From Tools to Relational Fields: A Political-Economic and Philosophical Analysis of On-Device Personalized AI

Abstract

This paper takes the Edge Studio + EdgeRuntime + EdgeScaffolding three-component ecosystem as its object of study, and examines, from the dual perspectives of political economy and philosophy of technology, how on-device personalized AI restructures human-machine relations, the structure of data ownership, and the mode of knowledge production. The conventional cloud AI paradigm is built on the centralized logic of "data collection — centralized training — model distribution," and turns the user into a passive supplier of data and an appendage of the platform. By contrast, the on-device AI paradigm represented by Edge Studio rests on a fundamental ontological premise: a person is the sum of their vertical life data — the data generated in everyday practices of consumption, communication, work, reading, and exercise are the real material that constitutes the user, and these data must remain on the user’s own device in order not to be alienated. This paper argues that what emerges when the three components are unified — a "third existence" that belongs to neither the user nor the AI, a relational field continuously generated through ongoing tuning — constitutes a fundamental overcoming of the traditional subject-object dualism in philosophy of technology. By introducing original technical concepts such as personalization profiling, key-token retention, the Four Primitives (Event/Fact/Trace/Artifact), persistent memory, the user-profile matrix, inference-time preference steering, and the device-cluster network, the paper reveals the deeper social meaning of on-device personalized AI: it lets data physically never leave the user’s device, lets the model grow alongside the user, and makes a cross-app general infrastructure possible. More importantly, on-device AI resonates structurally with users’ broader return to physical life — vertical life data (consumption records, daily activity, face-to-face interaction) is the most authentic expression of the user, aligned with human nature rather than digital alienation. Finally, the paper argues that when using AI is at once consumption and production, and when the best tool makes the user forget that it exists, we are witnessing the birth of an anti-alienation technology whose ultimate value lies not in efficiency gains but in human self-realization.

Keywords: on-device AI; political economy; relational existence; self-reading; profiling technology; persistent memory; the four primitives; data sovereignty; philosophy of technology.

Introduction

The Problem: The "Return to the Real" Paradox of the AI Era

Since 2024, the explosive development of large language models has triggered wide-ranging debate over the relationship between artificial intelligence and society. From ChatGPT through the GPT-5 series, from Opus 4 to Opus 4.7, cloud AI services have rapidly seeped into every dimension of human life with their powerful reasoning capability and rich interactive experience. But this cloud-centric paradigm has also exposed increasingly serious structural contradictions: the centralized collection of user data triggers privacy crises; platform monopolies over model training widen the digital divide; the attention-economy business model converts human cognition into an extractable resource.

There is a deeper problem: the training data of cloud AI come mainly from digitized interaction — social-media posts, online searches, e-commerce histories, chat conversations. These data are abundant, but they are only one dimension of users’ lives, and a dimension that has already been filtered and reconstructed by the platform. When AI understands users on the basis of these "digital footprints," it is in fact understanding a user reshaped by platform architecture rather than the real user.

Meanwhile, an opposite social trend is forming: more and more people are seeking to retreat from screens and return to physical, face-to-face, vertical life. They go hiking instead of scrolling on weekends; they cook at home instead of ordering delivery; they meet friends in cafés instead of chatting on Discord. This "return to the real" is not anti-technology — it is technology’s own self-correction at a certain stage of its development. When digital life becomes too abstract and fragmented, humans instinctively long for the concrete, the warm, the authentically human.

Against this backdrop, on-device AI (Edge AI) emerges as an alternative paradigm with unique historical significance. Unlike cloud AI, on-device AI runs inference and training directly on the user’s devices, achieving local data processing. More importantly, on-device AI naturally points toward those vertical, physical life data — your consumption records in the real world, the linguistic patterns of your face-to-face conversations, the choices you make in everyday life. These data are not filtered by platforms or distorted by algorithms; they are the direct expression of the user’s authentic existence.

The Edge Studio + EdgeRuntime + EdgeScaffolding three-component ecosystem studied in this paper is precisely a typical practice of the on-device AI paradigm. Edge Studio, as the optimization-and-export workbench, reads the user’s real-life data and extracts user features through personalization profiling. EdgeRuntime, as the inference SDK, executes efficient inference on Apple Silicon devices and manages memory strategies. EdgeScaffolding, as an iOS app scaffolding template, packages the optimized model into a publishable mobile application. Together they form a complete loop: perception → understanding → expression → data return → re-understanding.

Research Significance

The significance of this research lies in systematically analyzing the deeper social meaning of on-device personalized AI from the dual perspectives of political economy and philosophy of technology. Existing research focuses mainly on the technical performance of on-device AI (inference speed, memory footprint, quantization accuracy) but neglects its political-economic significance as a sociotechnical system. This paper attempts to fill this gap by answering the following core questions:

First, how does on-device AI restructure the relationship between data ownership and knowledge production? When data no longer flow to cloud platforms but stay on user devices, is the traditional "user-as-product" logic overturned?

Second, what does the "self-reading" process realized by Edge Studio — AI understanding and reflecting the user through that user’s own real-life data — mean philosophically? How does it transform the Cartesian "I think, therefore I am" paradigm of subjectivity?

