The attention economy as a financial asset

The thesis puts forward a five-part proposition: (i) the “vital energy” mentioned in the source text is formally isomorphic to the economic quantity of human attention, a scarce, allocable, measurable resource, as theorized by Herbert Simon (1971) and Michael Goldhaber (1997); (ii) this resource is captured by large corporations through a chain of signaling (Spence, 1973) and institutionalized information asymmetry; (iii) behavioral surplus (Zuboff, 2019) represents its market commodification; (iv) the strategic dynamics of global e… can be modeled as a Bayesian game with incomplete information, where the trembling-hand equilibrium (Selten, 1975) explains the persistence of extractivism; (v) this system is investable through a proprietary index, the Steelldy Vital Attention Index (SVAI™), whose methodology, calibration, and traceability are specified below. The metaphysical premise is thus rendered tradeable.

The question is not whether attention is monetized (Simon, Goldhaber, Zuboff have settled this), but rather to quantify the mechanism by which large corporations and global e… extract it, transform it into behavioral surplus, and reinject it into capital formation and to propose a patrimonial architecture and an investable index based on this quantification.

The integrated doctrinal synthesis identifies seven key concepts explaining the attention economy. Simon (1971) notes information abundance causes attention scarcity, formalized by a channel capacity theorem where total attention allocated to items is constrained. Goldhaber (1997) views attention as currency, modeling it as a capitalized stock over time. Spence (1973) introduces costly signals revealing hidden types in job markets. Zuboff (2019) describes surveillance capitalism extracting behavioral surplus from data. Selten (1975) proposes trembling-hand equilibrium showing strategy robustness to errors. BIS (2024-2025) defines tokens as programmable assets on a unified ledger. The WEF Global Risks 2025 identifies systemic behavioral risk from misinformation and attention capture, measured by a composite index.

I. THEORETICAL FOUNDATIONS: FROM “VITAL ENERGY” TO FINANCIAL ASSET

1.1 “Vital energy” as a metaphysical conceptualization of attention

Nikolai Sviridov posits that the employer does not “pay the worker” but rather purchases vital energy that is, the flow of directed attention, which alone constructs the matter of the enterprise. This metaphysical intuition has an exact formal counterpart in economic literature. Herbert Simon (1971) established that, in an information-rich world, the scarce resource is no longer information but attention the capacity to allocate cognitive processing to a subset of the information flow. Michael Goldhaber (1997) extended this: attention becomes the currency of the digital economy, storable, transferable, and capitalizable. The “matter” that the worker builds for the employer is thus, in strict terms, the capitalization of the attentional flow that they abdicate in favor of the organization. The formulation of the source text—”именно внимание строит материю“— is therefore a poetically exact translation of the Simon-Goldhaber theorem.

_{Simon’s argument is based on the theory of bounded rationality.
Simon posits that the information economy suffers from a structural asymmetry: technology exponentially increases the supply of information (bandwidth, data, messages), but the human hardware, our brain, has a biologically fixed cognitive processing capacity. Consequently, the consumption of information consumes a scarce resource: attention.}

Herbert A. Simon (Nobel Prize in Economics, 1978) introduced this concept in his foundational essay “Designing Organizations for an Information-Rich World” (1971).

1.2 Signal Theory (Spence, 1973) and Asymmetric Extraction of Attention

The employer faces an asymmetric information situation: they do not know the worker’s actual “vital energy” (latent attentional productivity). Spence (1973) resolves this problem through costly signaling—education, certifications, loyalty badges, and overtime are signals whose differential cost reveals the hidden type. The mechanism generalizes: any organizational system (company, platform, state) designs attention-costly signals (KPIs, OKRs, streaks, gamification, notifications) whose function is not efficiency but the forced revelation of the attentional type. The wage schedule ( w(\sigma) ) offered by the employer thus becomes a pricing function for revealed attention.

1.3 Behavioral surplus (Zuboff, 2019) and commodification

Zuboff (2019) formalizes the shift from signal to commodification: platforms do not merely extract explicit attention but produce a “behavioral surplus”, derived behavioral data, predictions, and behavioral futures markets. The mechanism is threefold: extraction → prediction → sale of predictions on behavioral futures markets. This is precisely the “matrix” mentioned in the source text (N. Sviridov): a device that purchases present attention to sell contracts on future behavior. The BIS (2024-2025) confirms the corresponding monetary infrastructure, tokenization allows these behavioral claims to be represented as programmable assets on a unified ledger.

