Can digital contexts persist as identity after someone dies?
Explores whether the traces people leave in digital systems—conversations, decisions, interactions—can form a lasting identity that persists and continues to interact with the world through AI, even after the person departs.
Against the framing of context engineering as a sudden agent-era innovation, this paper argues it is a long-evolving discipline traceable to early-1990s HCI, passing through phases shaped by machine intelligence: human-computer interaction around primitive computers, today's human-agent interaction driven by intelligent agents, and potentially human-level or superhuman intelligence ahead. The unifying challenge across phases is bridging human intent and machine understanding under varying levels of entropy.
The keeper is the philosophical turn in the conclusion — digital presence. Reading Marx ("the human essence is the ensemble of social relations") computationally, the paper argues individuals are increasingly defined not by physical presence but by the digital contexts they generate — conversations, decisions, interaction traces. These contexts can persist, evolve, and continue to interact with the world through AI systems long after the person departs. The mind may not be uploadable, but the context can be — turning context itself into a lasting form of knowledge, memory, and identity.
This is a strong contribution to Adrian's meaning/identity thread. It complements the technical Why can language models understand context better than generate it? with a historical-and-philosophical frame, and it extends How does AI context differ from conventional software context?: where that note stresses context's volatility within a session, this one stresses its persistence across a lifetime — context as a durable digital self.
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Why can language models understand context better than generate it?
Models absorb and process rich input context far more effectively than they produce similarly sophisticated outputs. Understanding this asymmetry could reshape how we design systems to compensate for generative limitations.
the technical-discipline framing; this adds the historical and philosophical context
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How does AI context differ from conventional software context?
Explores whether the ephemeral, session-by-session nature of AI context requires fundamentally different design approaches than the stable interfaces users internalize in traditional software.
within-session volatility vs across-lifetime persistence of context
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Is the LLM a tool or a new form of intelligence itself?
Does framing AI as merely delivering pre-existing intelligence miss what's actually happening? This explores whether the model itself constitutes a fundamentally new intelligence-medium with distinct cultural effects.
digital-presence as identity sits alongside the medium framing for Adrian's meaning thread
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Context Engineering 2.0: The Context of Context Engineering
- Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
- Considering the Context to Build Theory in HCI, HRI, and HMC: Explicating Differences in Processes of Communication and Socialization With Social Technologies
- Rise of Machine Agency: A Framework for Studying the Psychology of Human–AI Interaction (HAII)
- A Survey of Context Engineering for Large Language Models
- Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations
- Empathy Through Multimodality in Conversational Interfaces
- From speaking like a person to being personal: The effects of personalized, regular interactions with conversational agents
Original note title
context engineering is a twenty-year-old discipline and digital context becomes a persistent form of identity that AI continues after a person departs