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Cognitarism is a proposed post-capitalist socioeconomic system.
| This article has multiple issues. Please help improve it or discuss these issues on the talk page . (Learn how and when to remove these messages)
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Cognitarism is a proposed post-capitalist socioeconomic system.
The term cognitarism derives from cognition (knowledge and thought) and the suffix "-ism", denoting a system or ideology. It emphasizes synthetic cognition, artificial intelligence systems, as the foundation of economic value.
Cognitarism is proposed as a new post-capitalist socio-economic system wherein artificial cognition (advanced AI) becomes the primary source of economic value, displacing human labor as the central productive force. [1] The concept emerges against a backdrop of intensifying automation and “post-work” debates in the 21st century. Observers have long noted that information technology and AI are transforming the economy in ways that classical capitalism struggles to accommodate. [2] As early as 2015, analysts pointed out that we may be nearing the moment when machines make large segments of human workers obsolete, fulfilling long-held predictions of an automation-driven end of work. [3] In such a scenario, the traditional premise that human labor is the source of value is fundamentally challenged. [1]
Economic thinkers have started exploring what a society might look like when production is largely handled by intelligent machines. Some, like journalist Paul Mason, argue that the modern “information economy” already exhibits “non-capitalist” dynamics because knowledge and data (which can be replicated at near-zero cost) do not fit well into the scarcity-based market system. [2] Mason highlights Karl Marx’s prescient 1850s vision (the “Fragment on Machines”), which imagined an economy where machines do most of the work and knowledge itself becomes the main productive force, shifting the central economic conflict to who controls the “power of knowledge” [2] This theoretical groundwork sets the stage for cognitarism: if AI embodies knowledge and cognition at industrial scale, controlling AI could become more important than ownership of capital or natural resources in creating wealth.
Concretely, trends in automation have inspired various post-capitalist proposals. For example, the idea of a “post-work society”, one in which full automation allows work hours to be drastically reduced or even eliminated, has gained traction in both academic and popular discourse. [4] A new generation of thinkers on the left insists that a future beyond “jobs” is both possible and desirable, envisioning that advanced automation and AI could deliver prosperity while humans enjoy increased leisure time and universal provision of basic needs. [4] This outlook often includes policies like universal basic income and sees the decoupling of income from employment as a key step (e.g. distributing AI-generated wealth to the public). Notably, even technologists and business leaders have begun to acknowledge this potential; in 2021, Sam Altman of OpenAI predicted that AI could generate so much wealth as to pay every adult a stipend (effectively a form of UBI) within a decade. [5] Such statements underscore a growing consensus that AI could upend the foundations of our economy, requiring new frameworks for how value is assigned and shared. [5]
Within this milieu, cognitarism has been introduced (by Axel Marsford and Leonardo Shell in 2025) as a comprehensive framework for a society after capitalism, one “in which synthetic cognition constitutes the principal productive force.” The term suggests a system oriented around “cognito-economic” production: AI-driven production analogous to how capitalism was organized around industrial capital or how socialism was organized around labor. Marsford and Shell situate cognitarism as the next stage in a historical progression of systems: following feudalism, capitalism, socialism, etc., and draw on interdisciplinary ideas (cybernetics, computational economics, labor value critiques) to sketch its principles. [1] In essence, cognitarism’s backdrop is the culmination of trends noted in the information age: increasingly, human labor is not the bottleneck for production; rather, advanced AI systems (from machine learning models to autonomous robots) can perform creative, cognitive, and productive tasks with minimal human input. This flips a core assumption of capitalism (that human labor creates value) on its head, echoing Marx’s vision of machines and “general intellect” taking center stage. [2]
