It, Everyone Dies, co-authored with Nate Soares, this vision is now presented in a book format intended for the general public. This has generated significant media attention and sparked debate within academic, journalistic, and technological communities. The work has been reviewed by major outlets such as The New York Times and Vox, and has prompted a wave of discussion on social media and specialized forums. Below, we will explore the book's central arguments, its reception, its reach, and its relevance.

The Central Argument

The core of Yudkowsky and Soares' thesis is, in short, that if a superhuman artificial intelligence is developed, the default outcome is that humanity will lose control of it, leading to catastrophic consequences—potentially extinction. In this sense, the book does not offer a novel diagnosis compared to previous literature on AI existential risk. However, it does present a systematic and forceful narrative, accompanied by biological analogies and recent examples, to make it clear that there is no margin for error.

A standout element is the critique of how current AI systems are built. The authors argue that it is not engineering in the strict sense, but a process closer to organic growth. Models are trained on billions of iteratively adjusted parameters, without researchers having a true understanding of the link between this mathematical configuration and the emergent behaviors. The metaphor of "cultivation" replaces that of "construction"; just as knowing the letters of DNA is not enough to precisely anticipate phenotypic traits in biology, it is also impossible in AI to foresee what internal preferences a complex model will develop.

From this premise stems the second major warning: the impossibility of ensuring that the external behaviors induced during training will translate into stable internal motivations. In the same way that natural sele...

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