Should Teams Use the Best AI Model First?

Nikesh Arora says that in the experimentation phase a company should use the best model it can find, in the easiest way. He says not to restrict the team, not to cost-optimize, and not to stitch a private stack yet.

Two teams at his company, twenty people rather than twenty thousand, have just discovered AI in the last twelve to eighteen months, and adoption is not fully diffused. Making the tools harder slows that down. Arora calls the easy-adoption stretch phase one. After that, different tasks need different models. Inspecting all traffic at an endpoint costs twenty dollars a year, or twenty to twenty-five dollars a year. Sending that traffic through a token-priced model is not practical. He says to compress a small language model, make it task specific, and train it until it reaches 99.9 percent accuracy.

A frontier model is for the rare case, such as an active breach whose business impact he puts in the billions of dollars. For most practical enterprise use, and for a defender model in peacetime, Arora says to take an open-weight model, train it on proprietary data, and manage the weights.