Sunday, September 6th, 2026
Enterprises spent a year making sure they could switch between model providers. The switch turns out to run through a small number of clouds, chips and inference platforms that no procurement policy addressed.
Training got the headlines and the capital budget. The recurring cost of running models in production is what finance departments are now trying to forecast, mostly badly.
The most consequential AI deployments of the year are not chatbots. They are agents reconciling invoices, chasing documents and closing tickets nobody wanted to touch.
After years of scraping first and negotiating later, AI developers are building formal licensing pipelines, and content owners are learning what their archives are worth.
Deployment has run well ahead of measurement. A striking number of production systems have no defensible answer to whether they are performing better than what they replaced.
Away from enterprise pilots and keynote demos, small firms are putting generative tools to work on the least glamorous parts of operating a business.
Procurement departments, tired of negotiating novel terms for every AI vendor, are converging on shared contract language for data handling, evaluation and exit.
The frontier gets the headlines, but the volume is moving to compact models that run cheaply, privately and close to the work.
Companies built production systems on models that vendors retire on their own schedule. The resulting migrations are unplanned, unbudgeted and increasingly frequent.