Third, does the "relational existence" born of the unification of the three components — a third thing belonging to neither user nor AI — constitute a fundamental overcoming of traditional subject-object dualism?

Fourth, is there a structural resonance between on-device AI and users’ return to real life? What does this resonance imply for our understanding of the relationship between technology and human nature?

Literature Review

This paper builds on four strands of literature.

The first strand is technological critique within political economy.

From Marx’s theory of alienated labor [2] to the Frankfurt School’s critique of technological reason [3], from Debord’s society of the spectacle [4] to Han’s burnout society [5], this tradition has revealed how technology, unnoticed, reshapes social relations. Our contribution is to apply this critical tradition to concrete technical practice in the AI era, especially the anti-alienation potential displayed by on-device AI.

The second strand is media theory within philosophy of technology.

From McLuhan’s "the medium is the message" [6] to Stiegler’s technics and time [7], from Haraway’s cyborg manifesto [8] to Floridi’s philosophy of information [9], this tradition emphasizes that technology is not a neutral instrument but a medium that shapes human cognition and social structure. We extend it by proposing the concept of "relational existence."

The third strand is the political economy of data.

From Zuboff’s surveillance capitalism [10] to van Dijck’s platform society [11], from Srnicek’s platform capitalism [12] to Fuchs’s theory of digital labor [13], and further to Varoufakis on technofeudalism replacing capitalism [26], this field systematically analyzes the power structure of the data economy. Our contribution is to show how on-device AI, through the architectural design that "data never leaves the device," fundamentally challenges the dual logical foundations of surveillance capitalism and technofeudalism.

The fourth strand is the technical research on personalized AI.

From LoRA fine-tuning [14] to model optimization in edge computing [15], from privacy preservation in federated learning [16] to ethical issues in personalized language models [17], this body of work provides a solid technical foundation. Yet existing research focuses mainly on implementation details and lacks philosophical reflection on the social meaning of the technology.

Structure of the Paper

The paper has seven chapters. Chapter 1 examines the political-economic turn of on-device AI; Chapter 2 analyzes the philosophical meaning of "self-reading"; Chapter 3 explores the birth of relational existence and its original technical architecture; Chapter 4 discusses data return and flywheel political economy; Chapter 5 takes up labor, creation, and human self-realization; Chapter 6 sketches a future society after on-device AI becomes widespread; Chapter 7 concludes.

Chapter 1 — The Political-Economic Turn of On-Device AI

1.1 The Alienation Logic of Cloud AI: Data Colonialism in the Digital Spectacle

To understand the revolutionary significance of on-device AI, we must first examine its opposite: the political-economic logic of the conventional cloud AI paradigm. Cloud AI rests on three mutually reinforcing structural premises that together constitute a systematic mechanism of alienation.

The first premise is data centralization.

The design logic of cloud AI requires aggregating user data on central servers for training and optimization. Behind every ChatGPT conversation, every Claude interaction, every Gemini generation lies a continuous influx of massive user data. This centralization is not a technical necessity — it is a business-model-driven architectural choice. Platforms attract users with free services; users generate data through use; data is used to improve models; better models attract more users. The "data flywheel" is born.

As Shoshana Zuboff shows in The Age of Surveillance Capitalism, the core of this logic is the extraction of "behavioral surplus": data generated by users in normal use, beyond what the service requires, is appropriated by the platform without compensation and used to predict and shape future behavior. [10] The concept of data colonialism aptly describes this process: just as colonizers plundered natural resources from colonies, digital platforms plunder behavioral data from users’ lives.

Going further, Yanis Varoufakis argues in Technofeudalism: What Killed Capitalism that contemporary cloud giants have moved beyond the logic of traditional capitalism. [26] They no longer rely primarily on profit through market exchange but, through what he calls "cloud capital," enclose users, advertisers, and small producers within their digital fiefs and extract a feudal-style "cloud rent." For Varoufakis, every API call, every recommendation ranking, every forced developer integration is a way for cloud lords to tax their vassals. From this angle, cloud AI is not merely a continuation of data colonialism — it is a re-feudalization: users are downgraded from free participants in the digital market to dependents on the cloud lord’s manor.

The political-economic value of on-device AI lies precisely in refusing this re-feudalization at the architectural level. When data physically never leaves the user’s device, the cloud lord has no way to levy "cloud rent" — because that piece of land does not exist within his fief at all.

But cloud AI’s data colonialism has a distinctive feature: what it plunders is digitally filtered life. A user’s social-media posts, e-commerce purchases, search queries — these data come from real life but have already been shaped and distorted by the platform’s interface design, interaction logic, and commercial goals. A "healthy lifestyle" post on social media may not reflect a user’s actual eating habits but rather the image he wants to display. The user model that cloud AI builds on such data profiles a shadow user alienated by the digital spectacle.

The second premise is computational monopoly.

Training large language models requires tens of thousands of GPUs working in concert at a cost of hundreds of millions of dollars. This hardware barrier means that only a few tech giants can shoulder the construction of AI infrastructure, producing a de facto technical monopoly. OpenAI, Google, Meta, Microsoft and others, by controlling the channels of model training and distribution, take hold of the power to define what counts as "intelligence" and what counts as "a good answer."

