The concept of "echo chamber" has become a dominant metaphor for describing the epistemic risks of algorithmically curated information environments. In the context of AI-assisted work, the same metaphor is routinely deployed to characterize the dangers of personalized language models that adapt to their users’ preferences, vocabularies, and intellectual frameworks. This paper challenges that framing by introducing the concept of "ego chamber" — a deliberately constructed, memory-rich working environment in which the adaptive capacity of conversational AI becomes a productive epistemic tool rather than a distorting filter. Drawing on phenomenological epistemology, the philosophy of cognitive scaffolding, and emerging practices in AI-assisted intellectual work, I argue that the personalization afforded by persistent memory in large language models enables a qualitatively different relationship between user and tool — one closer to a trained research assistant than to a recommendation algorithm. The ego chamber is not the pathological byproduct of algorithmic filtering; it is the intentional construction of a cognitive workspace calibrated to one’s own intellectual project. The paper examines the conditions under which this transition from echo chamber to ego chamber becomes possible, its epistemological implications, and its limits.

This draft was produced with the assistance of a large language model (Claude, Anthropic) as a writing and structuring tool. The conceptual framework, original arguments, and theoretical contributions are entirely the author's.