Goldman Sachs reported strong earnings this week alongside rising shares, with executives highlighting AI investments as a strategic priority. What makes this announcement theoretically significant is not the technology deployment itself, but what it reveals about a fundamental coordination problem: enterprises can acquire algorithmic capability without developing the organizational competencies necessary to coordinate through that capability.

The gap between technological adoption and coordination effectiveness represents what Kellogg, Valentine, and Christin (2020) identified as the core puzzle in algorithmic work arrangements. Organizations assume that deploying AI systems will automatically improve decision-making and coordination. This assumption collapses awareness of algorithmic systems with the capacity to work effectively through them. Goldman's strategic positioning suggests they recognize something their competitors may not: the competitive advantage lies not in having AI tools, but in developing distributed competence to coordinate through algorithmically-mediated environments.

The Institutional Inversion Problem

Traditional financial institutions like Goldman operate under what organizational theorists call "market coordination" (Williamson, 1975), where participants bring pre-existing competencies to transactions. AI systems invert this logic. Platform coordination develops competencies endogenously through participation in algorithmically-mediated environments (Rahman, 2021). When Goldman deploys AI trading systems, risk assessment tools, or client recommendation engines, they are not simply automating existing workflows. They are creating new coordination mechanisms where competence must be learned through interaction with opaque algorithmic processes.

The challenge becomes acute at the organizational level. Individual traders or analysts may develop folk theories about how AI systems behave, mental models built from pattern recognition and anecdotal experience (Gagrain et al., 2024). But folk theories are individual impressions, not structural understanding. Without schema-level knowledge of how algorithmic systems actually function, organizations cannot transfer learning across contexts or scale coordination practices effectively.

Why Ramp's Enterprise Spending Data Matters

Concurrent reporting from Ramp shows business spending on OpenAI models jumped to record levels in December 2024, with OpenAI outpacing Anthropic and Google in enterprise adoption. This creates an empirical puzzle: if organizations are increasing AI spending, why are we not seeing corresponding improvements in coordination effectiveness? The answer lies in what Hatano and Inagaki (1986) distinguished as routine versus adaptive expertise.

Organizations purchasing AI access are developing routine expertise: procedural knowledge about how to use specific tools for specific tasks. This produces performance gains in stable contexts but fails when algorithmic behavior changes, when contexts shift, or when learning needs to transfer across platforms. Goldman's competitive positioning suggests they may be investing not just in AI tools, but in developing adaptive expertise: structural understanding of how algorithmic coordination actually works.

The Transfer Problem at Scale

The financial services sector provides a natural experiment for testing whether schema induction produces better transfer than platform-specific training. Goldman competes with firms that have identical access to AI technology. The variance in outcomes cannot be explained by technology access alone. Some institutions will develop algorithmic literacy as a distributed organizational competence. Others will accumulate platform-specific procedural knowledge that does not transfer.

This is not about individual skill development. Hancock et al. (2020) demonstrated that AI-mediated communication fundamentally alters interaction patterns, creating new coordination challenges that individuals cannot solve through effort alone. Organizations need structural interventions: training systems that teach the topology of algorithmic constraints, not just the topography of specific platforms. Knowing the shape of algorithmic decision-making differs fundamentally from knowing how to navigate one particular AI tool.

What Goldman's Strategy Reveals

Goldman's simultaneous emphasis on strong financial performance and AI investment suggests they understand the coordination mechanism question that most enterprise AI adoption ignores. The power-law distributions we observe in platform work outcomes (Schor et al., 2020) will likely emerge at the organizational level. Firms with identical AI budgets will show dramatically different coordination effectiveness. The difference will stem from whether they treated AI adoption as a technology problem or as a coordination competence problem requiring schema-level organizational learning.

The awareness-capability gap documented in platform worker studies applies equally to institutions. Goldman knows AI exists. So does every competitor. The question is whether they are building the organizational capacity to coordinate through algorithmic systems, or simply accumulating tools they do not structurally understand.