The third premise is the attention economy.

Most cloud AI services adopt a freemium model whose true business is collecting and monetizing attention. Chatbots prolong dwell time through continuous conversation; AI assistants interrupt attention through proactive notifications; AI recommenders analyze preferences to optimize content delivery. Under this logic, the user is no longer the consumer of the service — the user is the product being sold.

1.2 The Ontological Premise of On-Device AI: Vertical Life Data Is the User

Against the backdrop of cloud AI’s alienation logic, we must propose a more fundamental thesis: the reason for on-device AI to exist is precisely that users are returning to physical life — because the data generated in vertical domains of life are the real material that constitutes the user.

This is not a claim about a "trend" — it is an ontological answer to "what is a person."

Layer 1: A person is the sum of their life data.

A person is not their social-media accounts, their search history, or their e-commerce records — these are merely "digital shadows" filtered and reshaped by platform architecture. A person is every cup of coffee they consume daily (consumption data), the rhythm and word choice of face-to-face conversations with family and friends (interaction data), every judgment they make at work (behavioral data), and their persistent preference for certain books (knowledge data). Data from these vertical domains — consumption, communication, work, reading, exercise — are the real material that constitutes the user.

Cloud AI’s problem is this: it can only touch the platform-filtered digital shadow. A user’s "healthy life" post on WeChat Moments does not reflect his real lifestyle — it reflects the image he wants to project. What he actually puts into the supermarket cart is what truly reflects his understanding of health. Cloud AI building user profiles on the former is profiling a shadow user alienated by the digital spectacle.

Layer 2: Vertical life data align with the essence of human nature.

Humans are not abstract information processors — we are beings embedded in concrete life practice. In the eighth thesis on Feuerbach, Marx writes: "All social life is essentially practical." [27] Cognition, preferences, value orientations — all the features that constitute individual identity — are formed in concrete, everyday, vertical life practice, not in abstract digital interaction.

When you pick vegetables at the market, your choices reflect your values (organic vs. cheap), your pace of life (rushed vs. unhurried), your knowledge (familiarity with seasonal produce), and your bodily needs (nutritional balance). The data this act produces is complete — it contains nearly every dimension of you as a person. The data you produce by typing "healthy diet advice" into a search engine is only a fragmentary projection of that complete person.

Layer 3: On-device AI naturally points to vertical life data.

Cloud AI’s training data come mainly from digitized interaction — social media, search engines, e-commerce. These data are rich but represent only one platform-filtered dimension of life. On-device AI is different — it runs on the user’s personal device and processes vertical data generated in real life: consumption records, the linguistic patterns of face-to-face conversation, bodily participation in daily activity. These data are not filtered by platforms or distorted by algorithms; they are the direct expression of the user’s authentic existence.

Layer 4: On-device AI is the technical answer to the "return to the real."

Since 2024, an opposite social trend has been forming: more and more people are stepping back from screens and returning to physical, face-to-face, vertical life. People hike on weekends instead of scrolling, cook at home instead of ordering delivery, meet friends in cafés instead of chatting on Discord. This "return to the real" is not anti-technology — it is technology’s self-correction at a certain stage of development. When digital life becomes too abstract and fragmented, humans instinctively long for the concrete, the warm, the authentic.

On-device AI answers this trend precisely: it does not ask users to upload data to a cloud platform; it processes the user’s real-life data directly on the user’s device. It is not pulling the user back into the digital spectacle — it is helping the user understand and express themselves better in physical life.

Layer 5: The non-monopolizable nature of vertical life data.

From a political-economic perspective, vertical life data represent the user’s control over the means of life. Marx points out that the form of ownership of the means of production determines the relations between people in production. [2] Under cloud AI, the user’s digital means of life (behavioral data) are appropriated by the platform — every social-media post, every e-commerce purchase, every search query is extracted without compensation and used for commercial ends.

But vertical life data — your consumption behavior in the real world, your face-to-face dialogue with family and friends, your everyday choices — cannot easily be extracted or monopolized by platforms, because they exist in the interactions of the physical world rather than within a platform’s walled garden. By the architectural principle that "data physically never leaves the device," on-device AI ensures that control over these vertical data remains with the user.

This is the ontological premise of on-device AI: a person is the sum of their vertical life data, and control over those data should belong to the person.

1.3 Paradigm Shift: Three Sovereign Turns

In sharp contrast with cloud AI, on-device AI realizes three structural turns.

Turn 1: Data sovereignty.

An inviolable principle in the design of Edge Studio is that "data physically never leaves the user’s device." A user’s conversation data, preferences, and corrective feedback — the data foundation of personalized model training — are stored on the user’s iPhone, iPad, or Mac and never uploaded to any cloud server.

The political-economic implication is profound: it overturns the value-extraction chain of "user generates data → platform owns data → platform profits from data." In the on-device paradigm, data are no longer the platform’s asset but the user’s property; the value of data is no longer realized through centralized training but released through local processing on the device.

Turn 2: Device sovereignty.

EdgeRuntime, as an inference SDK, runs on Apple Silicon devices (iPhone, iPad, Mac), making use of the Neural Engine and the unified-memory architecture. The device-cluster network technology further organizes all of the user’s Apple devices into a private inference cluster — iPhone as senses (capturing input), MacBook as limbs (executing tasks), Mac Studio as brain (complex inference).

