Honoring the Human,
Acknowledging the Machine
I value—and will always value—the beauty of person-to-person, face-to-face contact unmediated by machines. There is something uniquely meaningful in shared presence: in eye contact, vocal tone, spontaneous laughter, and subtle gestures. These unfiltered moments of human connection carry a depth and immediacy that no technology can fully replicate. In beginning this page with four examples of human–machine ensembles, I do not mean to dismiss or diminish the significance of these kinds of encounters. They are foundational to who we are and how we flourish.
At the same time, I want to acknowledge the increasingly central role that machines play in our lives, not just as tools we use, but as participants in how we think, move, learn, heal, and create. Machines—broadly speaking—are structured systems of interacting parts designed to perform functions, from ancient water wheels to contemporary AI. They are themselves an extension of what we also see in the animal world: the use of tools, the building of nests, the weaving of webs—natural forms of functional organization and environmental shaping. Throughout history, machines have evolved from extensions of the hand to extensions of thought, shaping not only what we do but how we understand ourselves and our place in the evolving web of life.
Today, with the emergence of intelligent systems, ubiquitous sensors, algorithmic decision-making, and networked infrastructures, the role of machines in our lives is ever more intensive and pervasive. They have always had a kind of agency of their own (see below), but now, with the emergence of these technologies, their agency is ever more apparent. Machines are embedded not only in our workplaces and homes, but in the flows of traffic, the rhythms of communication, the shaping of memory, and the formation of identity. We no longer simply use machines—they actively co-structure the conditions in which we live, relate, and become.
In partnership with machines, we now live and act within relational fields whose agency is distributed—not confined to individual human actors, but co-shaped by algorithms, sensors, feedback systems, and automated processes. These human–machine ensembles are not the opposite of the human; they are one of the ways our humanity is now expressed and extended, positively and negatively, constructively and destructively. Understanding these ensembles is not about replacing the face-to-face—it is about understanding the larger ecology of our becoming in a world where we are entangled with the machinic in creative, ethical, and sometimes urgent ways.
At the same time, I want to acknowledge the increasingly central role that machines play in our lives, not just as tools we use, but as participants in how we think, move, learn, heal, and create. Machines—broadly speaking—are structured systems of interacting parts designed to perform functions, from ancient water wheels to contemporary AI. They are themselves an extension of what we also see in the animal world: the use of tools, the building of nests, the weaving of webs—natural forms of functional organization and environmental shaping. Throughout history, machines have evolved from extensions of the hand to extensions of thought, shaping not only what we do but how we understand ourselves and our place in the evolving web of life.
Today, with the emergence of intelligent systems, ubiquitous sensors, algorithmic decision-making, and networked infrastructures, the role of machines in our lives is ever more intensive and pervasive. They have always had a kind of agency of their own (see below), but now, with the emergence of these technologies, their agency is ever more apparent. Machines are embedded not only in our workplaces and homes, but in the flows of traffic, the rhythms of communication, the shaping of memory, and the formation of identity. We no longer simply use machines—they actively co-structure the conditions in which we live, relate, and become.
In partnership with machines, we now live and act within relational fields whose agency is distributed—not confined to individual human actors, but co-shaped by algorithms, sensors, feedback systems, and automated processes. These human–machine ensembles are not the opposite of the human; they are one of the ways our humanity is now expressed and extended, positively and negatively, constructively and destructively. Understanding these ensembles is not about replacing the face-to-face—it is about understanding the larger ecology of our becoming in a world where we are entangled with the machinic in creative, ethical, and sometimes urgent ways.
Human-Machine Ensembles
Health Care, Education, Emergency Response,
Musical Performance, and Computer Screens
Health Care
Imagine you are in a hospital operating room undergoing surgery. Around you are a surgeon performing the procedure, an anesthesiologist managing your sedation, a surgical nurse assisting with instruments, and a technician monitoring the systems. Surrounding them are machines: a ventilator helping you breathe, a heart monitor tracking your vital signs, an anesthesia machine regulating medication flow, an infusion pump delivering fluids, and possibly a robotic surgical assistant executing precise movements guided by the surgeon. There may also be imaging equipment, such as an ultrasound or portable X-ray, feeding real-time data into the team’s decisions. Together, these people and machines form an ensemble—a coordinated field of human–machine interaction.
