CUDO Compute

Out today! Land. Power. Compute. This report brings together some of the most influential voices in the industry, drawing on an independent survey of 701 AI infrastructure leaders and decision-makers across the UK, the US, and Europe, to uncover the real challenges and opportunities shaping the sector. What to expect inside: - 71% of AI deployment cost is infrastructure, not GPUs - Power availability is the most cited deployment blocker across all regions - 96% of decision-makers are actively adjusting where they build in response to geopolitical and grid conditions. Just 4.3% have made no change. The next phase of #AI won’t be won by whoever buys the most silicon. It’ll be won by whoever can actually switch it on. That’s what we do. Download Land. Power. Compute. today. tinyurl.com/land-power-com…

The AI infrastructure conversation has spent the last two years focused on GPUs. But the reality is, infrastructure strategy is increasingly being shaped long before hardware even arrives. Power availability. Grid certainty. Geopolitical stability. Land that can actually support deployment at scale. According to our Land. Power. Compute. report, 96% of infrastructure leaders have already adjusted infrastructure location strategy due to geopolitical or grid conditions. This is exactly why AI infrastructure can no longer be viewed as isolated layers of land, power and compute. They are now deeply interconnected strategic decisions that directly impact deployment timelines, operational resilience and long term scalability. The industry is moving beyond simply securing compute capacity. The focus is shifting towards securing deployable, sustainable and sovereign ready infrastructure. Read the full report today: tinyurl.com/land-power-com…

Over the last two years, GPU access has dominated the AI conversation. Now, the industry is confronting a different challenge entirely: Can infrastructure keep pace with AI demand? Land. Power. Compute. Our latest report explores the physical realities now shaping AI deployment at scale, from power availability and grid constraints to permitting, cooling and long term infrastructure readiness. Built from insights gathered across 700+ infrastructure leaders across the US, UK and Europe, the findings point to a market increasingly constrained not just by hardware, but by the systems required to support it. Some of the clearest findings: • Infrastructure now accounts for 71% of AI deployment cost pressures • GPU pricing ranked sixth among deployment cost drivers • 96% of organisations say geopolitical and infrastructure conditions are influencing deployment strategy • Power availability is increasingly shaping where AI infrastructure gets built The AI industry may have started as a software revolution. Scaling it is rapidly becoming an infrastructure challenge. Download the report today: tinyurl.com/land-power-com…

A huge thank you to the contributors and partners involved in helping bring this report to life, including @Arkdatacentres, AVK-SEG, @BloczIo, @CambridgeManag1, Conapto, @CanopyCloud, CloudAdvise, @ClusterPower_, @DigitalRealty, Holmes & Hills Solicitors, @intel, @KaoDataDC, Novara Infrastructure, @SchneiderElec, @Supermicro and @wsp. This report was built to move the conversation beyond GPU headlines and toward the infrastructure realities that will define the next era of AI. Because deploying AI at scale is no longer just about compute. It is about Land. Power. Compute. Read the full article today: cudocompute.com/land-power-com…

Land. Power. Compute. is not a conversation that happens in silos. It takes an industry. From power and cooling, to infrastructure, legal, colocation, engineering, architecture and delivery, the future of AI infrastructure will be shaped by the organisations solving the realities behind deployment at scale.

There is a growing gap between AI ambition and what can actually be delivered. In this @techradar feature, our CEO @HawkinsTech sets out the conditions required to attract AI investment. The message is clear. AI is no longer constrained by models or funding. It is constrained by infrastructure. Access to power. The ability to deploy quickly. Compute that is actually available and operational. These are now the deciding factors. When those foundations are not in place, investment does not wait. It moves to where they are. This is what will define the next wave of AI investment. Read the full article here: techradar.com/pro/the-five-t…

And if you want the deeper view, our Land. Power. Compute. report breaks down the real constraints shaping AI infrastructure today: tinyurl.com/land-power-com…

Everyone talks about the future of AI. Models. GPUs. Breakthroughs. But far fewer people understand the infrastructure behind it — or even what a data centre actually is. That gap doesn’t start in the workplace. It starts much earlier. If the next generation isn’t aware of the systems that power everything from AI to everyday digital life, they won’t be the ones building what comes next. That’s exactly why #NDCD26 matters. Proud to be part of it. Watch the full video: nationaldatacentreday.com

