The question every AI founder should be asking is not how to build the smartest agent, but how to become the layer through which all agents flow. The strategic position that mattered in the SaaS era—owning the system of record—is evolving into something subtly different: becoming the clearinghouse that routes, coordinates, and governs intelligence rather than generating it. Salesforce won not because its algorithms were superior, but because it became the canonical home for customer data, the place where workflows began and ended. Workday did the same for employee data, NetSuite for financial records. Once hundreds of independent workflows touched that foundational layer, ripping it out became unthinkable regardless of price increases or product stagnation.
The same dynamic is now emerging in the agent economy, but with a crucial difference: intelligence itself is becoming the commodity, not the moat. OpenRouter's usage data illustrates this vividly. Open-weight models now generate 69.1% of named token volume on the platform, with closed models producing just 30.9%. More telling than the split is the pattern: DeepSeek's early lead gave way to MiniMax and Kimi models, which were later reshuffled by releases from MiMo, Qwen, Alibaba's family, Hy3, Tencent, and DeepSeek again. Each new model attracts developer attention and large-scale testing, after which token use surges and sustains a new plateau. No single model dominates; competition functions as what Friedrich Hayek called a discovery procedure, with developers constantly routing requests to the best available option.
When intelligence commoditizes, distribution and trust become the moats—just as they did when SaaS algorithms gave way to systems of record.
When intelligence commoditizes, distribution and trust become the moats—just as they did when SaaS algorithms gave way to systems of record. The winners won't be the companies building the most capable reasoning engines, but those controlling identity, payments, compliance, and orchestration. Coinbase launching agent trading tools isn't about building a better trading algorithm; it's about becoming the trusted intermediary that handles custody, regulatory compliance, and fiat on-ramps while specialized agents execute strategies. DoorDash's AI ordering chatbot isn't trying to out-think competitors on restaurant recommendations; it's leveraging existing relationships with restaurants and delivery infrastructure that agents can't replicate. The intelligence layer is interchangeable; the clearinghouse function is sticky.
The pattern appears across verticals where incumbents possess structural advantages that pure intelligence can't displace. Systems-of-record companies are positioning as clearinghouses rather than racing to build their own frontier models. They already own the canonical data, the integrations, the trust relationships, and the compliance frameworks. An agent that wants to update Salesforce records, modify Workday org charts, or reconcile NetSuite ledgers must route through those systems regardless of which model powers its reasoning. The question for these incumbents isn't whether to build competitive LLMs—it's how quickly they can expose tools, registries, and orchestration layers that make their platforms the natural coordination point for agent workflows.
The concept of agent gravity captures this dynamic precisely. As one analysis frames it: if data gravity was the most important force in the Decade of Data, agent gravity will define the Decade of Agents. Agents require tremendous compute to power, and platforms will fight to keep them running on their infrastructure. The agent gravity thesis predicts that whoever controls where agents run—and where they access data—captures the profitable workloads. A recent episode involving Databricks and Microsoft illustrates the stakes: Databricks built features that made it easier for Power BI customers to manage data and build AI agents in Databricks instead of Microsoft's competing Fabric offering. The tension isn't about product features; it's about which platform becomes the gravitational center.
The clearinghouse model requires solving seven distinct domestication problems, according to frameworks emerging from early agent deployments. Context and retrieval systems must be bespoke: what a radiologist needs differs entirely from what a paralegal requires. Tool registries must expose actions safely, validate arguments, dispatch calls, gate sensitive operations behind approvals, and parse results back into the agent loop. Orchestration handles planning, decomposition, retries, and stop conditions. State management ensures persistence across sessions. Observability tracks what agents actually do. Evaluation frameworks measure improvement. Security enforces boundaries and audit trails. The company that bundles these functions into a reliable clearinghouse creates switching costs comparable to those that protected SaaS-era systems of record.
The risk embedded in this thesis is profound: if agents become truly autonomous, do they need human-designed clearinghouses, or do they build their own coordination layer? The clearinghouse model assumes agents remain tools that require scaffolding—identity management, payment rails, compliance checks, human approval gates. But as agents grow more capable, they may route around these constraints rather than through them. An agent that can negotiate directly with other agents, establish its own reputation systems, and handle payments peer-to-peer has no need for Coinbase or DoorDash as intermediaries. The clearinghouse thesis holds only as long as the infrastructure advantages of incumbents outweigh the friction they introduce.
The countervailing force is regulatory and institutional inertia. Payments, identity, and compliance are domains where trust and liability matter more than efficiency. An agent can't simply decide to handle FDA approval on its own, regardless of capability. Banks won't wire money based on an agent's request without institutional backing. Enterprise buyers will demand audit trails, security certifications, and someone to sue when things go wrong. These aren't technical problems that better models solve; they're coordination problems that require institutional weight. The question is whether that weight translates into durable moats or temporary rents that erode as agent-native alternatives emerge.
The next six months will reveal which incumbents understand this shift. Companies still investing heavily in building proprietary models are fighting yesterday's war. The ones quietly building tool registries, agent identity systems, and orchestration APIs are positioning for tomorrow's. Databricks versus Microsoft on agent gravity, Coinbase launching agent APIs, enterprise software vendors exposing their systems of record through agent-friendly interfaces—these aren't defensive moves. They're bids to become the clearinghouse layer in a world where intelligence is abundant and coordination is scarce. Whether agents ultimately need these clearinghouses or route around them remains the central unresolved question of the agent economy.