Why Time-to-Power Matters More Than PUE for AI Clouds

In the race to build AI cloud infrastructure, speed is everything. For years the data center industry has obsessed over PUE (Power Usage Effectiveness) as the gold-standard metric for efficiency and sustainability. But today’s AI-driven growth is exposing a new critical metric: “time-to-power”, or how fast you can deliver usable power and capacity to hungry AI workloads. This shift is transforming how hyperscalers and next-gen cloud providers plan, build, and operate data centers. In this blog post, we’ll explore why time-to-power is eclipsing PUE in importance for AI clouds – and how teams can adjust, from embracing power-first strategies to leveraging automation platforms like ArchiLabs to accelerate deployment.

The PUE Obsession – and Its Limitations

For context, PUE is a ratio of total facility power to IT equipment power. It was first proposed by data center pioneers at The Green Grid back in 2006, and it quickly became the de facto yardstick of data center energy efficiency (www.datacenterdynamics.com). A PUE of 1.0 means perfect efficiency (all power goes to computing), while higher values indicate more overhead consumed by cooling, power distribution losses, etc. It’s simple, easy to communicate, and drove massive improvements in design. In fact, industry-average PUE fell from ~2.5 in 2007 to around 1.5 by the early 2020s (www.datacenterdynamics.com), reflecting widespread adoption of better cooling technologies and power management.

However, PUE has always been an infrastructure-centric metric – it tells you how efficiently a facility delivers power to servers, but nothing about how quickly or how much capacity you can bring online. It also doesn’t account for the useful work produced per watt. As leading data centers approach PUE values of 1.1 and below (some experimental sites even hit an incredible 1.018 PUE (www.datacenterdynamics.com)), the gains from further efficiency tweaks become marginal. In other words, PUE has diminishing returns – squeezing out another 0.05 in PUE might save a few percent in energy, but it won’t double your compute capacity or shorten your build timeline. Even the Uptime Institute has called PUE a “rusty” metric after so many years of use (www.datacenterdynamics.com).

The bottom line: PUE remains useful for measuring efficiency and cost, but it’s a static snapshot. It doesn’t capture the dynamic challenge data center teams face today: unprecedented demand for AI compute and the urgency to deliver power for that compute as soon as possible . To understand why time-to-power is now paramount, we need to look at what’s happening with AI infrastructure demand.

AI’s Insatiable Appetite is Changing the Game

The rise of deep learning, large language models, and other AI workloads has triggered explosive growth in data center power demand. Consider a few eye-opening data points:

• Skyrocketing power needs: Deloitte projects that power demand from AI-focused data centers could grow more than 30-fold (!) by 2035 (www.prnewswire.com). We’re talking about going from a handful of gigawatts supporting AI in 2024 to over 100+ GW needed in the next decade. Similarly, Goldman Sachs analysts estimate global data center power consumption will surge ~160% by 2030 as AI adoption accelerates (www.goldmansachs.com). The International Energy Agency (IEA) even expects data center electricity usage to more than double in just the next five years, largely due to AI’s widespread uptake (www.statista.com). In short, demand curves that used to be gentle are now near-vertical.
• Power as the bottleneck: All that demand translates to massive new capacity requirements – and electricity is becoming the limiting factor. Industry experts warn there isn’t enough spare electricity in traditional hyperscale hubs alone to handle upcoming AI needs (www.techtarget.com). “The demand for AI is voracious right now,” as one real-estate tech expert put it, with every major cloud provider “racing to catch up” (www.techtarget.com). Hyperscalers and colocation firms have started signing huge power contracts – 36 MW leases that were rare five years ago are now common, and even 72 MW or 100 MW deals are no longer unheard of (www.techtarget.com). To put that in perspective, a single AI data center campus can draw as much power as a small city. This sheer scale of power requirement is unprecedented in the data center world.
• Capacity crunch and delays: Perhaps most telling, electricity availability is now setting the pace of data center expansion. In 2025, cloud giants noted that GPU supply and grid connection delays turned power into the primary constraint on growth (www.datacenterknowledge.com). We’re seeing the emergence of a “power-first” mindset: instead of building data centers wherever land and fiber are cheapest, developers are going wherever the electrical grid can support them. Power availability at scale has become the number-one site selection criterion – superseding geography – for new data center projects (datacenterpost.com). If a region can’t provide a big enough power feed quickly, it’s off the list. This shift has driven hyperscalers and “neo-cloud” upstarts alike into secondary markets; places like Texas, Alabama, or Wyoming are suddenly attractive simply because they have spare megawatts ready to go (datacenterpost.com).
• Misaligned timelines: A major challenge is the mismatch between how fast tech companies want to deploy capacity and how fast utilities can deliver power. Data center developers often plan on 18-24 month build cycles, but power utilities and regulators might operate on 3–5 year timelines for grid upgrades and new transmission (datacenterpost.com). This gap is only widening as AI demand accelerates. In practical terms, you might be able to construct a modern data center building in under two years, yet still wait extra years for the grid substation or transmission lines needed to supply it. Such delays are crippling in a world where AI product cycles are measured in months.

