How CIOs Can Win The Power and Capacity Race in The Age of AI

Old procurement cycles won’t work in this age of AI

Remember when data center decisions felt manageable? You'd scout a metro, draft the RFP, negotiate terms, and flip the switch by quarter's end. That playbook worked for decades, until AI arrived and rewrote the rules.

AI workloads don't just want power; they devour it. Suddenly, your biggest constraint wasn't square footage or monthly rates, it was whether the local grid could actually deliver the megawatts you needed. The old question "How much space?" became "Where can I get reliable power, and how fast?".  

Developers felt it first. They stopped following demand patterns and started chasing transmission lines instead. Secondary markets with solid substations and available capacity became gold mines. Places like Phoenix, Atlanta, and Northern Virginia, anywhere electrons flowed freely and permits moved quickly.

Enterprises followed the power trail, not by choice but by necessity. If you wanted your AI infrastructure online this year instead of next, you went where the watts were waiting. Geography became destiny.

Planning horizons have doubled, and you can now expect 12-24 months for viable capacity. Permitting, substation/transmission upgrades, and long hardware lead times compound delays. Saturated Tier-1 metros and an exponential demand for data centers have forced a seller-first market.  

Providers, suddenly holding all the cards, shifted to standard terms and first-come reservations. The leisurely 6-month evaluation or procurement cycles that CIOs once relied on became a luxury nobody could afford. Speed trumped perfection. Teams that could commit fast got the capacity; everyone else got the waiting list.

Inside the data halls, the physics caught up. Traditional air cooling, built for racks pulling 5-10 kW, hit a wall when AI pushed densities to 50, 100, even 200 kW per rack. Liquid cooling stopped being experimental and became essential. Direct-to-chip systems, immersion tanks, rear-door heat exchangers, if your site wasn't liquid-ready, your GPUs stayed dark.

Water joined the conversation. Not just for cooling, but for community relations and regulatory approval. Closed-loop systems and heat recovery moved from nice-to-have to must-have. Sustainability metrics started driving site selection as much as latency requirements.

Scale exploded in every direction. Single-phase deployments grew into multi-hundred-megawatt campuses, often pre-leased before construction began. Capital concentrated around these mega-projects, squeezing out smaller players and tightening timelines for everyone.

Meanwhile, a quieter revolution unfolded: data gravity. Training models and running inference became more efficient when compute sat close to datasets and cloud on-ramps. Moving terabytes costs time and money. Smart architects started designing around data locality, not just processing power.

The new reality is that power availability sets your timeline, cooling density defines your architecture, and decision speed determines whether you get capacity at all. The infrastructure that seemed abundant just two years ago now requires the kind of strategic thinking usually reserved for M&A deals.

For CIOs and tech leaders juggling these pressures, the old procurement playbook won't work anymore.

What can you do as a CIO?

The rules changed while you were running the old playbook. As a CIO, your next moves need to match this new reality where power scarcity drives timelines and speed beats perfection.

Govern like infrastructure matters

Treat power, cooling, and site selection as strategic risks that deserve board-level attention. Give your team the tools to move fast: pre-approved spending authority for letters of intent and deposits, standard contract terms you'll accept without a month of redlines, and clear triggers for when to reserve capacity before your requirements are perfectly defined. In today's seller's market, the team that hesitates loses.

Rethink your infrastructure mix

Anchor your core datasets and predictable workloads in colocation or private environments with solid cloud connections. Use public cloud for specialized AI services and experimental workloads that need rapid scaling. Design these environments to complement each other; private infrastructure for control and predictable costs, cloud for speed and innovation. Bare metal becomes your bridge, offering hardware isolation for compliance-sensitive work while your long-term colocation builds come online.

Plan for heat from the start

Choose your cooling strategy—liquid cooling, rear-door heat exchangers, or immersion—based on the rack densities you actually need, not what you hope to get away with. Size the fabric for east–west traffic at 400G/800G. Feed the GPUs with storage that can keep up, whether that’s NVMe/TCP, RDMA, or a parallel file system, so that storage doesn’t become a bottleneck. Keep your clusters physically together; spreading them across floors might look impressive on diagrams, but it kills performance in practice.

Make location part of the design

Bring location into the architecture. Choose markets that can prove grid timelines and power quality. Favor campuses with diverse fiber, cloud adjacency, and credible water strategies. Sustainability isn’t a side quest: WUE/PUE telemetry, reverse osmosis or closed-loop options, and heat reuse potential should sit in the same checklist as SLAs and cross-connects. You’ll need those numbers for audits, and they’ll affect where you’re allowed to build.

Accelerate your decision cycles

Replace lengthy RFPs with prequalified vendor lists. Build a library of standard contract terms so legal reviews take days, not months. Coordinate hardware deliveries with facility readiness to avoid expensive equipment sitting in empty rooms. Sync carrier installations with cluster deployments. The orchestration matters as much as the individual pieces.

