bradshaw.cloud: Audio Edition

The practical AI FinOps metrics that connect token, GPU, retrieval and agent cost to accountable operational and business decisions.

Read the written version, with its diagrams and tables: https://bradshaw.cloud/writing/finops-for-ai-metrics/

Personal views, not those of my employer.

A practical control map for deploying Model Context Protocol safely: identity, permissions, supply chain, approval gates, logging and operational ownership.

Read the written version, with its diagrams and tables: https://bradshaw.cloud/writing/mcp-security-governance/

Personal views, not those of my employer.

Strengths frameworks sell the upside and bury the cost. Every strength is a default, and defaults misfire under load — here's how to read your results for the failure modes, not the flattery.

Read the written version, with its diagrams and tables: https://bradshaw.cloud/writing/strengths-shadow-side/

Personal views, not those of my employer.

AI governance fails when it is a policy without decision rights. A practical operating model for governed AI delivery: lifecycle gates, accountable roles and what you run after go-live.

Read the written version, with its diagrams and tables: https://bradshaw.cloud/writing/ai-governance-operating-model/

Personal views, not those of my employer.

The Digitalisation World interview covered the AI Grid concept in eight minutes. Here's what that format couldn't fit: the three questions I get asked most often afterwards, and why the answers matter more than the headline.

Read the written version, with its diagrams and tables: https://bradshaw.cloud/writing/ai-grid-pole-position-followup/

Personal views, not those of my employer.

Traditional cloud bills scale with traffic. AI bills scale with autonomy — and an agent stuck in a loop spends like an intern with a corporate credit card. The circuit breakers, caching, and chargeback discipline that keep agentic spend answerable to somebody.

Read the written version, with its diagrams and tables: https://bradshaw.cloud/writing/finops-for-ai/

Personal views, not those of my employer.

Streaming solved access and then broke it again — eight services, $1,100 a year, and a discovery experience worse than cable. The platforms that survive won't be the ones that spent the most on content. They'll be the ones that removed the friction.

Read the written version, with its diagrams and tables: https://bradshaw.cloud/writing/streaming-experience-wars/

Personal views, not those of my employer.

Most people use "quantisation" and "compression" interchangeably. They aren't the same thing — and knowing the difference is what separates a deliberate self-hosting strategy from cargo-culting whatever ran on someone's laptop last week.

Read the written version, with its diagrams and tables: https://bradshaw.cloud/writing/model-compression-family-not-cousin/

Personal views, not those of my employer.

The repatriation conversation has become tribal — cloud believers vs data-centre revivalists. Both camps are wrong. The right question is workload-by-workload: where do the economics, performance, and compliance of this specific system land?

Read the written version, with its diagrams and tables: https://bradshaw.cloud/writing/cloud-repatriation-is-portfolio-management/

Personal views, not those of my employer.

Training built the centralised AI factory. Inference is quietly un-building it — because serving predictions to users is a latency, geography, and cost problem the industry already solved once, for content, twenty-five years ago.

Read the written version, with its diagrams and tables: https://bradshaw.cloud/writing/ai-grid-inference-is-a-cdn-problem/

Personal views, not those of my employer.