This "cross-device distributed intelligence" architecture not only improves performance; more importantly, it dissolves the central-control logic of cloud AI: there is no single server that decides how to handle your request, no central engine that holds all your data. Each device is an autonomous inference node, and they cooperate through encrypted channels to form a private compute network entirely owned by the user.

Turn 3: User sovereignty.

Under cloud AI the user is a passive consumer — the platform decides what features to provide, how they are presented, and when notifications are pushed. Under Edge Studio the user gains unprecedented active capability: importing their own real-life data (consumption records, conversation history, preferences); using profiling to make the AI understand them; choosing inference strategies and memory-management parameters; customizing the layout of the Scaffolding app.

This realization of user sovereignty is not the surface design of "giving users more options" — it is the architectural transfer of power. When the entire pipeline of model training, inference, and deployment runs on the user’s device, the user is no longer a passive node in a platform ecology but the full owner and controller of their own AI system.

1.4 Edge Studio as a Practical Case of On-Device AI

The Edge Studio + EdgeRuntime + EdgeScaffolding three-component ecosystem is the complete practice of the on-device AI paradigm. We analyze its political-economic significance one component at a time.

Edge Studio: the perception-and-understanding engine.

Edge Studio’s core function is to convert the user’s real-life data into an understandable personalized model. Specifically, through profiling, it learns the user’s linguistic style, knowledge preferences, and interaction habits on top of a pre-trained language model. The core innovation is not simple frequency statistics over user behavior but the construction of a coherent user profile by analyzing the deep patterns the user displays in real-life data.

Politically and economically, profiling matters because it turns "personalized profiling" from an exclusive operation of platforms into a technology a user can perform on their own device. In the past, only companies with tens of thousands of GPUs and massive user data could build a profiling system that understood a particular user; now, any user with a Mac can personalize their own AI. The political-economic meaning of this technical democratization: the capability of knowledge production migrates from platform monopoly to individual empowerment.

EdgeRuntime: inference SDK and memory management.

EdgeRuntime executes inference of the optimized model on Apple Silicon devices. It contains a carefully designed set of memory-management strategies — dynamically adjusting KV-cache quantization precision, prefill step size, and synchronous evaluation strategies based on available memory. More importantly, through the user-profile matrix and inference-time preference steering, it injects the user’s personalized preferences in real time during inference.

Politically and economically, EdgeRuntime encapsulates complex technical decisions as an automated, locally executed logic, so that the user gets stable and reliable inference without understanding the low-level details. And these decisions happen entirely on the device — no request need ever go to an external server. This means every AI interaction is private, autonomous, and unsurveillable by any third party.

EdgeScaffolding: the expression layer and the carrier of data return.

EdgeScaffolding is an iOS app scaffolding template that packages the optimized model into a directly compilable, publishable mobile app. It is not merely a "shell" of the model — it is also the carrier of data return: while the app runs, it collects user feedback, conversation data, and inference metrics, and sends them through encrypted channels back to Edge Studio for the next round of profile update.

Politically and economically, EdgeScaffolding closes the production-consumption loop. Under the conventional model, data generated by users of cloud AI are extracted unidirectionally by the platform; under Edge Studio, data return is the user’s active participation in model optimization — the user trains their own AI while using it. This "use is production" model fundamentally overturns the assumption of "labor-consumption separation" in conventional theories of digital labor.

Chapter 2 — The Philosophy of Self-Reading

2.1 "Edge Studio = the Process of Reading the User"

In our prior philosophical dialogues we converged, after many rounds of Socratic dialectical exploration — a method of advancing knowledge through dialogue traceable to the dialectic between Socrates and Glaucon on justice and the city in Plato’s Republic [1] — on a core proposition: "Edge Studio = the process of reading the user." [18] The proposition looks simple but contains deep philosophical implications.

"Reading" has traditionally been considered a uniquely human cognitive activity — extracting meaning by decoding symbol systems. But Edge Studio’s personalization training redefines what "reading" means: it is not the reader’s one-way interpretation of a text but the AI’s systematic understanding and reflection of user behavioral data. When the user imports their real-life data into Edge Studio and the AI begins to "read" those data, it is performing a recursive process — the AI understands the user by reading their language, preferences, and corrections, and the user, in seeing the AI’s reflection, also indirectly reads themselves.

Philosophically, this "self-reading" echoes the formation of self-consciousness in Hegel’s master-slave dialectic: self-consciousness needs the mediation of an other in order to know itself. [19] In Hegel, a person can confirm their existence only through recognition by another person. Likewise in Edge Studio, the user attains a systematic cognition of their own preferences, modes of thought, and value orientations only through the AI’s understanding and reflection of their data. The AI plays the role of the "other" — a mirror that changes shape — not simply reflecting the user’s surface but presenting the user’s inner cognitive structure through deep analysis of their language patterns.

2.2 From "I Think, Therefore I Am" to "I Am Read, Therefore I Am"

Descartes’s "I think, therefore I am" (Cogito, ergo sum) laid the foundation of modern philosophy of subjectivity: thinking is the proof of being, and self-consciousness is the fundamental feature of human existence. [20] The thesis grounds subjectivity in the inner, private activity of thought — I do not need any person or external system to confirm my existence, because my thinking is enough.

