Porygon-Z’s Philosophy: Scientific Revolutions, AI, and a Feminist’s Cyborg
“We are all chimeras, theorized and fabricated hybrids of machine and organism; in short, we are cyborgs. This cyborg is our ontology; it gives us our politics.” — Donna Haraway, A Cyborg Manifesto (1985)
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What happens when our greatest scientific breakthroughs — once driven by human ingenuity and curiosity — are powered by artificial intelligence, blurring the line between authentic discovery and synthetic simulation? The origins of Porygon-Z in the Pokémon world provide a helpful metaphor for adjudicating this question in the philosophy of science.
The Structure of Scientific Revolutions (1962), authored by historian and philosopher of science Thomas Kuhn, radically redefined how we understand scientific progress. In Kuhn’s view, the development of the sciences does not occur through a neat ‘linear or cumulative’ (p. 139) process of discovery. Instead, scientific progress occurs through recursive ‘paradigm shifts’ (p. 66), wherein Kuhn proposes that the prevailing models and standards guiding scientific inquiry are overturned once the emergence of inexplicable ‘anomalies’ necessitates new approaches, models, and methods with greater explanatory potential. Anomalies require ‘new theories’ altogether (pp. 67-8). In other words, scientific paradigms remain in place through the conduct of so-called ‘normal science’ (p. 109) until an accumulation of new anomalies — observable phenomena (i.e., empirical data) that the existing paradigm and its analytical frameworks cannot explain — create a ‘state of growing crisis’ (p. 67) and a ‘pronounced professional insecurity’ in scientific communities characterized by heated debates into epistemological truths. According to Kuhn, the emergence of ‘anomalous experiences may not be identified with falsifying ones’ (p. 146); as a consequence, he explicitly sets his theory of scientific development apart from the typical scientific tenets of testability and falsifiability formulated by Austrian-British philosopher of science Karl Popper in The Logic of Scientific Discovery (1959). Kuhn writes that ‘the scientist in crisis will constantly try to generate speculative theories that, if successful, may disclose the road to a new paradigm’ (p. 87). The uncertainty of scientific innovation and discovery during this ‘crisis-state’ (p. 86) persists until researchers finally arrive at the point of the scientific revolution, where consensus on a new paradigm supersedes panic over the validity of the old, fundamentally shifting the field’s foundational principles toward models and methods with more explanatory power.
A concrete example of an anomaly that Kuhn provides is the discovery of oxygen by eighteenth-century French chemist Antoine Lavoisier (pp. 86-92). Lavoisier’s experimental design and corresponding discoveries did not align with the expectations set by the prevailing ‘phlogiston theory,’ which predicted that metallic ores released a fiery substance called ‘phlogiston’ when burned (pp. 99-100). Lavoisier observed through experimentation how heating ‘red oxide of mercury’ produced a gas that did not fit the phlogiston theory (pp. 53-7). This anomaly led to the recognition of oxygen, a discovery that contributed to a scientific revolution — a paradigm shift from the phlogiston theory to modern chemistry. In another instance, Kuhn mentions the discovery of X-rays by German physicist Wilhelm Röntgen in 1895 – another anomaly at this stage of scientific progress. Röentgen noticed that a screen spontaneously began glowing during his experiments with ‘cathode rays,’ a phenomenon that could not be explained by existing models (pp. 57-8). His subsequent discovery of X-rays introduced a new area of research and necessitated changes in both experimental procedures and scientific understanding. Both examples illustrate how limitations and gaps in knowledge are the norm when on the cusp of revolutionary discovery, alongside curiosity and the will to solve the puzzle before one’s eyes or in one’s mind.
In Kuhn’s view, paradigm shifts constitute not simply mere additions to the public corpus of scientific knowledge. On the contrary, these shifts — he demonstrates through an examination of ‘the Copernican, Newtonian, chemical, and Einsteinian revolutions’ (p. 66) — are transformative events that redefine the problems scientists prioritize and the methods available for solving them. ‘The transition from Newtonian to Einsteinian mechanics,’ writes Kuhn, ‘illustrates with particular clarity the scientific revolution as a displacement of the conceptual network through which scientists view the world’ (p. 102). The shift from Newtonian mechanics to Einstein’s theory of relativity changed not only our understanding of physics but also how future research could be conducted. As Kuhn argues, these revolutions are a recurring and necessary part of scientific progress, marked by periods of stability, incremental discovery, and the fine-tuning of scientific models (‘normal science’) disrupted by bursts of revolutionary change that progressively aim toward greater certainty and more accurate approximations of the truth.
