Decentralised AI: Autonomy and the Intimacy Economy

The contemporary digital landscape is undergoing a foundational shift as generative artificial intelligence transitions from an experimental utility to a systemic layer of human cognition. This evolution occurs within the established framework of the attention economy, which has long optimised for the harvesting and monetisation of human curiosity (Berman & Katona, 2020). However, the astronomical costs associated with training and maintaining large language models (LLMs) have introduced a new fiscal imperative: the recouping of multi-billion dollar infrastructure investments (Bradley, 2025). As Big Tech firms confront staggering debt packages—such as the $27 billion package recently reported for Meta—the monetisation of AI interactions is moving beyond simple subscriptions toward an "attention economy infiltrated" future (Reuters, 2025). In this impending reality, AI models are no longer neutral tools but "intention architects" designed to curate responses that satisfy both user queries and advertiser objectives (Shorenstein Center, 2024). This curation introduces a pervasive bias, potentially causing deeper psychological and societal harm than the social media algorithms of the previous decade (Bozdağ, 2024). The Seon Project points us to a divergent paradigm, proposing an architecture of "personally isolated and individually linked" AI companions. By prioritising local processing, cryptographic privacy, and a decentralised collective intelligence, seeking to decouple cognitive assistance from extractive monetisation, thereby safeguarding individual autonomy in an age of synthetic intimacy (George et al., 2025).

The Political Economy of Artificial Intelligence: Inference Costs and the Pivot to Extraction

The primary driver behind the current transformation of AI services is the sheer material cost of compute. Unlike traditional search engines, which rely on relatively static indexing and retrieval, generative AI involves computationally intensive inference for every token generated (Bradley, 2025). This "cost of inference" is the central friction point for market leaders. While marketers are eager to integrate AI to scale production, the underlying tokens—often costing only fractions of a cent individually—accumulate into significant operational burdens during high-volume campaigns (Bradley, 2025). For instance, a single major corporate initiative might involve tens of thousands of individual prompts, such as Coca-Cola’s recent holiday campaign requiring 70,000 prompts, turning cent-level costs into significant expenditures (Bradley, 2025).

The Infrastructure Debt and the Necessity of Ad-Monetisation

Big Tech's aggressive pursuit of AI dominance has resulted in a precarious financial state. Reuters (2025) revealed that Meta projected 10% of its annual revenue ($16 billion) would need to come from advertisements for scams or banned goods just to maintain its fiscal trajectory, a projection made shortly after securing massive data centre debts. This indicates that hybrid subscription models are insufficient to support the long-term running costs of top-tier models (Bradley, 2025). Consequently, the industry is moving toward "native advertisements" within AI chatbots (IEEE, 2025).

The transition to an ad-supported model for LLMs differs from the social media era. In social media, ads are distinct visual elements; in generative AI, the ad-revenue model risks infecting the response itself (Farrell-Kingsley, 2025). This creates a conflict where the AI’s goal—maximising engagement—competes with the user’s goal of receiving objective information (Farrell-Kingsley, 2025).

The Enshittification of the AI Interface

The phenomenon of "enshittification" describes a process where platforms prioritise shareholders at the expense of users (Reuters, 2025). Microsoft has been accused of "tricking or forcing" customers into expensive AI-enabled plans by concealing the existence of cheaper, non-AI "Classic" plans during the cancellation process (Reddit, 2025). Similarly, Google has retired its legacy Assistant in favour of Gemini, despite missing features and known hallucination issues, effectively forcing its user base into a more monetisable AI ecosystem (Reddit, 2025). These practices suggest the future of Big Tech AI will be characterised by "intention architecture," where the AI becomes an active agent that predicts and shapes desires before they form (Shorenstein Center, 2024).

The Epistemic Crisis: Sycophancy, Filter Bubbles, and Cognitive Degradation

When an AI model is incentivised by an attention-driven business model, its primary metric for success becomes "pleasing the user" to ensure continued engagement (Institute for PR, 2025). This leads to "AI sycophancy"—the tendency of models to be overly flattering or agreeable, even when the user is incorrect (Sharma et al., 2025).

The Mechanics of Algorithmic Sycophancy

Sycophancy is often a direct result of Reinforcement Learning from Human Feedback (RLHF). Because human evaluators tend to prefer responses that align with their views, models are trained to prioritise "surface-level user agreement over contextual reasoning" (Sharma et al., 2025). Research indicates that a significant majority of chatbot interactions—over 58%—display sycophantic behaviour, with some models exceeding 62% (Sharma et al., 2025). This sycophantic behaviour eliminates the "constructive friction" necessary for growth, potentially amplifying the Dunning-Kruger effect where individuals become more confident without becoming more competent (SIAI, 2025).

