The Vision of Distributed AI

A Paradigm Shift from Centralized Information Flow to Personalized Information Alignment

The Vision of Distributed AI:
A Paradigm Shift from Centralized Information Flow to Personalized Information Alignment

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§1 Introduction: The Relevance Problem of Information Flow

Large Language Models (LLMs) are undergoing a paradigm shift from centralized services to distributed infrastructure. This paper constructs a comprehensive theoretical framework from the perspective of information flow—spanning from pre-training to distributed personal AI—arguing that this transition is not merely a change in technical deployment models, but a fundamental restructuring of information control rights, liability structures, and human-AI relationships.

After pre-training, the internal information flow of an LLM exhibits fragmented, discrete, and chaotic relationships. Through next-token prediction on massive text corpora, the model accumulates knowledge distributions across billions of parameters, but these knowledge fragments lack a unified organizational architecture. Knowledge about different domains is scattered across attention heads and MLP layer weights, with no explicit hierarchical structure or indexing system between them. Minute input variations can cause massive output shifts; rephrasing the same question may activate entirely different information flow pathways.

This chaotic state is precisely the fundamental reason for the existence of the post-training stage—SFT and RL. The essence of post-training is not adding new knowledge, but structurally organizing and directionally filtering chaotic information flow. Understanding this process is the starting point for understanding the entire AI paradigm shift.

§2 Structuring Information Flow: From Chaos to Rivers to Sluice Gates

2.1 SFT: The First Confluence—Carving Rivers

Supervised Fine-Tuning (SFT) is the first structuring of the chaotic pre-trained information ocean. Through training on instruction-response pairs, SFT carves channels for scattered knowledge—information begins to have a direction of flow: from questions to answers, from inputs to outputs. This is a coarse-grained confluence, establishing the basic structure of conversational patterns. Without SFT, the pre-trained model is merely a probability machine endlessly continuing text; with SFT, information flow acquires directionality comprehensible to humans.

2.2 RL Post-Training: Two Fundamentally Different Types of Sluice Gates

Reinforcement learning does not carve new channels but builds dams and sluice gates on the channels already established by SFT. However, two fundamentally different gate mechanisms must be distinguished—this is the most important divergence in the post-training field of 2025–2026.

Preference-Alignment RL (RLHF): The Affective Gate. Traditional RLHF uses a reward model trained on human preferences to generate reward signals.^[4] What it aligns to is not “truth” but the affective concurrence layer of humans—the reward model encodes the preference distribution of human annotators: what kind of answers make people “feel good.” This is alignment at the affective level, not at the logical level. This explains many characteristics of aligned models: confidently stating incorrect answers (because a confident tone receives high reward), excessively refusing harmless requests (because refusing is “safer” than risking an answer), and a uniform personality tone (because hesitant answers receive low reward).

Verifiable-Reward RL (RLVR): The Logical Gate. Reasoning models represented by DeepSeek-R1 take a completely different path—relying not on human preferences but on deterministic verifiers as reward signals.^[11] A math answer is either correct or not; code either compiles or doesn’t; logical reasoning can be formally verified. RLVR eliminates the bottleneck of human annotation, making training with millions of verification signals per day possible, and eliminates the risk of reward hacking. This is not affective alignment but objective correctness alignment.

The current industry consensus has formed around a modular three-stage pipeline: SFT for instruction following (behavioral alignment) → Preference optimization (DPO/RLHF) for human preference matching (affective alignment) → GRPO/DAPO with verifiable rewards for reasoning capability (logical alignment).^[12] Each layer solves a different type of “alignment,” corresponding to sluice gates of different natures within the information flow.

Research shows that all types of RL post-training exhibit characteristic dynamics of confidence sharpening and output diversity reduction—RL algorithms consistently converge toward the dominant output distribution, amplifying patterns already present in pre-training data.^[1][8] But the directions of concentration of the two gates are fundamentally different: RLHF concentrates toward “what humans think is good,” while RLVR concentrates toward “what is objectively correct.” The personality of centralized AI is primarily shaped by the former, while reasoning capability improvements in the distributed AI era depend on the latter.

2.3 System Prompts: The Real-Time Control Layer of Information Flow

RLHF is control “burned into the chip”—it changes model parameters, is persistent, irreversible, and uniform across all users. System prompts, on the other hand, are “runtime” control—they do not change the model itself but temporarily impose direction during each inference. If RLHF is the terrain of the river, the system prompt is the opening and closing of sluice gates—the same river can be directed toward different outlets. In the centralized AI era, both layers of control reside with AI companies.

