Machine intelligence as it moves from demo to deployed system — models, chips, regulation, agents, and the businesses being rebuilt around them.
Legal and security teams have quietly rewritten the checklist that kept open models out of production stacks.
Another order-of-magnitude drop in token prices is quietly re-pricing every AI product roadmap — and resetting the build-versus-buy math for everyone downstream.
Public benchmarks have decoupled from production performance. Enterprises are building private evals — and keeping them secret.
The burden-of-proof reversal survives. Compliance teams have until Monday to get their documentation in order.
Forty vendors, four architectures, one procurement checklist. What actually matters when the demos all look the same.
Longer contexts and cheaper retrieval are eating the fine-tuning budget, line by line.
Beyond the pilot programs and the keynote slides, a pragmatic playbook is emerging in 200-to-2,000-person companies — and it looks nothing like the enterprise story.
