Insights

Frameworks and mental models for the decisions that define your architecture.

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New here? Start with these three

Start with the fundamentals4

AI Fundamentals

A business leader's guide to the building blocks of AI — no code, no jargon, just the mental models that matter.

Frontier Models

The most capable AI models available — and when a frontier model earns its cost vs when a small specialised model wins.

Model Context Protocol

How to assess MCP as an integration contract: where it reduces repeated connector work, and where identity, trust, and operating responsibility remain.

Agentic AI Patterns

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From single agents to orchestrated teams — the architecture patterns behind AI that acts, and how to choose between them without over-engineering.

Running AI: infrastructure and cost4

AI Cost Curves

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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.

FinOps for AI

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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.

AI Grid

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How NVIDIA's AI Grid reference architecture distributes AI inference across edge locations — and why it changes the economics of running AI in production.

Model Compression

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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.

Cloud economics4

Build vs Buy

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The decision framework every CTO faces — when to build, when to buy, and when to compose. A structured way through the trade-offs that define your stack.

Economic Sovereignty

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From cloud chaos to cost control — the framework for regaining economic independence from hyperscaler lock-in.

FinOps Maturity

Cloud cost control is a capability you grow, not a dashboard you buy. The maturity ladder from raw visibility to unit economics — and the practices on each rung.

Cloud Repatriation

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A decision framework for 2026 — the real survey data, a workload scoring matrix, a 3–5 year TCO model, and the risks. Repatriation as portfolio management, not ideology.

Platform and teams5

Platform Operating Model

How a central platform team serves dozens of autonomous teams without becoming the bottleneck — the operating model behind an internal developer platform.

Developers as Customers

Treat the people who use your internal platform like customers, not ticket-raisers. The four journeys every platform has to nail — and how to measure whether it does.

Team Topology Decisions

Team design is an architectural act, not an HR one. The four team types and three interaction modes from Team Topologies — plus the decision layer: when each shape fits, and what breaks when it does not.

Technical Debt Prioritisation

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Not all debt is worth repaying, and the messiest code is rarely the most expensive. A prioritisation framework built on interest rate, change frequency, blast radius, and remaining life — so the argument stops being about taste.

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.

Security2

Agentic API Security

The caller is no longer a person. The identity, task-scoping, reversibility gates, and observability an API estate needs once autonomous agents hold the credentials.

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.

Where the market is going2

From Content Wars to Experience Wars

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The streaming industry solved access; now it needs to solve experience. A framework for the shift from content wars to experience wars — and what comes next.

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.