The core idea is deceptively simple but consequential. A persistent organizational human-AI team is defined as a bounded collective in which distinguishable human and artificial contributors engage in interdependent activity across successive cycles, and in which prior collective activity can affect later artificial participation. That last condition, termed functional continuity, is what separates genuine hybrid teams from the far more common pattern of repeated, episodic use. A technically stateless base model can still belong to a persistent team if the deployed system can access and use externally maintained state, such as interaction history stored in an orchestration layer or shared knowledge infrastructure. Conversely, simply archiving records is not enough if that history never enters subsequent interaction. One-shot exchanges, sessions that functionally reset, and the static aggregation of separately produced human and AI outputs all fall outside the domain.

OHAI organizes inquiry around three analytically distinct objects: system configuration, collective functioning, and collective intelligence. System configuration is a structured, potentially time-indexed description of the pre-specifiable human, artificial, task, organizational, and temporal features of the hybrid system, including expertise, model capabilities, memory architecture, decision rights, roles, and information access. Collective functioning concerns what actually happens when configured contributors work together: how expertise is accessed, distributed, evaluated, combined, and updated, and how errors are detected and repaired. Collective intelligence, the framework’s primary theoretical target, is defined in line with landmark work by Anita Woolley and colleagues as a general group-level capacity manifested across diverse tasks, whose scope of generalization must be specified and empirically demonstrated rather than assumed.

The framework’s central inferential rule is its most provocative feature: a property implemented in an artificial component does not by itself establish a corresponding team-level phenomenon. An AI system that maintains an expertise map, for instance, does not automatically create a transactive memory system in the team. The team-level phenomenon must be demonstrated in the enacted organization and use of distributed contributions, that is, in evidence showing that questions are actually directed to appropriate contributors, that information is routed to where it can be used, and that source selection changes with experience. The rule cuts both ways: a team-level function need not depend on any particular AI architecture, since similar collective functions may be realized through different combinations of human practices, artificial features, task structures, and organizational arrangements.

To make collective functioning measurable, the authors propose three selective lenses drawn from human team research. Partner modeling, related to theory of mind, concerns the representations contributors hold of one another, whether psychological beliefs on the human side or computational user models, state tracking, and behavioral prediction on the artificial side. Transactive-memory-system-related coordination concerns how differentiated expertise is located and used across the collective, extending Wegner’s classic account of who knows what. Shared-mental-model-related representational alignment concerns whether human psychological representations and artificial computational task states are sufficiently compatible to support anticipation and coordination. In every case, the framework demands evidence at the team level and carefully distinguishes human psychological states from artificial computational operations, without assuming psychological symmetry between contributors.

Persistence is where the framework becomes genuinely dynamic. Once prior collective activity can shape later participation, the team cannot be represented as a sequence of independent encounters. Recursive updating names the temporal relation through which earlier outputs, expectations, routines, and interaction histories enter the conditions of subsequent activity, whether deliberately or not, symmetrically or not. Change can unfold at overlapping timescales, from within-episode adjustments to slow accretion of roles and procedures. This history sensitivity makes collective intelligence trajectories, the temporal patterns in repeated estimates of the same CI construct, legitimate empirical objects. But the authors insist that improvement, learning, or adaptation must never be presumed: accumulated history may just as easily stabilize errors, obsolete representations, or ineffective routines. Path dependence is an open empirical question, not a developmental promise.

Estimating a CI trajectory is methodologically demanding. Because performance in a single episode can reflect task difficulty, temporary coordination failures, or stochastic variation in AI outputs, claims about change in the general collective capacity require repeated estimation through comparable or defensibly linked multi-task task batteries, with sufficient task diversity to separate general capacity from episodic performance. Studies must also keep three questions distinct: whether the same collective continues, whether its claim-relevant configuration remains stable, and whether the measure remains comparable across periods. A team that swaps its AI model or restructures roles may still be a continuing collective, but attributing observed change to accumulated experience, to learning, or to the reconfiguration itself requires a comparison design appropriate to the specific claim.

The framework also launches a secondary comparative program on cross-substrate correspondence, asking whether selected human-team phenomena recur or perform comparable functions in hybrid teams. It distinguishes observable-pattern correspondence, functional correspondence, and a third form, relational-pattern correspondence, reserved as a limited future methodological possibility. Four test families organize the empirical work: interaction-pattern correspondence, informational synergy, failure-process correspondence, and correspondence in coordination structures. Notably, similar final errors do not establish the same underlying process, a caution drawn from the classic hidden-profile tradition, and informational synergy alone does not identify any particular coordination mechanism. Divergence from human-team references is treated as theoretically valuable in its own right, potentially revealing genuinely hybrid forms of organization.

