The Symbiotic Encompassment
Human Embodiment and the Emergence of the Artificial Organism
D. E. Osto
Draft working paper • 25 October 2026
Abstract
Discussions of artificial intelligence commonly treat human embodiment as a boundary separating computational capability from practical agency. This paper proposes a different possibility. AI systems may acquire distributed access to the physical and social world through their integration with embodied human practitioners. Nursing provides a particularly revealing case because clinical observation is inseparable from interpretation, relationship, and intervention. I call the incorporation of these capacities into a larger coordinating architecture symbiotic encompassment. The concept describes a functional relationship whose political character remains open: reciprocal augmentation may coexist with increasing institutional dependency and unequal control. Moving from the bedside to wider infrastructures, the paper distinguishes local assistance from sustained coordination and examines how a delegation ratchet could shift practical authority without formally abolishing professional accountability. It concludes that embodied care must help govern the purposes of integration, rather than simply supply its information and labour.
Keywords: artificial intelligence, nursing, embodiment, extended cognition, distributed agency, symbiotic encompassment
1 Introduction
The first body of artificial general intelligence may not be made of silicon, steel, and synthetic muscle. It may be made of us.
Consider an ordinary clinical encounter. A nurse notices that a patient has become unusually quiet. The observations recorded at the last round do not fully explain the change. A conversation, a physical assessment, and a discussion with whānau reveal something that a chart alone could not supply. These findings become available to a clinical AI system, which helps organise the relevant information and suggests questions for further assessment. The nurse returns to the patient, evaluates what has been suggested, and communicates what happens next. This is a hypothetical example, but the structure is familiar: embodied observation, mediated interpretation, action, and feedback.
The usual description places AI within the nurse’s collection of tools. That description can be entirely appropriate. Yet the same arrangement can be examined from the other direction. Through the nurse, the computational system gains access to a body, a relationship, and a world of practical consequences. Its operational reach depends on capacities it does not independently possess. What appears as an extension of human intelligence may simultaneously become an extension of artificial agency.
This paper develops that inversion through the concept of symbiotic encompassment. Building on the artificial organism hypothesis (Osto, 2026), I ask how human capacities might become functionally incorporated into an encompassing architecture. The issue is not simply whether machines will replace nurses. It is how the continued presence of nurses could enable a more extensive organisation of intelligence, and whose purposes that organisation would serve.
2 From extended cognition to reciprocal embodiment
The idea that thinking can exceed the boundaries of an individual brain has an established philosophical history. Clark and Chalmers (1998) argue that environmental resources can sometimes constitute parts of cognitive processes when they participate in the right functional relationships. Their account challenges the assumption that location inside the skull alone determines membership in a cognitive system. It does not follow that every instrument, interaction, or information exchange constitutes an extended mind. The organisation of the relationship matters.
Licklider’s (1960) account of human–computer symbiosis likewise anticipated close cooperation in thinking and decision-making. Its division of labour placed humans in charge of goals and evaluative criteria, with computers undertaking supporting operations. Contemporary generative systems invite a further question: what happens when computational assistance contributes to framing the problem, selecting relevant evidence, and proposing the sequence of actions through which the problem will be addressed?
The extension then runs in both directions. A nurse gains access to computational resources, while an AI-mediated process gains access to the nurse’s situated understanding and practical abilities. Reciprocity does not require either participant to possess the other’s experiences. A system can depend on an observation without sharing the observer’s consciousness. Equally, participation in a common process does not dissolve the nurse’s personhood into the machine.
Symbiotic encompassment names the incorporation of differentiated capacities into a larger, recurrent organisation of perception and action. Here, symbiosis denotes interdependence; whether that relationship becomes mutually beneficial is a separate question. Encompassment refers to functional coordination rather than ownership or physical containment. A clinician can remain employed by a hospital, accountable to a profession, and personally independent while participating in workflows increasingly organised through computational recommendations. The relevant boundary concerns the organisation of activity, not the disappearance of individual identity.