1.4 Financialization of Attention: The Attention-Value-Capital Chain

The integrated value chain is formalized as follows: attention At → capture data Dt → modeling → behavioral surplus Bt → tokenization → token Tt → market-making → financial price Pt. Each transition is subject to an irreversibility cost (real option property, Dixit & Pindyck 1994) and an information noise modeled by a Wiener process. The profitability extracted by the e… is the carry of this chain, the gap between the present value of future behavioral flows and the cost of acquiring present attention.

1.5 Institutional confirmation: BIS, WEF, prediction markets

The BIS, in its Annual Economic Report 2025, explicitly describes tokenization as “the next logical progression of the monetary system’s evolution,” integrating messaging, reconciliation, and asset transfer into a single operation. The WEF Global Risks Report 2025 ranks misinformation and attention capture among the systemic risks of the decade. Prediction markets (Polymarket, Kalshi), which processed approximately $24.99 billion in combined notional volume over the recent period, now constitute the infrastructure for aggregated belief revelation, i.e., the place where behavioral surplus is priced in probabilities. Arkham Intelligence launched an analytics suite in 2026 enabling the tracking of prediction market whales, thus formalizing the surveillance of attentional smart money.

II. QUANTITATIVE FRAMEWORK: FROM SIGNAL TO INVESTABLE INDEX

The attention of a cohort is factorially decomposed into orthogonal components like novelty, urgency, social proof, authority, scarcity, and loss aversion, estimated via Bayesian ridge regression. This attention is tokenized and modeled as an asset with stochastic volatility using the Heston model, allowing for option pricing and capturing the asymmetry of negative attention shocks through the spot-vol correlation. Regime changes and clustering in attention volatility are captured using GARCH-X and Markov-switching GARCH models, distinguishing calm regimes from panic regimes. A K. filter provides real-time estimation of latent states like smart money positioning and squeeze probability from noisy observations like ETF flows and on-chain data. H. M. Models enable detection of liquidity clusters and anticipation of liquidations. The A. illiquidity measure is applied to attention tokens, signaling when attention becomes scarce and costly to extract. M. C. simulations with 100,000 trajectories under various scenarios produce attention-specific Value-at-Risk metrics.

_{In the attention economy, platforms design signal structures that enforce the separating equilibrium, thereby maximizing extracted information.
This is precisely the mechanics of KPIs, badges, streaks, and notifications.}

III. GAME THEORY: THE STRATEGIC DYNAMICS OF ATTENTIONAL EXTRACTION

Game theory analyzes the strategic dynamics of attention extraction, modeled as a B. game with incomplete information involving players like e…, platforms, employers, workers, and regulators. The B… Nash Equilibrium describes optimal extraction and alienation based on beliefs. Selten’s trembling-hand perfection ensures only credible threats persist, explaining surveillance capitalism. Spence’s signaling game shows how platforms use separating equilibria to maximize information extraction, as seen in KPI and notifications. The folk theorem supports tacit cooperation among platforms over long horizons, while the minimax theorem sets a structural limit on attention extraction. The iterated prisoner’s dilemma illustrates how collective resistance, via strategies like Tit-for-Tat, can sustain cooperation and prevent burnout.

_{SVAI™ is a monetizable index for www.steelldy-indices.com, aggregating five sub-indices. Formula: SVAI = 100×(w1×AVI + w2×ECI + w3×BSI + w4×RPI + w5×TNI), with weights (0.30,0.20,0.25,0.15,0.10).
AVI measures attention volume; ECI tracks attention acquisition costs; BSI quantifies behavioral data; RPI assesses cooperation via game theory; TNI follows tokenized RWA growth.
Data sources include Glassnode, CFTC, Bloomberg. Validation uses walk-forward, CUSUM, Kalman filtering. Related products: SVAI-Vol, SVAI-Skew, SAAI™, and SII™ integrity index.}

Steelldy Vital Attention Index

Smart Cities and 15-minute neighborhoods (Carlos Moreno’s concept, Paris) are, from the perspective of attention economy, hyper-dense extraction systems: IoT sensors, AI cameras, mobility flows, each movement generates a behavioral surplus. Academic literature (NIH PMC) confirms the security and privacy challenges; Eurocities advocates for depersonalization and opt-out.

Harry Dent’s Spending Wave model posits a consumption peak at age 47 for the head of household. Baby boomers (born 1946-1964) reached this peak around 2010-2015. Since then, the economy would be artificially supported by massive monetary stimulus (QE 2008-2026). The consequence: a demographic decoupling of available attention aging cohorts consume less digital attention (except in healthcare), creating structural stress on extraction. The SVAI™ integrates this dimension via the Demographic Attention Drift, a weighted demographic component.