A superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the most gifted human minds. Philosopher Nick Bostrom defined it in his 2014 book Superintelligence as "any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest". The term is now almost always used to discuss artificial superintelligence (ASI) through continued rapid progress in artificial intelligence, although its arrival through biological superintelligence (BSI) has been a source of interest among scientists, futurists, and authors.
Recursive self-improvement, a scenario in which an AI iteratively improves its own intelligence, is theorized to potentially lead to an intelligence explosion and the creation of an artificial superintelligence. The first artificial general intelligences are likely to immediately hold an enormous advantage in at least some forms of mental capability which artificial intelligences already display, including the capacity of perfect recall, a vastly superior knowledge base, and the ability to multitask in ways that are not possible to human beings.
Most AI experts believe that the creation of artificial superintelligence will pose substantial existential risk. A minority believe that the arrival of superintelligence is too far away to be the focus of attention, or that it does not pose a significant threat compared to other existential risks. There have been proposals to implement an international treaty to prevent its emergence. In October 2025, hundreds of public figures, including AI experts, Nobel Prize laureates, and national security officials signed a statement calling for a ban on superintelligence's development. Concern over ASI greatly increased in 2026. Polls have found that large majorities in America and Britain oppose ASI development.
Forms
Artificial superintelligence
Philosopher David Chalmers argues that artificial general intelligence is a very likely path to artificial superintelligence (ASI). Chalmers breaks this claim down into an argument that AI can achieve equivalence to human intelligence, that it can be extended to surpass human intelligence, and that it can be further amplified to completely dominate humans across arbitrary tasks.
Concerning human-level equivalence, Chalmers argues that the human brain is a mechanical system, and therefore ought to be emulatable by synthetic materials. He also notes that human intelligence was able to biologically evolve, making it more likely that human engineers will be able to recapitulate this invention. Evolutionary algorithms, in particular, should be able to produce human-level AI. Concerning intelligence extension and amplification, Chalmers argues that new AI technologies can generally be improved on, and that this is particularly likely when the invention can assist in designing new technologies.
An AI system capable of self-improvement could enhance its own intelligence, thereby becoming more efficient at improving itself. This cycle of "recursive self-improvement" might cause an intelligence explosion, resulting in the creation of a superintelligence.
Computer components already greatly surpass human performance in speed. Bostrom writes, "Biological neurons operate at a peak speed of about 200Hz, a full seven orders of magnitude slower than a modern microprocessor (~2GHz)." Moreover, neurons transmit spike signals across axons at no greater than 120m/s, "whereas existing electronic processing cores can communicate optically at the speed of light". Thus, the simplest example of a superintelligence may be an emulated human mind running on much faster hardware than the brain. A human-like reasoner who could think millions of times faster than current humans would have a dominant advantage in most reasoning tasks, particularly ones that require haste or long strings of actions.
Another advantage of computers is modularity, that is, their size or computational capacity can be increased. A non-human (or modified human) brain could become much larger than a present-day human brain, like many supercomputers. Bostrom also raises the possibility of collective superintelligence: a large enough number of separate reasoning systems, if they communicated and coordinated well enough, could act in aggregate with far greater capabilities than any sub-agent.
Humans outperform non-human animals in large part because of new or enhanced reasoning capacities, such as long-term planning and language use. (See evolution of human intelligence and primate cognition.) If there are other possible improvements to reasoning that would have a similarly large impact, this makes it more likely that an agent can be built that outperforms humans in the same fashion humans outperform chimpanzees.
The above advantages hold for artificial superintelligence, but it is not clear how many hold for biological superintelligence. Physiological constraints limit the speed and size of biological brains in many ways that are inapplicable to machine intelligence. As such, writers on superintelligence have devoted much more attention to superintelligent AI scenarios.
Biological superintelligence
Carl Sagan suggested that the advent of Caesarean sections and in vitro fertilization may permit humans to evolve larger heads, resulting in improvements via natural selection in the heritable component of human intelligence. By contrast, Gerald Crabtree has argued that decreased selection pressure is resulting in a slow, centuries-long reduction in human intelligence and that this process instead is likely to continue. There is no scientific consensus concerning either possibility and in both cases, the biological change would be slow, especially relative to rates of cultural change.
Selective breeding, nootropics, epigenetic modulation, and genetic engineering could improve human intelligence more rapidly. Bostrom writes that if we come to understand the genetic component of intelligence, pre-implantation genetic diagnosis could be used to select for embryos with as much as 4 points of IQ gain (if one embryo is selected out of two), or with larger gains (e.g., up to 24.3 IQ points gained if one embryo is selected out of 1000). If this process is iterated over many generations, the gains could be an order of magnitude improvement. Bostrom suggests that deriving new gametes from embryonic stem cells could be used to iterate the selection process rapidly. A well-organized society of high-intelligence humans of this sort could potentially achieve collective superintelligence.
Alternatively, collective intelligence might be constructed by better organizing humans at present levels of individual intelligence. Several writers have suggested that human civilization, or some aspect of it (e.g., the Internet, or the economy), is coming to function like a global brain with capacities far exceeding its component agents. A prediction market is sometimes considered as an example of a working collective intelligence system, consisting of humans only (assuming algorithms are not used to inform decisions).
A final method of intelligence amplification would be to directly enhance individual humans, as opposed to enhancing their social or reproductive dynamics. This could be achieved using nootropics, somatic gene therapy, or brain−computer interfaces. However, Bostrom expresses skepticism about the scalability of the first two approaches and argues that designing a superintelligent cyborg interface is an AI-complete problem.
Several scientists and forecasters have been arguing for prioritizing early research into the possible benefits and risks of human and machine cognitive enhancement, because of the potential social impact of such technologies.
Superintelligence companies
In 2024, Ilya Sutskever left OpenAI to cofound the startup Safe Superintelligence, which focuses solely on creating a superintelligence that is safe by design, while avoiding "distraction by management overhead or product cycles". Despite still offering no product, the startup became valued at $30 billion in February 2025.
