CUDO Ventures

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…

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…

Transformer lead times moving to 3–4 years forces a new question: which infrastructure decisions are reversible vs locked in? That’s the real risk model for #AI buildouts. Read our full capacity planning framework: cudocompute.com/blog/ai-data-c…

Our cofounders @HawkinsTech and @CudoPete had an eventful week at @CiscoLive EMEA in Amsterdam. Big takeaway: #AI wins need infrastructure that performs within real power and deployment limits. Thanks to everyone who connected with us and shared industry insights.

Data centers built a decade ago were never designed for modern #AI workloads. Facilities engineered for 5-10 kW racks are now being pushed to sustained densities of 30-100 kW. This breaks old assumptions fast. Read our thread on infrastructure bottlenecks below.

Regulation will always lag behind innovation. But designing for compliance can’t wait until later. As @cudopete points out, teams that plan for compliance early avoid costly rework when rules catch up. Build for it from day one.

Data center slowdowns usually begin with a small operational drift. Left unchecked, it compounds into real outages. Reliability involves playbooks, spares, and teams trained to execute under pressure. We share why operations decide outcomes: cudocompute.com/blog/designing…

We are here at @CiscoLive in Amsterdam. The focus today is performance consistency at scale: keeping throughput predictable once workloads hit production. @CudoPete & @HawkinsTech are on site at #CiscoLive throughout the entire week.

Failure in #AI training usually first shows up as underperformance. Small power or thermal drift becomes longer step times at scale. Full-path telemetry and fast controls protect throughput before it erodes. See how telemetry becomes a performance tool: cudocompute.com/blog/designing…

We’re attending @CiscoLive next week in Amsterdam. As #AI infrastructure scales, the discussion gets concrete: power headroom, site constraints, fabric design, and readiness. @CudoPete & @HawkinsTech will be on the ground with teams planning real 2026 deployments at #CiscoLive.

Underestimating compute is one of the fastest ways to kill #AI adoption. Models are becoming more sophisticated, and capacity gaps manifest as latency, queues, and poor user experience. As @cudopete has warned, you have to plan for more than you think you’ll need.

In multi-year #AI deployments, hardware is often less than half of the total cost. Power, cooling, & operations dominate. Facility efficiency improvements can save millions & unlock more usable compute. We share the numbers behind AI infrastructure TCO: cudocompute.com/blog/designing…

#AI-ready is not a label. It means power and cooling never become the bottleneck. If infrastructure is visible to the workload through throttling or slowdowns, the facility is already interfering. See what an AI-ready design really requires: cudocompute.com/blog/designing…

Progress happens when engineers, operators, partners, & platforms share what’s working and what’s breaking. @RedHat One was a reminder that production #AI is a team effort. Dean & Mark were on the ground at #LifeAtRedHat in Vegas, sharing real operator insight on production AI.

Scale isn’t a cloud vs on-prem debate. Some workloads belong on-prem. Others fit the cloud. The real work is optimizing how scale happens. As @cudopete puts it, you don’t “finish” scaling. You engineer for it.

At modern rack densities, air cooling hits a wall. Heat flux outruns airflow. Direct-to-chip liquid cooling removes most of the GPU's heat. Air handles the rest. Read our complete analysis on why cooling now limits #AI infrastructure: cudocompute.com/blog/designing…

Production #AI breaks where operations are ignored. At #RedHatOne Las Vegas, discussions are on power density, activation timelines, and day two control as clusters scale. Dean & Mark from CUDO Compute are in @RedHat ecosystem discussions on what keeps systems stable once live.

#AI at scale is an operations problem before it is a model problem. In Las Vegas today, CUDO Compute is in @RedHat ecosystem discussions on power density, activation timelines, and control at scale. Dean & Mark from our team are attending. #LifeAtRedHat

48V is not a trend. It is math. A 4x lower current than 12V means far lower losses. At 100 kW racks, 48V makes high density practical and improves cost per token. Read our breakdown on why power architecture is now a performance lever: cudocompute.com/blog/designing…

If you’re building #AI apps, models aren’t the hard part. Infrastructure is. @metaverski breaks down: ⚡ How AI compute works ⚡ Why GPUs matter ⚡ Inference + scaling costs Watch his presentation from our hackathon with @imperialcollege to see what your AI really runs on.