Nearly four years after ChatGPT brought artificial intelligence into the mainstream, the industry is confronting a new constraint.
For years, progress was measured largely through scale: more parameters, more computing power, and more training data. That formula produced major gains. But as businesses move from experimenting with chatbots to deploying AI agents inside real workflows, model capability is no longer the only issue. Companies also need to know whether the data, evaluations, and outputs behind those systems can be trusted.