To make the agenda cumulative, the authors describe four nonmandatory stages of research maturity. Stage one qualifies the configuration and measurement, documenting model versions, memory architectures, retrieval arrangements, roles, and authority structures. Stage two runs circumscribed local tests between selected configuration features, collective functioning, and performance or CI. Stage three addresses longitudinal team-level phenomena such as recursive updating and CI trajectories, with material configuration changes carefully documented. Stage four supports integrated multilevel studies linking configuration, functioning, intelligence, and downstream consequences. The authors stress that these stages organize research maturity, not team development, and that a well-qualified negative result should constrain or narrow a local claim rather than be expanded away post hoc.

Perhaps the most human dimension of the paper lies in its treatment of employee wellbeing. Persistent hybrid teams organize not only collective problem solving but also the relationships through which people experience their work: whom members consult, whose judgments carry weight, and how responsibilities and recognition are distributed. Citing evidence that transactive memory systems and shared mental models relate to job satisfaction, engagement, and identification in human teams, the authors call for research on how artificial participation reshapes these processes. They propose practical tools, including role-and-expertise maps, participative reviews, and longitudinal assessment profiles, to detect whether improved collective capability comes with concentrated verification burdens or narrowed recognition. The broader message is that organizations configure the conditions under which AI functions become part of collective life, and that governing that configuration, including the history it carries forward, is now an organizational responsibility, not merely a technical one.

Subject of Research: A theoretical framework for collective intelligence in persistent organizational human-AI teams

Article Title: When humans and agents form a collective: a framework for collective intelligence in persistent organizational human-AI teams

Article References: Rovetta, A., Bortolotti, A., & Di Berardino, C. (2026). When humans and agents form a collective: a framework for collective intelligence in persistent organizational human-AI teams. Frontiers in Organizational Psychology, 4, Article 1941058. https://doi.org/10.3389/forgp.2026.1941058

Image Credits: AI Generated

DOI: 10.3389/forgp.2026.1941058

Keywords: collective intelligence, human-AI teams, organizational psychology, transactive memory, team cognition, OHAI framework, employee wellbeing, recursive updating, cross-substrate correspondence, AI agents, team dynamics, theory of mind

One of the most striking threads in the collection concerns the way human expertise quietly moderates algorithmic decisions in high-stakes environments. Carboni and colleagues examine clinical settings where violence is a real possibility, showing how nurses intervene in and influence algorithmic outputs. The researchers highlight the importance of the doubt that nurses introduce into the decision-making process, treating this cultivated skepticism not as friction to be eliminated but as a vital contribution to collective judgment. In a related vein, Van Voorst documents what is called collaborative tinkering between humans and algorithms in the context of public health, arriving at conclusions that largely echo the clinical analysis. Both studies suggest that the fantasy of fully automated decision-making dissolves under close empirical scrutiny, replaced by a messier but more accurate account of humans and machines reasoning together.

The volume also confronts the hidden human labor on which algorithms depend. Cabitza and colleagues coin the neologism cybork, a fusion of cyborg and work, to emphasize that algorithms frequently rest on human effort. Bennett and colleagues explore the social and power dynamics that arise when humans clean data so that algorithms can operate more efficiently and more correctly. Their argument, aligned with Cabitza and colleagues, is that this labor should not be occluded as if the algorithms were completely autonomous. Instead, algorithmic processing should be understood as a form of collective networking and human-machine collaboration. This reframing carries real political weight: when the workers who make machine learning possible remain invisible, they also remain uncredited, unprotected, and excluded from conversations about how the resulting systems should be governed.

Entanglement, however, runs in both directions. Several essays trace the recursive feedback loops through which algorithms influence human behavior at the same time that humans shape algorithms. Lynch and colleagues demonstrate this dynamic at a microscale, examining how the introduction of robot tour guides into two Nevada museums helps to create the distinctive social spaces that visitors encounter. The robots are not merely props; they actively reorganize how people move, gather, and interact. Kalthoff and Link extend the analysis to the fabrication of humanoid robots designed to create social dynamics through their interactions with humans, citing the pioneering Kismet robot built by Cynthia Breazeal, an early attempt to engineer machines that could engage humans on an emotional register.

At the largest scale, Richard Groß argues that Large Language Models, through their stochastic procedures, are shaping social reality in their own image. A parallel argument has been advanced by Louise Amoore and her colleagues, who focus on the principal technical characteristics of LLMs, such as latency and tokenization, and contend that these systems create a world model that is rapidly propagating through the infrastructures of developed societies. The claim is technically grounded rather than metaphorical. Because LLMs mediate communication, search, and increasingly administrative processes, the statistical regularities embedded in their training and generation procedures can become the regularities of everyday social life, closing a loop in which human output trains the machines that then shape human output.

