As artificial intelligence (AI) increasingly restructures global economic, political, and social frameworks, the parallels with medieval European feudalism are too critical to ignore. What was once a power struggle bound by land ownership and inherited privilege is now reshaped by centralized data monopolies, algorithmic gatekeeping, and digital dependence. The ideological resurgence of AI is not simply technological—it is socio-political, revealing renewed forms of hierarchy under the guise of innovation. From data serfdom to platform lords, the echoes of feudal power are reproducible in today’s digital infrastructure, particularly as AI advances into its next economic phase with little public oversight or labor reprieve.
Data as the New Land: Platform Sovereignty and Digital Vassalage
In medieval feudalism, wealth and control were derived largely from land, a finite resource distributed by kings and tilled by vassals and serfs. In 2025, land has been supplanted by data—the lifeblood of AI training. According to a recent 2025 report by the World Economic Forum, over 90% of global AI models are trained using data controlled by fewer than 12 commercial entities. This concentration creates digital sovereigns that not only manage data pipelines but also monopolize the value chain of AI deployment.
Corporations like Alphabet, Meta, and Amazon now own the bulk of internet infrastructure, cloud ecosystems, and user behavior telemetry. Much like feudal lords, they offer “digital tenancy”—access to platforms and markets—in exchange for continuous data contribution. This power exchange is rarely transparent. Users generate torrents of behavioral data used to train proprietary models without remuneration or recourse. In this ecosystem, individuals resemble serfs more than citizens, bound not by legal compulsion but by the necessity of digital mobility.
This arrangement intensifies when AI is inserted into workplace management. In logistics and delivery sectors especially, gig workers now operate under AI-managed schedules and metrics. A 2025 Harvard Business Review study found that 74% of independent contractors in AI-managed platforms feel they “lack agency over how tasks are assigned,” suggesting algorithmic control may be replacing traditional managerial oversight with less accountability. The dynamic is reminiscent of manorial oversight—opaque, unilateral, and difficult to challenge.
Reinforcing Hierarchies: LLM Barons and Model Dependency
Another key echo of feudal structures lies in the rise of foundational large language models (LLMs). These models—such as GPT-5, Claude 3 Opus, and Gemini Ultra—form the epistemological base layer on which thousands of downstream applications and businesses depend. Their control is increasingly centralized. According to the December 2025 editorial in The Guardian, the elite AI labs producing these models have established de facto gatekeeping over what constitutes “truth” in digital interfaces. As governments and institutions integrate LLMs into customer service, education, and medical triage, these models become interpretive layers between the populace and reality—much like clerics mediating access to divine scripture in feudal times.
Access to model weights, fine-tuning APIs, or decision rationales is tightly restricted. Even among developers, the asymmetry is expanding. A recent survey by Kaggle (February 2025) reports that nearly 61% of data scientists feel “locked out” of meaningful LLM innovation due to financial, infrastructural, or licensing constraints. As a result, business builders must pay homage—through API fees or compute contracts—to model holders, mirroring the lord-vassal economic loop of medieval enterprise.
| Company | LLM Released (2025) | Commercial Access Type |
|---|---|---|
| OpenAI (Microsoft) | GPT-5 Turbo | API subscription only |
| Anthropic | Claude 3 Opus | License + usage fees (no weights) |
| Google DeepMind | Gemini Ultra | Cloud-integrated, no public weights |
The above table illustrates how three dominant AI model producers have structured public interaction as monetized access layers, not shared commons. Just as medieval peasants needed permission to till their lord’s lands, modern developers must purchase compute allotments to co-create with proprietary AI systems. The symbiosis is not collaborative—it is conditional.
Neo-Guilds and the Fragmentation of AI Labor Justice
In parallel, regions of skilled practitioners—particularly prompt engineers, fine-tuners, and safety auditors—are morphing into digital guilds. On platforms like Hugging Face and Runway, skilled AI practitioners provide services in closed or semi-closed communities, sometimes under opaque governance terms. While these guilds offer specialization and resilience against general labor displacement, they also mirror medieval guild protections—exclusive, technically layered, and localized.