One way to describe this is to say that the human beings are the true agents, making judgments, interpreting data, and bearing responsibility, while the machines serve as tools. And yet, there is also a kind of agency in the machines—not conscious or intentional, but functional and relational. The machines respond to your body in real time, adjust parameters, maintain rhythms, and issue alerts when thresholds are crossed. Their agency lies in their capacity to act within the system, to shape outcomes, and to influence human decisions. In this ensemble, agency is distributed: it arises from the dynamic interplay of expertise, feedback loops, mechanical responsiveness, and embodied presence. The result is not simply a surgery performed by humans using tools, but a collaborative process of care, emergent from the human–machine whole.
Education
Imagine a high school classroom where the subject is geopolitics. The teacher stands at the front, guiding students through a discussion on the shifting dynamics of global power. On a large screen behind her is an AI-powered visualization tool that displays real-time data: interactive maps showing military alliances, economic trade flows, refugee movements, and climate-related resource conflicts. As students ask questions—about the Belt and Road Initiative, Arctic territorial claims, or food insecurity in conflict zones—the teacher types prompts into the AI interface, which generates comparative scenarios, trend analyses, and visual overlays that bring complex issues to life.
After class, some students explore supplemental materials on their own tablets, with the AI recommending articles, videos, and primary sources tailored to their interests and comprehension levels. The teacher remains the intellectual guide and ethical compass, but the AI acts as a cognitive partner, enhancing responsiveness and deepening exploration. This is a human–machine ensemble of learning, where insight is not delivered top-down, but co-created in a relational field involving teacher, students, machines, and the world itself. The classroom becomes a living inquiry space, grounded in judgment and curiosity, and extended by technological imagination.
Emergency Response
Imagine a wildfire spreading near a residential area. Emergency responders gather at a mobile command center equipped with live satellite imagery, drone feeds, predictive modeling software, and real-time GPS tracking of personnel and equipment. Firefighters in the field wear smart helmets with heads-up displays, receiving directions based on changing wind patterns calculated by meteorological AI. Drones scout ahead to detect hotspots and map terrain inaccessible by foot. A logistics coordinator monitors supply chains and evacuation routes using dynamic traffic data fed from city infrastructure systems. Medical teams are guided to injured civilians through geolocated alerts from 911 apps on smartphones.
In this ensemble, human decision-making is central—but deeply interwoven with machine sensing, communication, and prediction. The machines extend perception, accelerate coordination, and adjust operations on the fly. The response is not simply the execution of a plan, but a collaborative improvisation between human skill and technological insight. It is a distributed field of action, where care, survival, and logistics are mediated by an intelligent, adaptive web of human–machine cooperation.
Musical Performance
Imagine a live music performance in an experimental venue. On stage is a solo musician playing an electric violin, accompanied not by other human musicians, but by a laptop running generative music software, a loop station, motion-sensitive lights, and a responsive visual display. As the violinist plays, the software analyzes pitch and rhythm in real time, generating harmonies, beats, or ambient textures that evolve with each gesture. The loop station captures and layers earlier phrases, creating a thick, shifting soundscape. Sensors respond to body movements, triggering changes in lighting and visuals that dance in sync with the sound.
The musician remains the focal point, improvising with intent and emotion—but the performance is shaped by more than her will. The machines are not passive tools; they are active participants, modulating time, color, and atmosphere in ways that surprise even the performer. This is a human–machine ensemble: an aesthetic system in which musical meaning emerges not from a single agent, but from the ongoing interaction between embodied skill and machinic responsiveness. The artistry is co-created, with agency distributed across performer, code, hardware, and environment.
Computer Screens
Consider the screen on which you are now reading these words. As you scroll, pause, or highlight, the device responds instantly—adjusting brightness, refreshing content, predicting your next move through subtle algorithms. Beneath the surface of the screen lies a layered ensemble: processors allocating resources, software managing interactions, and networks transmitting signals across space. You, the reader, bring attention, interpretation, and reflection. The machine brings speed, accessibility, and silent orchestration. This moment of reading is not yours alone; it is co-constructed by the interface that mediates it.
