The Infrastructure Announcement as Theoretical Signal

Nvidia's move this week to chase the $200 billion CPU market through AI agent PCs built with Microsoft, Dell, and HP is being covered primarily as a hardware competition story. That framing misses what is organizationally interesting about the announcement. The bet Nvidia is making is not simply that AI agents will run on more devices. The bet is that competence in deploying those agents will distribute evenly enough across enterprise users to justify the infrastructure investment. That assumption deserves scrutiny, and it is one that coordination theory gives us tools to examine.

What Agentic AI Actually Assumes About Users

Classical coordination theory, whether through markets, hierarchies, or networks, assumes that participants arrive with ex-ante competence. They know the rules, understand the structure, and can act on information. Platform coordination inverts this. Competencies develop endogenously through participation, and workers with identical access routinely produce power-law distributions in outcomes (Kellogg, Valentine, & Christin, 2020). The AI agent PC initiative embeds a version of the classical assumption: give users the hardware and access, and productive use will follow. There is no structural reason to believe this will happen uniformly.

The Nvidia-Microsoft-Dell-HP configuration is, at its core, a new application layer. AI agents operating on local hardware will mediate how enterprise users retrieve information, delegate tasks, and communicate across systems. The algorithmic logic governing agent behavior will be opaque to most users in the same way that platform recommendation systems are opaque to most content workers. Awareness that an agent is making decisions on your behalf does not translate into an ability to direct or audit those decisions effectively. This is the awareness-capability gap that Gagrain, Naab, and Grub (2024) identify in their work on algorithmic media use, and it applies with equal force here.

The Topology Problem in Agentic Deployment

There is a distinction in my research between topology and topography. Topology describes the shape of a constraint system - the underlying rules governing how an algorithm routes, prioritizes, or filters. Topography describes the surface features a user actually navigates. Most enterprise training programs for new software tools teach topography: here is where the button is, here is the workflow, here is the output format. Nvidia's partner ecosystem will almost certainly produce exactly this kind of documentation.

The problem is that agentic AI systems are structurally dynamic. The agent PC architecture Nvidia announced is designed to allow agents to reason across tasks, not simply execute fixed procedures. A user trained on topography - on the specific steps to interact with a particular agent configuration - will be poorly equipped when that configuration changes, when the agent encounters an edge case, or when the task itself requires the user to override or redirect the agent. This is the routine versus adaptive expertise distinction Hatano and Inagaki (1986) draw. Routine expertise performs well under stable conditions. Adaptive expertise is what survives novelty.

Agency Asymmetry as an Organizational Risk

Sundar (2020) describes the rise of machine agency as a condition in which AI systems increasingly act as communicative agents rather than passive tools. The AI agent PC is precisely this: a device where the machine is not simply processing input but initiating actions, filtering information, and completing tasks with varying degrees of user oversight. The organizational risk this creates is not primarily a security risk, though that is real. It is a coordination risk. When agents mediate communication across an organization, the quality of organizational outcomes becomes a function of how well individuals can specify, monitor, and correct agent behavior. If that capacity is unevenly distributed across employees, you get a new source of variance that management structures are not currently designed to address.

Hancock, Naaman, and Levy (2020) argue that AI-mediated communication introduces systematic distortions that are invisible to participants who lack structural awareness of how the mediation works. An organization deploying AI agent PCs across a workforce without investing in schema-level understanding - not procedural manuals, but genuine structural comprehension of how agents prioritize and act - is building asymmetry into its coordination infrastructure from the start.

What the Infrastructure Rollout Actually Requires

Nvidia's announcement is credible as a hardware strategy. The question it does not answer, and that no hardware announcement can answer, is what the competence distribution across adopting organizations will look like eighteen months after deployment. If Gentner's (1983) structure-mapping logic holds, users who develop accurate schemas of how agentic systems operate will transfer that understanding across tool updates and configuration changes. Users who learn procedures will face repeated retraining costs every time the underlying system shifts. Nvidia is building a powerful new application layer. The organizations that extract value from it will be the ones that treat competence development as a structural problem, not a documentation problem.

References

Gagrain, A., Naab, T. K., & Grub, J. (2024). Algorithmic media use and algorithm literacy. New Media & Society.

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

Hancock, J. T., Naaman, M., & Levy, K. (2020). AI-mediated communication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication, 25(1), 89-100.

Hatano, G., & Inagaki, K. (1986). Two courses of expertise. In H. Stevenson, H. Azuma, & K. Hakuta (Eds.), Child development and education in Japan (pp. 262-272). Freeman.

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.