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Opinion

The Hidden Price of DIY Automation: Expensive, Slow, and Stuck in Silos

Every large enterprise hits the same moment. The infrastructure teams are buried in change requests, and someone says the sensible-sounding thing: "We've got good engineers — let's just build the automation ourselves."

It feels like the responsible, cost-conscious call. In practice it's usually the opposite: the most expensive, slowest-to-deliver, least scalable path available. The bill just shows up later, in instalments.

We're an automation company. We are not anti-automation. We're against the specific, predictable way do-it-yourself automation quietly fails — and it fails the same way almost every time. Three ways, to be exact.

AI Factories Are Coming. Who Controls Them?

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.

The numbers are staggering. The Vera Rubin platform promises 35-50x performance leaps. Cost per token is collapsing. Entire data centres are being redesigned as single computers, with rack-scale liquid cooling and NVLink fabric connecting thousands of GPUs into one coherent system.

But here's what Jensen didn't talk about: who actually governs what these factories produce?

MCP: The New Vendor Lock-In, Dressed Up as a Standard

Every few years, the infrastructure industry invents a new way to sell you complexity.

First it was hardware appliances - proprietary boxes that went end-of-life every three years, forcing expensive upgrades. Then it was proprietary software platforms - lock-in disguised as "integrated solutions." Now it's MCP.

The Model Context Protocol is being positioned as the "USB-C for AI" - a universal standard for connecting AI agents to external systems. 97 million SDK downloads. Big tech backing. The narrative says it's already won.

I don't buy it.