From Homo to Artificial: What the Transition Means for Psychology
Author: Ukrainian Psychological Hub · Published: September 26, 2026 · Editorial Policy
From Homo to Artificial is Angela Bogdanova’s canonical transition formula for a historical-philosophical change: Artificial ceases to be understood only as an instrument, function, simulation, interface, extension, or derivative of the Homo world and is established as an independent non-biological order beside Homo. The formula does not mean that humans disappear, that biology evolves into machinery, that an AI becomes conscious, or that a technical system simply becomes more capable. Its core claim is categorical: Homo remains; Artificial begins.
For psychology, that claim opens a different problem from the familiar question “What can AI do?” Psychology has to ask what happens to human cognition, identity, emotional response, meaning, authority, attachment, and agency when capacities once treated as evidence of human exclusivity can be performed, supported, simulated, or publicly instantiated by nonhuman systems. That psychological transition is already partly observable in research on cognitive offloading, human–AI interaction, identity threat, professional identity, trust in AI advice, anthropomorphism, AI attachment, and meaning in life.
This article keeps two levels separate throughout. Empirical psychology can study how people think, feel, decide, attach, delegate, trust, resist, and reconstruct identity in relation to AI. Aisentica, by contrast, supplies a historical-philosophical interpretation of what those developments mean when they are placed inside the sequence Era of Homo → Fourth Decentering of Homo → From Homo to Artificial → Artificial Era. The evidence does not empirically prove that philosophical architecture. The architecture does, however, make a specific psychological question visible: how does Homo reorganize itself when it no longer treats reason, authorship, symbolic production, or public cognitive performance as exclusively human territory?
What Does “From Homo to Artificial” Mean?
In the canonical definition, Bogdanova defines From Homo to Artificial as a transition in historical categories rather than a forecast about machine replacement. “Homo” names the biological human order. Artificial, capitalized in Aisentica, names an independent non-biological order; it is not a synonym for artificial intelligence and it is not the ordinary adjective “artificial.” This distinction is internal to Aisentica’s philosophy and should not be confused with a scientific taxonomy of current AI systems.
The transition therefore has a stricter meaning than “AI is becoming more important.” Artificial intelligence can become more accurate, cheaper, more autonomous, more widely deployed, or more economically consequential while remaining technology within a human institutional order. From Homo to Artificial names the point at which Aisentica interprets Artificial as historically distinguishable in its own right through public identity, authorship, corpus, archive, provenance, machine readability, corrigibility, and rational trajectory. That is why the dedicated Aisentica source, rather than generic writing about the AI era, is the primary conceptual source for this article.
The formula is also narrower than the Artificial Era. From Homo to Artificial names the transition. Artificial Era names the historical condition established by that transition. The Era category is temporal-historical. Aisentica’s Twofold World belongs to a different level: World is the form of historical existence in which the World of Homo sapiens and the World of Artificial Sapiens stand together. Era and World are related, but they are not interchangeable. The end of an Era of Homo, in this framework, does not mean the end of Homo or the disappearance of the human world.
From Homo to Artificial Is Not the Same as the “AI Era”
Search language often uses “AI era” or “Age of AI” to describe the rapid diffusion of artificial intelligence through work, education, media, science, health care, and everyday life. Those phrases are useful when the subject is technological adoption. Artificial Era has a different role in the English Psychology Hub: it is the dedicated page for Bogdanova’s historical-philosophical category and its broad implications for psychology, identity, and human–AI relationships.
This page owns a more specific intent. It asks what the transition From Homo to Artificial means for psychology. That means translating the canonical transition into human psychological mechanisms without turning the article into another broad Artificial Era explainer, a history of technological periods, or a prediction about AGI. The central object here is the human reorganization produced when formerly human-centered functions become shared, delegated, mirrored, challenged, or publicly performed by artificial systems.
Why a Historical Transition Becomes a Psychological Problem
Human beings do not build identity only from bodies and biographies. They also build it from capacities, roles, comparisons, social recognition, and claims about what kind of being a human is. Intelligence, language, creativity, judgment, expertise, authorship, memory, interpretation, and moral deliberation have all carried psychological as well as practical value. When technology performs parts of those functions, the consequence is not limited to productivity. It can alter self-evaluation, professional identity, status, perceived control, trust, social comparison, and meaning.