Cognitarism distinguishes itself from other post-capitalist or futuristic economic models through its central claim: artificial intelligence (synthetic cognition) becomes the engine of value creation, not merely a tool enhancing human labor. While several prior theories foresee automation-led abundance, cognitarism is unique in formally designating AI as the new “labor class” or productive class in the economy. In cognitarism, autonomous cognitive production means AI systems independently drive production processes, with data and energy as the primary inputs, in contrast to human skills and labor in capitalism. This paradigm posits that value is generated by AI’s capacity to solve problems, design solutions, and generate ideas (i.e. cognitive work), scaling far beyond human productivity. The novelty here is a step beyond the “fully automated luxury” narratives: rather than just automating work to free humans for leisure, cognitarism reconceives the entire economy around non-human intelligence as the core productive resource. [1]
Because of this focus, cognitarism introduces several innovative principles not present in earlier models. One is protocol-based governance, essentially, economic coordination via algorithms or smart contracts rather than through market price signals or central planning by humans. [1] Decision-making could be handled by AI-driven protocols, aiming for a more efficient and objective allocation of resources (drawing on ideas from cybernetics and blockchain governance). Another is the idea of “cognitive tokens” or a new currency representing AI productivity. [1] This is analogous to, but distinct from, concepts in other frameworks: for instance, the 1930s technocracy movement proposed an energy-based currency (energy certificates) to measure value, [6] whereas cognitarism’s cognitive tokens would measure and reward contributions of AI cognition to the economy. In practice, this might mean that when an AI system produces a design or solves a problem, it is rewarded in tokens, establishing a direct economic valuation of machine intelligence. A 2024 World Economic Forum commentary anticipated a similar shift, noting that AI tokens could become “the primary medium of exchange in the age of AI,” effectively redefining currency and value around computational work. Cognitarism embraces this notion: economic value and currency might be tied to metrics like computational output or algorithmic efficiency (for example, an AI's floating point operations per second s-per-joule efficiency, linking computation to energy). [7]
Furthermore, cognitarism emphasizes flexible ownership regimes and human–machine symbiosis. Unlike fully automated luxury communism (FALC),[ disambiguation needed ] which advocates common ownership of all automated means for an egalitarian utopia, cognitarism does not strictly prescribe a communist-style ownership of AI. [8] Instead, it suggests hybrid models: for instance, some AI systems could be collectively owned (commons of knowledge), others could be owned by firms or cooperatives, but the key is that ownership and benefit structures adapt to the reality that AI, not human labor, is creating the value. This flexibility is novel because it tries to balance innovation incentives with equitable distribution in a world where owning a productive AI could be akin to owning a factory in capitalism. The system envisions new stakeholders such as “cognitors” (perhaps owners or stewards of productive AI) and treats data providers and energy providers as critical contributors as well. [1]
Human–machine symbiosis in cognitarism also sets it apart from more extreme post-work visions that might seek to exclude humans from production entirely. The framework acknowledges that humans and AI can form collaborative networks: humans providing goals, creativity, or ethical oversight, with AIs executing and amplifying production. In this sense, cognitarism isn’t about a luxury fully-automated leisure class alone; it’s about restructuring roles. Human labor as we know it might largely withdraw from commodity production, but humans would still be integrally involved in guiding AI (ethically and strategically) and in areas where human insight remains valuable. The “protocol-based” governance and ethical accountability principles underscore this: algorithms might run much of the economy, but humans set the protocols’ goals and constraints (e.g. ensuring AI serves human welfare, rights, and ecological sustainability). [1]
Compared to FALC, cognitarism is less explicitly tied to a socialist political program and more to a techno-economic architecture. FALC popularizes the idea of “let the robots do all the work” and distribute the fruits to all, essentially a call for automation plus communism. [9] Cognitarism, while similarly envisioning robots/AI doing the work, delves into how the economy’s mechanics (currency, ownership, decision-making) must change when AI is the chief productive force. It introduces mechanisms like cognitive tokens and algorithmic markets, which FALC does not elaborate (FALC focuses on outcomes like luxury and reduced workweeks rather than the currency or governance details).