71% of the cost pressures shaping AI deployment now point to infrastructure. GPU pricing ranks sixth. That gap between what organisations are investing in and what they are actually able to deploy is widening. Our Land. Power. Compute. report, built on insights from 700+ infrastructure leaders, makes one thing clear: AI is no longer constrained by innovation. It is constrained by execution. Land. Power. Compute. is where that execution either succeeds or fails. Read the full report today: tinyurl.com/land-power-com…

37% of #AI leaders say GPU investment is outpacing the infrastructure required to run it. That means organisations are securing compute before solving for the fundamentals: land, power, cooling, deployment readiness, and operational delivery. GPUs may win headlines. Infrastructure determines whether AI actually scales. That is exactly why we released Land. Power. Compute. Based on insights from 701 infrastructure leaders across the US, UK and Europe, the report explores the real constraints shaping AI growth in 2026. If your roadmap starts with chips and ends there, it may be time to rethink the sequence. Read the report today: tinyurl.com/land-power-com…

Download the report today. tinyurl.com/land-power-com…

The core thesis: the bottleneck in AI has shifted from silicon to infrastructure. The next phase will not be won by those who buy the most GPUs. It will be won by those who can deploy them. If you are building, buying or investing in AI infrastructure, this report was written for you.

We have published “Land. Power. Compute.” It is the most ambitious piece of research we have produced at CUDO. 701 respondents. 15 in-depth interviews. Nine chapters covering every physical constraint on AI deployment.

The AI infrastructure bottleneck has shifted. For the past three years, the conversation has been about GPU access. Who has the chips? Who placed their order first? That conversation is now outdated. The real constraint on AI deployment is physical infrastructure. Land. Power. Grid connections. Cooling capacity. The ability to actually put GPUs into buildings that can run them. Infrastructure accounts for 71% of all cost-driver selections in AI deployment. GPU pricing ranks Sixth. We surveyed 701 AI infrastructure decision-makers across the UK, US and Europe to understand where the real bottlenecks sit. The findings launch this week.

Yesterday the UK launched a £500m Sovereign AI fund, a significant moment. But the hard question remains: where does the infrastructure sit? Sovereign AI isn't just about funding. It's about data centres on UK soil, UK grid connections, UK jurisdiction. Our upcoming research shows 96% are adjusting where they build due to geopolitics. 37.2% say data sovereignty is a primary driver. Report launches Monday 21 April, check out the comments for access 👇 #SovereignAI #LandPowerCompute #UKTech

Power efficiency is being treated like a sustainability metric. It’s not. Most of the waste we’re seeing isn’t coming from one obvious place either. It’s across the system. GPUs sitting idle because data isn’t ready. Jobs not balanced properly. Checkpoints dragging things out longer than they should. Individually, none of that feels like a big issue. But at scale, it eats into capacity fast. And when power is already the constraint, that’s the difference between a cluster that performs and one that never quite delivers what it should. This is why efficiency isn’t just about hardware or chasing better PUE numbers. It’s about how these systems are actually run. Because right now, power is setting the limit. Efficiency is what decides how much you get out of it. If you’re planning or scaling AI infrastructure, this is worth understanding properly: cudocompute.com/blog/optimizin…

Getting AI hardware installed and running properly has become the rate limiting factor. What used to be a straightforward process is now one of the hardest parts of deployment. Racks are pushing beyond 100 kW, cooling is shifting to liquid, and power needs to hold under sustained load, not theoretical capacity. Most environments simply weren’t designed for this level of density. So the issues don’t show up on paper, they show up when systems go live. When everything is under real conditions and assumptions get tested. That’s when delays start, rework kicks in, and hardware sits there not doing what it was bought to do. That’s the shift. It’s less about access, more about execution. If you’re planning AI infrastructure, this is the part worth paying attention to: cudocompute.com/blog/ai-hardwa…

Efficiency isn’t a nice to have in AI infrastructure, it’s what drives your cost base. Better cooling and faster activation don’t just improve performance, they change your unit economics immediately. And with demand accelerating, capacity planning has shifted from space to power, thermal design, and how quickly you can actually bring systems online. 🔗 cudocompute.com/blog/ai-data-c…