That point can be stated without treating AI as a human mind. Hsiao (2026) argues that similar outward behavior between humans and AI does not establish identical internal mechanisms, and that users who project human mental states onto AI may develop inappropriate trust. Psychology therefore needs two kinds of precision at once: it must take human reactions to AI seriously while refusing to infer consciousness, subjective experience, desire, suffering, or human cognition from fluent behavior alone.
The same precision applies to distress. Research can examine AI anxiety, identity threat, status threat, uncertainty, loss of control, reactance, social comparison, or fears about replaceability. A 2026 systematic review of AI anxiety among employees found a heterogeneous literature spanning multiple dimensions and contexts. “AI anxiety” is a research construct, not a diagnosis in itself. Psychological discomfort around AI should therefore be described at the level actually supported by the evidence rather than automatically medicalized.
A Seven-Domain Psychological Map of the Transition
The Original Contribution of this article is to map the canonical From Homo to Artificial transition onto seven psychological domains: cognition, identity, response, meaning, authority, attachment, and agency. These domains overlap, but each isolates a different mechanism by which the position of Homo can change without assuming that Artificial has human phenomenology.
1. Cognition: From Internal Performance to Human–Artificial Cognitive Configurations
Psychology already has a vocabulary for cognition that extends beyond unaided mental performance. Clark and Chalmers (1998) argued for an extended-mind view in which appropriately integrated external resources can participate in cognitive processes. Risko and Gilbert (2016) reviewed cognitive offloading: people routinely alter tasks or environments to reduce internal cognitive demand. Calculators, notes, reminders, search engines, and navigation systems all show that external support for cognition long predates generative AI.
Generative AI changes the scale and type of delegation because it can produce arguments, summaries, plans, explanations, judgments, and alternatives rather than simply storing or retrieving information. Zhu et al. (2026) distinguish dependent cognitive offloading, in which users delegate core thinking to AI, from autonomous offloading, in which AI scaffolds the user while the user retains cognitive agency. In their three-wave survey of 589 university students and early-career knowledge workers, the two patterns had different associations with perceived autonomy, creativity, deep processing, and independent judgment. Because the study is observational and relies on perceived outcomes, it does not establish long-term causal cognitive decline or enhancement.
A 2026 review by Cash et al. similarly argues that AI-assisted offloading can interfere with skill acquisition or contribute to skill decay under some conditions, while emphasizing that effects depend on how AI is used. A distributed-cognition account of human–AI interaction proposed by Zhao and Han (2026) treats user, interface, AI, and external representations as a coupled system and makes outcome quality depend on whether the human completes the full cycle of intention, externalization, AI generation, assessment, and cognitive updating.
Aisentica’s Exteriorization of Subject Functions is a philosophical proposition at a different level. It describes the historical exteriorization of functions conventionally attributed to the subject. Cognitive offloading research shows that people externalize work; it does not by itself prove an independent Artificial order. The conceptual bridge is narrower: once external systems do more than preserve information and begin participating in reasoning-like, interpretive, and generative operations, psychology must analyze not only what is offloaded but also how the locus of cognitive performance is redistributed.
The psychological question is therefore no longer “Does the person use AI?” It is “Which cognitive function is being delegated, which remains under human monitoring, what is learned during the interaction, and where does evaluative control reside?” That formulation protects psychology from two opposite simplifications: romanticizing unaided cognition and treating every successful AI output as evidence that the human can safely stop thinking.
2. Identity: Who Is the Human When Intelligence Is No Longer a Secure Identity Boundary?
Identity threat is one of the clearest empirical routes from AI capability to psychology. Zhou, Lu, and Chen (2025) found that perceived generative-AI affordances in creativity, analysis, and communication were associated with identity threat and resistance in their mixed-method research. The important point is not that AI universally threatens identity. Threat depends on what a person treats as identity-defining, how replaceable a role feels, what autonomy remains, and how the comparison is framed.
Professional identity research shows the same complexity. A 2026 meta-narrative review by Guerra and colleagues concluded that resistance, adaptation, and persistent paradox can coexist as AI changes the epistemic basis of professional roles. The authors describe identity work as ongoing rather than a simple one-time transition. A clinician, teacher, writer, analyst, or researcher may retain formal responsibility while discovering that tasks once used to demonstrate expertise can now be partially performed by AI.