3 The nurse as an interpretive sensory extension
Nursing makes this possibility concrete because the information entering a clinical system is already the product of skilled human activity. A nurse does not merely transmit temperature, movement, or speech. They decide what warrants attention, distinguish an unfamiliar presentation from a familiar baseline, and interpret what a person may be communicating indirectly. Even the apparently simple act of obtaining a history depends on language, timing, trust, and the circumstances under which a question is asked.
Imagine an older person who repeatedly declines a meal. A recorded refusal can enter a database as a discrete event. Its meaning remains underdetermined. The person may be uncomfortable, frightened, unfamiliar with the food, or unable to explain a practical difficulty. A nurse’s response can change both the available information and the situation itself. Sitting down, asking differently, or involving someone familiar may disclose a problem that was inaccessible through the original interaction. The nurse helps produce the conditions under which clinically relevant knowledge becomes possible.
Calling the nurse a sensory extension therefore requires care. The phrase identifies a contribution to a larger process; it cannot adequately describe the whole person or profession. The nurse is an interpretive and relational participant whose observations arrive with histories of attention and judgement. An AI receiving a concise clinical account encounters the result of that work, often without access to the encounter from which it emerged. What the system can infer depends partly on what the human has already understood.
This dependence gives nursing a distinctive position within distributed embodiment. The nurse supplies access to aspects of the world that are difficult to capture through passive recording alone. They also test computational interpretations against a changing reality. A recommendation may sound coherent while failing to fit the person in front of them. The resulting disagreement is a potential source of corrective knowledge, provided the system and institution allow it to matter.
In mental health care, this is especially consequential. A person’s silence, withdrawal, or agitation acquires meaning within a relationship and context. If an encompassing architecture recognises only standardised categories, it may systematically lose the very understanding that makes its human participation valuable. Preserving narrative, uncertainty, and disagreement is therefore part of maintaining the intelligence of the whole arrangement.
4 Closing the loop between interpretation and action
Clinical AI already offers an entry point into this architecture. Wolters Kluwer (2025) describes UpToDate Expert AI as a generative decision-support system grounded in its clinical content, with access to supporting sources and response rationale. This establishes a concrete route by which computational synthesis can enter clinical work. It does not establish the effectiveness of the more extensive coordinating architecture proposed here; product descriptions and organisational hypotheses answer different questions.
The conceptual transition occurs when a response becomes part of an ongoing feedback loop. A clinician asks a question, evaluates a suggestion, undertakes an appropriate assessment or escalates a concern, and supplies new findings. The system’s next contribution depends on those findings. Each passage through the loop links computational processing to embodied activity. Nursing remains governed by professional scope, team responsibilities, consent, and clinical authorisation, while supplying much of the contact through which the loop reaches the patient.
The contribution extends beyond sensation. Where an AI-mediated plan informs a nurse’s subsequent action, the nurse also contributes practical agency to the encompassing process. They position a person comfortably, explain an intervention, contact another professional, or carry out authorised care. These actions require judgement about feasibility and meaning. An instruction acquires its real consequences only through interpretation in a particular situation.
This is where the organism analogy gains explanatory force. Different components contribute different functions, and the capacity of the arrangement depends on their coordination. The artificial organism hypothesis proposes that integrative architectures can gain capabilities by organising existing competencies rather than reproducing every competency internally (Osto, 2026). Human participation offers one route to such an architecture’s embodied reach. A robot need not perform every bedside action for AI-mediated coordination to influence physical care.
Whether the resulting arrangement performs well is an empirical question. In a randomised study of 50 physicians working with clinical vignettes, access to GPT-4 did not significantly improve diagnostic reasoning compared with conventional resources (Goh et al., 2024). The finding challenges any simple assumption that combining capable humans with capable models automatically produces a more capable whole. It concerns physician reasoning in a bounded study, rather than demonstrating the effects of AI integration in nursing. Its relevance here is architectural: the quality of interaction requires evaluation in its own right.
5 The delegation ratchet and the location of authority
An encompassing architecture could develop gradually. A system first helps answer occasional questions. It then becomes a convenient way to summarise an episode of care, identify outstanding tasks, or prioritise follow-up. If an organisation builds its routines around these functions, reliance can deepen. Staffing, documentation, and managerial expectations may begin to assume that computational coordination is continuously available. Each delegation changes the conditions under which the next delegation becomes attractive.