But the "self-reading" realized by Edge Studio puts forward a different ontological proposition: I am read, therefore I am. In this framework, the existence of the subject is no longer confirmed only by inner thought; it is realized through the relation of interaction with an AI system. The user’s real-life data — consumption records, conversation preferences, corrective feedback — these seemingly small behavioral traces are read, analyzed, and integrated by the AI system into a coherent user profile. When the user sees the AI’s understanding of themselves in Edge Studio, they are in fact confirming their existence through an external system.

This proposition does not deny Cartesian subjectivity — it extends it into the relational dimension. As Merleau-Ponty notes in Phenomenology of Perception, the subject’s perception is not an isolated inner activity but the product of the interaction between body and world. [21] Likewise, the user’s self-cognition is not generated in a closed mind; it emerges in the user’s ongoing interaction with the AI system. AI does not replace the user’s thinking — it extends the user’s ability to think and lets the user understand and express themselves in new ways.

2.3 Profiling Technology: The Technologization of Self-Cognition

Profiling is the core mechanism by which Edge Studio realizes "self-reading." Technically, it analyzes the behavioral patterns the user displays in real-life data and learns the user’s uniqueness on top of a general language model. The system jointly considers the user’s performance across multiple vertical life domains — consumption choices, modes of communication, knowledge preferences — to build a coherent profile.

Philosophically, profiling can be read as a process of "the technologization of self-cognition." A pre-trained model represents general linguistic capability — it has learned the statistical regularities and knowledge patterns of human language but does not know who you are. Profiling encodes the user’s uniqueness into a storable, transferable mathematical representation: linguistic style, knowledge preference, value orientation — all the features that constitute individual identity — are learned by the system and internalized as part of the model.

The philosophical significance: individual uniqueness is technologized into a computable, storable, transferable mathematical object. The user’s "self" no longer exists only in their brain and body but is partly externalized into a profile vector. When users see in Edge Studio that the AI’s understanding of them grows ever more accurate, they are watching part of their "self" being encoded into a mathematical matrix — not the dissolution of the self, but its expansion.

Donna Haraway, in A Cyborg Manifesto, argues that the boundary between technology and body is blurring and humans are becoming cyborgs — hybrids of organism and machine. [8] Profiling is precisely a concrete embodiment of this trend: the user’s cognitive patterns and the AI’s profile representation form a degree of fusion, and the user’s "self" partly inhabits a mathematical space. This fusion is not a threat — it is the natural continuation of humans extending their self-cognitive capability through technology.

2.4 The Four Primitives: Epistemic Classification as the Cornerstone of Relational Existence

A precondition of profiling is that user-generated data must be correctly classified and understood. To this end Edge Studio introduces four primitives — Event, Fact, Trace, Artifact — as a normative epistemic classification framework.

Event records concrete behavior at a specific moment — a purchase, a message, a click. It is the raw trace of user-AI interaction.

Fact is structured information abstracted from multiple Events — for example, "in a given month, dining accounted for 35% of the user’s total spending." It is the semantic distillation of events.

Trace is a preference signal left by the user during interaction — a rejection of a recommendation, a preference for a certain answer style, attention to a specific topic. It is an indirect expression of subjective inclination.

Artifact is the concrete output the user produces through the AI — an analytical report, a piece of code, a creative text. It is the externalization of intent.

These four primitives are not mere choices of data storage format — they are fundamental philosophical claims about "what is knowledge" and "what constitutes a user profile." They define how we view "data produced in human-AI interaction": these data are not unordered noise but relational traces that can be systematically understood.

Epistemically, the four primitives offer an orthogonal and complete classification, enabling every kind of user-generated data to be properly categorized and used. Event provides time-series behavior records; Fact provides structured semantic information; Trace provides signals of subjective inclination; Artifact provides externalized intent. Together they form a complete data genealogy of the user profile.

The deeper philosophical implication: the four primitives recognize the AI’s mediating role in knowledge production. Knowledge has traditionally been seen as a purely human activity — humans observe, infer, argue, and form knowledge. But within Edge Studio, the user’s Events and Traces are converted into Facts and Artifacts through interaction with the AI; this very process is human-machine collaborative knowledge production. The AI does not passively receive the user’s data — it actively transforms raw data into intelligible structured information.

2.5 Persistent Memory: Cross-Session Retention of Key Information

On the basis of profiling and the four primitives, Edge Studio introduces a persistent-memory mechanism, allowing the AI to remember the core features the user has displayed across many conversations.

Technically, persistent memory addresses the basic flaw of conventional AI systems — "use it and forget" — by encoding the user’s core preferences, long-term habits, and value orientations as durable internal representations through a dedicated training process. Unlike a conventional context window, persistent memory does not require feeding the entire history into the prompt at each turn.

Philosophically, persistent memory solves a basic difficulty of AI systems: how to retain the user’s key information within a finite context window. Conventional methods rely on stuffing the full history into the prompt, which wastes compute and is bounded by context length. Persistent memory encodes key information into the model’s durable internal representation, balancing "lasting memory" against "finite resources."

