AI Foundations & Adoption
Most of what gets called an AI strategy is really an infrastructure decision wearing a different hat. This is the broad end of my AI thinking — what the models actually are, where the compute has to sit, what enterprises keep getting wrong on the way in, and the running commentary I've given on all of it.
85 items, 2021 to 2026.
Frameworks
All frameworks →- Agent Authority An AI agent needs more than an API token. The framework for assigning a sponsor, mandate, permissions, autonomy and evidence before it can act.
- AI Platform Operating Model AI does not replace platform engineering. It expands the platform’s job from delivery pipelines to model access, data context, evaluation and agent governance.
- Sovereign AI Data residency is one control in a larger AI sovereignty model. Score data, models, operations, jurisdiction and exit paths before you choose a deployment.
- FinOps for AI AI spend scales with autonomy, not traffic. The circuit breakers, tier routing, caching, and chargeback discipline that keep LLM and agent spend answerable to somebody.
- Model Compression Compression is the family. Quantisation is one cousin. The techniques that shrink language models for self-hosting — what they do, what they cost in quality, and which ones you actually reach for.
- Frontier Models The most capable AI models available — and when a frontier model earns its cost vs when a small specialised model wins.
- AI Grid How NVIDIA's AI Grid reference architecture distributes AI inference across edge locations — and why it changes the economics of running AI in production.
- AI Cost Curves When API costs cross the self-hosting line — the economics that drive AI infrastructure decisions, and how to spot the crossover before it hits your bill.
Talks
All talks →- The Compute Infrastructure Questions Every AI Buyer Should Ask
- Debate: Hybrid cloud in the AI era – Best practice or a model under pressure?
- Akamai Inference Cloud Overview
- Applied AI in Practice: Deploying Tools Inside Real Client Workflows
- From Data to Dollars: Using Real-Time Data and AI to Lift Revenue and Customer Experience
- Architecting the Agentic Web
- Cloud Innovation: Powering Next-gen Apps
- API Security Made Simple
Writing
All writing →- FinOps for AI Metrics: The Scorecard That Turns Token Spend into Decisions The practical AI FinOps metrics that connect token, GPU, retrieval and agent cost to accountable operational and business decisions.
- MCP Security and Governance: The Control Map Enterprises Actually Need A practical control map for deploying Model Context Protocol safely: identity, permissions, supply chain, approval gates, logging and operational ownership.
- AI Governance Operating Models: Who Decides, Who Enforces, Who Gets Paged AI governance fails when it is a policy without decision rights. A practical operating model for lifecycle gates, accountable roles, enforcement and time-bound exceptions.
- Data Residency Is Not Sovereign AI A local model region answers one question about location. Sovereign AI also requires control of prompts, embeddings, model provenance, operations, jurisdiction and a tested exit path when conditions change.
- After the Interview: What I Wish I'd Had More Time to Say About the AI Grid 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.
- FinOps for AI: Why Your LLM Bill Is Exploding — and How to Stop It 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.
- The AI Grid: Why Inference Is Becoming a CDN Problem 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.
- Compression Is the Family. Quantisation Is One Cousin. 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.
In the press
All in the press →- The State of AI in Media 2026
- John Bradshaw cited in AS Watson technology partnership announcement
- Preparing to take pole position on the AI grid
- John Bradshaw (Akamai): «La inteligencia artificial es tan buena, o tan mala, como los datos que la alimentan»
- GenAI data center infrastructure reshapes business processes
- Exploring the Future of AI at the Edge
- How Consumer Experience Is Driving Enterprise UX
- From Core to Edge: Akamai on Where AI Inference Must Live Next