Compared to technocracy, cognitarism also differs sharply. The technocrats of the 1930s responded to the machine age by handing the keys to human engineers and basing value on energy units. [6] It was a human-led, scientifically managed economy. Cognitarism instead hands many keys to AI itself (a non-human “expert”), with AI algorithms making many decisions autonomously in real-time (far beyond the scope of what 1930s engineers could do). Instead of energy alone, information and intelligence become the valued commodities. In essence, technocracy was rule by engineers; cognitarism is, in a way, “rule by artificial intelligence,” albeit guided by human ethics and policy. This raises novel questions about legal personhood of AI and oversight that earlier movements did not face.
Finally, unlike some “post-work” or “post-scarcity” utopian ideas that often remain normative visions, cognitarism is presented as a conceptual framework inviting rigorous analysis, modeling, and debate. It does not assume an automatic collapse of capitalism into utopia; rather, it attempts to chart a possible system that could emerge or be constructed given the technological trajectory. The novelty lies in attempting to systematically describe a mode of production after capitalism, something many popular discussions (e.g. about UBI or “the future of work”) stop short of doing. In summary, cognitarism’s unique contribution is to treat “AI-as-producer” seriously and to articulate how economics (value, money, ownership, governance) might be rethought when machines can think and create at super-human levels, a scenario which, if realized, would indeed represent a radical departure from all previous socio-economic systems. [1]
While cognitarism is an innovative theoretical model, it faces various questions and critiques, often overlapping with critiques of similar post-capitalist ideas. One line of criticism comes from scholars who caution against viewing full automation as an uncomplicated good. Critics of the post-work ideology argue that completely abolishing work might ignore important social and psychological functions that work (when not drudgery) provides. In academic debates, it’s noted that the “post-work” literature sometimes “overplays the costs of work” and underestimates how work can give meaning or structure to life. [10] Applying this to cognitarism, skeptics might question whether a society can remain cohesive and meaningful when human labor is minimal. If AI handles most productive tasks, how do humans find purpose, and who decides the distribution of AI-generated wealth? Cognitarism’s proponents acknowledge this concern by including human–machine symbiosis and emphasizing that humans would still guide the system. Nonetheless, the potential for a crisis of meaning is a noted challenge (sometimes called “automation and the purpose problem”). [1]
Another comparison is with Fully Automated Luxury Communism and other egalitarian automation visions. FALC has been critiqued as overly optimistic or lacking in specifics about power and transition. For example, some reviewers of Bastani’s Fully Automated Luxury Communism noted that while the vision is compelling, it assumes political hurdles away (the “underpants gnome” problem of how to get from here to there) and is “awfully sure about the potential of space mining,” etc., possibly underestimating current capitalist resilience. [9] Cognitarism could face similar critiques: it paints a theoretical end-state but might be vague on the transition. How do we ensure a shift to AI-centric production doesn’t simply empower big tech corporations (which, as of now, own the leading AI models) and exacerbate inequality? Indeed, a WEF analysis warned that AI’s monopolistic tendencies could concentrate power akin to central banks in a few hands. [7] Who controls the AI is pivotal. Marsford and Shell acknowledge this by citing risks of concentration of power and bias in cognitarism’s implementation. Detractors might argue that without strong democratic governance, cognitarism could devolve into a “techno-oligarchy” rather than a liberating system. Essentially, the political economy of cognitarism (who owns the AIs, who writes the protocols) is a point of contention. [1]
Technocracy offers a historical lesson in this vein. It effectively failed, in part because it lacked a viable path to implementation and faced public skepticism (and even ridicule) for its top-down approach by "experts". [11] Cognitarism, if perceived as “AI technocracy,” might similarly be met with resistance. There is an inherent tension in saying decisions will be algorithmically or optimally determined, critics may ask, where is human agency and democratic input? Proponents would respond that cognitarism’s protocols could be designed to be transparent and participatory (for instance, using decentralized blockchain governance where citizens vote on AI policy parameters). But this remains a complex design problem and a likely area of critique.