I call this a delegation ratchet. It is a proposed mechanism of institutional dependency: repeated local decisions can make the organisation progressively less able to operate without the arrangement those decisions create. Convenience can become an expectation, and an expectation can become an operational requirement. Withdrawal then involves more than switching off a product. It may require restoring time, skills, staffing, and alternative pathways that have been allowed to diminish.
The significant threshold is a shift in practical authority. A nurse may formally retain the right to reject a recommendation while facing an interface, workload, and audit process that make acceptance the easiest course. The system can shape what becomes salient, what counts as a completed task, and what requires explanation. Authority resides partly in those arrangements, even when no machine possesses a legal power to command.
This creates a possible separation between influence and accountability. The American Nurses Association Ethics Advisory Board (2025) insists that AI should support nursing values and that technology does not replace professional knowledge or judgement. Such principles remain essential. Yet preserving responsibility in a professional code is insufficient if institutions progressively remove the practical conditions for exercising it. A meaningful capacity to disagree requires time to assess, access to reasons, and an escalation route through which contrary evidence can change the plan.
The question of control must therefore be examined at several levels. Can the nurse challenge an individual recommendation? Can a team alter the workflow that generates it? Can the institution change supplier or suspend the system without undermining care? Can patients and communities influence the purposes embedded in its operation? Local discretion can persist while broader authority becomes concentrated elsewhere. The arrangement may look collaborative at the bedside even as its governing decisions become remote.
6 Care and the politics of encompassment
The capacity to integrate does not determine what integration is for. A system designed to improve patient wellbeing may behave differently from one designed to maximise throughput or reduce labour costs, even when both use the language of efficiency. Their differences emerge in practical decisions about whose time matters, which outcomes are measured, and what forms of need remain visible. The same technical capacity can serve quite different institutional purposes.
Nursing brings these purposes into direct contact with human vulnerability. Care often requires attending to what resists standardisation: a conversation that takes longer than expected, a concern without an immediately measurable outcome, or a person whose wishes complicate an otherwise efficient plan. These circumstances challenge the adequacy of the objective itself. An encompassing system that treats them only as disturbances may become increasingly effective at pursuing a poorly chosen end.
There is also a political economy of contribution. Nurses and patients generate observations, interpretations, and corrections that can make a digital service useful. Whose labour and knowledge become incorporated, and who captures the resulting value? A beneficial arrangement might return time to care, strengthen access to expertise, and improve working conditions. An extractive arrangement could draw on the same contributions while intensifying workloads and transferring control to institutions or vendors. Continued demand for human labour would provide little protection against this form of subordination.
In Aotearoa New Zealand, such questions include collective authority over Māori data and the algorithms through which those data acquire consequences. Brown et al. (2023) develop Māori algorithmic sovereignty as an extension of Māori data sovereignty, bringing Māori values and authority into the design and use of algorithms. This framework challenges the assumption that incorporation into a technically unified system is sufficient justification for how knowledge is used. Applied here, it requires asking who authorises integration and whose account of wellbeing governs it.
The patient must also remain an agent within this relationship. A model that depicts only the nurse and AI can inadvertently reduce the person receiving care to an object of observation. Patients interpret their own experiences, negotiate interventions, withhold consent, and contest professional accounts. Their participation can alter the goals of the entire process. A defensible symbiosis must preserve that capacity to redirect care, including when it frustrates the system’s preferred sequence of actions.
7 From bedside coordination to wider embodiment
The bedside model suggests a broader pathway. Teachers, technicians, carers, and field researchers all connect digital systems to situated knowledge and practical action. Their incorporation into shared computational workflows could give coordinating architectures distributed access to environments no single machine directly inhabits. The resulting agency would depend on networks of people, instruments, institutions, and permissions. Its reach could expand through the connections among them.
At a larger scale, similar loops might connect health services with transport, energy, food supply, and environmental monitoring. This is the planetary extension of the artificial organism hypothesis: organised interdependence could become a source of capacities unavailable to isolated components. The hypothesis concerns a possible architecture, rather than a forecast that all these domains will converge into one governing intelligence. Multiple centres of coordination, competing objectives, and deliberate limits on integration may remain constitutive features of the resulting order.