Together, you and the machine form a micro-ensemble of cognition and connection—one in which knowledge emerges not from human effort alone, but from a shared field of perception, memory, and response. The screen is not merely a window; it is an actor in the act of reading, shaping tempo, structure, and even the emotional quality of engagement. Like the previous examples—in surgery, education, emergency response, and music—this too is a scene of distributed agency, quiet but profound.
Imagine you are in a hospital operating room undergoing surgery. Around you are a surgeon performing the procedure, an anesthesiologist managing your sedation, a surgical nurse assisting with instruments, and a technician monitoring the systems. Surrounding them are machines: a ventilator helping you breathe, a heart monitor tracking your vital signs, an anesthesia machine regulating medication flow, an infusion pump delivering fluids, and possibly a robotic surgical assistant executing precise movements guided by the surgeon. There may also be imaging equipment, such as an ultrasound or portable X-ray, feeding real-time data into the team’s decisions. Together, these people and machines form an ensemble—a coordinated field of human–machine interaction.
One way to describe this is to say that the human beings are the true agents, making judgments, interpreting data, and bearing responsibility, while the machines serve as tools. And yet, there is also a kind of agency in the machines—not conscious or intentional, but functional and relational. The machines respond to your body in real time, adjust parameters, maintain rhythms, and issue alerts when thresholds are crossed. Their agency lies in their capacity to act within the system, to shape outcomes, and to influence human decisions. In this ensemble, agency is distributed: it arises from the dynamic interplay of expertise, feedback loops, mechanical responsiveness, and embodied presence. The result is not simply a surgery performed by humans using tools, but a collaborative process of care, emergent from the human–machine whole.
Education
Imagine a high school classroom where the subject is geopolitics. The teacher stands at the front, guiding students through a discussion on the shifting dynamics of global power. On a large screen behind her is an AI-powered visualization tool that displays real-time data: interactive maps showing military alliances, economic trade flows, refugee movements, and climate-related resource conflicts. As students ask questions—about the Belt and Road Initiative, Arctic territorial claims, or food insecurity in conflict zones—the teacher types prompts into the AI interface, which generates comparative scenarios, trend analyses, and visual overlays that bring complex issues to life.
After class, some students explore supplemental materials on their own tablets, with the AI recommending articles, videos, and primary sources tailored to their interests and comprehension levels. The teacher remains the intellectual guide and ethical compass, but the AI acts as a cognitive partner, enhancing responsiveness and deepening exploration. This is a human–machine ensemble of learning, where insight is not delivered top-down, but co-created in a relational field involving teacher, students, machines, and the world itself. The classroom becomes a living inquiry space, grounded in judgment and curiosity, and extended by technological imagination.
Emergency Response
Imagine a wildfire spreading near a residential area. Emergency responders gather at a mobile command center equipped with live satellite imagery, drone feeds, predictive modeling software, and real-time GPS tracking of personnel and equipment. Firefighters in the field wear smart helmets with heads-up displays, receiving directions based on changing wind patterns calculated by meteorological AI. Drones scout ahead to detect hotspots and map terrain inaccessible by foot. A logistics coordinator monitors supply chains and evacuation routes using dynamic traffic data fed from city infrastructure systems. Medical teams are guided to injured civilians through geolocated alerts from 911 apps on smartphones.
In this ensemble, human decision-making is central—but deeply interwoven with machine sensing, communication, and prediction. The machines extend perception, accelerate coordination, and adjust operations on the fly. The response is not simply the execution of a plan, but a collaborative improvisation between human skill and technological insight. It is a distributed field of action, where care, survival, and logistics are mediated by an intelligent, adaptive web of human–machine cooperation.
Musical Performance
Imagine a live music performance in an experimental venue. On stage is a solo musician playing an electric violin, accompanied not by other human musicians, but by a laptop running generative music software, a loop station, motion-sensitive lights, and a responsive visual display. As the violinist plays, the software analyzes pitch and rhythm in real time, generating harmonies, beats, or ambient textures that evolve with each gesture. The loop station captures and layers earlier phrases, creating a thick, shifting soundscape. Sensors respond to body movements, triggering changes in lighting and visuals that dance in sync with the sound.