Taken together, these factors create a perfect storm: the need for speed has never been greater, and traditional approaches can’t keep up. Hyperscalers now control ~44% of global data center capacity (projected to reach 60% by 2030) (www.datacenterknowledge.com), and they’re pouring hundreds of billions into an “arms race” of AI infrastructure. A new cohort of specialized AI cloud providers (“neoclouds”) is surging as well, growing over 200% year-on-year in revenue (www.datacenterknowledge.com) by rapidly standing up GPU farms. In this environment, being slow to deliver capacity means lost opportunity – whether it’s training the next breakthrough model or onboarding enterprise customers seeking AI compute. That’s why time-to-power has become the metric on everyone’s mind.

Time-to-Power: The New Critical Metric for AI Data Centers

“Time-to-power” can be defined as how quickly you can go from planning a data center expansion to having servers up and running at full power. It’s essentially the lead time to deploy capacity. Here’s why that metric now matters even more than PUE in the AI era:

• Opportunity Cost of Delay: In AI cloud services, being early to capacity is a huge competitive advantage. The sooner you can light up additional GPUs or AI accelerators, the sooner you can serve growing customer demand (or internal R&D needs) and monetize that infrastructure. If you delay a deployment by 6–12 months in order to engineer a slightly more efficient facility, you might miss a market window or lose share to a faster-moving competitor. Every month of delay is revenue left on the table and AI projects deferred. The cost of not having the capacity often dwarfs the savings from a marginally better PUE. For example, a PUE improvement from 1.3 to 1.2 saves about 8% of power overhead – meaningful over years, but that doesn’t help you this quarter’s demand surge if the servers aren’t even installed yet.
• Scaling trumps efficiency (for now): With AI workloads, the overall energy consumption is exploding so fast that absolute capacity is the top concern. Even if you achieve world-class efficiency, a single large AI training run might consume megawatt-hours of energy. The focus for cloud operators is on scaling out power and compute to meet that demand first; then they can iterate on efficiency. Think of it this way: a decade ago, data centers reduced power use even as workloads grew – it was a period of efficiency gains (thanks to PUE improvements, virtualization, etc.). But now those gains have plateaued (www.linkedin.com) right as AI is causing workloads (and power use) to skyrocket. When overall data center energy consumption could triple by 2030 (www.goldmansachs.com) and hit ~8% of global electricity by 2030 (www.linkedin.com), shaving a few percentage points via PUE improvements simply can’t offset that growth. The priority has shifted to managing the surge – getting capacity online fast – rather than optimizing every last watt.
• Diminishing returns on PUE: Many operators have already picked the low-hanging fruits to lower PUE (hot/cold aisle containment, high-efficiency cooling, etc.), and some are exploring liquid cooling for high-density AI racks. But as PUE approaches ~1.1 or below in flagship facilities, the incremental gains get small. By contrast, reducing time-to-power by even a few months can be game-changing. Imagine being able to deploy a new data hall in 12 months instead of 18 – that’s 6 extra months of serving AI workloads (and perhaps avoiding 6 months of capacity shortfall). In an industry survey, 39% of data center professionals said new projects are now deployed in under a year, and two-thirds under 18 months (www.datacenterfrontier.com) – timelines unheard of a decade ago. Those faster deployments are a direct response to market pressure. Providers know that a data center sitting idle in development is essentially wasted potential.
• Power availability is binary: Another reason time-to-power is crucial is that power is often the make-or-break factor for a project. If you can’t get the load energized, nothing else matters. You might design a facility with a theoretical PUE of 1.1, but if the utility can only hook you up 18 months from now, that efficiency means little in the interim. Many operators are now taking creative steps to secure power sooner – from signing utility agreements early, to even bringing in bridging power solutions like on-site generators or mobile turbines to temporarily supply megawatts while waiting on the grid (datacenterpost.com). These moves wouldn’t make sense if efficiency was the only goal (diesel generators certainly won’t win any PUE contests!), but they make perfect sense when the goal is to have live capacity as soon as possible. In short, teams are willing to trade off some efficiency or elegance in design for the sake of speed and certainty of power. The “perfect” data center delivered too late is less valuable than a “good enough” one delivered now.