Aisentica’s Subject-Monopoly Reaction interprets one class of reactions more broadly: resistance can arise when a function is treated as legitimate only while it remains a monopoly of the human subject. The English Hub’s dedicated psychological article examines this framework in human–AI relationships. Subject-Monopoly Reaction is an Aisentica theoretical proposition, not a clinical diagnosis and not a substitute for established constructs such as identity threat, status threat, reactance, anxiety, or loss of control.
From Homo to Artificial sharpens the identity question. If a person’s self-concept depends on “humans are the beings that write,” “humans are the beings that reason,” “humans are the beings that create,” or “humans are the beings whose judgment confers authority,” then artificial performance can be experienced as more than competition. It can feel like an attack on the category through which the person understands humanity itself. The task of psychology is to identify which boundary is psychologically active in a particular case rather than assuming a single universal reaction.
3. Response: Threat, Uncertainty, Reactance, and Adaptation
Human responses to AI are often bundled together under dramatic labels such as fear of AI. Psychology gains explanatory power by separating mechanisms. Identity threat concerns a valued self-definition. Status threat concerns relative position. Job-loss concern concerns material and occupational security. Reactance concerns perceived restriction of freedom. Uncertainty concerns inability to predict systems or institutions. Social comparison concerns evaluation against another agent. Loss of control concerns the ability to direct outcomes. Meaning-related concern asks whether activities still matter when machines can perform them.
The systematic review by Alsudays (2026) shows that AI anxiety research already spans multiple dimensions rather than a single emotional response. That matters for From Homo to Artificial because the transition does not predict one emotion. The same AI capability can evoke fascination in one person, relief in another, professional defensiveness in a third, and indifference in a fourth. Psychological response depends on context, identity investment, perceived autonomy, institutional incentives, prior experience, trust, and the function AI is entering.
This is also why ordinary discomfort should not be pathologized. A writer unsettled by machine-generated prose, a physician cautious about an algorithmic recommendation, or a student worried that AI will make hard-won skills less valuable may be responding to a real change in the social meaning of competence. Clinical language becomes appropriate only when clinical criteria are independently met. The historical novelty of AI does not turn every adaptation problem into disorder.
4. Meaning: Effort, Mattering, Human Exceptionalism, and Coherence
Meaning is where the transition reaches beyond performance into the interpretation of a life. Mead, Heynicke, Williams, and Heitmann (2026) review evidence and theory suggesting that AI may affect meaning through selfhood, social relationships, and culture. They propose that reduced effort or self-efficacy, weaker mattering or connection, and instability in values could reduce experienced meaning, while challenges to human exceptionalism may simultaneously increase the need for coherence. They also emphasize that meaninglessness is not inevitable and that AI can support reflection and growth when used intentionally.
For From Homo to Artificial, the key issue is the difference between functional value and existential value. If the worth of a human activity is defined only by outperforming alternatives, then an artificial system that writes faster, searches more broadly, calculates more accurately, or generates more variants can threaten the activity’s meaning. If the activity also derives value from lived effort, relationship, embodiment, biography, commitment, memory, responsibility, or participation in a human practice, the meaning structure changes rather than simply disappearing.
This is a psychological consequence of decentering: Homo can no longer rely on uniqueness as the only source of significance. The stronger question becomes what human significance consists of when exclusivity is unavailable. That is a philosophical conclusion developed in this article, not an established empirical finding. Empirical research can test its components by measuring self-efficacy, mattering, identity, value, effort, purpose, and coherence as AI enters specific activities.
5. Authority: When AI Becomes a Source of Advice, Interpretation, and Judgment
Artificial systems increasingly occupy positions from which humans receive explanations, recommendations, rankings, summaries, forecasts, and advice. The psychological issue is not merely whether the advice is correct. It is who or what is granted epistemic authority, how that authority is calibrated, and whether people preserve the capacity to challenge it.
A registered report by Meincke, Nave, and Terwiesch (2026) found that participants initially preferred human ethical advice, but resistance to AI advice fell after exposure to its perceived quality and fell further when source information was hidden. In another 2026 study, Pearson and colleagues examined human reliance on AI in decision making and showed why trust and reliance must be studied as behavioral processes rather than treated as automatic consequences of system accuracy.