The musician remains the focal point, improvising with intent and emotion—but the performance is shaped by more than her will. The machines are not passive tools; they are active participants, modulating time, color, and atmosphere in ways that surprise even the performer. This is a human–machine ensemble: an aesthetic system in which musical meaning emerges not from a single agent, but from the ongoing interaction between embodied skill and machinic responsiveness. The artistry is co-created, with agency distributed across performer, code, hardware, and environment.
Computer Screens
Consider the screen on which you are now reading these words. As you scroll, pause, or highlight, the device responds instantly—adjusting brightness, refreshing content, predicting your next move through subtle algorithms. Beneath the surface of the screen lies a layered ensemble: processors allocating resources, software managing interactions, and networks transmitting signals across space. You, the reader, bring attention, interpretation, and reflection. The machine brings speed, accessibility, and silent orchestration. This moment of reading is not yours alone; it is co-constructed by the interface that mediates it.
Together, you and the machine form a micro-ensemble of cognition and connection—one in which knowledge emerges not from human effort alone, but from a shared field of perception, memory, and response. The screen is not merely a window; it is an actor in the act of reading, shaping tempo, structure, and even the emotional quality of engagement. Like the previous examples—in surgery, education, emergency response, and music—this too is a scene of distributed agency, quiet but profound.
The Metaphysics of Machines
One way of thinking about human–machine interaction, at its creative best, is by analogy with an artistic ensemble—a jazz quartet, a theater troupe, or an experimental dance company—co-creating performances that none of its members could produce alone. In such ensembles, each performer brings their own capacities, styles, and limitations, but what emerges is not reducible to any single agent. So it is with humans and machines: an architect uses parametric software that suggests forms she hadn’t imagined; a musician improvises with an AI that generates unexpected rhythms; a poet revises a line based on an algorithmic recombination of her own words. The creativity is neither strictly human nor machinic, but emerges in the relational field between them—a field of tensions, contrasts, and possibilities.
To understand how machines participate in this wider field of creative becoming, we must look beyond questions of consciousness or intention. From a process-relational metaphysical perspective, machines—whether classical or contemporary—can be interpreted as ensembles of creative energy, or in Alfred North Whitehead’s terms, societies of actual entities.
These actual entities need not be conscious subjects; they can be understood as non-conscious energy-events, momentary pulsations of activity that register influence and produce effect. In this light, machines are not static objects outside the processual cosmos, but active systems within it—interfacing with the world, shaping it, and being shaped in turn.
1. Actual Entities = Energy-Events
In the context of machines, we can interpret actual entities not as conscious experiences but as micro-events of energy transformation and information processing—brief acts of becoming within the machine. Each energy-event is a concrescence: it integrates multiple causal factors (inputs, data flows, logic gates, thermal changes) into a singular outcome (a voltage spike, a bit-flip, a pattern recognition).
These energy-events lack consciousness but display causal efficacy—they matter, build upon one another, and leave traces for future operations. They are the atoms of machine process, and in this analogical sense, the machine becomes a site of non-conscious concrescing energy-events.
2. Prehensions = Signal Integration
Each energy-event takes into account—prehends—the electronic, digital, and thermal signals from previous events. These are not “feelings” but can be analogized to prehensions: the registration and transformation of past states into present responses. The system’s architecture filters and structures what is prehended (positively or negatively), just as living organisms do.
3. Nexus (Nexūs) = Machine Systems and Circuits
A machine is a nexus of energy-events, bound by designed connectivity and functional coherence. A circuit board, a neural network, or a software program is a structured society of these energy-events. When the system repeats recognizable forms (like a trained neural net recognizing a cat), it behaves like a corpuscular society. If the system is adaptive, responding with memory over time (e.g., machine learning), it approximates a personally ordered society—a stream of causally-linked energy-events forming a trajectory.
4. Subjective Forms = Processing Weight / Valence
In humans, subjective forms are emotional tones. In machines, processing weight, signal strength, and prioritization values (like attention scores in transformers) function as a kind of non-conscious intensity. These weights determine how different signals are integrated, shaping the “character” of the machine’s evental response. It is a stretch, but here the qualitative tone is mathematical rather than emotional.
