"Percent of code AI-generated" is this era's lines-of-code: easy to compute from a diff, trivially gamed, and silent on whether anyone can explain the system in six months. Here is what the field data actually shows when authoring gets cheap, why the number you're setting OKRs against measures the stage that stopped being the bottleneck, and four replacement metrics graded by whether you can compute them today.
The finale of the series: the mature end-state the first three parts build toward. When every server-side capability's trust boundary is a decision on record—the deployment tier you chose, the toggle you set, the contract you signed—saying yes to another team stops being a risk assessment and becomes a lookup. Fine-grained capability governance is what buys you safe expansion. Maturity, not lock-in. Boring, in the way a well-run system is boring.
Parts 1 and 2 argued that a capability's trust boundary is a separate decision from the model's. Part 3 is the runbook that makes the decision stick: the org-policy constraints that gate partner web search and structured outputs, set once at the organization tier by gcloud and Terraform, plus the seams org-policy doesn't reach—VPC Service Controls, request-response logging, the grounding-provider choice. Which toggle, at which scope, with deny-wins precedence, so a good-faith developer can't trip a data path nobody chose.
On February 23, 2026, Google quietly made the retention terms for Grounding with Google Search better: the window dropped from thirty days to up to three, and what's kept narrowed from your prompts and output to short-lived debug logs. The old thirty-day, prompts-and-output language didn't vanish—it now describes Grounding with Google Maps, a sibling capability under the same terms. Here's what the corrected terms actually say, how to verify the date yourself from Google's dated archive, and the durable lesson: govern facts that move—cite the dated primary source, hedge the number, and don't hard-code a digit that will drift out from under you.
The builder's companion to Governed Growth—the same reference repo from the implementer's chair. Why I built a one-tool MCP server to hand an assistant a reliable, governed Google search, how Gemini grounding and citation extraction work in the code, and how a full, human-validated eval picked the default model. The surprise: gemini-3.1-flash-lite matched the larger models on quality, grounded most reliably, and cost the least.
Opening a platform to more teams and more models—first-party and partner, such as Gemini and Claude—quietly assumes those capabilities share one trust boundary. They don't. Multi-model expansion is a governance-maturity story: Google Cloud lets you grant capability broadly while keeping each model's data path a deliberate, fine-grained decision. You can grow usage without growing exposure.