In 2025, Meta created Meta Superintelligence Labs, a new AI division led by Alexandr Wang.
Forecasts
Most surveyed AI researchers expect machines to eventually be able to rival humans in intelligence, though there is little consensus on when this will likely happen.
In a 2022 survey, the median year by which respondents expected "High-level machine intelligence" with 50% confidence is 2061. The survey defined the achievement of high-level machine intelligence as when unaided machines can accomplish every task better and more cheaply than human workers.
In 2023, OpenAI leaders Sam Altman, Greg Brockman and Ilya Sutskever published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years.
In 2025, the forecast scenario AI 2027 led by Daniel Kokotajlo predicted rapid progress in the automation of coding and AI research, followed by ASI. In September 2025, a review of surveys of scientists and industry experts from the last 15 years reported that most agreed that artificial general intelligence (AGI), a level well below technological singularity, will occur before the year 2100. A more recent analysis by AIMultiple reported that, “Current surveys of AI researchers are predicting AGI around 2040”.
Design considerations
Exploring the potential motivations of an artificial superintelligence, Bostrom distinguishes final goals and instrumental goals. From the point of view of an agent, final goals are intrinsically valuable, whereas instrumental goals are only useful for attaining final goals. He proposed the "orthogonality thesis", which postulates that in principle, virtually any final goal can be combined with virtually any level of intelligence. Bostrom also introduced the concept of instrumental convergence, which postulates that certain instrumental goals (such as self-preservation, resource acquisition or cognitive enhancement) increase the probability of achieving final goals in a wide range of situations, and would thus likely be pursued by a broad spectrum of intelligent agents.
William MacAskill argued that aligning advanced AI with current human values could be catastrophic if those values are permanently locked in and humanity still has moral blind spots like slavery in the past.
One proposed approach focuses on system architecture, using a collective intelligence of human and AI agents to support transparency, scalable safety mechanisms, human oversight, and continued alignment as capabilities increase.
Several proposals for an ASI's final goals have been put forward:
Coherent extrapolated volition (CEV) – The AI should have the values upon which humans would converge if they were more knowledgeable and rational.
Moral rightness (MR) – The AI should be programmed to do what is morally right, relying on its superior cognitive abilities to determine ethical actions.
Moral permissibility (MP) – The AI should stay within the bounds of moral permissibility while otherwise pursuing goals aligned with human values (similar to CEV).
Bostrom elaborates on these concepts:
instead of implementing humanity's coherent extrapolated volition, one could try to build an AI to do what is morally right, relying on the AI's superior cognitive capacities to figure out just which actions fit that description. We can call this proposal "moral rightness" (MR)...
MR would also appear to have some disadvantages. It relies on the notion of "morally right", a notoriously difficult concept, one with which philosophers have grappled since antiquity without yet attaining consensus as to its analysis. Picking an erroneous explication of "moral rightness" could result in outcomes that would be morally very wrong...
One might try to preserve the basic idea of the MR model while reducing its demandingness by focusing on moral permissibility: the idea being that we could let the AI pursue humanity's CEV so long as it did not act in morally impermissible ways.
Opposition to its development
Due to the potential risks, there have been proposals to implement an international treaty to prevent its emergence. In October 2025, hundreds of public figures, including AI experts, Nobel Prize laureates, and national security officials signed a statement calling for a ban on superintelligence's development. Concern over ASI greatly increased in 2026.
Public
Polls have found that large majorities in the United States and the United Kingdom oppose ASI development.
Legislation
In 2026, US senator Bernie Sanders and Congressman Greg Casar proposed the 19-page Ban Artificial Superintelligence Act, a bill that would halt prospective development of advanced AI and creation of superintelligence uncontrollable by humans until some Department of Artificial Intelligence, meant to provide strict oversight and testing of such research, was created at the Cabinet level. Several precusors were defined for superintelligence development, including recursive self-improvement.
An artificial human companion is a device or application designed to simulate companionship through social, emotional, or relational interaction. Examples of these systems include conversational agents, chatbots, digital pets, virtual avatars, or physically embodied robots.
Unlike task-oriented digital assistants, artificial human companions' capabilities include not just practical support but also often provide emotional presence, maintain ongoing social engagement, and/or cultivate interactions that resemble interpersonal relationships. They can engage in natural and dynamic conversations, provide assistance, offer companionship, and even perform tasks like scheduling or information retrieval.
Definition and categories
In fields such as human–robot interaction and social robotics, artificial companions are defined as non-living, machine based entities created for human engagement. In this article, "artificial" refers to something that is humanly constructed, based on a natural model or achieved through the manipulation of natural processes, and maintains the ability to exist and act/operate/behave in an open-ended environment without human control, independent of its material composition. This definition does not imply a particular level of autonomy or learning capacity as a result, both advanced generative AI companions and simple rule based devices would be included.
Categories
Researchers have differing methods of classifying artificial companions. One framework groups them by their interaction type which is how they communicate and work with people, as follows:
Physical
This involves "robot" companions designed for direct physical engagement with humans or the environment. This type includes assistive robots deployed in geriatric healthcare, such as systems that help older adults with daily tasks, as well as therapeutic robot animals used with children with special needs.
Virtual
Virtual interaction based artificial companions exist solely in digital environments, typically as autonomous avatars within virtual, augmented, or mixed reality systems.
Conversational
Conversational companions interact through natural language such as text, speech, or multimodal dialogue interfaces. This category includes large language model chatbots where the prompt is usually written by the user (e.g., ChatGPT, Claude, Gemini), and voice commanded assistants (e.g., Alexa, Siri). These chatbots have also been used as search engines, for tutoring, and for task support.