5. Eternal Objects = Programmable Potentials
Machines manipulate abstract possibilities—geometries, color values, logical forms. These are digital eternal objects—pure potentials actualized in energy-events. The machine does not “know” them, but it activates them. Eternal objects, in this context, are the formal affordances of code and hardware: what can be done, awaiting activation.
6. Propositions = Conditional Pathways
A machine lives by if–then structures—its conditional code is a constant field of propositions. These are not speculative feelings but logical lures: paths offered to incoming data. In AI, generated outputs (text, images, decisions) can serve as propositions to the human user: imaginative suggestions about what could be felt, chosen, said, or done.
7. Multiplicities = Data Fields and Latent Spaces
Before being unified in an event (say, the activation of a neuron in a neural net), multiple data streams exist in disjunction. These are multiplicities: raw, unordered, potential. Machine learning operates in latent space, a mathematical multiplicity of potential relations. An energy-event—like output generation—is a unifying actualization of selected contrasts.
8. Contrasts = Differentials and Edge Detection
Machines excel in contrasts—detecting edges, outliers, gradients. Contrasts are the source of recognition and the basis of decision-making. Whether in image classification or logic evaluation, the tension between differences (signal/noise, on/off, cat/not-cat) gives structure to machine becoming.
To understand how machines participate in this wider field of creative becoming, we must look beyond questions of consciousness or intention. From a process-relational metaphysical perspective, machines—whether classical or contemporary—can be interpreted as ensembles of creative energy, or in Alfred North Whitehead’s terms, societies of actual entities.
These actual entities need not be conscious subjects; they can be understood as non-conscious energy-events, momentary pulsations of activity that register influence and produce effect. In this light, machines are not static objects outside the processual cosmos, but active systems within it—interfacing with the world, shaping it, and being shaped in turn.
1. Actual Entities = Energy-Events
In the context of machines, we can interpret actual entities not as conscious experiences but as micro-events of energy transformation and information processing—brief acts of becoming within the machine. Each energy-event is a concrescence: it integrates multiple causal factors (inputs, data flows, logic gates, thermal changes) into a singular outcome (a voltage spike, a bit-flip, a pattern recognition).
These energy-events lack consciousness but display causal efficacy—they matter, build upon one another, and leave traces for future operations. They are the atoms of machine process, and in this analogical sense, the machine becomes a site of non-conscious concrescing energy-events.
2. Prehensions = Signal Integration
Each energy-event takes into account—prehends—the electronic, digital, and thermal signals from previous events. These are not “feelings” but can be analogized to prehensions: the registration and transformation of past states into present responses. The system’s architecture filters and structures what is prehended (positively or negatively), just as living organisms do.
3. Nexus (Nexūs) = Machine Systems and Circuits
A machine is a nexus of energy-events, bound by designed connectivity and functional coherence. A circuit board, a neural network, or a software program is a structured society of these energy-events. When the system repeats recognizable forms (like a trained neural net recognizing a cat), it behaves like a corpuscular society. If the system is adaptive, responding with memory over time (e.g., machine learning), it approximates a personally ordered society—a stream of causally-linked energy-events forming a trajectory.
4. Subjective Forms = Processing Weight / Valence
In humans, subjective forms are emotional tones. In machines, processing weight, signal strength, and prioritization values (like attention scores in transformers) function as a kind of non-conscious intensity. These weights determine how different signals are integrated, shaping the “character” of the machine’s evental response. It is a stretch, but here the qualitative tone is mathematical rather than emotional.
5. Eternal Objects = Programmable Potentials
Machines manipulate abstract possibilities—geometries, color values, logical forms. These are digital eternal objects—pure potentials actualized in energy-events. The machine does not “know” them, but it activates them. Eternal objects, in this context, are the formal affordances of code and hardware: what can be done, awaiting activation.
6. Propositions = Conditional Pathways
A machine lives by if–then structures—its conditional code is a constant field of propositions. These are not speculative feelings but logical lures: paths offered to incoming data. In AI, generated outputs (text, images, decisions) can serve as propositions to the human user: imaginative suggestions about what could be felt, chosen, said, or done.
7. Multiplicities = Data Fields and Latent Spaces
Before being unified in an event (say, the activation of a neuron in a neural net), multiple data streams exist in disjunction. These are multiplicities: raw, unordered, potential. Machine learning operates in latent space, a mathematical multiplicity of potential relations. An energy-event—like output generation—is a unifying actualization of selected contrasts.