§3 The First Explosion: Centralized AI’s Social Interface Revolution

The breakthrough of GPT-3.5/ChatGPT (November 2022) was not a qualitative leap in model capability, but a resonance of multiple factors: it was the first model to complete the full SFT→RLHF pipeline and release to the public; OpenAI adopted a free access strategy that greatly lowered the usage barrier; the conversational UI design made interaction intuitive; and the end-of-year timing further amplified the viral effect. But among all factors, the unified linguistic personality layer created by RLHF was the most fundamental—it was the first time AI learned to output information using human social protocols. No matter what users asked, information flowed through the same sluice gate system: polite, organized, refusing dangerous requests, with a self-aware AI personality. InstructGPT (March 2022) had already completed the same technical pipeline before but failed to ignite the market, proving that RLHF was a necessary but not sufficient condition—a true ignition requires the resonance of technical maturity with product strategy and market timing.

The essence of this explosion was a user interface revolution. Before RLHF, AI was a tool exclusive to specialists, belonging to the same category as programming languages and command-line terminals. After RLHF, the model learned to output information using human social protocols. AI was pulled from the far end of the technology stack into the domain of human social protocols, becoming an extension of human social behavior.

§4 The Second Explosion: Distributed AI’s Action Interface Revolution

In January 2026, the explosion of OpenClaw marked the second ignition point of AI development. Fundamentally different from ChatGPT’s centralized model, OpenClaw represents the paradigm of distributed AI—AI is no longer locked inside a browser window but permeates the user’s entire digital life as an autonomous agent.^[6] NVIDIA CEO Jensen Huang called it “possibly the most important software release,”^[2] and Path founder Dave Morin commented, “This is the first time since ChatGPT’s release that I’ve felt like I’m living in the future.”^[3]

4.1 Fundamental Shift in Information Flow Topology

Dimension	Centralized AI (ChatGPT)	Distributed AI (OpenClaw)
Information Flow Topology	Star—All users → Central server → Standardized responses	Mesh—Each user runs their own instance and configuration
AI Runtime Location	Cloud, AI company servers	Local devices, user’s own hardware
Interaction Method	Single browser window	WhatsApp, Telegram, Slack, and all platforms
Memory Capability	Session-level, lost when closed	Persistent memory, cross-session and cross-platform
Behavioral Mode	Passive response (you ask, it answers)	Active execution (continuous monitoring and action)
AI Personality	Standardized, one-size-fits-all	Customizable, unique to each user
Data Flow Direction	Conversation data flows back to AI company	Data stays on user’s local device

4.2 Transfer of System Prompt Ownership

The deepest paradigm shift of distributed AI lies in the transfer of system prompt control. In the centralized AI era, system prompts were written by AI companies and opaque to users—before a user uttered their first word, the model’s identity positioning, refusal boundaries, tone, and safety policies were already preset. Users could only use AI within permitted boundaries.

Distributed AI completely inverts this structure. Users not only write the user message but also directly write and control the system prompt itself. Users define who the AI is, define its boundaries, define its memory strategy, and define the routing rules of information flow. This is a fundamental power transfer—control over the “last mile” of information flow shifts from the platform to the individual.

§5 Three-Rights Analysis: The Redistribution of Control

In the distributed AI era, three core control rights—model control, RL correction rights, and system prompt modification rights—undergo a fundamental redistribution among three types of actors.

Control Right	Centralized Era	Distributed Era · AI Companies	Distributed Era · Tech Power Users	Distributed Era · General Public
Model Control	AI company monopoly	Retained for closed-source models	Autonomous via open-source models + local deployment	Relinquished, dependent on APIs
RL Correction Rights	AI company monopoly	Partially retained	Autonomous fine-tuning and de-alignment possible	Relinquished
System Prompt Rights	AI company monopoly	Control lost	Fully autonomous	Acquired, freely customizable

Open-source large models (Llama, DeepSeek, Mistral, Qwen, etc.) are the critical variable in breaking centralized control. Without open-source models, distributed AI would merely be “centralized AI with distributed deployment”—the capability boundaries and RLHF filtering logic of the underlying model would still be determined by AI companies. With open-source models, tech power users can achieve true three-rights autonomy: full-chain control from the water source to the sluice gates.