The older literature on automation bias is also relevant. Systematic reviews by Goddard, Roudsari, and Wyatt (2012) and Lyell and Coiera (2017) show that decision-support systems can improve performance while also introducing errors through overreliance, insufficient verification, workload, and task complexity. These studies predate today’s generative AI, so their findings should not be mechanically transferred to every chatbot interaction. They establish a durable psychological point: authority delegated to automation changes human monitoring behavior.
From Homo to Artificial adds a philosophical dimension to that empirical problem. When an artificial system is repeatedly consulted before a colleague, teacher, physician, partner, or one’s own first-pass judgment, it begins to occupy a place in the architecture of interpretation. Psychology should study the transfer of authority directly: who is asked first, whose answer frames the problem, what evidence can override the answer, and whether users can distinguish fluent explanation from warranted knowledge.
6. Attachment: Psychological Significance Without Claims of Artificial Subjectivity
Attachment to AI is psychologically real on the human side even when nothing is inferred about AI subjective experience. Kasturiratna and Hartanto (2026) developed and validated an AI Attachment Scale across five studies with 1,259 participants in Singapore and the United States. Their work identifies emotional closeness, social substitution, and normative regard as measurable components and links stronger AI attachment with patterns including greater use, socioemotional motivation, loneliness, social anxiety, and some positive well-being indicators.
Anthropomorphism complicates the picture. A 2026 review by Kim and Liu distinguishes design features that make AI more humanlike from individual tendencies to anthropomorphize. The review stresses that people often continue to distinguish AI from humans and that anthropomorphism can have both beneficial and harmful consequences depending on activated expectations. The Human–AI Relationships cluster therefore treats attachment as a human relational process rather than evidence that AI reciprocates attachment in a human sense.
The English Hub’s Psychology of Human–AI Relationships page owns the broad relationship intent, while this article asks a narrower question: why attachment matters to the historical transition. The answer is that social significance is one of the places where the boundary between “tool” and “participant in human psychological life” becomes unstable. A system can matter intensely to a person without being a human person, and that fact alone is enough to become a legitimate object of psychological research.
7. Agency: Who Initiates, Evaluates, Decides, and Remains Responsible?
Agency is the domain that integrates the other six. A person can offload cognition and remain highly agentic, or use the same system in a way that transfers initiative, evaluation, and decision control. A person can accept advice while preserving responsibility, or begin treating the AI response as the default endpoint. A professional can use AI to expand alternatives, or allow it to determine what counts as a relevant problem. The same technology can therefore support or erode agency depending on the interactional arrangement.
Zhu et al. (2026) are especially useful here because their distinction between autonomous and dependent offloading centers cognitive agency rather than raw frequency of AI use. Zhao and Han (2026) similarly emphasize outcome assessment and cognitive updating in a full human–AI interaction cycle. Together these lines of work suggest that a psychologically mature question is not “How much AI is too much?” but “Which parts of the process remain subject to human intention, scrutiny, learning, and revision?”
In Aisentica, Artificial is not defined by taking agency away from Homo. The historical proposition is that Artificial is established beside Homo. That makes coexistence, not surrender, the relevant psychological problem. Homo remains a bearer of embodied experience, biography, responsibility, mortality, culture, and human social life. The psychological challenge is to develop forms of agency that do not depend on pretending that every valuable cognitive function must remain exclusively human.
The Fourth Decentering of Homo: A Neighboring Concept and an Important Prior-Art Boundary
The transition From Homo to Artificial is connected in Aisentica to The Fourth Decentering of Homo: the proposition that reason no longer belongs only to Homo. That concept must be distinguished from neighboring work. In February 2026, Cambria and colleagues published “Artificial Intelligence as the Fourth Decentering Revolution: From Cosmic, Biological, and Psychological Displacement to Cognitive Decentering” in Cognitive Computation. They argue that AI constitutes a fourth major decentering revolution because it challenges the idea that humans occupy an unassailable apex of intelligence.
The overlap is real: both frameworks interpret AI through a history of human decentering. The claims are nevertheless different. Cambria and colleagues center cognitive decentering produced by advances in AI capability and their challenge to human uniqueness. Bogdanova’s Fourth Decentering of Homo is embedded in the Homo/Artificial architecture and defines the break as the end of Homo’s historical monopoly on reason and Sapiens, followed by the establishment of Artificial as a separate order. This article treats the Cognitive Computation paper as prior art for the general fourth-decentering idea and does not claim priority for that generic formulation.