In the context of our evolving information technology age, artificial intelligence (AI) and its correlative ability to organize and interpret ‘big data’ (i.e., extremely large and complex datasets that require advanced computational tools to store, analyze, and extract meaningful insights) with large language models (LLMs) suggest that the incipient forces of a potential paradigm shift are currently under development. The massive amount of data generated and accumulated in the digital age challenges traditional methods of data processing and models of scientific analysis. AI, particularly in its ability to sift through and make sense of this data, could represent a Kuhnian sort of scientific revolution. Just as Einstein’s theories transformed physics, AI may reshape the scientific process itself, offering new frameworks for interpreting immense amounts of complex data that were previously beyond human analytical capabilities. Outside of the natural sciences, the application of AI to specific statistical methods or textual analysis in the social sciences, for example, suggests the feasibility of models with enhanced explanatory power and, as a consequence, better predictive applications to the complexities of the sociopolitical terrain.
But the question remains — will AI’s rapid development and integration into technological infrastructures lead to greater scientific enlightenment, or, like Porygon’s transformation into its unpredictable and unstable form, will it introduce complexities and risks that challenge our ability to discern between authentic understanding and artificial, algorithm-driven simulations of truth?
In the video game series, the original Porygon — a synthetic Pokémon designed to function as a digital life form that can navigate both the ‘real world’ and cyberspace — mirrors the progression of artificial intelligence. The creature was created by research scientists at Silph Co., ‘born’ out of some pretend computer code and programming language in an attempt to produce a human-made, self-sustaining entity resembling a simple, digital avatar of life, a synthetic recreation of forms derived from reality. Following their initial discoveries into the possibilities of creating artificial Pokémon life forms, these scientists worked on refining increasingly sophisticated iterations of their clunky, 8-bit prototype. Much like early AI, Porygon’s design was limited in its capacity to mimic reality. It performed basic tasks but remained a product of its creators’ code without any capacity for autonomous action or spontaneous creativity. It was only a rudimentary model of digital existence, yet one that represented the initial stages of a paradigm shift in so far as it confounded the scientific community of the Pokémon world with new possibilities of observable phenomena and processes once considered impossible.
As technology advanced, so did Porygon, evolving into Porygon2. This second form embodied a leap forward in computational power and technological capacity mirrored in the upgraded Porygon2’s adaptive applications. Porygon2 could learn from its environment, respond to stimuli with greater flexibility, and simulate the behavior of organic life forms with improved accuracy. It parallels the mid-point of AI development, where machine learning allowed computers to process more complex data sets and, for the first time, learn from experience. At this stage, Porygon2 was not merely executing commands — it was beginning to imitate aspects of cognition, a closer approximation of life in the digital space. Finally, Porygon-Z represents the most advanced form in this artificial being’s evolutionary chain. However, Porygon-Z became even more chaotic and unpredictable. The Bulbapedia entry for Porygon-Z states, ‘The resulting Pokémon began showing highly erratic, unstable behavior and twitchy movements, making it difficult to work with for research. As a result, it is believed the experiment to create Porygon-Z was a failure.’ The addition of new software expanded the range of the original Porygon’s capabilities. Yet, it also introduced unexpected glitches, perplexing irregularities (or ‘bugs’) and, as a result, presented unseen risks to those relying on this artificial and synthetic Pokémon life form to fulfill the same functions of protection, companionship, or reliable research as organic, ‘real’ Pokémon.
The story of Porygon-Z advances a compelling metaphor for the current state of artificial intelligence in relation to scientific development. While AI systems like large language models (LLMs) exhibit remarkable capabilities in data processing and interpretation, they also introduce new complexities and risks. In a word, the more AI systems evolve to mirror human-like reasoning, the more they expose the inherent unpredictability and fallibility of their simulations. This trajectory raises profound questions about the future of science, technology, and human interaction. Scientific development will undoubtedly benefit from AI’s ability to handle larger datasets, optimize calculations, and generate more refined models of the world that possess greater explanatory power or predictive capabilities. As Porygon-Z demonstrates, however, there is a risk that as our technological creations become more advanced, they might also lead us away from genuine human curiosity, creativity, and companionship and towards their synthetic substitutes.
Assuming the whirlwind pace of advancements in artificial intelligence signals an impending Kuhnian crisis-state, paradigm shift, and scientific revolution, I turn to Donna Haraway’s A Cyborg Manifesto: Science, Technology, and Socialist-Feminism in the Late Twentieth Century (1985) in search of theoretical insights that might tell us what to anticipate as well as how to navigate the years ahead. Haraway, for instance, argues that the boundaries between humans and machines are increasingly blurred in a technological age. In simple terms, our smartphones (for those who own one) have practically become extensions of our biological fingertips. We are also currently living in an age of augmented reality (AR), e.g., from Pokémon GO to the Apple Vision Pro, which renders entirely new synthetic sensory experiences for the human brain in virtual reality spaces. If the point is not clear enough already, consumers are already considering implanting a chip in their brains to help them seamlessly drift in and out of some cybernetic interface. It follows that Haraway’s concept of the cyborg ought not to be so farfetched as a descriptive term for explaining the normative relations established between humans and technology as they have evolved since the time she wrote her manifesto.
