Why do most AI projects end up becoming platforms?

Because the entry point determines everything, and @dgrid_ai chooses not to be the entry point. Instead, it builds the underlying infrastructure—a network that all entry points can connect to.
This may sound unglamorous, but it’s extremely critical because once it becomes the default routing layer, upper-layer applications can’t bypass you regardless of how they change.
Looking at its structure, models provide capabilities, nodes offer computing power, and protocols handle scheduling and validation.
The three are decoupled but linked through $DGAI , with each inference call triggering a value distribution.
This isn’t SaaS billing but a real-time economic system.
More importantly, the results are auditable— the inference process leaves records, and outputs can be verified.
This enables AI to enter more serious scenarios, such as finance and automated execution.
Once trustworthiness is addressed, AI can truly begin to expand.
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