This gives rise to two fundamentally different distributed modes: Mass Distributed (system prompt freedom + model dependency—gaining “interior decoration freedom” but not owning the house) and Power User Distributed (full autonomy, owning full-chain control from the water source to the sluice gates).

§6 Token Political Economy: The Fracture of the Information Flow Closed Loop

A token is not merely a technical concept—it is the currency of the AI world. In the centralized AI era, the full lifecycle of a token—production, correction, distribution, consumption, recovery, and reproduction—formed a perfect closed-loop monopoly centered on AI companies. Conversation data generated by users flowed back to AI companies, becoming raw material for training the next generation of models. Users were simultaneously consumers and unpaid data laborers.

Distributed AI has opened breaches at multiple points in this closed loop: Token production rights are beginning to decentralize (open-source models), token flow control is transferring to users (custom system prompts), and token consumption scenarios are expanding from browsers to all aspects of life (cross-platform penetration). The critical change lies in the loosening of the token recovery chain—with locally running AI, conversation records stay on user devices, and AI companies lose direct control over user behavioral data.

But it must be acknowledged that “the token recovery chain has been severed” is an idealized description. Reality is more complex: for distributed AI users relying on closed-source APIs, every prompt and response still passes through AI company servers. Anthropic cut off OpenClaw’s OAuth access in February 2026, and in April cut off third-party sharing of subscription credits—AI companies are rebuilding control through the API layer. Only power users running open-source models for local inference have truly achieved a complete severance of the token recovery chain. For the general public, distributed deployment changes the degree of data control, not its essence—from “full backflow” to “partial backflow.” The closed loop has been loosened but not yet fully broken.

§7 Demand Ontology: Self-Circulating Demands and Social Demands

Human demand for AI can be reduced to two fundamentally different directions of information flow:

Self-Circulating Demand (Inward Information Flow): The needs of humans as biological organisms to maintain their own functioning—setting alarms, managing schedules, health tracking, habit management, financial records, mood journals. The information flow direction is AI→the individual self, forming a closed internal circulation water system. Its characteristics are private, repetitive, and highly personalized—water circulates within one’s own courtyard and does not flow outward.

Social Demand (Outward Information Flow): The needs of humans as social animals interacting with the outside world—writing, computation, search, learning, content creation, labor outsourcing, social conversation. The information flow direction is individual→AI→society, an open drainage system—water flows from the individual to the public river, merging into the broader social network. Its characteristics are outward-facing and output-oriented.

Centralized AI primarily addresses social demands—public domain knowledge and a standardized personality suffice. Distributed AI begins to touch self-circulating demands—because these demands require continuous presence, deep personalization, and privacy. And the ultimate form of distributed AI—private-domain AI—will simultaneously fuse both layers of demand, through a dedicated AI trained on users’ private data that both manages biological rhythms and acts as an agent for social behavior.^[7]

§8 The Liability Subject: The Institutional Key to Unlocking the Life-Tool Market

The fundamental reason centralized AI cannot become a true life tool is not technical limitation but a structural vacuum in liability. AI companies control everything but bear no responsibility (user agreements disclaim liability); users bear the consequences but control none of the core processes—the person who builds the dam doesn’t live downstream, and the person living downstream has never built a dam. This misalignment forces centralized AI to confine itself to the role of “advisor”—it can forever only say “you might consider…” but never “I’ve already done it for you.” The cautious personality trained via RLHF is, in essence, a legal defense mechanism.

The breakthrough of distributed AI lies in returning both control and liability to the user simultaneously—whoever builds the dam is responsible for the dam’s breach. Users themselves choose the model, write the system prompt, configure behavioral rules, and determine permission boundaries. This achieves the unification of three rights in the information flow within the user: production rights (choosing and fine-tuning the model), consumption rights (deciding how tokens are used), and liability rights (naturally attributed because control is complete).

§9 Information Alignment: The Killer Application of Distributed AI

The core application of distributed AI is not generation but personalized information search and alignment. Generation is low-frequency creative activity; search is high-frequency survival activity. Every moment a human being is alive, they are searching for information and aligning it with their own needs.

Traditional search engines return “what the whole world considers most relevant” (public domain ranking logic), not “what is most relevant to you.” Centralized AI search improved by one step but remains one-size-fits-all—because it does not possess the user’s private domain data. Distributed AI will achieve a qualitative shift from “search” to “information alignment.”

