Analysis, perspectives, and practical insight on AI strategy, infrastructure, and what it all means for mid-market organizations.
Organizations invest heavily in AI tools and platforms, but skip the foundational work that determines whether any of it creates value. The problem isn't the technology — it's the sequence.
The Moon holds extraordinary reserves of materials that can be mined, processed, and delivered back to Earth at a fraction of conventional logistics costs. The physics make the economic case almost inevitable.
The dangerous failure mode isn't AI not knowing things — it's AI confidently explaining its own errors. A real incident, three layers deep, and what it reveals about deploying agents operationally.
A plain-language breakdown of the metrics that actually matter in AI infrastructure — compute efficiency, energy cost, and chip economics — and what they mean for mid-market decision-makers.
Hyperscalers are committing hundreds of billions to AI infrastructure. Understanding this buildout helps mid-market buyers make smarter decisions about cloud, colocation, and timing.
GPU allocation, export controls, and chip design cycles determine which AI capabilities are accessible and at what cost. Here's what decision-makers need to understand.
As frontier model performance converges, the differentiation shifts elsewhere — to data, workflows, and integration depth. What that means for how you buy and build.
Training and inference at scale consume significant energy. For infrastructure-adjacent businesses, this creates both risk exposure and genuine commercial opportunity.
Generative engine optimisation is not just SEO with a new name. The ranking signals, content formats, and buyer journeys are fundamentally different. Here's how to adapt.
The build vs buy question is more nuanced for AI than for traditional software. This framework helps mid-market leaders make the call based on strategic fit, not vendor pressure.