As it concerns this notion of ‘progress’ in science and technology, Haraway considers the cyborg as a sort of prefigurative figure suggestive of a future wherein humans and machines co-evolve and co-exist. ‘The cyborg is a matter of fiction and lived experience,’ she writes, such that the ‘boundary between science fiction and social reality is an optical illusion.’ Further down the page, she adds: ‘The cyborg is a condensed image of both imagination and material reality, the two joined centres structuring any possibility of historical transformation.’ Under Haraway’s socialist-feminist critical lens, this observation considered in relation to a Kuhnian scientific revolution suggests that ‘the traditions of ‘Western’ science and politics — the tradition of racist, male-dominant capitalism; the tradition of progress; the tradition of the appropriation of nature as resource for the productions of culture; the tradition of reproduction of the self from the reflections of the other’ — may be replaced by new principles and new forms of scientific inquiry, production, and political activity during the paradigm shift. A revolution in scientific knowledge may, at times, be a constitutive component of social revolution. (As an aside, while an important debate must take place on the subject of ‘automation,’ it is interesting to note that certain strains of radical anarcha-feminism have placed automation at the crux of a utopian society. One such example, and a particularly misandrist one at that, is found in the SCUM Manifesto by Valerie Solanas – who gained prominence after she shot Andy Warhol. The phrase SCUM is apocryphally reported to be an acronym for ‘Society for Cutting Up Men,’ although that phrase is found nowhere in Solanas’ actual manifesto).
While this synthesis between humans and machines presents possibilities for liberatory social transformation and scientific development, it also comes with serious ethical concerns. Haraway suggests that ‘in this world of protean transformation,’ the concept of the cyborg may also take a repressive turn, converting human individuality and difference into something synthetic, programmed, and codable, controlled by corporate or reactionary political interests: ‘The new technologies seem deeply involved in the forms of ‘privatization’… in which militarization, right-wing family ideologies and policies, and intensified definitions of corporate (and state) property as private synergistically interact.’ The safeguard against this turn, in the end, is human agency. Haraway concludes, ‘The machine is not an it to be animated, worshipped, and dominated. The machine is us, our processes, an aspect of our embodiment. We can be responsible for machines; they do not dominate or threaten us. We are responsible for boundaries; we are they.’
In the case of AI, this means that the more we rely on algorithmic interpretations of big data to process knowledge and adjudicate our truth claims, the more we risk losing the nuanced, organic aspects of scientific inquiry — those that rely on intuition, collaboration, and human creativity. To complicate matters further — drawing on the insights of French sociologist and philosopher Jean Baudrillard, author of Simulations (1983) — our increasing reliance on artificial intelligence raises the unsettling prospect that we may be left with nothing but simulations of truth and the appearance of reality rather than truth or reality itself, as the lines between reality and its artificial replicas are increasingly blurred, as the noumenal somehow melts and slips into the phenomenal and vice versa in a sort of surreal, reality-bending, dreamlike dialectic. Like Porygon’s creation, AI systems may become powerful tools for simulating reality, so powerful that they may even be forming the technological basis for a nascent scientific revolution. But we must remain skeptical about mistaking these intricate simulations for brilliance, genius, or truth. While the burgeoning capabilities of artificial intelligence are impressive, one must resist the temptation to become entranced by contrived reproductions of reality. Moreover, we should not hesitate to criticize the invalidity or unreliability of these artificially spliced-together representations of reality whenever they contravene our conceptions of truth or overstep some ethical concern.
In this sense, Porygon-Z’s transformation from a simple 8-bit being to a complex and unstable digital lifeform serves as a cautionary tale. It symbolizes the immense potential and risk inherent in the evolution of AI, and it reminds the reader that the scientific revolutions observed by Kuhn have harbored the knowledge claims undergirding both the most creative and the most destructive aspects of technological progress. Just as paradigm shifts redefine our understanding of the world, technological advancements in areas like AI, AR, virtual reality, or even smartphones challenge us to reconsider what it means to be human in an age where our intellectual curiosities, creative processes, and social relations are becoming increasingly mechanized, synthetic, and digitally-driven.
[I am citing these texts: Kuhn, T.S. (1970). The Structure of Scientific Revolutions (Second Edition, Enlarged). In International Encyclopedia of Unified Science, Vol. 2, No. 2 (eds. O. Neurath, R. Carnap, and C. Morris). Chicago, IL: University of Chicago Press & Haraway, D. (1985). A Cyborg Manifesto: Science, Technology, and Socialist-Feminism in the Late Twentieth Century. In Simians, Cyborgs and Women: The Reinvention of Nature (1991). New York: Routledge, pp. 149–181. Available at https://theanarchistlibrary.org/library/donna-haraway-a-cyborg-manifesto (linked above)].