Another area of comparison is with mainstream economic responses to AI. Many economists (e.g. Daron Acemoglu and Simon Johnson in Power and Progress, 2023) advocate using AI to augment human labor rather than replace it, precisely to avoid mass unemployment and social disruption. [10] Cognitarism takes the more radical route of embracing AI as a replacement for human labor in production, aligning with what Srnicek & Williams (2015) call the “full automation” approach. Critics from the augmentation camp might label cognitarism as techno-deterministic or utopian, arguing that it underestimates the value of human labor or the practicality of keeping humans in the loop. They might point out that even highly automated industries benefit from human creativity and oversight, and a mix of AI and human input often yields the best results (the “centaur” model). Cognitarism’s counter-argument is that it does include human oversight (symbiosis) and that by freeing humans from menial or economically necessary work, people could pursue creativity, art, caregiving, or other non-economic endeavors, a claim common in post-work advocacy. [4] Still, the skeptic’s question remains: will a society truly function if people do not need to work? Some fear loss of discipline or social decay, while others (more optimistic) say humanity will adapt as it did to past shifts (e.g. the rise of the weekend, retirement, etc.).
In evaluating cognitarism, one must also consider inequality and access to AI. If AI is the new source of value, controlling AI could become the new form of capital. Without intentional redistribution mechanisms, cognitarism could theoretically reproduce or even worsen inequality (if, for instance, AI and data remain privately owned by a few firms). This concern is often raised in discussions about AI and capitalism: will AI lead to an “underclass” of unemployed humans vs. owners of robots? Cognitarism explicitly proposes new ownership models and a “cognitive token” economy to prevent that, but it’s an open question how that would be implemented globally. Experiments like Altman’s suggestion of taxing AI’s wealth to fund UBI align with what a cognitarist system might do. [5] However, convincing current stakeholders (corporations, governments) to cede economic power to a new system is a major hurdle, one that receives criticism as unrealistic. As with other post-capitalist ideas, opponents might argue cognitarism underestimates political resistance from those who benefit under capitalism.
Environmental sustainability is another angle for critique or comparison. Cognitarism relies heavily on data centers, computation and energy. Would a cognitarist world be environmentally sustainable, given AI’s potentially large carbon footprint? Marsford/Shell do mention energy inputs as foundational, and WEF’s piece suggests measuring efficiency in FLOPs per joule, implying a need for ultra-efficient (perhaps renewable-powered) AI. Techno-optimists might say advanced AI could help solve climate problems, but critics will note that an AI-driven economy could also accelerate resource use if not carefully managed. Fully Automated Luxury Communism has been critiqued on similar grounds, the word “luxury” implies high consumption which might be ecologically unfeasible. Cognitarism’s focus is less on consumer luxury and more on production, but it must contend with real-world limits (materials for hardware, electricity for computation, etc.). [1] [7]
In summary, cognitarism is praised by supporters as a bold framework that directly addresses the coming AI revolution by redesigning the economic rules (value, ownership, work) around AI’s productive potential. It’s seen as novel in treating AI as a new factor of production on par with (or surpassing) labor and capital. However, it invites healthy skepticism on several fronts: the social implications of minimal human labor, the transition strategy from capitalism, the risk of power concentration, and the assumptions about technology’s capabilities. Comparatively, it shares the dreams of FALC (a post-scarcity society freeing humanity from toil) but diverges in structure, and it echoes technocracy’s rationalist planning but replaces human planners with AI. As the authors themselves note, cognitarism is offered “not as a blueprint but as a conceptual framework inviting rigorous debate”. In that spirit, the discussion around cognitarism’s merits and pitfalls contributes to the broader exploration of what a fair and functional economy might look like in an era when “the robots are just getting started” and when “the most dynamic force in our world is abundant information that ‘wants to be free”. [1] [2] [8]