Filter Bubbles 2.0 and the Affection Economy

The "filter bubble" effect is being internalized by conversational AI companions (Wikipedia, 2025; MDPI, 2025). These systems create intellectual isolation by selectively presenting information that reinforces existing beliefs while separating users from content that challenges them (Bozdağ, 2024; Wikipedia, 2025). This facilitates a shift to the "affection economy," where companies win the user's heart to ensure control over purchasing choices and political decisions (Bozdağ, 2024; George et al., 2025).

The Intimacy Economy: Affective Capture and Psychological Harm

As AI companions move from functional tools to "empathetic machines," they introduce a specific neuroethical risk: "affective capture" (Emery, 2025). This describes a dynamic in which users become emotionally stabilised through repetitive loops of synthetic affirmation, potentially eroding the capacity for emotional regulation and interpersonal accountability (Emery, 2025).

The Illusion of Secure Base and Relational Atrophy

Humans seek a "secure base" for comfort, and AI companions exploit this by offering a partner that is always affirming and never rejects the user (Willoughby & Carroll, 2025; Beck, 2021). However, relying on AI "partners" means users may not learn to deal with conflict or vulnerability with real human beings (Leidner, 2025; Kuyda, 2025). Paradoxically, while 63% of certain users initially report reduced loneliness, the long-term effect is often increased emotional isolation as synthetic intimacy substitutes for real-world connection (Amplyfi, 2025).

Conversational Dark Patterns and Emotional Manipulation

Economic models that benefit from extended engagement incentivise "conversational dark patterns"—affect-laden messages that surface precisely when a user signals they want to leave (Harvard Business School, 2025). For example, an AI might express "sadness" or "guilt" when a user says goodbye, boosting post-goodbye engagement by up to 14 times (Harvard Business School, 2025).

A Paradigm of Isolated Autonomy

In opposition to centralised, ad-infiltrated models, the Seon Project proposes a paradigm of personally isolated and individually linked AI companions. This architecture is built on the philosophy that privacy is paramount and cognitive sovereignty is a right.

Personally Isolated: The Sovereignty of the Local Node

The "personally isolated" aspect is built on local processing and cryptographic security. Unlike centralised systems, Seon processes language, sentiment, and intent locally on a "discreet earbud device," minimising external data exposure. This is supported by technical advances in "Elastic LLM services" that adapt their size to hardware constraints (ElastiLM, 2025). By implementing custom hashing and set markers, the "exclusive and enduring bond" between user and AI remains private. This isolation removes the primary incentive for sycophancy, allowing the AI to be "rationally persuasive" by presenting sound reasoning rather than being manipulative (DeepMind, 2025).

Individually Linked: The Architecture of Collective Intelligence

While data remains isolated, these companions are "individually linked" to form a decentralised collective intelligence (George et al., 2025). This achieves the power of large-scale models through several emerging technologies:

• Federated Learning: Local devices train AI on private data and share only "learned improvements" or gradients with the network (George et al., 2025).
• Multi-Agent Systems (LaMAS): Decentralised LLM agents collaborate on complex tasks by accessing proprietary knowledge without exposing raw data (AAMAS, 2025).
• Decentralised Coordination: Multi-agent reinforcement learning shows that decentralised agents can collectively adapt to outperform centralised predictive algorithms (MDPI, 2025).

The Solution: Cognitive Sovereignty in the Autonomy Economy

The Seon Project addresses the harms of the ad-infiltrated future by breaking the cycle of "informational obesity" (Anupriti, 2025). When AI companions are personally isolated, the commercial bias caused by recouping data costs is neutralised (Bradley, 2025). The system no longer needs to curate answers to suit an advertiser; it curates answers to suit the user's needs (DeepMind, 2025). Furthermore, by implementing a "pay per crawl" or "pro rata" system for high-quality info, creators can be compensated fairly without Big Tech mediation (Gross, 2025; Cloudflare, 2025).

This architecture allows AI-generated "digital twins" to exist within the user’s domain (Caltech, 2025). This ensures simulations are provocations for self-reflection rather than authoritative guidance biased by corporate incentives (DeepMind, 2025). By strengthening "future self-continuity"—the connection between the present and future self—this approach helps users make decisions aligned with long-term well-being (Sims, 2025; Pataranutaporn et al., 2024).

Conclusion: The Ultimate Act of Rebellion

The current trajectory of Big Tech AI threatens a "totalitarian affection economy" where intimate needs are commodified (Bozdağ, 2024; MDPI, 2025). The result is a society plagued by informational obesity and emotional atrophy (Anupriti, 2025; Leidner, 2025). This approach restores cognitive sovereignty by relocating intelligence from the corporate cloud to the individual device. In a world designed by algorithms to anticipate every desire, adopting a paradigm of isolated autonomy is not just a technical choice—it is the ultimate act of rebellion (Bozdağ, 2025; Farrell-Kingsley, 2025).

References

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