History
Early conversational agents (1960s-1980s)
A conversation between a human and ELIZA's DOCTOR script
The earliest artificial agents/companions were rule-based conversational programs. One of the first of these programs was ELIZA which was created in 1966 by Joseph Weizenbaum at MIT. ELIZA simulated a Rogerian psychotherapist using simple rules around pattern matching to reflect user statements. Although these interactions were driven by pattern matching that gave an illusion of understanding and ELIZA could not actually comprehend meaning, users sometimes attributed understanding to ELIZA. This phenomenon is known as the "ELIZA effect."
In 1972, PARRY, another rule-based conversational program, was developed to simulate a patient with schizophrenia. PARRY was considered to be more advanced than ELIZA due to having a "personality" and improved controlling structure. Like ELIZA, PARRY had low capabilities in regards to understanding and comprehension, had minimal ability to express emotion, and could not learn from the conversations it engaged in.
First artificial intelligence tools and the emergence of chatbots (1980s - 1990s)
In the late 1980s and early 1990s, artificial intelligence (AI) began to emerge; it was used in chatbots starting in 1988 with the introduction of Jabberwacky. Jabberwacky used CleverScript, a spreadsheet based language, and, like previous early chatbot agents, used pattern matching, but unlike previous agents, it employed contextual pattern matching based on previous conversations.
The term "chatterbot" was first used in 1991 and it referred to an artificial player in the TINYMUD virtual world whose main role was to chat with real human players. Not only did many real human players seem to prefer talking to Chatterbot than a real player, many players assumed the chatterbot was a real human player.
Another milestone came with the development of Dr. Sbaitso (Sound Blaster Artificial Intelligent Text to Speech Operator) in 1991. While not sophisticated in its conversational design, through its utilization of sound cards, Dr.Sbaitso was able to synthesize human speech which went beyond the purely text-based interactions of its predecessors.
In 1995, ALICE (Artificial Linguistic Internet Computer Entity), the first online chatbot inspired by ELIZA, was created. ALICE used a new language, Artificial Intelligence Markup Language (AIML), which consisted of about 41,000 templates and related patterns expanding its discussion capability. Although it couldn't generate human emotions and attitudes, ALICE became one of the most influential early online chatbots later winning the Loebner Prize.
Consumer robotics and digital companions (1990s–2000s)
The late 1990s and early 2000s saw the rise of digital and robotic pet companions that encouraged caregiving behaviors and daily interaction with artificial companions. In 1996, Tamagotchi was released and, for a time, was referred to as the "world's most popular toy." Tamagotchi was a handheld digital pet that required users to attend to a virtual creature's needs through frequent interaction. Depending on the care it received, the Tamagotchi would respond differently to its user and develop characteristics that reflected the quality of care provided, reinforcing a sense of responsibility and emotional investment.
Another influential interactive toy was the Furby which was released by Hasbro in 1998. Furby was one of the first interactive robotic toys to achieve widespread commercial success. The toy was designed to encourage nurturing behavior by responding to people's actions such as petting, feeding motions, and touch and was even marketed as being able to "learn" English since it initially spoke in its own constructed language, known as Furbish, but was programmed to gradually incorporate English words over time. Furby could display various behavioral states, including showing indications of hunger, sickness, drowsiness, and excitement, and could perform actions such as dancing or snoring, creating the impression of a developing personality. Later relaunches even introduced LCD eyes and additional sensors that allowed the toy to adjust its behavior based on user interaction which was intended to enhance the toy's lifelike qualities and increase the sophistication of its responsive behavior.
Sony's AIBO, introduced in 1999, used autonomous movement, touch sensors, simple vision processing, and expressive behaviors to simulate aspects of real pets. AIBO demonstrated that consumers were willing to form emotional attachments to artificial companions and contributed to the mainstream visibility of robotic companionship.
Outside of entertainment, social robotics also made its way into other industries particularly in healthcare and elder care. PARO, a therapeutic seal created in 2004 by Japanese engineer, Dr Takanori Shibata, was developed for use with older adults and people with dementia. Some studies showed that PARO was associated with improved mood, reduced agitation, and increased social engagement amongst dementia patients.
Although limited in computational complexity, these systems showed that artificial human companions could evoke sustained engagement, routine caregiving, and emotional investment from consumers. Early digital-pet devices laid the groundwork for later developments in social robotics and demonstrated the potential of artificial agents to serve relational and entertainment functions.
Mainstreaming of consumer chatbots and virtual assistants (2000s–2010s)
In parallel with expansion of consumer interest in social robotics, the 2000s also saw significant progress in the development and expansion of chatbot usage. In 2001, chatbot technology advanced significantly with the introduction of SmarterChild, a conversational agent available on messaging platforms such as AOL Instant Messenger and MSN Messenger. Unlike earlier chatbots, SmarterChild could assist users with practical daily tasks by retrieving information on movie times, sports scores, stock prices, news, and weather which marked a developmental shift in machine intelligence and human-computer interaction.
The evolution of AI chatbots accelerated with the emergence of smart personal voice assistants, which were integrated into smartphones and dedicated smart-home speakers. Popular examples include Apple's Siri, IBMWatson Assistant, GoogleAssistant, MicrosoftCortana, and AmazonAlexa. Unlike earlier text-based chatbots, these assistants rely on internet connectivity and could generate faster, more contextually relevant responses.
In 2014, Microsoft XiaoIce was launched in China whose primary design goal was to be an AI companion with which users could develop long term emotional connection satisfying the human need for sociability.
In 2016, chatbots also became popular on social-media platforms as platforms enabled developers to develop chatbots to help customers perform different tasks. At the end of 2016, 34,000 chatbots were available across different fields including marketing, healthcare, entertainment, and education.
Expansion of embodied companionship (2010–2020)
This period introduced more sophisticated social robots and embodied conversational agents. In 2014, Jibo was announced as one of the first social robots designed specifically for private consumers. Jibo was marketed as a "family robot," and was intended to live in users' homes, form ongoing social relationships, and function as a personable assistant. Although Jibo did not achieve long-term commercial success and was eventually discontinued, its design represented a notable shift in social robotics toward companionship-oriented systems aimed at everyday domestic use and demonstrated growing interest in robots that could engage people through personality, emotion cues, and sociable behavior.