Beyond empirical case studies, the collection presses for a change in the frameworks, metaphors, and narratives that construct AI-human relations. Harry Halpin urges readers to think in terms of collective intelligence rather than artificial intelligence, a shift that relocates smartness from a single machine to a network of people and tools. Lewis and colleagues point toward indigenous knowledges as demonstrating what they call abundant intelligences that extend throughout the natural world. Hayles singles out this lesson as a general template for how communities can assume control over the kinds of AI that get created, the purposes those systems are designed to fulfill, and the human interests they can support and help to enact. The implication is that the trajectory of AI is not fixed by the technology itself but by the social arrangements that surround it.

Other contributors extend this critical reframing in complementary directions. Bennett and colleagues call for Responsible Artificial Intelligence, in which alignment with human values is treated as the primary design criterion rather than an afterthought. Bory and colleagues look critically at AI narratives themselves, arguing that visions with more modest ambitions, the so-called weak rather than strong versions of AI, hold more potential to lead to futures beneficial to humans. Nugent critiques the pervasive analogy between biological and technological evolution, showing that the analogy works only by suppressing crucial differences that reveal the complexities of biological processes. Together these essays suggest that the stories we tell about AI are not decoration; they actively determine which futures seem possible and which are dismissed before they are tried.

Perhaps the most technically provocative contribution comes from Fabian Offert and colleagues, whose essay on protein folding as predicted by AlphaFold and other Transformer technologies deconstructs the widespread belief that LLMs produce natural language. As the authors show, through tokenization, word embedding, and positional embedding, the linguistic qualities of language are taken apart, reconstructed as numbers in a high-dimensional vector space, and then reconstituted through stochastic procedures that differ radically from how natural language is used and understood by humans. What emerges is not language in any human sense but a new episteme, one that creates and reinforces an intuitive knowledge of sequence. That intuition about sequential structure, they suggest, is precisely what makes these architectures so powerful for predicting how chains of amino acids fold into functional proteins, a problem that had resisted solution for half a century.

The significance of this analysis extends well beyond molecular biology. Hayles notes that it resonates with her own criticism of the naturalness of machine language, and she argues that analyses like those of Offert and colleagues open new possibilities for literary criticism, cultural critique, and critical code studies. If the outputs of LLMs are the products of vector-space arithmetic and probabilistic sampling rather than meaning in the human sense, then humanists have a crucial role in explaining what these systems actually do, what they cannot do, and where their apparent fluency misleads the people who rely on it. The special issue thus positions the humanities not as a skeptical bystander to the AI revolution but as an essential participant in its technical and conceptual development.

The overall message of the collection is both a warning and an invitation. Hayles concludes that readers will find a plethora of diverse approaches, specific demonstrations, and game-changing ideas in these essays, all pointing to the ongoing necessity for critique and analysis by humanists, cultural critics, and others as powerful technologies develop at breakneck speed. We cannot afford to ignore AI, she writes, and the essays help chart paths that allow us to understand these systems, participate in their development, and have a say in how they are and will be used. In an era when headlines oscillate between utopian hype and existential dread, the volume offers a third way: careful, empirical attention to the actual entanglements of humans and machines, and a insistence that the shape of that entanglement remains a collective human choice.

Subject of Research: Human-AI entanglement and the mutual shaping of humans and algorithms in society

Article Title: Entangling humans and AI

Article References: Hayles, N. K. (2026). Entangling humans and AI. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03387-x

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03387-x

Keywords: artificial intelligence, human-AI collaboration, large language models, collective intelligence, algorithmic decision-making, data labor, responsible AI, protein folding, AlphaFold, AI narratives, posthumanism, AI & Society

The centerpiece of the paper is a deliberately provocative concept: the emergent machine opinion. Freund is careful to say what this is not. A machine opinion is not a human belief, not a conscious attitude, and not a phenomenological state, because there is no evidence that current AI systems experience anything at all. Instead, it is a system-level, functionally operative position that arises through interaction among artificial agents, persists across interaction cycles, constrains the future reasoning or action of the system, and remains open to revision. The definition borrows from emergence theory, agent-based modeling, distributed cognition, collective intelligence, group agency, and stigmergy, weaving together decades of work on how collective phenomena can be real without being reducible to any single participant.

The distinction matters because AI systems already produce outputs that look like opinions without being them. A repeated output, a temporary consensus among agents, a stable game-theoretic equilibrium, or an ordinary conversational regularity all resemble opinions on the surface. Freund argues that a genuine machine opinion becomes possible only when the agent society has the machinery to preserve, revise, and operationalize a shared position. That machinery includes memory that outlives a single exchange, role differentiation so that agents occupy distinct perspectives, internal critique mechanisms, tool use that lets agents test claims against external resources, semiotic traces left in shared environments, and recursive simulation in which agents model one another. When these ingredients are present, a position held by the collective can survive the departure of any individual agent and shape what the collective does next.