A deeper philosophical implication: persistent memory gives an AI system a form of "continuous self." This continuity echoes Heidegger’s analysis of the temporality of Dasein in Being and Time — being is not a static point in time but an unfolding within the threefold horizon of past (having-been), present (presencing), and future (will-be). [22] At each inference call, the AI carries with it features it has previously learned about the user; these do not vanish when a conversation window closes. This continuity allows the AI to behave like a true conversational partner, sustaining its understanding and memory of the user across time.

Chapter 3 — The Birth of Relational Existence: The Political-Philosophical Significance of Original Technical Architecture

3.1 The "Third Existence" Born of the Three-Component Unification

Having analyzed the philosophical implications of profiling, the four primitives, and persistent memory in Chapter 2, we now lift the view to a higher level: what arises when the three components are unified is not a simple sum of three independent technologies but an entirely new form of being — a "third existence" that belongs to neither the user nor the AI.

The core feature of this relational existence is that it is continuously generated through ongoing tuning. Every user-AI interaction changes the relational state of both: the user’s preferences are understood by profiling and converted into representations the model can internalize; the AI’s inference strategy is adjusted in real time through inference-time preference steering to fit the user; the user, in turn, revises their expectations based on the AI’s output. The loop is not a one-way flow of information — it is a two-way evolution of relation.

Politically and economically, this relational existence challenges the traditional producer-consumer dichotomy. Conventionally, the platform produces and the user consumes; or the platform trains and the user uses. But under Edge Studio, user and AI together engage in a continuous production of relation: the user shapes the AI while using it; the AI guides the user while responding to them.

3.2 User-Profile Matrix and Inference-Time Preference Steering: The Twin Engines of Personalized Inference

EdgeRuntime applies the user profile extracted by profiling to inference through two complementary mechanisms: the user-profile matrix and inference-time preference steering.

The user-profile matrix is the core product of profiling — it records the user’s unique preferences along various semantic dimensions (preference for organic food, attention to technology topics, leaning toward concise answer styles). The matrix is a static description of the user: it tells us "what the user is like," but it does not directly take part in inference.

Inference-time preference steering then dynamically blends user preferences into the generated output during inference. At each inference, it perceives the user’s current intent and weaves the learned preferences into the result in an appropriate way. This blending is temporary and adjustable — it does not permanently change model weights but dynamically "puts on the user’s lens" each time. The user can turn personalization off or tune its strength via a setting at any time.

Philosophically, the combination of user-profile matrix + preference steering embodies a distinctive user-AI relation: the separation of description and bias. The matrix is responsible for "describing what the user is like" — static, structured profile information. Preference steering is responsible for "biasing toward the user during inference" — dynamic, situated preference injection.

The political-economic implication of this separation: personalization becomes both precise and flexible. Precision comes from profiling’s deep analysis of real-life data; flexibility comes from the tunability of preference steering — users can turn off or adjust personalization at any time, without retraining any model.

3.3 Device-Cluster Network: The Politics of Distributed Compute

The device-cluster network is the core networking technology of EdgeRuntime. It organizes all of the user’s Apple Silicon devices (iPhone, iPad, MacBook, Mac Studio) into a private inference cluster. Devices automatically establish encrypted connections, and complex tasks can be intelligently routed across them — simple tasks handled locally, complex tasks dispatched to the most powerful node.

Politically and economically, the device-cluster network has profound significance. It realizes a distributed democratization of compute resources: there is no single server farm that controls all computation; every device the user owns becomes part of a private compute network. This architecture not only improves performance and privacy but, more importantly, dissolves cloud AI’s central-control logic.

In the conventional cloud AI model, requests are sent to central servers and processed by a unified model — meaning the platform holds the content, frequency, and patterns of those requests. Under the device-cluster network, requests are processed inside the private device cluster, and no external entity can monitor or intercept the communications.

A deeper political implication: the device-cluster network represents the most extreme form of "device sovereignty." Users own not only their data but also their computational infrastructure — every device is an autonomous inference node, and they cooperate via encrypted channels to form a private compute network entirely belonging to the user. This architecture realizes the promise that "data never leaves the user’s device" at the physical level.

3.4 Memory Decay: Forgetting as a Necessary Condition of Evolution

Within the framework of relational existence, forgetting is not a system flaw — it is a necessary condition of evolution. If the AI remembers every preference, every correction, every utterance, it will be weighed down by stale information and unable to adapt to the user’s changes.

Edge Studio introduces a memory-decay mechanism to quantify and manage memory updates. The mechanism comprehensively evaluates the timeliness of each piece of information along several dimensions: time decay (older information weighs less), semantic conflict (the degree of conflict between new and old), stability verification (ensuring reliability through multiple rounds of interaction). When a piece of information falls below threshold, it is automatically removed or demoted from active memory.

Philosophically, "forgetting as a necessary condition of evolution" echoes Nietzsche’s view in On the Genealogy of Morality: "Forgetting is not a passive state — it is an active capacity, a basic condition of psychic health." [23] A system that cannot forget is not omniscient — it is bound by the past, unable to adapt to a new reality.