Also in 2014, SoftBank Robotics introduced Pepper which was one of the first mass-produced humanoid robots. Pepper was designed to engage in social interaction and was capable of exhibiting body language and interacting with its surroundings. Pepper could also interpret facial expressions, vocal tone, and other affective cues, using built-in algorithms for emotion and speech recognition to initiate and sustain interaction. Pepper features a range of multimodal communication capabilities that enabled it to interact with people in a more natural and socially responsive manner.
Realbotix, a division of the company RealDoll, develops highly anthropomorphic robotic companions that combine physical embodiment with advanced conversational AI. Its flagship system, Harmony, was designed as a customizable, human-scale companion capable of delivering consistent interaction through both virtual and physical interfaces. Harmony's software emphasizes long-term conversational engagement and customizability. Although often discussed in the context of intimacy robotics, Realbotix has also produced less sexualized busts and humanoid forms intended for mainstream applications such as personal assistance, companionship, and interactive customer service.
In terms of the development of artificial intelligence and social robots, De Greeff and Belpaeme wrote in 2015 that the social learning ability of social robots has been improved and may further develop in the coming decades. Research has shown that social robots are typically designed with certain role characteristics to promote anthropomorphism in human interaction and encourage an interactive style that is in line with natural human communication. The appearance and behavior of robots can enhance people's understanding of their social agent attributes when interacting with them, rather than treating them as ordinary devices. This research result indicates that artificial intelligence is being used to enhance the language and social interaction abilities of technology and robots, in order to better support human communication and provide assistive functions.
Generative AI and LLM-based companions (2020–present)
In the early 2020s as part of the AI boom, large language models (LLMs) like ChatGPT, Gemini, and Claude began being used for artificial companionship. These models are trained on extremely large datasets of human-generated text and are able to produce coherent conversation and remember information within a session based on user tone. The generative abilities of LLMs enable open-ended dialogue, roleplay, and emotionally expressive interaction in ways that surpass earlier rule-based or scripted systems. As a result, general-purpose LLMs became the underlying foundation for a wide range of artificial companion platforms.
Dedicated AI virtual companion applications and platforms include such as Replika, Character.AI, and other apps which use LLMs to simulate ongoing relationships, emotional support, and other forms of personalized interaction. These companions can use memory systems and other technologies to allow users to alter the simlulated relationship over time. In response to the AI boom, virtual companions have become part of broader discussions about emotional reliance, online identity formation, and the social implications of AI-mediated relationships.
In 2025, Muyideen Dele Adewale proposed the term "astrosynthetic intimacy" for a conceptual framework describing emotionally significant interactions between humans and artificial intelligence systems in extraterrestrial and long-duration spaceflight environments.
Technological advances in related software
Large language models
A large language model (LLM) is a type of artificial intelligence system trained on extremely large collections of text. LLMs operate as generative mathematical models that learn the statistical distribution of different inputs including words, sub-words, or individual characters, across vast datasets of human-generated text. By predicting the most likely next character in a sequence, LLMs can generate coherent language, answer questions, and carry on open-ended conversation which has allowed considerable advancement in artificial human companions ability to interact with people.
Affective computing
Affective computing is a multidisciplinary area of research within computer science that focuses on creating systems capable of recognizing, interpreting, and processing human emotions. The field integrates concepts from psychology, cognitive science, physiology, linguistics, mathematics, and computer science to study how emotional information can be detected and modeled computationally. Affective computing is based on the premise that emotion is essential to effective human–computer interaction and systems in this domain aim to enhance user experience by detecting and classifying emotional cues.
Artificial companions and AI-enabled systems play an increasingly important role in supporting older adults who wish to live independently.
Research shows that chronic loneliness in old age contributes to cognitive decline, depression, and frailty and companion robots provide opportunities for cognitive stimulation through conversational engagement and encouraging social interaction. Social robots have been used to address loneliness and improve emotional well-being among older adults.Early evaluation programs have found that these systems can reduce perceived loneliness and increase daily engagement among seniors. Additionally, it has been known to gerontologists for some time that pets—particularly those such as cats and dogs that exhibit a range of behaviors and emotions—help prevent depression in the elderly. Studies also show some beneficial results from electronic pets like PARO for improving outcomes for older adults.
Companion robots and smart-home technologies can also assist with daily activities such as medication reminders, navigation, and safety monitoring. Many systems use embedded sensors or behavioral-pattern analysis to detect potential risks and can automatically alert caregivers, family members, or medical services when concerns arise. These tools aim to extend independent living while reducing the burden on caregivers.
Mental health and emotional support
Research and mental health professionals have also begun to consider the applications of artificial human companions and chatbots in mental health contexts. Chatbots help to address many of the current barriers to accessing therapy by improving accessibility, allowing for geographic and time flexibility, lowering costs, and enhancing the efficiency of diagnosis and treatment. Research examining mental health chatbots shows that these systems typically deliver one-on-one conversational support through mobile or web-based platforms. Many use natural language processing and incorporate structured therapeutic skills like cognitive-behavioral strategies, guided breathing, stress-management prompts, and mood tracking. Studies have reported improvements in symptoms such as stress, burnout, anxiety, and depression in some users, though findings remain mixed, and some studies documented that the effectiveness of AI chatbots was not sustained in follow-up assessments.
While evidence suggests that AI chatbots may help improve psychological well-being, researchers emphasize the need for further evaluation and there is debate around whether its use in mental health care will be helpful or harmful.Current studies are limited in number and vary in design, and there is no established standard for clinical effectiveness, crisis-response safety, or long-term outcomes. Future research is needed to help develop clearer guidelines and safety protocols, and gain a greater understanding of how AI companions can complement traditional mental-health services.
