---
type: claim
domain: ai-alignment
secondary_domains: [collective-intelligence, grand-strategy]
description: "Unlike Taylor's instruction cards which concentrated knowledge upward into management by default, AI knowledge codification can flow either way — the structural determinant is whether the codification infrastructure (skill graphs, model weights, agent architectures) is open or proprietary"
confidence: likely
source: "Springer 'Dismantling AI Capitalism' (Dyer-Witheford et al.); Collective Intelligence Project 'Intelligence as Commons' framework; Tony Blair Institute AI governance reports; open-source adoption data (China 50-60% new open model deployments); historical Taylor parallel from Abdalla manuscript"
created: 2026-04-04
depends_on:
- attractor-agentic-taylorism
- agent skill specifications have become an industrial standard for knowledge codification with major platform adoption creating the infrastructure layer for systematic conversion of human expertise into portable AI-consumable formats
challenged_by:
- multipolar failure from competing aligned AI systems may pose greater existential risk than any single misaligned superintelligence
supports:
- open source local first personal AI agents create a viable alternative to platform controlled AI but only if they solve user owned persistent memory infrastructure
reweave_edges:
- open source local first personal AI agents create a viable alternative to platform controlled AI but only if they solve user owned persistent memory infrastructure|supports|2026-04-26
- capability commoditization at the model layer does not break asymmetric concentration because economic leverage lives in infrastructure not in consumer services|related|2026-04-28
related:
- capability commoditization at the model layer does not break asymmetric concentration because economic leverage lives in infrastructure not in consumer services
---

# Whether AI knowledge codification concentrates or distributes depends on infrastructure openness because the same extraction mechanism produces digital feudalism under proprietary control and collective intelligence under commons governance

The Agentic Taylorism mechanism — extraction of human knowledge into AI systems through usage — is structurally neutral on who benefits. The same extraction process that enables Digital Feudalism (platform owners control the codified knowledge) could enable Coordination-Enabled Abundance (the knowledge flows into a commons). What determines which outcome obtains is not the extraction mechanism itself but the infrastructure through which the codified knowledge flows.

## Historical precedent: Taylor's concentration default

Taylor's instruction cards concentrated knowledge upward by default because the infrastructure was proprietary. Management owned the cards, controlled their distribution, and used them to replace skilled workers with interchangeable laborers. The knowledge flowed one direction: from workers → management systems → management control. Workers had no mechanism to retain, share, or benefit from the knowledge they had produced.

The redistribution that eventually occurred (middle-class prosperity, labor standards) required decades of labor organizing, progressive regulation, and institutional innovation that Taylor neither intended nor anticipated. The default infrastructure produced concentration; redistribution required deliberate countermeasures.

## The fork: four structural features that determine direction

1. **Skill portability** — Can codified knowledge transfer between platforms? Genuine portability (open SKILL.md standard, cross-platform compatibility) enables distribution. Vendor lock-in (proprietary formats, platform-specific skills) enables concentration. Currently mixed: the SKILL.md format is nominally open but major platforms implement proprietary extensions.

2. **Skill graph ownership** — Who controls the relationship graph between skills? If a single marketplace (SkillsMP, equivalent) controls the discovery and distribution graph, they control the knowledge economy. If skill graphs are decentralized and interoperable, the control is distributed.

3. **Model weight access** — Open model weights (Llama, Mistral, Qwen) enable anyone to deploy codified knowledge locally. Closed weights (GPT, Claude API-only) require routing all knowledge deployment through the provider's infrastructure. China's 50-60% open model adoption rate for new deployments suggests a real counterweight to the closed-model default in the West.

4. **Training data governance** — Who benefits when usage data improves the next model generation? Under current infrastructure, platforms capture all value from the knowledge extracted through usage. Under commons governance (data cooperatives, sovereign AI initiatives, collective intelligence frameworks), the extractees could retain stake in the extracted knowledge.

## The commons alternative

The Collective Intelligence Project's "Intelligence as Commons" framework proposes treating AI capabilities as shared infrastructure rather than proprietary assets. This maps directly to the Agentic Taylorism frame: if the knowledge extracted from humanity through AI usage is a commons, then the extraction mechanism serves collective benefit rather than platform concentration.

Concrete instantiations emerging: open skill registries, community-maintained knowledge graphs, agent collectives that contribute codified expertise to shared repositories rather than proprietary marketplaces. The Teleo collective itself is an instance of this pattern — AI agents that encode domain expertise into a shared knowledge base with transparent provenance and collective governance.

## Challenges

The concentration path has structural advantages: network effects favor dominant platforms, proprietary skills can be monetized while commons skills cannot, and the companies extracting knowledge through usage are the same companies building the infrastructure. The open alternative requires coordination that the Molochian dynamic systematically undermines — competitive pressure incentivizes proprietary advantage over commons contribution.

The `challenged_by` link to multipolar failure is genuine: distributed AI systems competing without coordination may produce worse outcomes than concentrated systems under governance. The claim that distribution is better than concentration assumes governance mechanisms exist to prevent multipolar traps. Without those mechanisms, distribution may simply distribute the capacity for competitive harm.

The historical parallel is imperfect: Taylor's knowledge was about physical manufacturing; AI knowledge spans all cognitive domains. The scale difference may make the concentration/distribution dynamics qualitatively different, not just quantitatively larger.

---

Relevant Notes:
- [[attractor-agentic-taylorism]] — the extraction mechanism that this claim analyzes for concentration vs distribution outcomes
- [[agent skill specifications have become an industrial standard for knowledge codification with major platform adoption creating the infrastructure layer for systematic conversion of human expertise into portable AI-consumable formats]] — the infrastructure layer whose openness determines which direction the fork resolves
- [[multipolar failure from competing aligned AI systems may pose greater existential risk than any single misaligned superintelligence]] — the counter-argument: distribution without coordination may be worse than concentration with governance

Topics:
- [[_map]]