This fracturing poses risks for large-scale labor equity. According to Accenture (January 2025), only 18% of AI-literate professionals globally have access to cross-platform certification standards, reducing mobility and increasing wage arbitrage. The decentralization of expertise contrasts sharply with the centralization of infrastructure, magnifying inequality through digital caste systems.
Moreover, AI does little to democratize opportunity. In poorer regions, rather than empower people with creative or analytical AI, tech transfers tend to focus on content moderation or low-pay annotation work. Kenya’s ongoing debates over compensating local labelers hurt by AI’s psychological toll now serve as a case study in data colonialism. As with medieval feudal serfs, some labor is extractive and essential—yet voiceless in design governance or profit sharing.
AI Monarchy: Political Capture and Automated Sovereignty
National governance is also being reshaped. While western medieval monarchs claimed divine right, today’s AI-fueled political actors claim algorithmic inevitability. The 2025 elections in Indonesia and Mexico have triggered international scrutiny over automated content moderation strategies used to filter political speech—often built on U.S.-based LLMs without regional fine-tuning (VentureBeat, March 2025). In both cases, local political norms were subordinated to technical priors trained elsewhere.
This outsourcing of epistemic sovereignty has deep implications. As nations employ AI to manage bureaucracies, eligibility benefits, or judicial guidance—as seen in recent EU pilot programs for court sentencing support (Deloitte, February 2025)—concerns mount that national policy is becoming reducible to vendor inputs. Rather than constitutional or popular input, systems increasingly privilege vendor epistemology—a return to divine judgment, now via inference algorithms.
Democratic institutions are struggling to check this paradigm. Only five out of the G20 countries have passed updated laws mandating AI outcome audits for public-sector deployments, despite public backlash. Gallup (April 2025) shows 63% of citizens in developed democracies “distrust government-owned AI systems,” particularly as lobbying influence from AI vendors continues to rise. The push for “AI monarchies”—technical rule with minimal consent—threatens meaningful public participation.
Challenging the Hierarchies: What Radical AI Decentralization Looks Like
Despite these feudal reverberations, there are grassroot movements modernizing resistance. OpenWeights.org, backed by Mozilla and the European AI Alliance, recently launched a decentralized compute and model storage platform aiming to democratize model training across university and co-op groups. Their decentralized LLM, Forge 1.2, launched publicly in March 2025 and has seen over 2.5 million downloads in under a month (MIT Technology Review). Unlike traditional foundational models, Forge offers transparent training data documentation, adjustable weight sharing, and federated governance councils.
These projects are not utopian—they face resource limitations, trust bootstrapping problems, and integration hurdles—but they represent tangible efforts to prevent AI governance from defaulting into control regimes. According to Pew Research (March 2025), 56% of surveyed developers believe “open tooling is necessary to prevent social inequality via AI.” However, only 12% say their employer is committed to open standards, suggesting industrial inertia remains a significant challenge.
Moreover, states like Brazil and Indonesia are considering introducing “Data Sovereignty Mandates” that compel AI firms to catalog local language training sets in exchange for commercial licenses, moving governance closer to the user. This follows the lead from India’s 2025 “Bhashini Act,” a law requiring language parity for all LLMs operating commercially within the country. These approaches aim to dissolve platform feudalism by asserting jurisdictional sovereignty—a digital Magna Carta of sorts.
Looking Ahead: A Fragmented Enlightenment or a Locked Dark Age?
The trajectory of AI’s resurgence presents a dual vision, interlaced with historic resonance. On one hand, the promise of collective intelligence and automated bandwidth could lead to broader economic liberation and creative flourishing. On the other hand, the current pattern of intensified capture—by firms, gatekeepers, and opaque models—recreates systems designed for narrow control rather than collective emancipation.
If these new digital kingdoms—fortified by proprietary models and extractive labor rule sets—go unchallenged, AI may not usher in a renaissance, but harden into a computational neo-feudalism. As we hurtle toward general-purpose systems and autonomous cognition in machines, the societal counterweights we install—transparency mandates, computational commons, labor protections—will determine whether AI magnifies democracy or entombs it behind feudal algorithms.