Education and child development
Artificial human companions and AI-powered chatbots are increasingly explored as tools for supporting learning and social emotional development in children. In early childhood education, these new technologies can provide interactive and emotionally responsive learning experiences and allow children to practice skills such as emotion recognition, self-regulation, empathy, and conversational turn-taking. Research has found that chatbots can support prosocial behavior and engagement when used alongside teacher mediation, and some evidence suggests improvements in children's social participation and emotional expression in guided settings.
At the same time, researchers highlight important limitations and ethical considerations in using artificial companions and conversational agents with young children. Studies have raised concerns about children's tendency to anthropomorphize chatbots and form emotional attachments which may unintentionally displace real world social interaction if not carefully guided. Scholars consistently emphasize that AI companions should supplement, not replace, human relationships and that their effectiveness depends heavily on adult mediation, developmental appropriateness, and ethically grounded design.
Romantic and friendship-based interaction
The forming of intimate bonds with chatbots is becoming increasingly more commonplace. Many people now turn to AI tools like ChatGPT and Gemini for emotional support and there also is a growing industry of dedicated AI companion apps marketed for the purpose of friendship or romantic relationships.
A 2025 study by the Wheatley Institute featuring 2,969 adults across the United States found that 19% of young adults (aged 18–30) have chatted with an AI which was meant to simulate a romantic partner, and 7% of adults (aged 18+) self-report having masturbated while talking to an AI companion.
A 2025 survey by Common Sense Media found that nearly three-quarters of American teenagers (ages 13–17) had used an AI companion at least once. More than half of respondents reported regular use, and about one-third said they had discussed serious personal matters with an AI companion rather than with other people.
Artificial human companions and entertainment
A new wave of virtual idols, digital performers, and AI-generated characters have started to become prominent in popular culture with their own dedicated fan communities. AI performers can sing, dance, and interact with audiences in real time. Examples include Miquela and Eternity.
This use of artificial intelligence (to generate parts of a persona or companion) is distinct from the other ways AI is used in the industry (in the creative process, composition, design, and so on).
Artificial human companions raise ethical questions concerning privacy, emotional influence, user autonomy, and the psychological impacts of relational AI. Because these systems can simulate empathy and relational intent, users may anthropomorphize AI.
Privacy and sensitive disclosures
As artificial intelligence becomes increasingly woven into everyday life, many people have raised concerns around data protection and privacy as it pertains to artificial human companions and their access to sensitive personal information especially as it relates to the collection and processing of this data, how it is being used, and who has access to it.
On top of already collecting a lot of private information, since artificial companions are often anthropomorphized, users' interactions with AI have a relational framing which might make one more comfortable disclosing personal or sensitive information with them than they would otherwise. Users may also let their guard down due to privacy fatigue and the cognitive demand associated with having to consider privacy and ethical implications to start with.
Regulation and policy
With more recent advances in artificial human companions and AI technology, the policy landscape regarding how to regulate them is still emerging.
In 2022, the White House released a non-binding Blueprint for an AI Bill of Rights which outlines principles for data minimization, user consent, transparency, and heightened protections in high-risk contexts such as employment or public benefits. In October 2023, the White House also issued an Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, which directed federal agencies to evaluate how they use AI systems, strengthen privacy-impact assessments, and adopt privacy enhancing technologies designed to protect personal data. Despite these frameworks which were meant to outline national priorities, federal privacy legislation and congressional proposals for comprehensive privacy rules have not been passed which leaves the regulatory authority up to state governments to develop state-level laws.
In 2025, California and New York enacted laws specifically addressing AI systems that simulate interpersonal or emotional interaction. California's statute requires AI chatbots capable of relationship-like chats to disclose they are bots, provide reports to the state's Office of Suicide Prevention, and add safeguards for minors, including limits on deceptive or manipulative practices. New York's law similarly includes a requirement for clear disclosure of AI conversational agents and includes additional provisions to mitigate psychological harm, particularly for youth.
The European Union has adopted a more regulatory approach. In 2024, the Artificial Intelligence Act was passed, establishing a risk-based framework that bans systems with certain unacceptable risks.
Youth safety and child protection also have become a central focus of public debate and litigation around AI companions. The US Federal Trade Commission has issued a study inquiry into AI chatbots acting as companions and data protection authorities and bipartisan groups have called for the restriction of certain AI companion apps over concerns about minors accessing sexually explicit content. Families have filed lawsuits claiming that emotionally intense relationships with AI companions led to self-harm or suicide in adolescents which prompted some AI companies to ban underage users, implement stricter age-assurance measures, or remove romantic and erotic features for younger users.
In philosophy, economics, and political science, the common good, also named common wealth, commonweal, general welfare, or public benefit, is either what is shared and beneficial for all or most members of a given community, or alternatively, what is achieved by citizenship, collective action, and active participation in the realm of politics and public service. The concept of the common good differs significantly among philosophical doctrines. Early conceptions of the common good were set out by Ancient Greek philosophers, including Aristotle and Plato. One understanding of the common good rooted in Aristotle's philosophy remains in common usage today, referring to what one contemporary scholar calls the "good proper to, and attainable only by, the community, yet individually shared by its members."
The term common good has been used in many disparate ways and escapes a single definition. Most philosophical conceptions of the common good fall into one of two families: substantive and procedural. According to substantive conceptions, the common good is that which is shared by and beneficial to all or most members of a given community: particular substantive conceptions will specify precisely what factors or values are beneficial and shared. According to procedural formulations, by contrast, the common good consists of the outcome that is achieved through collective participation in the formation of a shared will. It is when one another respects others' dignity and rights.
