How Does Tribe AI Decide What to Build Next?
Pooja Brown says Tribe AI decides what to build next from two places: how frontier models are developing, and the outcomes the businesses they work with want to achieve. Two years ago the models could help a person do the work. Today the models can do the work themselves, so the product has to change with them.
It started as a smart layer on top of a model. The person still did the job, and the model drafted or suggested. Now the agent does the job and the person checks it. Every time a person corrects the agent, Tribe AI learns from the correction, and the agent gets a little more room to work on its own. They measure the work against what actually happens in the business, asking whether the outcome they promised got achieved. Pooja Brown calls the shift a move from digitizing the keyboard to digitizing the human, a journey that followed as model development increased and the scope changed. The roadmap builds itself. The first product in a company is always expensive because it sets up the integrations, the evals, and the security the agents and models need. The second product reuses all of that setup. Afterward the roadmap follows the enterprise they are working with: the outcomes it wants, and what the system already built can unlock.
The larger aim is learning loops in AI products that outlive the engagement. An AI product, Pooja Brown says, is a product that gets better as it is being used, and the systems have to keep evolving as context changes, as new information comes in, and as business processes evolve.
