AI governance starts before the first token
A piece in Data Center Dynamics argues that AI governance needs to begin at the infrastructure level, before a model ever processes data, rather than being applied after deployment.
Why this matters: Most governance conversations start with the model output. This one starts earlier. The argument is that decisions made in data centers — how compute is provisioned, where data lives, who has access — shape what AI systems can and cannot do before any policy document is written. That matters because rules applied late are harder to enforce. If the accountability structure is not built into the infrastructure, it is much easier to bolt on something that looks like governance without actually providing it.
Who should care: AI governance · Lawyers · Administrators · General readers · Policy
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