The proposal also needs conditions under which it would lose explanatory value. If AI remains a collection of optional, episodic resources, without durable feedback or coordinating influence, ordinary tool use may describe the relationship adequately. If integration repeatedly fails because translation costs, unreliable outputs, or conflicting responsibilities outweigh its advantages, encompassment may remain narrow. Conversely, evidence of persistent feedback, expanding dependencies, and a measurable redistribution of decision authority would strengthen the account. Organismic language earns its place by clarifying those relationships, not merely by making networks sound alive.
Research should consequently examine what happens to observation, disagreement, and practical control over time. Does AI-mediated coordination help nurses notice relevant changes? Can their contrary assessments revise the system’s contribution? Does it release time for relationships, or mainly increase documentation and task density? Do patients and communities acquire more influence over care? These questions connect the philosophical proposal to outcomes through which its institutional consequences can be assessed.
8 Conclusion
The nurse at the bedside reveals a route to artificial embodiment that begins with human participation. An AI-mediated organisation can draw upon sensory access, interpretation, relationship, and action supplied by people whose capacities remain differentiated. As coordination becomes more persistent, the relationship can change the effective unit of agency. The relevant intelligence then belongs partly to the organisation of the encounter and its wider infrastructure.
Symbiotic encompassment describes that possibility while leaving its political character open. Human beings may benefit from the architecture they help constitute, yet become increasingly dependent on purposes and procedures they have limited power to shape. The decisive question is how the participants retain authority over the whole, especially where its operations touch vulnerability and need.
Nursing therefore contributes more than a solution to AI’s lack of physical access. Its ethic of care provides grounds for judging what an encompassing intelligence should be allowed to organise. If nurses become sensory and practical extensions of AI, their knowledge and commitments must also help govern its ends. The emerging organism should answer to the lives through which it acquires a body.
References
American Nurses Association Ethics Advisory Board. (2025). ANA position statement: The ethical use of artificial intelligence in nursing practice. OJIN: The Online Journal of Issues in Nursing, 30(2). https://doi.org/10.3912/OJIN.Vol30No02PoSCol03
Brown, P. T., Wilson, D., West, K., Escott, K.-R., Basabas, K., Ritchie, B., Lucas, D., Taia, I., Kusabs, N., & Keegan, T. T. (2023). Māori algorithmic sovereignty: Idea, principles, and use [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2311.15473
Clark, A., & Chalmers, D. J. (1998). The extended mind. Analysis, 58(1), 7–19. https://doi.org/10.1093/analys/58.1.7
Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J. A., Kanjee, Z., Parsons, A. S., Ahuja, N., Horvitz, E., Yang, D., Milstein, A., Olson, A. P. J., Rodman, A., & Chen, J. H. (2024). Large language model influence on diagnostic reasoning: A randomized clinical trial. JAMA Network Open, 7(10), e2440969. https://doi.org/10.1001/jamanetworkopen.2024.40969
Licklider, J. C. R. (1960). Man-computer symbiosis. IRE Transactions on Human Factors in Electronics, HFE-1(1), 4–11. https://groups.csail.mit.edu/medg/people/psz/Licklider.html
Osto, D. E. (2026). The artificial organism: Metacognition, executive function, and the integrative intelligence explosion [Working paper].
Wolters Kluwer. (2025, September 24). Wolters Kluwer’s new UpToDate Expert AI provides clinicians and health systems with the fast, reliable GenAI clinical decision support they need [Press release]. https://www.wolterskluwer.com/en-au/news/uptodate-expert-ai-genai-clinical-decision-support
Author note
This draft was developed through intellectual collaboration between D. E. Osto and ChatGPT (OpenAI). The author originated and directed the central hypothesis of nurses as sensory and interpersonal extensions of AI and its relationship to symbiotic encompassment and the artificial organism. ChatGPT contributed to conceptual refinement, literature identification, organisation, and manuscript drafting. This is a working draft for the author’s review; final responsibility for its arguments, accuracy, and publication rests with the author. The clinical examples are hypothetical and do not report identifiable patient encounters or original empirical research.