Rent general intelligence. Own the specialists.

I may need to amend something I’ve been saying: rent the model, own the memory. Coinbase just published a fascinating example of what happens when a company does more than that.

They took a relatively small open-weight model and trained it on Coinbase’s proprietary fraud data. On their internal fraud benchmark, the resulting model outperformed frontier models across every metric they tested. The actual training run cost less than $100.

There are obvious caveats. This is one narrow task, the benchmark belongs to Coinbase, and the real cost includes a lot more than the GPU time. But the architectural point is really interesting.

The original open-weight model was not particularly good at the task. The advantage appeared after Coinbase combined an open model with something nobody else has: its own fraud history, outcomes and understanding of what a good decision looks like.

So maybe the emerging enterprise architecture is more nuanced than “rent the model.” Rent general intelligence. Own specialized intelligence where your proprietary data and judgment actually create an advantage.

A frontier model can remain the generalist. But underneath it, companies may have dozens or eventually hundreds of smaller specialist models trained to do very specific things unusually well.

Fraud detection. Pricing. Customer retention. Ad yield. Forecasting. Claims. Underwriting. Whatever is specific to that company.

And Coinbase is making this available internally as a skill, so employees can spin up and fine-tune specialist models themselves. Brian Armstrong even suggests that some existing backend services could eventually be replaced by this architecture.

Maybe the future enterprise AI stack is not one giant model that knows everything. It is a generalist surrounded by a growing collection of specialists that know your business extraordinarily well.

And those specialists may be worth owning.