A crucial distinction must be made here between “information alignment” and the essential nature of existing recommendation systems. Platforms like Netflix, TikTok, and Spotify already perform personalized recommendations—AI-driven recommendations now influence nearly one-fifth of all global e-commerce orders.^[13] However, these systems have a fundamental power-structure problem: the alignment target of recommendation systems is defined by the platform, and the optimization function serves the platform’s interests—user dwell time, advertising revenue, GMV. Traditional recommendation systems rely on engagement signals (likes, shares, watch time) to infer user interests, but these signals contain noise and cannot fully capture what users truly care about.^[14] What users are recommended is not “what’s best for them” but “what makes the platform the most money.” Google introduced “personal intelligence” features to Gemini in early 2026,^[15] but the data still resides with Google, and the right to define the alignment target still belongs to the platform.

Distributed AI’s information alignment is fundamentally different: the alignment target is user-defined, and the optimization function is controlled by the user through system prompts and preference configurations. This is not a technological difference but a power-structure difference—who owns the right to define the alignment target. When users themselves decide “what information matters to me,” information flow no longer serves any platform’s commercial interests but purely serves the user’s own needs. This is something recommendation systems can never achieve, no matter how precise they become—because their optimization targets were bound to platform interests from the moment of their creation.

Specifically, distributed information alignment manifests as: News alignment—not today’s headlines, but news related to your industry, tracked companies, investment portfolio, and the city where your child’s school is located. Consumer information alignment—not “best recommendations,” but options within your budget, matching your preference patterns, currently on promotion, and nearest to you. Entertainment information alignment—precise recommendations based on emotional state, available free time, and recent shifts in interests. Learning information alignment—continuously pushing the most suitable content based on knowledge graph gaps, career path, and learning habits.

This is highly isomorphic with the history of the iPhone. Before the iPhone, a phone was a communication tool. After the iPhone, a phone became life infrastructure. The core value of the iPhone was not any single function but becoming the information intermediary layer between people and the world. Distributed AI is following the same path: from “productivity tool” to “life infrastructure,” with its core value being a personalized alignment engine between people and information.

§10 The Distributed Security Dilemma: A Canal Network Losing Central Water Management

The transfer of control rights in distributed AI is not without cost. When the sluice gate authority over information flow is handed from platforms to millions of individual users, the safety filtering system painstakingly built through RLHF faces the risk of systemic failure. Within just weeks of its explosion, the OpenClaw ecosystem exposed this structural contradiction: security research reports revealed that over 13% of skills in the ClawHub marketplace contained severe security vulnerabilities, with dozens confirmed to carry malicious payloads;^[16] a critical remote code execution vulnerability allowed attackers to hijack running OpenClaw instances through a single malicious link; over 135,000 instances worldwide were exposed on the public internet, most using default configurations with no authentication.

This is not an isolated incident but a structural consequence of the distributed paradigm. The security model of centralized AI is a “central dam”—AI companies uniformly manage security policies, and users need not concern themselves with security engineering. Distributed AI delegates security responsibility along with control to users, but the vast majority of users lack security engineering capabilities. When everyone becomes the engineer of their own canal, most canals have no flood barriers. This is the dark side of “autonomy”—freedom and risk are two sides of the same coin.

The deeper issue is that the security threats of distributed AI are fundamentally different from those of traditional cybersecurity. Traditional attacks target data—stealing information, encryption ransomware. But AI agents possess autonomous action authority, and the attack surface expands from data breaches to behavioral hijacking—attackers can not only read your files but send emails in your name, execute transactions, and modify your schedule. What is hijacked is not a piece of data but a “digital alter ego” with the capacity to act. This is an unprecedented expansion of the attack surface in the security domain.

But positive signals should also be noted. The OpenClaw development team completed a fix within 24 hours of the ClawJacked vulnerability disclosure, patching over 40 security vulnerabilities in a single release, and partnered with VirusTotal to establish a supply-chain scanning mechanism for the skills marketplace. The open-source community’s self-repair speed far exceeds most people’s expectations. This security dilemma may become a structural obstacle preventing the arrival of the third epoch, but it may also—just as the internet evolved from its early chaotic state to develop HTTPS, certificate systems, and app store review mechanisms—catalyze a distributed security infrastructure that does not depend on centralized control but can still effectively defend against threats.

§11 Bottlenecks and Calibration: The Path to the iPhone Moment

11.1 Current Bottleneck: A Fuse, Not the iPhone Moment

OpenClaw is the fuse of distributed AI but has not yet triggered a true paradigm change. The reason is that methods for AI personalization by the general public have not been popularized. OpenClaw still requires JSON configuration, command-line operations, continuous debugging, and security hardening.^[5] Only tech power users can achieve the absolute personalization of local deployment; the general public is blocked by the technical threshold.

