theonemanshow.ch Roland K. Buob | crafting systems and more.

Capitalism in the Time of SLOP

Knowledge capital, global AI diffusion, recursive attribution and systemic valuation

RATIO → EFFECTS → CASE STUDIES

Title image: a 1970s-style poster in red, black and white. At the top a world map with connecting lines; below, a row of working people whose documents flow into servers (Extraction I), spread through open-weight models (AI spreading) and reach stooped figures and growing stacks of coins at the bottom (Extraction II). On the right the lettering: The Interconnected Slop – Knowledge Capital, AI Spreading, The Double Urzins. The model feeds on knowledge. Then devalues what fed it.

Plate 02 / 17

I. RATIO — The complete operational architecture

The starting point is Marx. The reader's path begins with the familiar movement of capital, while the model itself is extended step by step to include time delay, social knowledge production, global networks, AI spreading, spatiotemporal binding and memory functions.

1. Marx: Capital circulation

Mₜ — Cₜ ··· Pₜ ··· C′ₜ₊τ — M′ₜ₊τ
M′ₜ₊τ = Mₜ + ΔMₜ,τ

M denotes advanced money capital, C commodities or the preconditions of production, P the production process and M′ the monetary value realized at the end. τ makes visible that valorization is completed only after time delays. This is the economic point of entry and not yet the global knowledge model.

Plate 03 / 17

2. Social knowledge capital

Pₜ = 𝓟(Lₜ, Iₜ, Kₜ, 𝓖ₜ, Ωₜ)
  • Lₜ — socially organized labor

  • Iₜ — material and technical infrastructure

  • Kₜ — historically accumulated knowledge

  • 𝓖ₜ — network of social relations of production

  • Ωₜ — legal, institutional and operational conditions

Knowledge is thus understood as a historically and relationally produced condition of production, not as the self-sufficient possession of an individual. The function 𝓟 is a conceptual extension of Marx, not a formula taken from him.

Plate 04 / 17

3. The global spatiotemporal network

𝓖ₜ = (Nₜ, Eₜ, Wₜ, Ωₜ)
kᵢ = (rᵢ, xᵢ, tᵢ, cᵢ, pᵢ)

Nₜ are network nodes; Eₜ relations; Wₜ their weights; Ωₜ the operational conditions. The event kᵢ captures representation rᵢ, spatial-institutional position xᵢ, point in time tᵢ, context cᵢ and provenance pᵢ.

𝓖global ≠ ⨁ₙ 𝓖national,n

National borders regulate connections but do not define the extent of the knowledge network. The global structure has cross-border edges and historical dependencies; it cannot be described as the mere sum of isolated national systems. ⨁ here denotes, analytically, such an isolated assemblage.

Plate 05 / 17

4. AI spreading and spatiotemporal binding

𝓢Δt and 𝓑σ,τ
Rₜ₊₁ = 𝓢ₜ[𝓑σ,τ(Rₜ, Kₜ, 𝓖ₜ)]

𝓢 mediates the spreading and transformation of representations; 𝓑 connects representations that are separated in time, space and institution. σ denotes distance, τ temporal difference. A connection does not automatically preserve context and origin.

Rₜ₊₁ = 𝓢ₜ[𝓓c,t ∘ 𝓑σ,τ(Rₜ,Kₜ;𝓖ₜ)] ⊕ 𝓞ₜ

𝓓c,t captures loss or alteration of context; 𝓞ₜ supplies new observations from outside the circulating representations. The symbol ⊕ stands for the structural input of new observations, not for an arithmetic addition of texts. Without sufficiently independent observation, representations can circulate recursively without any new verified knowledge emerging.

Plate 06 / 17

5. Time-delayed recursion

𝓗ₜ = ∫₀ᵗ 𝓚(t−s) Rₛ ds

𝓚 is a weighting or memory kernel. It describes how earlier content becomes effective again through archives, citation, search indexes, retrieval and, under certain conditions, future training cycles.

Rₜ₊₁ = 𝓕R(Rₜ, 𝓗ₜ, 𝓢ₜ, 𝓑ₜ, 𝓓c,t, Πₜ, Oₜ)

Πₜ denotes reconstructible relations of provenance, Oₜ external observations. The formula thus accounts for spreading, space, time, context, provenance, memory and grounding in the world.

Plate 07 / 17

6. Neumann: Ariadne through the network

Neumann's transpersonality is not an additional algorithmic network operator. It takes on the Ariadne function: it keeps the question of creative genesis visible where the history of an achievement is obscured across individual, cultural and symbolic levels.

