Large language models (LLMs) and other related systems have already been subsumed into the age-old struggle for political power, as seen in everything from Elon Musk’s AI-driven takeover of governmental agencies to technological competition between the United States and China. Are they compatible with democratic governance, or threats to its survival?

Computers have always been governance machines—tools used by bureaucracies to organize themselves to exert power, models used to understand how bureaucracies behave, and little bureaucratic organizations in and of themselves. Generative artificial intelligence systems are no exception; they are likely to transform how governments, corporations, and other entities organizationally behave.

Large language models (LLMs) and other related systems have already been subsumed into the age-old struggle for political power, as seen in everything from Elon Musk’s AI-driven takeover of governmental agencies to technological competition between the United States and China. Are they compatible with democratic governance, or threats to its survival?

In choosing to frame an always amorphously defined “intelligence” as an inherently singular and self-contained quality, AI designers have unconsciously selected systems that mirrored the centralized architectures of the institutions that utilize them.

AI has, throughout its history, emphasized particular solutions to intelligent behavior that trend towards centralization and top-down control. In turn, these tendencies have been reinforced by the manner in which patrons—such as governments and large corporations—see their own ideological and organizational assumptions reflected as computational artifacts.

Past need not be prologue, but less centralized AI may require breaking with the field’s governing assumptions.

In a 2019 talk, Peter Thiel suggested AI itself—independent of any particular AI flavor—might be inherently authoritarian.