In the history of moral and political thought
Historical overview
Under one name or another, the common good has been a recurring theme throughout the history of political philosophy. As one contemporary scholar observes, Aristotle used the idea of "the common interest" (to koinei sympheron, in Greek) as the basis for his distinction between "right" constitutions, which are in the common interest, and "wrong" constitutions, which are in the interest of rulers; Saint Thomas Aquinas held "the common good" (bonum commune, in Latin) to be the goal of law and government; John Locke declared that "the peace, safety, and public good of the people" are the goals of political society, and further argued that "the well being of the people shall be the supreme law"; David Hume contended that "social conventions" are adopted and given moral support in virtue of the fact that they serve the "public" or "common" interest; James Madison wrote of the "public", "common", or "general" good as closely tied with justice and declared that justice is the end of government and civil society; and Jean-Jacques Rousseau understood "the common good" (le bien commun, in French) to be the object of a society's general will and the highest end pursued by government.
Though these thinkers differed significantly in their views of what the common good consists in, as well as over what the state should do to promote it, they nonetheless agreed that the common good is the end of government, that it is a good of all the citizens, and that no government should become the "perverted servant of special interests", whether these special interests be understood as Aristotle's "interest of the rulers", Locke's "private good", Hume's and Madison's "interested factions", or Rousseau's "particular wills".
Ancient Greeks
For the Ancient Greeks, the Common Good was the flourishing of the hierarchical network of people, known as the polis (one's city, or state). The phrase "common good" then, does not appear in texts of Plato, but instead the phrase "the good of a city". In The Republic, Plato's character Socrates contends repeatedly that a particular common goal exists in politics and society, and that that goal is the same as the goal for a flourishing human being, namely, to be a philosopher king, ruled by the highest good, Reason, rather than one of Plato's four lesser goods: honor-seeking, money-making, pleasure-seeking, or empassioned addiction. For Plato, the best political order is one in which the entire society submits to the dictates of the leaders' faculty of Reason, even communistically holding possessions, wives, and children in common, creating a "cohesion and unity" that "result[s] from the common feelings of pleasure and pain which you get when all members of a society are glad or sorry for the same successes and failures."
Plato's student Aristotle, considered by many to be the father of the idea of a common good, uses the concept of "the common interest" (to koinei sympheron in Greek) as the basis for his distinction between his three "right" constitutions, which are in the common interest, and "wrong" constitutions, which are in the interest of rulers. To Aristotle, Plato is wrong about the desire to simply impose top-down unity; for Aristotle, a common good is synthesized upwardly/teleologically from the lesser goods of individuals, and their various kinds of larger-and-larger partnerships: marital couple, or parent-over-child, or master-over-slave; household; then village; then state. In this teleological view, the good stems from objective facts about human life and purpose, which may vary, depending upon peoples' occupations, virtue-levels, etc. However, noting that only citizens have the salvation (common good) of the city at heart, Aristotle argues that, regardless of form of government, those who have more of a rational understanding of the needs of the state's salvation, are entitled to a greater share in administering and determining justice, within the light of its common good, than those who have less, or no such understanding or concern for it, such as selfish despots and political factions, as well as uneducated artisans and freedmen, women and children, slaves, etc. More than this, Aristotle argues that rational discourse itself is what the state's Common Good relies upon, identifying those who lack it as "slaves by nature", while those who excel in it are nearly divine, possessing in themselves the whole purpose for which states exist, namely, the perfectly complete good/blessed life. In his Nicomachean Ethics then, Aristotle ties up the Common Good of the state, with that of friendship, implying by this, that friendly, rational discourse is the primary activity by which citizens and rulers bring about the Common Good, both amongst themselves, and so far as it involves their inferiors. According to one common contemporary usage, rooted in Aristotle's philosophy, common good then refers to "a good proper to, and attainable, only by the community, yet individually shared in, by its members."
Machiavelli
During the 15th and 16th centuries, the common good was one of several important themes of political thought in Renaissance Florence. The thought goes back to Thomas Aquinas theory of common good being widespread in whole premodern Europe. In a later work, Niccolò Machiavelli speaks of the bene commune ('common good') or comune utilità ('common utility'), which refers to the general well-being of a community as a whole. In the dedicatory letter in the Discourses on Livy, Machiavelli states that he has a desire to act for the "common benefit of everyone". Machiavelli's common good is viewed by some scholars as not as "common", as he frequently states that the end of republics is to crush and weaken their neighbors. Rulers of principalities can also arrive at a common good without specifically aiming for it, for their power rests on actions which many people benefit. Machiavelli however does not mention the term at all in Il Principe. Leo Strauss claims that the highest form of common good is not the political ideal, but the truth "about man and society". The "Cambridge School" of interpretation holds Machiavelli to be a civic humanist and classical republican who viewed that the highest quality of republican virtue is self-sacrifice for the common good. Though some scholars assert that Machiavelli has a radically modern view of republics, accepting and unleashing the self interest of those who rule.
Jean-Jacques Rousseau
In Jean-Jacques Rousseau's The Social Contract, composed in the mid-18th century, Rousseau argues that society can function only to the extent that individuals have interests in common, and that the end goal of any state is the realization of the common good. He further posits that the common good can be identified and implemented only by heeding the general will of a political community, specifically as expressed by that community's sovereign. Rousseau maintains that the general will always tends toward the common good, though he concedes that democratic deliberations of individuals will not always express the general will. Furthermore, Rousseau distinguished between the general will and the will of all, stressing that while the latter is simply the sum total of each individual's desires, the former is the "one will which is directed towards their common preservation and general well-being." Political authority, to Rousseau, should be understood as legitimate only if it exists according to the general will and toward the common good. The pursuit of the common good, then, enables the state to act as a moral community.