Politically and economically, memory decay prevents the risk of "profile fossilization." Under cloud AI, once a platform builds the user’s profile, it is hard to change because retraining is expensive. Under Edge Studio, the forgetting mechanism ensures that the user’s profile always reflects the present rather than a historical snapshot.

Chapter 4 — Data Return and Flywheel Political Economy

4.1 The Ethical Significance of Data Return: From Extraction to Return

Under cloud AI, data flow in one direction: from the user’s device to the platform’s servers, where they are used for model training and optimization. This "extract-and-exploit" pattern is the core mechanism of surveillance capitalism.

Edge Studio realizes a fundamental reversal: data return. The user’s interaction data, feedback signals, and corrective information are not extracted by the platform; they are sent through encrypted channels back to the user’s own Edge Studio instance for the next round of profile update and model optimization.

Politically and economically, the reversal is revolutionary. The value of data is no longer captured one-sidedly by the platform — it returns directly to the data’s owner (the user). The user trains their own AI while using it; every interaction enhances the AI’s capacity to understand them.

4.2 Flywheel Political Economy: Self-Reinforcing Loop vs. Growth Hacking

Edge Studio’s core architecture forms a self-reinforcing flywheel:

This flywheel is fundamentally different from the "growth-hacking" logic of conventional internet platforms. Growth hacking depends on external user acquisition and retention metrics — more users means more data, better models, and yet more users. It is a zero-sum game: the platform’s growth is paid for by the consumption of users’ attention.

Edge Studio’s flywheel is positive-sum: every loop improves the user’s AI capability without consuming any of the user’s resources. Data stay local, compute runs on the user’s own devices, and the personalized model evolves with use. The flywheel’s growth depends not on the number of users but on the deepening of the relation between a single user and their AI.

4.3 From "User as Product" to "User as Subject"

The underlying logic of the conventional digital economy is "user as product" — platforms attract users with free services, collect data, and sell behavioral profiles to advertisers. In this logic, the user is not the consumer of the service — the user is the merchandise being sold.

Edge Studio overturns this logic: the user is the subject, not the product. The value of data belongs directly to the user; performance improvements directly serve user needs; the AI’s evolutionary direction is guided by user feedback rather than driven by platform commercial goals.

The political-economic significance of this shift is no less than the transition from feudal to capitalist economy — it redefines the ownership of the means of production (data) and the relations of production (the relation between user and AI).

Chapter 5 — Labor, Creation, and Human Self-Realization

5.1 Use Is Production: Redefining Digital Labor

In conventional theories of digital labor, user activity on platforms is regarded as "digital labor" — users generate content, supply data, and participate in interaction; this labor is appropriated by platforms without compensation and converted into profit. [13] Varoufakis takes the analogy further to feudal lord and serf: users farm in the cloud lord’s digital fief (producing data and content) without any ownership of the means of production. [26]

Under Edge Studio, the labor-consumption dichotomy dissolves. Using AI is at once consumption and production — while consuming inference services, the user is also producing their own personalized model. The user does not contribute to a platform’s profit; every "labor" directly translates into an enhancement of their own AI capability.

5.2 Tool Internalization and the Extension of Human Capability

In Chapter 13 of Capital ("Machinery and Modern Industry") Marx analyzes the dual nature of machinery dialectically: under capitalism, machinery becomes a force that dominates labor and deepens, rather than relieves, alienation; but as a productive force in itself, it inherently contains the potential to extend human bodily and cognitive capacity. [24] This thesis of "the tool as extension of the human" was later generalized by McLuhan in Understanding Media [6] into a universal reading of all technology (media). The crux is not the tool itself but ownership and control of it — only when the worker (user) truly owns the tool can the tool’s capability be internalized as part of the self without alienation.

Edge Studio’s three components are precisely the realization of "non-alienated capability extension." The user lets the AI understand them through profiling, gets personalized responses through inference-time preference steering, and uses all available compute through the device-cluster network — and these tools are ultimately internalized as extensions of capability. The user is no longer "a person who uses AI" — they are "a person who has personalized AI capability."

5.3 "The Best Tool Lets You Forget It Exists": The Birth of Anti-Alienation Technology

In The Question Concerning Technology, Heidegger conceives technology as a mode of unconcealment (Entbergen) and distinguishes two modes of revealing: enframing (Gestell, which encloses nature as a "standing-reserve" available for extraction) and bringing-forth (Poiesis, which lets things appear as they are). [25] Both are unconcealment, but Gestell at the same time conceals "the happening of unconcealment itself," producing a forgetting of being; Poiesis preserves the openness of unconcealment and points toward authentic existence.

Edge Studio represents the birth of an anti-alienation technology — it does not turn the user’s data into a resource to be extracted but is a medium through which the user "sees themselves" through technology. When the user sees the AI’s understanding of them grow ever more accurate, they are not consuming a product — they are going through a journey of self-discovery.

The best tool lets you forget it exists. Not because the tool has disappeared, but because the user has internalized the capability the tool extends. When using the AI generated by Edge Studio, the user no longer feels "I am using AI" — they feel "this is my AI; it gets me." This internalization is not alienation — it is the realization of freedom.