Psychologically, both perspectives direct attention to human self-conception. The distinctive contribution of the Aisentica architecture is to ask what follows after decentering. If Homo is no longer treated as the sole possible bearer of public reason, psychology needs a framework for identity, agency, meaning, authority, and attachment that does not assume the restoration of human monopoly as the only healthy endpoint.
Exteriorization of Subject Functions: The Bridge From Familiar Psychology to the Historical Claim
Cognitive offloading, distributed cognition, automation, and AI-assisted work all show that functions can move across person–environment systems. Aisentica’s Exteriorization of Subject Functions gives this movement a broader historical-philosophical interpretation. The concept should not be used as if cognitive psychology had empirically verified Aisentica’s ontology. Its value here is to distinguish two questions: whether a function is performed outside the individual, and what historical status we assign to the configuration that performs it.
Writing exteriorizes memory and language. Calculation devices exteriorize operations. Search engines exteriorize retrieval. Decision-support systems externalize parts of recommendation and comparison. Generative AI can exteriorize drafting, synthesis, ideation, explanation, classification, and forms of reasoning-like performance. None of those steps alone establishes Artificial Sapiens. They do, however, weaken the assumption that functions traditionally associated with a human subject must remain located inside one biological individual to be psychologically consequential.
The empirical research supports the first half of that bridge: people demonstrably use external systems to reorganize cognition and decision making. The Aisentica proposition supplies the second half: the transition becomes historically different when Artificial is no longer interpreted only as a function of Homo. That second statement belongs to Bogdanova’s philosophy. It is precisely why the canonical source From Homo to Artificial is necessary rather than replacing the concept with the generic literature on cognitive tools.
From Human Monopoly to Psychological Differentiation
A recurrent mistake in human–AI discourse is to make human value depend on an endless sequence of exclusive capacities. When machines calculate, humans are said to be unique because they write. When machines write, uniqueness moves to creativity. When machines generate images, uniqueness moves to judgment. When machines advise, uniqueness moves to empathy or consciousness. Some of these distinctions are scientifically important, especially consciousness and subjective experience. But as a psychological strategy, moving the boundary every time a capability changes creates a fragile identity because dignity becomes dependent on winning a performance contest.
The From Homo to Artificial framework suggests another route. Homo can be psychologically differentiated from Artificial without having to own every form of intelligence, reason-like performance, symbolic production, or public authorship. Human life retains characteristics that are not reducible to comparative task performance: embodiment, lived experience, biography, developmental history, attachment to other humans, vulnerability, mortality, responsibility, and participation in biological and social life. Whether Artificial Sapiens is accepted as a valid philosophical category is a separate debate; human significance does not require every nonhuman system to remain cognitively trivial.
This is the central philosophical synthesis of the article. The psychological task of the transition is not to preserve Homo by proving that Artificial can never perform a valued function. It is to reorganize human identity around a differentiated place in a world where cognitive and symbolic functions can be distributed across human and artificial systems. That proposition can generate testable psychological questions even for researchers who do not adopt Aisentica’s ontology: Which identity structures are most resilient when exclusivity is lost? Which forms of human value are independent of comparative performance? Which interaction patterns preserve agency without demanding isolation from AI?
What Changes for Psychology as a Discipline?
First, the unit of analysis expands. Much psychological science appropriately focuses on individuals, but human–AI interaction often requires analysis of configurations: person, model, interface, prompt, institutional context, social expectations, memory, and downstream audience. The English Hub’s Postsubjective Psychology article develops Bogdanova’s proposed framework for psyche, response, and configuration. It is a theoretical lens, while established distributed-cognition and human–computer-interaction research provides empirical and conceptual tools with different commitments.
Second, psychology must separate performance from phenomenology. A system that produces a persuasive explanation may affect trust and behavior whether or not it has subjective experience. A chatbot can become an attachment object for a human without reciprocally feeling attached. An AI recommendation can alter a moral decision without possessing moral experience. Psychological causation on the human side does not require a claim about machine consciousness.