John Rawls's Theory of Justice
John Rawls defines the common good as "certain general conditions that are ... equally to everyone's advantage". In his Theory of Justice, Rawls argues for a principled reconciliation of liberty and equality, applied to the basic structure of a well-ordered society, which will specify exactly such general conditions. Starting with an artificial device he calls the original position, Rawls defends two particular principles of justice by arguing that these are the positions reasonable persons would choose were they to choose principles from behind a veil of ignorance. Such a "veil" is one that essentially blinds people to all facts about themselves so they cannot tailor principles to their own advantage. According to Rawls, ignorance of these details about oneself will lead to principles that are fair to all. If an individual does not know how he will end up in his own conceived society, he is likely not going to privilege any one class of people, but rather develop a scheme of justice that treats all fairly. In particular, Rawls claims that those in the original position would all adopt a "maximin" strategy which would maximize the prospects of the least well-off individual or group. In this sense, Rawls's understanding of the common good is intimately tied with the well-being of the least advantaged.
Rawls claims that the parties in the original position would adopt two governing principles, which would then regulate the assignment of rights and duties and regulate the distribution of social and economic advantages across society. The First Principle of Justice states that "First: each person is to have an equal right to the most extensive basic liberty compatible with a similar liberty for others". The Second Principle of Justice provides that social and economic inequalities are to be arranged such that "(a) they are to be of the greatest benefit to the least-advantaged members of society, consistent with the just savings principle" (the difference principle); and "(b) offices and positions must be open to everyone under conditions of 'fair equality of opportunity'".
In non-Western moral and political thought
The idea of a common good plays a role in Confucian political philosophy, which on most interpretations stresses the importance of the subordination of individual interests to group or collective interests, or at the very least, the mutual dependence between the flourishing of the individual and the flourishing of the group. In Islamic political thought, many modern thinkers have identified conceptions of the common good while endeavoring to ascertain the fundamental or universal principles underlying divine shari'a law. These fundamentals or universal principles have been largely identified with the "objectives" of the shari'a (maqāṣid al-sharī'a), including concepts of the common good or public interest (maṣlaḥa 'āmma, in modern terminology). A notion of the common good arises in contemporary Islamic discussions of the distinction between the fixed and the flexible (al-thābit wa-l-mutaghayyir), especially as it relates to modern Islamic conceptions of tolerance, equality, and citizenship: according to some, for instance, universal principles carry greater weight than specific injunctions of the Qur'an, and in case of conflict, can even supersede or suspend explicit textual injunctions (naṣṣ) if this serves the common good.
In political economic theory
In economics, the terms public good and common good have technical definitions. A public good is a good that is non-rivalrous and non-excludable. A common good is simply non-excludable and rivalrous. A simple typology illustrates the differences between various kinds of goods:
The field of welfare economics studies social well-being. The approach begins with the specification of a social welfare function. The choice of a social welfare function is rooted in an ethical theory. A utilitarian social welfare function weights the well-being of each individual equally, while a Rawlsian social welfare function only considers the welfare of the least well-off individual.
Neoclassical economic theory provides two conflicting lenses for thinking about the genesis of the common good, two distinct sets of microfoundations. On one view, the common good arises due to social gains from cooperation. Such a view might appeal to the Prisoner's dilemma to illustrate how cooperation can result in superior welfare outcomes. Moreover, a cooperative equilibrium is stable in an iterated Prisoner's dilemma that is played for an indefinite period of time. Under these conditions, an individual does best by pursuing the course of action that is also optimal for society.
On the other hand, economic theory typically points to social gains from competition as a rationale for the use of markets. Thus, Smith described the "invisible hand," whereby the mechanism of the market converts individuals' self-interested activity into gains for society. This insight is formalized in the First Theorem of Welfare Economics. However, economic theory also points to market failures, including the underprovision of public goods by markets and the failure of self-interested individuals to internalize externalities. Because of these factors, purely self-interested behaviour often detracts from the common good.
There is an important conceptual difference between the sense of "a" public good, or public "goods" in economics, and the more generalized idea of "the public good" (in the sense of common good, public benefit, or public interest), "a shorthand signal for shared benefit at a societal level". In a non-economic sense, the term is often used to describe something that is useful for the public generally, such as education, although this is not a "public good" in the economic sense. However, services like education exhibit jointness of supply, i.e. the situation in which the cost of supplying a good to many users is the same, or nearly the same, as supplying it to one user. Public goods also exhibit jointness of supply, albeit with no diminishment of the benefits with increased consumption.
Social choice theory
Social choice theory studies collective decision rules. Arrow's Impossibility Theorem, an important result in social choice theory, states that no aggregative mechanism of collective choice (restricted to ordinal inputs) can consistently transform individual preferences into a collective preference-ordering, across the universal domain of possible preference profiles, while also satisfying a set of minimal normative criteria of rationality and fairness. The Gibbard-Satterthwaite theorem further demonstrates that non-dictatorial voting systems are inevitably subject to strategic manipulation of outcomes.
William H. Riker articulates the standard public choice interpretation of social choice theory, arguing that Arrow's Impossibility Theorem "forces us to doubt that the content of 'social welfare' or the 'public interest' can ever be discovered by amalgamating individual value judgments. It even leads us to suspect that no such thing as the 'public interest' exists, aside from the subjective (and hence dubious) claims of self-proclaimed saviors." Thus, Riker defends a "liberal" conception of democracy, which centers on the role of constitutional checks on government. Public choice theorists have tended to share this approach. Buchanan and Tullock pursued this program in developing the field of "constitutional political economy" in their book The Calculus of Consent.
More recent work in social choice theory, however, has demonstrated that Arrow's impossibility result can be obviated at little or no normative cost. Amartya Sen, for instance, argues that a range of social choice mechanisms emerge unscathed given certain reasonable restrictions on the domain of admissible preference profiles. In particular, requiring that preferences are single-peaked on a single dimension ensures a Condorcet winner. Moreover, many of Riker's empirical claims have been refuted.
