When discussing AI applications, people are most easily attracted by the demo effects, but the real challenge lies in how to turn it into a system that can operate long-term. AINFT's exploration within the TRON ecosystem is somewhat like building an intelligent production line—breaking down the processes of generation, sorting, review, and distribution, which originally required a lot of manpower, into standard modules that can be repeatedly invoked.

Once the workflow is standardized, the value dimension shifts from single output to continuous iteration. For creators, this guarantees efficiency and stable output; for project teams, it reduces operational costs and helps control growth pace. Frankly, what is truly scarce is never the idea itself, but the capacity to replicate and scale production.

If we are to find new directions for growth in the next stage within the TRON ecosystem, engineering AI capabilities into practical applications will be a key focus. It is recommended to treat AINFT as a long-term observation target—continuously monitoring how it organizes single-point capabilities into a complete toolchain and how to truly integrate this toolchain into daily use scenarios. This might be the next real step for the ecosystem's supply side.