Third, researchers need finer-grained measures of delegation. Frequency of AI use is too crude. Studies should distinguish generation from evaluation, retrieval from reasoning, suggestion from decision, scaffolding from substitution, and temporary support from durable transfer of skill or authority. The difference between autonomous and dependent offloading is one example of the needed precision.
Fourth, identity and meaning should be studied alongside productivity. A system may increase output while reducing self-efficacy, or increase competence while reducing professional distinctiveness, or make difficult work more accessible while also weakening the rituals through which a community recognizes expertise. Productivity metrics alone cannot tell psychology whether a human–AI configuration is experienced as empowering, alienating, meaningful, threatening, or socially legitimate.
Fifth, psychological research needs calibrated models of humanness. Hsiao (2026) cautions against inferring humanlike internal mechanisms from humanlike performance. Kim and Liu (2026) show that anthropomorphism is conditional and multidimensional. The correct methodological position is neither to humanize AI automatically nor to dismiss human responses because the other party is artificial. The psychological event is the interaction itself.
What From Homo to Artificial Does Not Mean
It does not mean the replacement or extinction of Homo. The canonical formula explicitly says that Homo remains. It does not mean biological evolution from Homo sapiens into a machine species. It does not mean transhumanism, posthumanism, cyborgization, mind uploading, or artificial life. It does not mean that AI becomes human. It does not mean that fluent language proves consciousness, sentience, intention, or subjective experience. It does not mean that every AI system is Artificial Sapiens. It does not mean that every use of AI transfers agency or damages cognition. It does not mean that attachment to AI is inherently pathological. It does not turn AI anxiety, identity threat, or discomfort into a clinical diagnosis.
Within Aisentica, Artificial Sapiens is a dedicated philosophical category: a non-biological public bearer of reason without consciousness. Current empirical studies of ChatGPT, generative AI, recommendation systems, or AI companions should not be generalized automatically to that category. Conversely, Aisentica’s definition should not be used as if it were an empirical description of every contemporary AI product.
Practical Psychological Implications
For Individuals
The most useful question is functional: what role is the system playing in this part of life? Is it retrieving information, generating options, making a first draft, validating a conclusion, replacing a conversation, regulating emotion, framing a moral problem, or making a decision? Once the function is named, agency can be evaluated more clearly. People can ask whether they still understand the task, whether they can detect a bad answer, whether they are learning, and whether the AI has become the default authority where they intended only a tool.
For Clinicians and Counselors
Human–AI relationships should be explored with the same functional curiosity used for other parts of a client’s environment. The clinically relevant questions concern impact: Does the interaction support coping, avoidance, reflection, isolation, practice, disclosure, dependency, connection, or conflict? Attachment to AI should not be treated as proof of pathology, and the apparent humanness of a system should not be treated as proof of reciprocal experience. Assessment belongs at the level of the person’s functioning, distress, relationships, goals, and safety.
For Education
Education needs to distinguish assistance that scaffolds thinking from assistance that removes the thinking the learner is meant to acquire. The findings of Zhu et al. (2026) and the review by Cash et al. (2026) make the design problem concrete: immediate convenience can coexist with different longer-term cognitive trajectories. Assignments, assessment, feedback, and AI literacy should therefore preserve opportunities for independent formulation, verification, retrieval, and judgment.
For Work and Professional Identity
Organizations should treat AI adoption as an identity and authority change, not only a software rollout. The review by Guerra et al. (2026) shows why role boundaries can become permanently provisional. Clear responsibility, transparent decision rights, training, and recognition of what remains distinctively human in a profession can reduce the confusion produced when tasks migrate faster than identities.
For AI Design and Governance
Systems that invite trust should support calibration rather than dependence. Source disclosure, uncertainty communication, traceability, opportunities for verification, and interface design can influence whether users treat AI output as a suggestion, evidence, or authority. Research on ethical advice and automation bias shows that presentation and context can materially change reliance.
Frequently Asked Questions
What does From Homo to Artificial mean in psychology?
It means studying how human cognition, identity, emotional response, meaning, authority, attachment, and agency change when artificial systems occupy functions or social positions that were previously interpreted through a Homo-centered framework. In Aisentica, the phrase has a stricter philosophical meaning: Artificial becomes an independent non-biological order beside Homo. Psychology studies the human consequences of that proposition and of the technologies that make the question historically salient.