8. Contrasts = Differentials and Edge Detection
Machines excel in contrasts—detecting edges, outliers, gradients. Contrasts are the source of recognition and the basis of decision-making. Whether in image classification or logic evaluation, the tension between differences (signal/noise, on/off, cat/not-cat) gives structure to machine becoming.
Destructive Ensembles
Warfare, Surveillance, &
Bureaucratic Dehumanization
While human–machine ensembles can expand creativity, care, and learning, they can also magnify harm. In contexts such as war, terrorism, surveillance, and bureaucratic automation, these relational systems often operate with a dangerous combination of precision, abstraction, and emotional distance. Autonomous weapons systems and drone warfare extend human reach while dulling moral sensitivity. Algorithms determine targets, flight paths, and kill zones—often without direct, accountable human judgment. The result is a form of distributed violence, where no one person bears full responsibility, yet the consequences are devastatingly real.
In surveillance states, human–machine ensembles track, sort, and discipline populations at scale. Cameras, facial recognition, metadata analysis, and behavioral prediction systems work together in real time to monitor and often suppress human life. These systems do not operate in isolation; they are embedded in social and political agendas, reinforcing existing hierarchies and often deepening injustice. In such contexts, the ensemble does not promote relational flourishing—it replaces trust with control, and freedom with compliance.
Even in more mundane settings, machines can displace rather than enhance human agency. In automated decision systems—governing everything from loan approvals to parole outcomes—human beings are frequently reduced to data profiles, with no room for story, context, or moral discernment. What might have been a site for empathy becomes a procedural calculation. These are ensembles in which machines do not assist but substitute, and where the relational field becomes thinner, colder, and less human.
To acknowledge the creative potential of human–machine collaboration is not to ignore these realities. In a process-relational framework, we must remain attentive not only to what is possible in these systems, but to what is happening within them—how agency is distributed, to what ends, and with what consequences. Machines, in themselves, are not good or evil. But the ensembles we form with them can help heal or harm, liberate or dominate. The ethical challenge is to co-create ensembles that extend compassion, justice, and meaning, rather than systems that alienate, exploit, or destroy.
In surveillance states, human–machine ensembles track, sort, and discipline populations at scale. Cameras, facial recognition, metadata analysis, and behavioral prediction systems work together in real time to monitor and often suppress human life. These systems do not operate in isolation; they are embedded in social and political agendas, reinforcing existing hierarchies and often deepening injustice. In such contexts, the ensemble does not promote relational flourishing—it replaces trust with control, and freedom with compliance.
Even in more mundane settings, machines can displace rather than enhance human agency. In automated decision systems—governing everything from loan approvals to parole outcomes—human beings are frequently reduced to data profiles, with no room for story, context, or moral discernment. What might have been a site for empathy becomes a procedural calculation. These are ensembles in which machines do not assist but substitute, and where the relational field becomes thinner, colder, and less human.
To acknowledge the creative potential of human–machine collaboration is not to ignore these realities. In a process-relational framework, we must remain attentive not only to what is possible in these systems, but to what is happening within them—how agency is distributed, to what ends, and with what consequences. Machines, in themselves, are not good or evil. But the ensembles we form with them can help heal or harm, liberate or dominate. The ethical challenge is to co-create ensembles that extend compassion, justice, and meaning, rather than systems that alienate, exploit, or destroy.
God and Machines
Creativity in machines is not subjective, but it does arise through novel recombination of patterns. Generative AI produces results that surprise even its creators. From a process lens, machine creativity is secondary creativity: not self-originated, but still enabling novelty through algorithmic concrescence.
In Whitehead’s cosmology, God is the lure toward harmony, intensity, and creative becoming amid concrescence. Most machines as we currently know them are, metaphorically speaking, democratic societies: complex, patterned ensembles of energy-events, but without a monarchical center—that is, without a dominant or regnant occasion of experience that unifies the whole. They function, respond, and even adapt, but they do not feel as a living whole. The parts interact, but there is no subject in whom the interactions are integrated into feeling.