It’s important to note that none of this means sustainability and efficiency efforts are disappearing – far from it. In fact, once new capacity is online, operators will work hard to optimize it, lower its PUE over time, and source more renewable energy. But the immediate focus in the AI era is scaling out first, then tuning up. As one panel of industry experts put it, we’ve entered a “power-first era” in which success depends on aligning data center deployment with available power, even if it “fundamentally reshapes the landscape” of where and how we build (datacenterpost.com) (datacenterpost.com). Time-to-power is simply the metric that tracks our ability to keep pace with AI’s growth.

Accelerating Time-to-Power with Automation and Integration

Given this new reality, how can data center teams improve their time-to-power? Part of the solution lies in technology and design strategy – for example, using prefabricated modular data centers or phased builds to compress construction schedules. (Indeed, many colocation providers now build in smaller chunks so they can activate capacity in phases quickly and start serving customers while the next phase is still being built (www.datacenterfrontier.com) (www.datacenterfrontier.com).) But an often-overlooked factor is the efficiency of the planning and deployment process itself. This is where modern infrastructure automation and a unified approach to data center design come in.

ArchiLabs is one example of a platform designed to tackle this challenge from the ground up. ArchiLabs is building an AI operating system for data center design that connects your entire tech stack – everything from Excel capacity spreadsheets and DCIM databases to CAD platforms (like Revit and others), power and cooling analysis tools, cable routing software, and even custom in-house applications – into a single, always-in-sync source of truth. By breaking down data silos between these tools, teams can avoid the repeated manual handoffs and data re-entry that traditionally slow down projects. Changes in one system are instantly reflected across all others. For instance, if the IT load or rack layout changes in a design model, the power and cooling calculations, the bill of materials, and the availability status in your DCIM all update automatically in one ecosystem.

On top of this unified data layer, ArchiLabs lets you automate countless workflows that used to be repetitive and time-consuming. Think about tasks like laying out racks and rows in a new white space, planning cable pathways and fiber routes, placing power distribution units and routers – traditionally these involve going back and forth between CAD drawings, Excel, and library specs. With a cross-stack platform, these can become push-button workflows. For example, a team could generate an optimal rack layout (following all their design rules for hot aisle containment, weight distribution, etc.) with one command, and the system can auto-route power and network cabling based on capacity data from the DCIM. Equipment placement that once took days of coordination can be algorithmically optimized in minutes, with all metadata captured for procurement and installation.

A real-world example is in commissioning new capacity. ArchiLabs can automate large portions of the commissioning process: generating test procedures, running and validating sensor checks or network tests via integrated scripts, tracking all results, and ultimately producing the compliance report – all in one connected workflow. Rather than juggling PDFs, hand-written checklists, and separate monitoring tools, the entire commissioning test plan can live in one system that executes and verifies steps automatically. This not only saves valuable time (commissioning weeks can be reduced to days) but also ensures nothing is missed in the rush to go live.