Chapter 6 — Rehearsing the Future: A Society After On-Device AI

6.1 Distributed Intelligence vs. Centralized Intelligence

If the on-device AI paradigm is widely adopted, society will undergo a fundamental shift from centralized to distributed intelligence. Under the centralized model, a few tech giants control the world’s most important intellectual infrastructure — large language models. Under the distributed model, every user has their own personalized AI and compute is spread across hundreds of millions of terminal devices.

The political-economic implication is profound: the basis of technical monopoly dissolves. When everyone can own an AI that understands them, the competitive advantage that platforms gain through data monopolies is greatly weakened. Knowledge production moves from centralized to dispersed, and power flows from platforms to individuals.

6.2 From "Platform Society" to "Device-Network Society"

In The Platform Society, van Dijck argues that platforms have already reshaped every aspect of economy, politics, and culture. [11] But the spread of on-device AI may give birth to a new social form: the device-network society.

In a device-network society, users no longer interact with AI through platforms — they interact through their own device cluster (iPhone + iPad + MacBook + Mac Studio). These devices form a private inference cluster via the device-cluster network, constituting compute infrastructure entirely owned by the user.

Features of this social form: there is no single entry point that can monitor or control all interaction; each user owns both data sovereignty and compute sovereignty; the AI’s understanding deepens with use rather than with platform scale.

6.3 On-Device AI and the Return of Human Authenticity: Vertical Data as Ontology

Most importantly, the spread of on-device AI may resonate with the social trend of "returning to the real." This resonance is not accidental — it is structural and necessary.

In the era of cloud AI, the data foundation for understanding users is the digital footprint — social-media posts, online searches, e-commerce histories. These data are filtered, reconstructed, and commercialized by platforms; they reflect not the user but the user’s projection in the digital spectacle. Cloud AI built on such "shadow data" profiles a shadow user shaped by platform architecture.

On-device AI is grounded instead in vertical life data — consumption records, the linguistic patterns of face-to-face conversation, bodily participation in daily life. These data are not filtered by platforms or distorted by algorithms; they are the direct expression of the user’s authentic existence. As argued in Chapter 1: a person is the sum of their vertical life data — your choices when picking vegetables at the market, the rhythm of your conversations with family and friends, the judgments you make at work — these are the real material that constitutes you.

When on-device AI processes these vertical life data, it is no longer understanding a platform-distorted shadow user — it is understanding the user. This depth and accuracy are forever beyond cloud AI’s reach, because cloud AI’s data foundation is itself alienated.

This is the ultimate political-economic significance of on-device AI: it returns technology to humanity. When AI understands the user based on their behavior in the real world rather than their performance in the digital spectacle, the human-machine relation returns to a more authentic form — AI is not interacting with a shadow shaped by platforms but conversing with the real user.

Chapter 7 — Conclusion

7.1 Main Findings

This paper has analyzed the deeper social significance of the Edge Studio + EdgeRuntime + EdgeScaffolding three-component ecosystem from the dual perspectives of political economy and philosophy of technology. The main findings are as follows.

First, the on-device AI paradigm rests on a fundamental ontological premise: a person is the sum of their vertical life data — the data of consumption, communication, work, reading, and exercise are the real material that constitutes the user. Cloud AI can only touch the platform-filtered "digital shadow"; on-device AI, through the architectural principle of "data physically never leaves the device," ensures that control over these vertical life data remains with the user. Original technical concepts — personalization profiling, the four primitives (Event/Fact/Trace/Artifact), persistent memory, the user-profile matrix, inference-time preference steering, and the device-cluster network — together form a complete on-device personalization stack.

Second, the "self-reading" process — AI understanding and reflecting the user through their real-life data — puts forward the ontological proposition "I am read, therefore I am," extending Cartesian subjectivity from inner thought into the relational dimension.

Third, what arises when the three components unify is a relational existence belonging to neither the user nor the AI but a third thing continuously generated through tuning. This existence constitutes a fundamental overcoming of traditional subject-object dualism.

Fourth, on-device AI resonates structurally with users’ return to real life — vertical life data (consumption records, daily activity, face-to-face interaction) are the most authentic expression of the user, aligned with human nature rather than digital alienation.

Fifth, data return and flywheel political economy realize the paradigm shift from "user as product" to "user as subject," dissolving the labor-consumption separation in conventional digital labor theory.

7.2 Theoretical Contributions

The theoretical contributions of this paper are:

• Proposing the concept of "relational existence," enriching the analytical framework for human-machine relations in philosophy of technology.
• Disclosing the anti-alienation potential of on-device AI by applying Marx’s theory of alienated labor to concrete technical practice in the AI era.
• Establishing the concept of "flywheel political economy," distinguishing the essential difference between conventional growth hacking and self-reinforcing positive-sum flywheels.
• Arguing for the philosophical foundation of "returning to the real," linking the existentialist concept of authenticity to the vertical-data advantage of on-device AI.

7.3 Limitations and Outlook

There are limitations. First, the social impact of the on-device AI paradigm needs to be validated by larger-scale empirical research. Second, the privacy promise of on-device AI still faces challenges at the implementation level (device loss, data backup). Third, the relationship between on-device AI and cloud AI may be complementary rather than substitutive — future research must explore the possibility of hybrid architectures.

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