The Specific Event

EY recently disclosed that it has deployed what it calls an "invisible" AI router positioned behind its suite of AI tools. The router's function is to direct queries to the most cost-efficient language model capable of handling a given task, rather than defaulting every request to the most powerful and expensive option. The reported result is a reduction in token consumption of up to 60%. This is not a marginal operational tweak. For a Big Four firm running AI queries at scale across tens of thousands of professionals, a 60% reduction in token spend represents a structural intervention in how AI capacity is allocated inside a large organization.

The announcement is worth pausing on, because what EY is describing is not an AI literacy initiative. It is an infrastructural governance decision made above the level of individual workers. The router is invisible by design. Users do not choose which model handles their request. The organization has removed that decision from the worker entirely.

Governance Above the Awareness Layer

This design choice illuminates a distinction that algorithmic literacy research tends to underweight. Kellogg, Valentine, and Christin (2020) documented how algorithmic systems at work tend to create asymmetries between workers and the organizations deploying those systems. Workers develop awareness of the systems around them, but that awareness rarely translates into the capacity to influence how those systems are configured. EY's router makes this asymmetry structurally explicit. The worker does not need to know which model is being used, because the governance layer has already made that determination.

From the perspective of my own framework on Application Layer Communication, this is a meaningful case. The ALC framework is concerned with how competencies develop endogenously through participation in algorithmically-mediated environments. EY's router suggests that some organizations are now intervening at the infrastructure layer to prevent certain competence-development pathways from forming at all. If workers never encounter the decision of which model to use, they cannot develop schema-level understanding of why those differences matter. The awareness-capability gap does not close. It is simply made irrelevant by fiat.

The Routine Expertise Trap at Organizational Scale

Hatano and Inagaki (1986) drew a foundational distinction between routine expertise and adaptive expertise. Routine expertise enables reliable performance within stable task structures. Adaptive expertise enables reconfiguration when those structures change. EY's routing infrastructure optimizes for routine performance across its current AI stack, but it does so by abstracting away the structural features of the stack itself.

This is organizationally rational in the short term. Token costs are real, and 60% reduction is a significant efficiency gain. But the abstraction carries a longer-term risk that governance discussions about AI in organizations rarely surface directly. If the router is invisible, and if workers are never required to reason about model selection, then the organization's adaptive capacity becomes concentrated in whoever designed and maintains the router. Everyone else develops only procedural familiarity with the output layer. When the infrastructure changes, as it will, the organization's distributed AI competence will not transfer.

Gentner's (1983) structure-mapping theory is instructive here. Transfer across contexts depends on learners having access to structural, relational features of a domain, not just surface-level procedures. EY's architecture, however rational, systematically denies most workers access to those structural features. The organization trains procedural fluency. It does not train transferable schema.

What This Means for AI Governance Frameworks

The luxury board governance discussion circulating in parallel business press this week frames AI governance primarily as a risk management problem: boards need to understand AI well enough to ask the right questions. EY's router case reframes the problem. Governance is not only about board-level oversight. It is also about where in the organizational stack decisions get made, and what gets obscured when they are made at the infrastructure level rather than the worker level.

Sundar (2020) described the rise of machine agency as a process in which AI systems increasingly make decisions that humans formerly made, shifting accountability in ways that are not always visible to participants. The invisible router is a clean example. The efficiency gains are visible. The governance implications of removing model-selection from worker cognition are not.

The practical question for organizational theorists is whether large firms deploying AI routers, filters, and abstraction layers are building organizational AI competence or merely AI-adjacent workflow efficiency. Those are different assets with different durability. The 60% token reduction will show up in EY's cost reports. The competence deficit, if it materializes, will show up much later and be harder to attribute.

References

Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155-170.

Hatano, G., & Inagaki, K. (1986). Two courses of expertise. Research and Clinical Center for Child Development Annual Report, 8, 27-36.

Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410.

Sundar, S. S. (2020). Rise of machine agency: A framework for studying the psychology of human-AI interaction. Journal of Computer-Mediated Communication, 25(1), 74-88.