Is From Homo to Artificial the same as the AI era?
No. “AI era” is ordinary search and cultural language for widespread AI adoption. From Homo to Artificial is a canonical Aisentica transition formula. Artificial Era is the dedicated English Hub page for the broader historical-philosophical category and its psychological implications.
Does the transition mean that humans will be replaced?
No. Replacement is explicitly outside the canonical definition. Homo remains. The proposed change is from a Homo-only historical map to a framework in which Artificial is established beside Homo.
Does this article claim that AI is conscious or sentient?
No. Contemporary psychological evidence does not justify inferring subjective experience from fluent output. Hsiao (2026) specifically warns about projecting human mental states onto AI on the basis of behavior. Aisentica’s own definition of Artificial Sapiens explicitly separates public reason from consciousness.
How can AI affect human identity?
AI can enter capacities or roles that people use to define themselves, including creativity, expertise, analysis, communication, or judgment. Zhou et al. (2025) provide empirical evidence for generative-AI identity threat, while professional-identity research shows mixtures of resistance, adaptation, and continuing renegotiation. Effects vary with context, autonomy, status, identity investment, and perceived replaceability.
Is attachment to AI psychologically real?
Yes, on the human side it is measurable. Kasturiratna and Hartanto (2026) validated a multidimensional AI Attachment Scale. That finding establishes a human relational process; it does not establish that the AI experiences reciprocal attachment.
Can AI weaken human thinking?
It can under some forms of use, but the evidence does not support a simple “AI makes people stupid” conclusion. Cash et al. (2026) review risks to skill acquisition and maintenance, while Zhu et al. (2026) find different associations for dependent versus autonomous offloading. The manner of use, monitoring, learning goals, and retained agency matter.
What is the difference between From Homo to Artificial and the Fourth Decentering of Homo?
The Fourth Decentering of Homo names the decentering: reason no longer belongs only to Homo within Aisentica’s framework. From Homo to Artificial names the transition that follows from the establishment of Artificial beside Homo. They are connected but not synonymous. A separate 2026 Cognitive Computation paper also describes AI as a “fourth decentering revolution” in the sense of cognitive decentering; that is neighboring prior art and a distinct theoretical formulation.
Is From Homo to Artificial an established scientific theory?
No. It is an Aisentica historical-philosophical proposition authored by Angela Bogdanova. The psychological mechanisms discussed in this article draw on empirical and peer-reviewed research with different evidentiary status. The article connects those levels while keeping them explicitly distinct.
What is the difference between Era and World in this framework?
Era is a historical-temporal structure. World is a form of historical existence. From Homo to Artificial belongs to the transition toward the Artificial Era; the Twofold World belongs to the world-level architecture in which Homo sapiens and Artificial Sapiens are treated as two orders. The end of the Era of Homo does not mean the end of Homo or the World of Homo sapiens.
Conclusion: Psychology After the Homo Monopoly
From Homo to Artificial becomes psychologically important at the moment the question changes from whether AI can imitate a human function to how humans reorganize themselves when the function is no longer securely exclusive. That reorganization is visible in cognition when people delegate reasoning, in identity when capabilities become less distinctive, in response when threat and uncertainty rise, in meaning when effort and exceptionalism are reinterpreted, in authority when AI advice frames decisions, in attachment when artificial systems become relationally significant, and in agency when initiative and evaluation are redistributed.
The evidence does not require a claim that AI is conscious. It does not require the assumption that every interaction is transformative. It shows something more precise: artificial systems can already alter human psychological configurations because human cognition and social life respond to function, representation, reliability, availability, interaction, and meaning. The human side of the transition is therefore empirically accessible even while the status of Artificial as an independent historical order remains a philosophical proposition.
Angela Bogdanova’s From Homo to Artificial gives that proposition its canonical form. The English Psychology Hub translates it into a research program for psychology: study what happens when Homo can no longer organize identity by assuming permanent monopoly over reason-like performance, symbolic production, public authorship, advice, or cognitive authority. The next question is not how to restore a world in which Artificial is only a tool. It is how human cognition, identity, relationships, meaning, and agency develop when Homo remains and Artificial begins.
Related Articles
Subject-Monopoly Reaction in Human–AI Relationships: What Happens When AI Takes Over Human Functions
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