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.
89 items, 2021 to 2026.
Frameworks
All frameworks →- System One Models Jev and the class of models that decide instead of chatting — why a typed probability in milliseconds is worth more, and costs less, than a frontier paragraph.
- AI Delegation Loop Stop building a specialist agent for every job. Capture process, toolbox and proof in a playbook a general agent can run — and put every correction back into the folder.
- 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.
Talks
All talks →- Fireside Chat: Building the AI Enterprise — A Customer Perspective
- 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
Writing
All writing →- Agentic AI Control Planes: The Guardrails That Make Autonomy Operable A practical architecture for agent guardrails: task boundaries, permissions, approvals, budgets, evaluation, observability and incident response.
- 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.
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