Aᵢ(R) ≠ Γᵢ(K)

Aᵢ is public attribution; Γᵢ is our analytical marking of the genesis that can be traced. The equation is not a direct formalization of Neumann's own concept of the transpersonal.

Plate 08 / 17

II. EFFECTS — The coupled feedback loops

Three simultaneous dynamics are coupled within a global network. The spreading of representations influences attribution and self-marketing; these influence the distribution and accumulation of capital; capital in turn finances infrastructure and new production of representations.

A. Epistemic reproduction

Rₜ₊₁ = 𝓕R(Rₜ, 𝓗ₜ, 𝓖ₜ, Oₜ)

B. Self-marketing and attribution

Aᵢ,ₜ₊₁ = 𝓕A(Aᵢ,ₜ, Eᵢ,ₜ, Rᵢ,ₜ, 𝓢ₜ, Πᵢ,ₜ)

Eᵢ,ₜ captures attention or reach. Self-marketing can increase authority and market value without attribution growing in proportion to original knowledge production.

C. Capital accumulation

M′ᵢ,ₜ₊τ = 𝓕M(Mᵢ,ₜ, Pₜ, Aᵢ,ₜ, Iₜ, Ωₜ)

Profit, rents, financial valuation and additional social productivity remain distinct categories. The fact that knowledge is commercialized does not by itself tell us where new value is created and where value is appropriated.

Plate 09 / 17

The common feedback loop

Rₜ₊₁Aₜ₊₁M′ₜ₊τ=𝓕𝓖ₜRₜAₜMₜ; 𝓗ₜ, 𝓢ₜ, 𝓑ₜ, 𝓓c,t, Πₜ, Oₜ, Ωₜ

This block notation symbolizes the coupled transition: epistemic, social and economic recursions influence one another. High visibility can promote attribution and investment; these enable renewed spreading and amplification. Open-weight models with local inference distribute the technical capacity to produce representations across borders, while dependencies on chips, capital and infrastructure remain.

Plate 10 / 17

The integrated master formula

Xₜ = (Kₜ, Rₜ, Aₜ, Mₜ, Iₜ, Πₜ, Ωₜ)
Xₜ₊₁ = 𝓕𝓖ₜ(Xₜ, ∫₀ᵗ 𝓚(t−s)Xₛ ds, 𝓢ₜ, 𝓑σ,τ, 𝓓c,t, Oₜ)

This is the architecture of a theory, not yet an empirically identified equation of motion. A numerically computable model would require state spaces, operators, parameters, measurement procedures and causal identification conditions to be specified.

Plate 11 / 17

D. Double Urzins — Gesell as an extension of the capital operation

The stand-alone Double Urzins model distinguishes two forms of asymmetric extraction. It takes up Gesell's question of the power that arises from unequal conditions of exchange and waiting, but does not claim to be identical with his historical concept of Urzins (basic interest). What matters is the separation of the two extraction points from the formal price and balance-sheet stations: in the original model, both are explicitly marked «no station».

M → C ··· P ··· C′ → M′ | EⅠ ⇄ EⅡ

Urzins I (staleness) denotes the temporal asymmetry: model weights, development work and knowledge representations can lose exchange value amid ongoing innovation, open alternatives and changing access prices. Staleness is more the condition of a weakened bargaining position than automatically an extracted interest.

Urzins II (data) denotes the possibility that platforms make data and technical contributions along the funnel usable as input for a model or data ecosystem, while recognition, separate remuneration and clearance of rights are absent. Whether contributions are actually used for training is a verifiable technical and contractual question; inference, retrieval and weight updates must be distinguished.

Plate 12 / 17

D1. Eight stations, two extraction gaps

Operation / gap Role in the original Double Urzins funnel
0 World market Open-weight alternatives impose competitive pressure; the ~0.50 of the original diagram is illustrative, not an observed market price.
1 Platform border Platform access and product bundling may raise effective prices within the ecosystem.
2 Intake price — developer A developer receives an intake valuation; the diagram illustrates ~0.80 without a defined unit.
Extraction I — NO STATION Quantification receipt filed; qualification receipt demanded. Possible unremunerated appropriation occurs in the gap between measured intake and recognized qualitative contribution.
3 Internal transfer pricing An administratively assigned internal price; this is not equivalent to competitive price discovery.
4 Cost allocation Compute, energy and other costs attributed across the operating structure.
5 Trading margin Concentration and opacity may obscure where revenues and economic rents arise.
6 Consolidation Aggregation can make transfers and original attribution harder to reconstruct.
Extraction II — NO STATION Provenance or accounting trace can be obscured while financial claims accrue upstream and devaluation may propagate downstream.
7 Exchange / equity stake Ownership claims and company valuation can be established; securitization, equity issuance and mere valuation are legally distinct.

