AI is moving beyond chatbots. The more interesting question is what developers can actually build with it.

A powerful model is only one part of an AI application. Behind a useful product, there may be an API connecting the model to an application, a workflow that determines when it runs, external tools that provide information, and systems that turn its output into something actionable.

Think about an AI research assistant.

The model interprets a question, the application retrieves relevant documents, tools help verify information, and the workflow organizes the result into something a user can work with.

If one part of that system is unreliable, the experience suffers, even if the underlying model is impressive.

This is why AI infrastructure matters. Developers need more than access to intelligence; they need practical ways to integrate it into products, manage costs, and select models suited to different workloads.

At TOKEN2049 Singapore, B.AI is bringing the AI infrastructure conversation to the TRON booth.

The important question isn't just which model is trending. It's what people can build when model access, tools, and workflows come together.

What part of the AI stack do you think deserves more attention?

@justinsuntron @BAI_AGI #TRONEcoStar #BAIinToken2049