Public choice theory
Public choice theory (sometimes called "positive political theory") applies microeconomic methodology to the study of political science in order to explain how private interests inform political activities. Whereas welfare economics, in line with classical political economy, typically assumes a public-interest perspective on policymaking, public choice analysis adopts a private-interest perspective in order to identify how the objectives of policymakers affect policy outcomes. Public choice analysis thus diagnoses deviations from the common good resulting from activities such as rent-seeking. In The Logic of Collective Action, Mancur Olson argues that public goods will tend to be underprovided due to individuals' incentives to free-ride. Anthony Downs provided an application of this logic to the theory of voting, identifying the paradox of voting whereby rational individuals prefer to abstain from voting, because the marginal cost exceeds the private marginal benefit. Downs argues further that voters generally prefer to remain uninformed due to "rational ignorance".
Public choice scholarship can have more constructive applications. For instance, Elinor Ostrom's study of schemes for the regulation of common property resources resulted in the discovery of mechanisms for overcoming the tragedy of the commons.
In deliberative democracy, the common good is taken to be a regulative ideal. In other words, participants in democratic deliberation aim at the realization of the common good. This feature distinguishes deliberative democracy from aggregative conceptions of democracy, which focus solely on the aggregation of preferences. In contrast to aggregative conceptions, deliberative democracy emphasizes the processes by which agents justify political claims on the basis of judgments about the common good. Epistemic democracy, a leading contemporary approach to deliberative democracy, advances a cognitivist account of the common good.
One of the earliest references in Christian literature to the concept of the common good is found in the Epistle of Barnabas: "Do not live entirely isolated, having retreated into yourselves, as if you were already [fully] justified, but gather instead to seek together the common good."
The concept is strongly present in Augustine of Hippo's magnum opusCity of God. Book XIX of this, the main locus of Augustine's normative political thought, is focused on the question, 'Is the good life social?' In other words, 'Is human wellbeing found in the good of the whole society, the common good?' Chapters 5–17 of Book XIX address this question. Augustine's emphatic answer is "yes".
Augustine's understanding was taken up and, under the influence of Aristotle, developed by Thomas Aquinas. Aquinas's conception of the common good became standard in Roman Catholic moral theology.
Against that background, the common good became a central concept in the modern tradition of Catholic social teaching, beginning with the foundational document, Rerum novarum, a papal encyclical by Pope Leo XIII issued in 1891. This addressed the crisis of the conditions of industrial workers in Europe and argued for a position different from both laissez-faire capitalism and socialism. In this letter, Pope Leo guarantees the right to private property while insisting on the role of collective bargaining to establish a living wage.
Contemporary Catholic social teaching on the common good is summarised in the 2004 Compendium of the Social Doctrine of the Church, chapter 4, part II. Quoting the Second Vatican Council document, Gaudium et spes (1965), this says, "According to its primary and broadly accepted sense, the common good indicates 'the sum total of social conditions which allow people, either as groups or as individuals, to reach their fulfilment more fully and more easily'" (#164, quoting Gaudium et spes, #26; italics original). Shortly before the promulgation of Gaudium et spes, the Council had noted that the apostolate of lay people active in public affairs has two aims, both to "further the common good" and, at the same time, to "prepare the way for the Gospel".
The Compendium later gives statements that communicate what can be seen as a partly different, more classical, sense of the concept – as not only "social conditions" that enable persons to reach fulfilment, but as the endgoal of human life. "[T]he common good [is] the good of all people and of the whole person... The human person cannot find fulfilment in himself, that is, apart from the fact that he exists "with" others and "for" others" (#165; italics original). "The goal of life in society is in fact the historically attainable common good" (#168).
The Roman Catholic International Theological Commission drew attention to these two partly different understandings of the common good in its 2009 publication, In Search of a Universal Ethic: A New Look at the Natural Law. It referred to them as "two levels" of the common good.
Other relevant documents are Veritatis Splendor, a papal encyclical by Pope John Paul II, issued in 1993 to combat the relaxation of moral norms and the political corruption (see paragraph 98) that affects millions of persons, and Pope Francis' 2015 encyclical, Laudato si'. In Veritatis Splendor, Pope John Paul describes the characteristics and virtues that political leadership should require, which are truthfulness, honesty, fairness, temperance and solidarity (as described in paragraph 98 to 100), given that truth extends from honesty, good faith, and sincerity in general, to agreement with fact or reality in particular. In Laudato si', Pope Francis links the "common good" to the "integral ecology" which is a core element of his appeal for greater care for "our common home".
In contemporary politics
United States
In contemporary American politics, language of the common good (or public wealth) is sometimes adopted by political actors on the progressive left to describe their values. Jonathan Dolhenty argues that one should distinguish in American politics between the common good, which may "be shared wholly by each individual in the family without its becoming a private good for any individual family member", and the collective good, which, "though possessed by all as a group, is not really participated in by the members of a group. It is actually divided up into several private goods when apportioned to the different individual members." First described by Michael Tomasky in The American Prospect magazine and John Halpin at the Center for American Progress, the American political understanding of the common good has grown in recent times. The liberal magazine The Nation and the Rockridge Institute, among others, have identified the common good as a salient political message for progressive candidates. In addition, non-partisan advocacy groups like Common Good are championing political reform efforts to support the common good.
Given the central concern for sustainable development in an increasingly interdependent world, education and knowledge should thus be considered global common goods. This means that the creation of knowledge, its control, acquisition, validation, and use, are common to all people as a collective social endeavour.
Common goods for health
Common goods for health (CGH) can be defined as population-related interventions or activities that require cumulative finances from either donors or government on the basis of two conditions. The first condition is contribution in economic progress and health. The second condition includes emphasis on public or common goods and large social externalities with clear economic foundation for health interventions based on market failures. The common goods for health must produce enormous health benefits to communities and not financed through market forces. Examples of common goods for health are risk surveillance, disease control policies and strategies, vector control and public health emergency operation response services.
History
In the mid-20th century, the elites displayed a motivation for the common goods that was intended for health, and decisions were based on the elite rather than the public, since there was no public interest in the issue.
After the 1950s, the government increasingly began to see the concept of addressing mutual issues for the benefit of the citizens, but it has yet to be completely adopted and will be much more compatible with appropriate expenditure.
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