What does it take to get AI infrastructure live? Land. Power. Data centres. Networks. Compute. And, behind all of it, people who can make those pieces work together. That’s the challenge we’re building CUDO around. It means the work here rarely sits neatly in one box. Our teams are solving problems across the infrastructure stack, making decisions quickly and taking real ownership of getting things delivered. So rather than tell you what it’s like to work at CUDO, we asked some of the people doing it. As CUDO continues to grow, we’re growing the team behind it too. We’ll be sharing more opportunities to join us over the coming weeks. For now, meet some of the people building it.

Two identical GPU clusters. Same hardware. Same generation. Very different results in production. Because GPU count and spec sheets only tell you what the hardware could do. They don’t tell you what it will sustain once a real workload is running. Fabric design. Collective communication. Storage throughput. Cooling. Power. Straggler detection. Runtime configuration. Day-to-day operations. They all determine how much of the performance you paid for actually reaches the workload. The difference can be significant. Published examples covered in our latest blog show comparable-era training environments achieving dramatically different levels of useful GPU utilisation, while inference performance can shift materially on unchanged hardware simply through changes to the serving stack and configuration. That changes the questions buyers should be asking. Not just: How many GPUs? Which generation? But: What throughput can this system sustain under load, and who is responsible for making sure it does? Hardware sets the floor. Throughput is built. Read why identical GPU clusters diverge in production, and what to look for beyond the spec sheet. cudocompute.com/blog/why-ide…

Are we planning the UK's AI infrastructure around where compute is going, or only where it is today? This week, our Chief Business Development Officer and Co-Founder, @CudoPete, joined industry leaders for the opening keynote panel of The Xcelerated Compute Show, exploring UK sovereign compute in the age of frontier AI. The conversation moved across sovereignty, AI Growth Zones, power, domestic capacity and what the UK needs to build if it wants to capture more of the economic value created by AI. But one of Pete's points looked beyond the infrastructure challenge immediately in front of us. Right now, the direction of travel is towards increasingly power-dense compute. Pete pointed to the prospect of one megawatt racks within the next three to five years. That creates an obvious question for an industry already working through constraints around land, power and grid capacity: how much further does that curve go? Pete challenged the assumption that it only moves in one direction. Emerging areas such as biological compute could eventually change the relationship between compute output and power density altogether. We could spend the next few years moving towards unprecedented densities, only for new architectures to begin pushing that requirement back down. And when the infrastructure we are planning today will be operating for decades, that matters. As Pete put it: “How do we get ahead of this? And how do we change the future, not just in the UK, but across the world?” The UK has the research capability, universities and talent to play a role in answering that question. So while we absolutely need to solve for today's constraints, from deliverable power to the right locations for AI infrastructure, we also need to think further ahead. What does the next generation of compute require? What could come after it? And are the infrastructure decisions we make today flexible enough for both? Land takes years. Power takes years. Compute can change much faster. Planning for AI infrastructure means planning for that difference.

The individual pieces of AI infrastructure can all look viable. The deployment can still be years away. Land can be secured without deliverable power. Power can be announced without an energisation date. Data centre capacity can exist without the network, hardware or operational capability needed to turn it into usable compute. That is the coordination problem sitting underneath the AI infrastructure build-out. And it is showing up in the decisions companies are making. In our Land. Power. Compute. research, 96% of AI infrastructure decision makers said power constraints or geopolitics had already caused them to change infrastructure plans. Because hyperscale AI does not buy land, power, data centres, networks and compute on separate timelines. It buys capacity against a deployment window. The question is not whether each piece exists. It is whether they can be brought together in the right place, at the right time, with economics that work. That is the conversation we are taking to DCD Connect London on 16–17 September. @CudoPete, Kir Devaser, Kaitlin Argeaux, Mark Richman, Dean Fletcher, Barry Kick and Michael Jones will be there across both days. If you are working through where capacity is going, what will actually be deliverable and what has to line up to get compute live, find one of us. And if you think the industry is looking at the wrong constraints, we especially want to hear why.

Announced power and power you can actually draw on are rarely the same number. That is what we want to talk about in Austin this week. Power availability is the most cited deployment blocker across the US, UK and Europe. It stands at 32.1% according to the 701 AI infrastructure decision makers we surveyed for Land. Power. Compute. Which puts the risk in the chain, not the chip: whether the power behind the GPUs arrives inside your window. That gap is our subject because we carry it. CUDO delivers the whole chain under one contract, land, power, data centre, network and compute on an SLA, inside the window you buy in. One accountable party rather than four. Two views we are taking with us, on 2027 capacity: the number that matters is not what you have reserved, it is the date it energises. On placement: the workload and the deliverable power decide where compute sits, not land availability. If you see it differently, we would like to hear it in the comments or in person. Dean Fletcher, our Strategic Alliance Director, and Mohamed Alibi, our Senior HPC Engineer, are at Metro Connect Fall and Datacloud USA.

Advanced AI capabilities are no longer available only through closed third-party APIs. Enterprises can choose where models run, how they are adapted and what happens to the data. That gives organisations greater control over a capability that is fundamental to how they operate.

As enterprises connect AI systems to documents, databases and operational tools, the risk changes. A malicious instruction no longer needs to come directly from a user. It can be hidden in a webpage, email or file that an AI system has been asked to process. If the system treats that content as trusted, the model could disclose sensitive information, manipulate an output or attempt an unauthorised action. That is prompt injection, and no single filter or carefully written system prompt can eliminate the risk. The practical response is a defence-in-depth approach: • Treat external content as untrusted • Give models and agents the minimum access they need • Isolate sensitive workloads and data • Validate outputs before they reach other systems • Require human approval for consequential actions • Continuously test how the system behaves under attack This makes security architecture an essential part of the infrastructure decision. The question is no longer: “Which model should we deploy?” “It’s a case of zero trust regardless of how good the model is.” - Mohamed Alibi, Senior HPC Engineer at CUDO Compute. At CUDO, we believe enterprise AI infrastructure should be evaluated on the control and resilience it provides, not compute performance alone. What safeguards are you building around your AI systems before they move into production?