AI models are getting cheaper. But most organisations can't tell you what their AI agent actually costs per outcome.They see monthly bills. They see token counts. What they don't see is: "It cost us £0.47 to deploy that load balancer, and £2.30 to configure that firewall rule."Without that visibility, you can't optimise. You're just hoping costs go down as models get cheaper.There's a better way: build a system that finds the cheapest path to the correct outcome, automatically.

Everyone's trying to figure out how to make their APIs "AI-native." The current leading solution is MCP (Model Context Protocol) — a standardised way for LLMs to discover and interact with external tools.But MCP is complicated. What if there was a simpler way? MCP requires: - Implementing a protocol - SDKs and tooling - Infrastructure overhead - Another dependency to maintain And here's the thing: it's solving a problem that doesn't need to be this hard.

I’ve been the infrastructure manager on the receiving end of a “completed” platform handoff: the kit is racked, the fabric is flying, the benchmarks look great, and everyone wants to move on.In GPU-first data centre builds, that moment is happening at scale. Fabrics are tuned for raw throughput (RDMA/RoCE, lossless Ethernet, PFC/ECN), validated, and handed over to a neocloud operator.

NVIDIA's GTC 2026 keynote made one thing crystal clear: AI isn't a feature anymore — it's infrastructure. Jensen Huang didn't talk about models or chatbots. He talked about AI factories — purpose-built facilities that manufacture intelligence the way power plants manufacture electricity.

With over two decades of experience in Telecoms, Service Providers, Broadcast & Media sectors, ranging from large global corporates to SMEs and Start-ups, Mike has led major technology transformations, cloud migrations, and automation strategies across some of the most complex environments.

In the modern networking landscape, automation has become a crucial component of managing increasingly complex infrastructures. Traditional network automation methods, often implemented in a non-declarative manner, have served as the backbone for automating repetitive tasks, reducing human error, and improving overall efficiency. However, these methods come with inherent challenges and limitations that can hinder scalability and flexibility.

Universities are increasingly relying on IT infrastructure to meet the diverse needs of their faculty and students. As both the number of Cloud-based enterprise solutions and the users increase, there is a compelling need for automation and orchestration, and a user-friendly interface accessible to all members of the university community to ensure efficient and effective utilization of resources.

Automating the deployment of services within an organisation is an expensive proposition. Even with today's automation ready infrastructure, to get that automation being used successfully in production requires a large amount of time from expensive integration resources. In addition, the project management infrastructure that needs to go on top of it. In this article we discuss how defining and offering your services ahead of that automation spend can produce better and more valuable business outcomes.