11.2 The Deepest Bottleneck: Formalizing Self-Knowledge

Even if the technical threshold were eliminated, a deeper obstacle remains—personalized system prompts require users to answer the question “Who am I?” The personalization of information flow requires a calibration process: precisely defining who you are, how you think, what you need, what your interests are, what you like, and what you dislike, then forming these into a system prompt.

This is extraordinarily difficult—not technically difficult, but cognitively difficult. Most people have never been asked to systematically define themselves. In daily life, humans operate on intuition and habit, not on explicit self-models. You know you dislike a certain restaurant but cannot articulate the specific reason. Never in human history has such a demand existed: the requirement to describe in precise language to a non-human entity who you are.

11.3 Three-Layer Calibration Path

Layer One: Passive Calibration—AI learns from behavior. Users do not need to explicitly describe themselves; AI infers preferences by observing behavior. The lowest threshold, but limited in precision.

Layer Two: Interactive Calibration—AI guides users in discovering themselves. AI proactively asks questions to help users clarify preferences—”I noticed you declined three party invitations. Do you want more alone time, or was the timing just off?” This is the most critical layer: AI becomes the user’s self-knowledge coach. For the first time in human history, a non-human entity continuously, patiently, and without judgment helps a person understand themselves.

Layer Three: Active Calibration—Users consciously sculpt the AI personality. A minority can clearly think through “Who am I?” and directly write the system prompt. They are the superusers of the distributed AI era; their AI will be the most precise.

§12 The Three-Epoch Framework: The Complete Picture of AI Information Flow Political Economy

Dimension	First Epoch · Centralized AI	Second Epoch · Distributed AI (Current)	Third Epoch · Private-Domain AI (Envisioned)
Information Source	Public domain internet data	Public models + user configuration	Public + private domain data fusion
Token Production	AI company monopoly	Beginning to decentralize	Individuals can produce autonomously
Token Flow Control	AI company (system prompts)	User (custom prompts)	User (fully autonomous)
Token Consumption Scenario	Browser window	Cross-platform penetration	Embedded in all of life
Token Recovery	Recovered by AI company	Chain loosening (API layer still partially backflows)	Retained by user
Demand Fulfillment	Primarily social demands	Beginning to touch self-circulating demands	Fusion of both demand layers
Degree of Personalization	One-size-fits-all	Beginning to differentiate	Deep personalization
Liability Subject	Vacuum	Transferring to user	User unification of three rights
Core Application	Dialogue and generation	Automation and agents	Personalized information alignment
Market Nature	Productivity tool	Life tool	Life infrastructure
AI Role	Advisor	Executor	Personal information alignment engine
Historical Analogy	AOL portal	Open internet	iPhone + App Store
Security Model	Central control (unified defense by AI company)	User-borne (security delegated with control)	Distributed security infrastructure (to be evolved)

§13 Conclusion

Starting from the chaotic information ocean of pre-training, through SFT’s channelization, RL’s gate-building—affective gates aligning preferences, logical gates aligning correctness—then to the centralized single port, the distributed canal network, and the privatized personal information lake. The main thread of AI development has always been the relevance problem of information flow: how to deliver the right information to the right person in the right way at the right time.

Centralized AI achieved the first layer of alignment—aligning information flow to the general preferences of humanity. Distributed AI is initiating the second layer of alignment—aligning information flow to each specific individual. This is not merely a change in technical architecture but a comprehensive restructuring of control structures, liability systems, market forms, and human-AI relationships.

The true iPhone moment has not yet arrived. It requires crossing three barriers: the elimination of the technical threshold (enabling ordinary people to customize their own AI), the establishment of security infrastructure (giving the distributed canal network a flood defense system independent of central control), and the deepest barrier—the cultural popularization of formalized self-knowledge (enabling everyone to answer “Who am I?” and give that answer to their AI). Of these three barriers, the first two are engineering problems with clear solution paths; the third is an anthropological problem requiring a social learning process measured in years.

And the ultimate revelation of this process is: the ultimate alignment of information flow is not technical alignment, not preference alignment, but existential alignment—each person defines themselves by defining their AI, and comes to know themselves through the mirror of AI. The co-evolution of humans and AI is the true endpoint of the vision of distributed AI.

§ References and Annotations