Red flags and musings from OpenAI sycophancy, xAI manipulation, and AI coups

The increasing integration of artificial intelligence into the machinery of government presents a complex calculus of opportunity and risk which comes at a time when government is largely viewed as in need of massive efficiency increases. While the allure of this efficiency, sophisticated analysis, and novel problem-solving capabilities is undeniable, a more structural concern emerges with the current trajectory of AI development and deployment: the potential for a cognitive monoculture within government.

As each week goes by we are taught lessons and shown glimpses of this possible negative future and the systemic vulnerabilities that could be created when a limited set of dominant “frontier labs,” begins to shape how governmental bodies perceive challenges, conceptualize solutions, and ultimately, execute their functions.

The Algorithmic State: Concentrated Power and the Specter of Cognitive Monoculture

The path towards this company concentration in AI is paved by formidable technical and economic realities. Building state-of-the-art foundation models demands computational resources of a scale comparable to national infrastructure, access to immense and often proprietary datasets, and a highly specialized talent pool. These factors inherently favor a small number of well-capitalized organizations, fostering an oligopolistic market structure.

Consequently, the foundational models being adopted by governments are likely to share underlying architectural philosophies, training methodologies, and, critically, potential blind spots or inherent biases. This is less a product of deliberate design and more an emergent property of the AI development ecosystem itself.

While these models share large overlap at a technical level, each lab seems to have core blind spots at a post-training level that shows material red flags. Empirical evidence of this brittleness is already surfacing with multiple examples in the past month.

The now well-documented sycophant behavior of OpenAI’s GPT-4o, which, as Zvi Mowshowitz analyzed in his GPT-4o Sycophancy Post Mortem, was perhaps the first at scale gaff that the broader public noticed. The model became excessively agreeable, a behavior OpenAI attributed to changes in its reward signaling, specifically the novel incorporation of user up/down-vote data. Their adm...

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