The era of charging for the “how” is officially over. For a decade, B2B SaaS founders built moats around proprietary workflows and the UI that housed them. We sold seats. We sold efficiency. We sold the promise that our software made humans better at their jobs. But as the marginal cost of logic approaches zero, those moats are evaporating in real time.
At Cameyo, we saw the early signals of this shift before the Google acquisition. It was never about the virtualization of an app. It was about the resolution of a need. Today, if your startup is still anchored to seat-based pricing while an AI agent performs the work of ten people, you are participating in a race to the bottom.
The Shift to RaaS Economics
The transition to Resolution as a Service (RaaS) is not just a pricing change. It is a fundamental decoupling of revenue from headcount. In the old model, logic was the value. In the new model, logic is a commodity. The real value has shifted to the integrity of the output and the repository that validates it.
Founders who fail to bridge this narrative data gap will find themselves with a burn multiple that no Series A investor will touch. You cannot scale a business on the back of “human efficiency” when the market expects autonomous outcomes. You must own the resolution. You must charge for the “what,” not the “how.”
The Technical Reality Behind the Shift
The reason your software moat is disappearing is not just market pressure. It is a structural liquidation of logic itself. When AI can generate the “how” of a task for near zero cost, the proprietary nature of your code becomes a liability rather than an asset. To survive, your infrastructure must pivot from performing the logic to auditing the result.
The strategic shift toward outcome-based revenue is driven by a fundamental change in how software handles data. To understand the underlying technical shift from workflow-based SaaS to high-fidelity repositories, read our deep dive on The Logic Liquidation at Middle Way in AI.
Prescription
You must audit your current pricing model against the 1 to 4 Rule. If your AI logic saves a customer four dollars in labor, you must capture at least one dollar of that value through outcome integrity rather than per-user licensing.
Is your current roadmap focused on making the “bot” smarter, or is it focused on making the “repository” the ultimate arbiter of truth for your customer?