Big Tech profits are not simply 'technological rents' but a mixture of industrial and commercial surplus rooted in postwar U.S. capitalism, and value is actively produced within tech labor processes.

Main Finding

The article rejects the claim that large technology firms’ revenues are best interpreted as “technological rents” (a ground-rent analogue). Using a Marxian critique of ground rent, it argues that extending rent concepts to replace a theory of surplus profits is conceptually mistaken. Instead, the paper develops a preliminary framework showing that Big Tech’s surplus profits derive from a mix of industrial and commercial profit categories rooted in the historical development of U.S. capitalism after World War II. It also emphasizes that value creation occurs in the tech labor process, linking this to contemporary tech-worker organizing.

Key Points

• Critique of rival paradigms: The paper identifies shared concepts between the technofeudal hypothesis and digital-capitalism theories, and challenges the explanatory sufficiency of both when they collapse profit into a rent-type category.
• Marx’s ground rent revisited: Through a theoretical discussion of Marx’s ground-rent concept, the author argues against using “technological rents” as a stand-in for surplus profits extracted by tech firms.
• Surplus-profit framework: Big Tech revenues are not pure rent; they span industrial profit (profits from production, IP-enabled scale and scope, capital investments) and commercial profit (profits from distribution, market intermediation, advertising and platform monetization). The paper presents this as a working classificatory framework rather than a final empirical accounting.
• Historical grounding: The sources of these profit categories are traced to institutional and economic developments in postwar American capitalism, which shaped how production, distribution, and market power evolved in the tech sector.
• Labor and value creation: The article highlights instances of value creation inside the tech labor process (engineering, platform operations, content moderation, etc.) and connects this to the growing worker organizing movement in tech, arguing that labor is a site of real value production, not merely an input captured by rents.
• Methodological stance: Emphasis on careful conceptual distinctions—profit vs rent—rather than treating rent as a catch-all to explain tech-sector surplus.

Data & Methods

• Primary approach: The paper is theoretical and conceptual. It uses:
  • Critical reading of Marxist categories (especially ground rent) and their applicability.
  • Comparative analysis of existing literatures on technofeudalism and digital capitalism.
  • Historical contextualization linking institutional changes in U.S. capitalism to the organization of tech production and markets.
• No novel empirical dataset is claimed; the contribution is analytical: clarifying categories and proposing a provisional framework for where surplus profits come from in technology firms.
• The framework is positioned as preliminary and intended to guide subsequent empirical testing (e.g., decomposing firm returns into industrial vs commercial sources, tracing labor value-creation activities).

Implications for AI Economics

• Conceptual clarity for measurement: Researchers modeling returns to AI investments should distinguish surplus profits from rents. Treating platform or IP returns as “rents” risks mis-specifying income sources and dynamics.
• Empirical agenda: The paper motivates empirical work to decompose Big Tech profitability into industrial (production, R&D, IP) and commercial (platform intermediation, data monetization, advertising) components, and to trace how these map onto firm balance sheets and sectoral accounts.
• Labor valuation and dynamics: AI-economics should incorporate tech labor’s role in value creation (engineering, data labeling, model tuning, ops). This affects analyses of productivity, returns to capital vs labor, and the impact of automation on incomes.
• Policy and regulation: If surplus profits are not merely rents, antitrust and tax policy interventions should be informed by the specific mechanisms generating profits (market power, IP barriers, distributional control), rather than by a generalized rent-extraction narrative.
• Modeling implications: Macro and industry-level models of AI-driven growth should account for historical-institutional features (market structure, vertical integration, platform architectures) that condition how profits accrue, and should allow for multiple profit-generating mechanisms rather than a single rent channel.
• Labor organizing and institutional change: The finding that value is produced within tech labor processes suggests that worker organizing can meaningfully affect distributional outcomes; AI-economics should therefore consider the bargaining power and institutional responses of tech labor as endogenous determinants of returns.

JEL: B5, L63, N72