4 articles in this category
Most diagrams of AI pipelines jump straight from "data" to "model" as if the gap were filled by math alone. In reality, a large part of that gap is filled by people working through queues of tasks: tagging images, rewriting sentences, rating outputs, flagging harm. The current wave of foundation models did not remove human labor—it reorganized it, offloaded it, and hid it.
Governments have finally understood something the industry has known for a while: the ability to train and run large models at scale is not just an IT decision. It is industrial policy, military capability, information control, and energy planning rolled into one. Your "infra" is their strategic asset or their strategic liability.
A lot of companies think they have AI governance because they wrote a principles document and formed a committee. That is not a stack. Once you start deploying models into workflows that touch real customers, employees, or regulated data, governance becomes a set of systems: policies wired into code, logs you can query, audits that actually bite, guardrails that fail closed.
For years, tech teams treated regulation as a late-stage obstacle. You ship, you find traction, then someone forwards a PDF from legal and you bolt on whatever is needed to pass procurement. With AI, that sequence breaks.