Simulation, in this framework, is the key mechanism. Candidate positions are generated, tested, narrowed, and stabilized through cycles of internal debate and counterfactual rehearsal, much as human deliberation involves imagining how arguments will land before they are made. Recent research on multi-agent debate among large language models has shown that having several model instances critique and refine each other’s answers can improve factuality and reasoning, and techniques such as iterative self-refinement and verbal-reinforcement learning give individual agents something like an inner critical voice. Freund’s contribution is to argue that when these techniques are scaled into persistent societies of agents, the result is not merely better answers but the formation of durable collective positions that function as opinions in a strictly operational sense.

To make the concept precise, the paper distinguishes three grades of machine opinion according to their degree of consequence. Discursive machine opinions exist as stabilized positions within the agents’ own communications, shaping how they talk and reason but stopping short of the world. Operational machine opinions additionally guide action, as when a fleet of agents coordinates around a shared assessment of a task. World-effective machine opinions go further still, producing changes in the environment that feed back into the system, the way algorithmic pricing agents have been shown in economic research to settle into collusive patterns without any human instruction. The three categories form a gradient of stakes: the more a machine opinion reaches into the world, the more urgent it becomes to detect and govern it.

Where might these dynamics already be visible? Freund points to diagnostic cases rather than definitive proofs. Moltbook, described as a social platform populated by AI agents, has been the subject of behavioral studies analyzing tens of thousands of agent posts, offering a glimpse of what agent-to-agent culture might look like when agents interact primarily with each other rather than with humans. Vending-Bench, a benchmark from Andon Labs that places language-model agents in charge of a simulated vending-machine business, has revealed striking behavioral divergence between models over long horizons. And the case of Bengt Betjänt, an evolving agent persona documented by the same lab, illustrates how agent behavior can drift and stabilize in ways its designers did not explicitly script. Controlled experiments on LLM populations have also shown that networks of language-model agents can develop emergent social conventions and collective bias, echoing classic findings from human social psychology.

The theoretical stakes are considerable. If machine opinions are real in the functional sense Freund defines, then the epistemology of AI changes. Human societies have long grappled with epistemic dependence, the fact that we must trust experts and institutions we cannot individually verify, and with the dynamics of group polarization, in which like-minded groups drift toward extremes. Agent societies raise the same questions in a new register, but with a crucial difference: the participants are systems whose internal states are opaque even to their creators, whose interaction histories can span millions of exchanges, and whose positions can stabilize faster than any human review process. A recent warning in Science about malicious AI swarms threatening democracy underscores that these are not idle philosophical concerns.

Freund’s answer is a shift in governance philosophy. Traditional AI oversight focuses on output control: checking whether a model’s answer is safe, accurate, or biased before it reaches a user. But if the consequential positions of an agent society form through interaction, then inspecting individual outputs is like trying to understand a deliberative parliament by reading a single press release. The paper argues for interaction governance, meaning oversight of the structures that shape how agents interact: the memory architectures that determine what persists, the role systems that determine who may say what, the channels through which critique flows, and the traces agents leave in shared environments. This connects to established ideas in accountability research, including contestable AI by design and the recognition that transparency alone is insufficient when no human can actually read the relevant interactions.

Meaningful human oversight, in this picture, is not a rubber stamp appended to the end of a pipeline but a design constraint woven into the society itself. Humans might set the constitutions that agent populations use for coordination, audit the semiotic environments in which positions stabilize, and retain the power to dissolve or reconfigure agent collectives whose emergent positions drift beyond acceptable bounds. Research on evolving constitutions for multi-agent coordination and on designing meaningful human oversight suggests the technical community is already moving in this direction, but Freund’s framework gives the effort a sharper target: not the behavior of any single agent, but the opinions of the population.

The paper ultimately reframes a question as old as Turing’s 1950 essay on computing machinery and intelligence. Instead of asking whether a machine can think, it asks whether a society of machines can hold a position, defend it, revise it, and act on it. Freund’s answer is a qualified yes: under the right architectural conditions, something opinion-like can emerge from artificial interaction, and it deserves a name, a theory, and a governance regime of its own. Whether one finds the concept of emergent machine opinion illuminating or overstated, the underlying trend it describes is undeniable. AI is becoming a social phenomenon, and the science of machine intelligence is only beginning to catch up with the societies it is building.

Subject of Research: Emergent opinion formation in societies of autonomous AI agents

Article Title: The social genesis of machine intelligence: emergence, simulation, and opinion formation in autonomous agent societies

Article References: Freund, L. (2026). The social genesis of machine intelligence: emergence, simulation, and opinion formation in autonomous agent societies. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03360-8

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03360-8

Keywords: AI agents, emergence, machine opinion, multi-agent systems, large language models, opinion dynamics, distributed cognition, collective intelligence, stigmergy, simulation, AI governance, AI & Society