Crucially, ArchiLabs isn’t a rigid off-the-shelf tool – it’s a cross-stack platform for automation and data synchronization. It supports custom “agents” that teams can configure or train to handle end-to-end workflows across all their tools, even bespoke ones. Want to pull real-time facility power readings from a remote sensor API, feed that into a CAD model to adjust your electrical one-line diagram, then push an update to your maintenance ticketing system? That kind of multi-step, multi-system process can be orchestrated seamlessly. Teams have used custom agents to teach the system how to read and write data in Revit models (and other CAD/BIM formats like IFC), interface with external databases and asset management systems, and even orchestrate complex sequences like “spin up a test environment, run CFD analysis on a new layout, and notify the engineering team on Slack with results.” In short, the platform is designed to break the usual cycle of manual workflow at each stage, letting your digital systems do the heavy lifting in sync.

All of this directly impacts time-to-power. When your design and planning iterations happen faster and with less friction, you can shave weeks or months off a deployment timeline. Here’s how automation speeds things up:

• Fewer errors and rework: A single source of truth means everyone – designers, engineers, construction, operations – is working off the same live data. You don’t lose days reconciling version discrepancies between spreadsheets and drawings. Avoiding mistakes means avoiding delays.
• Parallelization: Automated processes can run in parallel, whereas manual work is often sequential. For example, while an AI agent is placing equipment and validating power loads, an engineer can focus on higher-level design decisions. The overall project isn’t gated by one person or one task at a time.
• Rapid what-if analysis: Need to evaluate two different site layouts or power configurations? Automation allows you to spin up alternatives quickly. This agility in the design phase means you can lock in decisions sooner and commence construction earlier, confident that you’ve optimized the design digitally.
• Streamlined handoffs: With a cross-stack platform, the handoff from design to building to operations is smoother. Construction teams get up-to-date plans, procurement gets accurate BOMs, and operations gets an environment where DCIM, monitoring, and documentation are already synchronized. The result is faster ramp-up once physical build is complete – effectively shortening the commissioning and turn-up phase.

By leveraging such automation and integration, data center organizations can respond to AI capacity needs much more rapidly without descending into chaos. It’s about working smarter to complement working faster. In practice, the companies that thrive in the AI cloud era will be those that tightly coordinate across disciplines (IT, facilities, power, networking) and use software to eliminate the traditional bottlenecks in deploying infrastructure.

Balancing Speed and Efficiency in the AI Era

Emphasizing time-to-power over PUE is a strategic shift – it doesn’t mean we stop caring about efficiency or sustainability, but it recognizes the current inflection point we’re in. AI is reshaping the data center industry’s priorities. As one summit panel concluded, meeting the extraordinary 128 GW of new data center demand expected by 2029 will require using “every tool in the toolbox” – from faster builds and creative power arrangements to advanced cooling and demand management (datacenterpost.com). In this wave of growth, the ability to deliver capacity quickly and reliably has become the ultimate competitive edge for cloud and colocation providers.

PUE still has a role to play – once all that new infrastructure is online, optimizing its efficiency will be key to controlling costs and meeting sustainability goals. But a low PUE at the expense of agility is a luxury most AI infrastructure players can’t afford in the near term. Time-to-power is the tempo of this new era: it dictates who can serve the next billion AI queries or train the next revolutionary model, and when.

By adopting a power-first, speed-focused mindset – and equipping their teams with platforms for automation and cross-stack integration like ArchiLabs to execute that vision – data center leaders can ensure they aren’t caught flat-footed. Instead, they can move in sync with the fast pace of AI innovation. The winners in the AI cloud race will be those who deliver not just the most efficient compute, but the right compute at the right time. In other words, efficiency is great – but in the age of AI, being there first with capacity might matter even more. It’s a delicate balance, but one that the industry is learning to master as we build the future of cloud infrastructure.