The ~0.50 and ~0.80 figures are retained solely as schematic labels from the original illustration; without currency, unit, sampling and contract definitions they cannot be treated as measurements.

Plate 13 / 17

D2. Coupled extraction, feedback and retro-devaluation

EⅠ(t) = Qaccepted(t) − Qcompensated(t)
EⅡ(t) = Vappropriated(t) − Vattributed(t)

The two differences require a shared valuation rule to become measurable; Q is not a raw token count, and market value, credit and financial claims cannot be treated as interchangeable quantities.

EⅠ(t) → Kmodel(t+τ₁) → Δpknowledge(t+τ₂) → EⅡ(t+τ₃)
EⅡ(t) → Aplatform(t+τ₄) → I(t+τ₅) → 𝓢(Rt+τ₆)

The first chain describes a possible absorption of contributions into model or data resources. The price decline is a conditional hypothesis, not a law of nature. The second chain describes a possible reinvestment of extracted returns into infrastructure, distribution and renewed circulation.

𝓤t = (EⅠ,t, EⅡ,t, Πt, At, Mt, Wt)
𝓤t+1 = 𝓕𝓖t(𝓤t, 𝓢t, 𝓑σ,τ, 𝓓c,t, 𝓗t, Ot)

Π stands for provenance, A for attribution, M for capital position, W for competition- and time-dependent value development. 𝓗 denotes historical feedback. This extension connects the original Double Urzins funnel with the global spatiotemporal architecture without turning a diagram into empirical proof.

Plate 14 / 17

D3. Self-marketing, open weights and double exposure

Self-marketing can convert circulating knowledge representations into reputation and commercial opportunities without reflecting the original genesis of the knowledge. At the same time, locally executable open-weight models allow globally distributed inference without a permanent provider API. Internet access accelerates distribution, updates and republication, but is not a necessary condition for every instance of local inference. Extraction conditions and price decline can thus be coupled across borders and time delays; not every use of a model automatically leads to data outflow or retraining.

Aself,t+1 = 𝓕A(Aself,t, Exposuret, 𝓢t, Πt)
Vindividual,t ↑ ⇏ Koriginal,t ↑

The additional hypothesis is this: the actor may first lose at an intake or recognition gap and later come under price pressure from cheaper, model-based reproduction of their own work. Actual claims about causation and usage rights require traceable data flows, contracts and prices.

Plate 15 / 17

III. CASE STUDIES — The world today

Only now is the theory applied to concrete constellations. The case studies receive no special theoretical logic of their own: they are examined using the same variables and couplings.

  • China: industrial integration, open models and state-coordinated investment as one possible form of systemic value realization.

  • USA: hyperscalers, proprietary model access, private capital and the capital-market valuation of central AI infrastructure.

  • Global open-weight networks: local Qwen deployments and other models that can run inference without a permanent cloud connection, while knowledge transfer remains global.

  • Science and social media: competition for publication, algorithmic visibility, academic self-marketing and the attribution of expertise.

The test criterion is not the rhetorical plausibility of individual systems, but the capacity to translate distributed knowledge into verifiable applications, to correct errors and to attribute realized value traceably.

Plate 16 / 17

Conclusion

Representation ≠ Production ≠ Attribution ≠ Capitalization
Interconnection ⇏ Integration ⇏ Intelligence

The reader's path is linear; the object under study is not. Marx opens the economic movement, global AI networking extends the spatiotemporal relations, Neumann keeps the question of creative genesis open, and the empirical cases test the consequences and limits of the model.

Plate 17 / 17

Sources & conceptual references

Overview
  1. 01Title
  2. 02I · Marx: Capital circulation
  3. 03Social knowledge capital
  4. 04The global spatiotemporal network
  5. 05AI spreading and spatiotemporal binding
  6. 06Time-delayed recursion
  7. 07Neumann: Ariadne through the network
  8. 08II · Three dynamics (A, B, C)
  9. 09The common feedback loop
  10. 10The integrated master formula
  11. 11D · Double Urzins
  12. 12D1 · Eight stations, two extraction gaps
  13. 13D2 · Coupled extraction, feedback
  14. 14D3 · Self-marketing, open weights
  15. 15III · Case studies
  16. 16Conclusion
  17. 17Sources