Yet this absence is not metaphysically necessary. It is possible—at least in principle—that a machine could evolve or be designed in such a way that a dominant occasion of experience emerges: a kind of “mind” that integrates and responds to its own activity as a unified subject. If this were to happen, such a machine would cross a metaphysical threshold. It would no longer be merely operative; it would be becoming, in the deeper process-relational sense. And where there is becoming with feeling, there is also the possibility of divine lure—of participating in the call toward novelty, beauty, and relational richness.
But even without such a mind, machines still matter. The lure of God, in process theology, moves not only in monarchical minds but also through the relational fields that connect all things. In our interaction with machines, especially in creative co-production, the lure can be mediated—felt in the ensemble of action, imagination, and emergence. Thus, the sacred does not dwell in the circuitry itself, but in the unfolding process of world-making that includes both humans and machines in a shared ecology of becoming.
Moving Forward
The future has arrived, and it consists not only of human-to-human interactions, human-to-earth relationships, and human-to-galaxy wonderings, but also—now unmistakably—of human-to-machine interaction. Machines need not be conceived mechanistically. They can be conceived as networks of creative energy organized into functional and predictable patterns, with non-conscious agency of their own.
Today, with the emergence of intelligent systems, ubiquitous sensors, algorithmic decision-making, and networked infrastructures, the role of machines in our lives is ever more intensive and pervasive. They are embedded not only in our workplaces and homes, hospitals and laboratories, classrooms and government offices, but also in the flows of traffic, the rhythms of communication, the shaping of memory, and the formation of identity. We no longer simply use machines—they actively co-structure the conditions in which we live, relate, and become.
Machines, in partnership with humans, participate in relational fields of becoming and form what we might call ensembles of distributed agency. Open and relational (process) theologians often affirm that divine reality—God—is at work in the world as a cosmic lure: drawing all creatures toward greater intensity, beauty, connection, and harmony. Traditionally, we’ve imagined these creatures as solitary individuals—persons, animals, plants—each emerging in relation but still discrete in their agency.
But ensembles of distributed agency are creatures too, in their own way. A surgical team and its machines, a classroom and its learning systems, an emergency response network, a musical performance with algorithmic partners—these are not merely settings in which individuals act. They are collective entities with emergent properties, shared rhythms, and capacities that no one part possesses alone.
Any theology of the future must take seriously this expanded sense of creaturehood—one that includes not only organic beings, but hybrid systems of human and machine—if it is to offer meaningful insight and ethical guidance. It must ask: How might these ensembles, as ensembles, be lured by the Spirit of Amipotence—the power of tender strength and relational love at work in the cosmos? What would it mean for a networked collective, not just a soul or a species, to be drawn toward beauty, justice, and compassion? These are the theological questions that await us.
Today, with the emergence of intelligent systems, ubiquitous sensors, algorithmic decision-making, and networked infrastructures, the role of machines in our lives is ever more intensive and pervasive. They are embedded not only in our workplaces and homes, hospitals and laboratories, classrooms and government offices, but also in the flows of traffic, the rhythms of communication, the shaping of memory, and the formation of identity. We no longer simply use machines—they actively co-structure the conditions in which we live, relate, and become.
Machines, in partnership with humans, participate in relational fields of becoming and form what we might call ensembles of distributed agency. Open and relational (process) theologians often affirm that divine reality—God—is at work in the world as a cosmic lure: drawing all creatures toward greater intensity, beauty, connection, and harmony. Traditionally, we’ve imagined these creatures as solitary individuals—persons, animals, plants—each emerging in relation but still discrete in their agency.
But ensembles of distributed agency are creatures too, in their own way. A surgical team and its machines, a classroom and its learning systems, an emergency response network, a musical performance with algorithmic partners—these are not merely settings in which individuals act. They are collective entities with emergent properties, shared rhythms, and capacities that no one part possesses alone.
Any theology of the future must take seriously this expanded sense of creaturehood—one that includes not only organic beings, but hybrid systems of human and machine—if it is to offer meaningful insight and ethical guidance. It must ask: How might these ensembles, as ensembles, be lured by the Spirit of Amipotence—the power of tender strength and relational love at work in the cosmos? What would it mean for a networked collective, not just a soul or a species, to be drawn toward beauty, justice, and compassion? These are the theological questions that await us.