Secure the supply chain early

New regions, modular construction, and private power arrangements introduce fresh risks. Develop site-level security blueprints covering physical access, remote management, network segmentation, and chain-of-custody procedures. Get provider attestations upfront. If the provider can’t show the controls you need, they’re not your site, no matter how tempting the delivery date looks.

Focus on momentum over perfection

Your teams face impossible pressure to ship AI models, control costs, and hit sustainability targets simultaneously. The path forward requires clarity: secure power capacity before everyone else wants it, keep AI workloads physically clustered, and place compute where your data and network connections are strongest. Do this right, and market constraints start working in your favor instead of against you.

What you shouldn’t be doing?

The new data center landscape is full of traps that look like opportunities. Here are the blind spots that catch even experienced tech leaders and decision-makers off guard. ‍

Mistaking space for capability

That empty cage in the Tier-1 metro feels like a win until you realize it can't handle AI densities. Before you sign, verify the facility can actually deliver 50-200 kW per rack with proper liquid cooling infrastructure. Check floor loading limits for cooling equipment and get real delivery dates from the utility company. An air-cooled room can't run modern AI workloads, no matter how prime the location.

Trusting optimistic timelines

"We'll have power by Q3" often becomes "maybe next year" when substations and transmission upgrades hit reality. Don't accept verbal promises. Demand documented proof: utility interconnect queue positions, transformer delivery schedules, and construction permits already in hand. Build backup options so one delay doesn't torpedo your entire roadmap.

Over-negotiating in a seller's market

Spending three months perfecting contract terms while capacity disappears to faster competitors is a costly mistake. Standardize the terms you can accept, get deposit authority approved in advance, and only fight for what truly matters. In today's market, a quick "yes" beats a perfect "maybe."

Treating cooling as an afterthought

Air cooling hits a wall at AI densities, and retrofitting liquid cooling is expensive and slow. Choose your cooling strategy early—direct-to-chip, rear-door heat exchangers, or immersion—and get facilities teams, hardware vendors, and operators aligned from day one. Confirm cooling distribution units, piping loops, leak detection, and maintenance access before you reserve any space.

Spreading clusters to fill available space

Splitting GPU clusters across multiple low-density halls might look efficient on spreadsheets, but it destroys performance and inflates costs. Keep training workloads physically together, design for 400G/800G interconnects, and let power availability drive your market selection instead of trying to make suboptimal space work.

Overlooking water and waste heat

Your cooling choices affect water usage efficiency, permitting approvals, and community relations. If a site can't support water recycling or closed-loop systems, or has no realistic plan for heat recovery, you're inheriting operational risks and sustainability problems. Water strategy should rank alongside power in your site selection criteria.

Placing compute far from data

Distance kills performance and explodes costs when you're moving massive datasets for AI training. Choose carrier-rich facilities with direct cloud connections and cross-cloud networking so your compute, data, and services stay tightly coupled.

Ordering hardware on hope

GPUs arrive months before the room is ready, or the room sits idle waiting for parts. Tie purchase orders to facility and carrier milestones, and set gates so delivery slips don’t strand capex or delay go-lives.

Deferring security to later

New regions, modular construction, and private power arrangements create security gaps in physical access, remote management, and supply chain controls. If providers can't show you proper attestations now, you won't pass compliance audits later. Make site-level security controls a prerequisite for any reservation.

Missing the campus wave

The largest facilities pre-lease capacity in phases, often before public announcements. Waiting for general availability means joining the waitlist. Track where the next major capacity will come online and reserve early. The difference between "on time" and "on hold" often comes down to a few weeks of decisive action.

How to prepare for these changes and act quickly (next 6–12 months)

Your next twelve months need to be about speed and precision. Here's how to move fast while the market still has options:

Build your decision engine first

Create a small capacity council with real authority: facilities, infrastructure, security, finance, legal, and procurement. Give them pre-approved spending limits and standard contract terms they can sign without endless reviews. Set clear thresholds for letters of intent and deposits so your team can act the day opportunities appear. Speed is a governance decision, not a technical one.

Scout where the power actually flows

Pick one primary market and two backups based on grid reliability, renewable energy access, and fiber connectivity. Focus on campuses with liquid-cooling infrastructure already in place, documented substation upgrade timelines, and strong carrier presence. Track utility paperwork like you track critical project milestones: interconnect queue positions, transformer delivery schedules, permits already approved. Choose markets that can prove their claims, not just promise them.

Lock capacity in stages

Secure a first phase you can use within 6-12 months, a second phase 6-12 months later, and options for a third. Accept minimum usage commitments if they guarantee delivery dates. Use public cloud or bare metal services to bridge gaps—especially for compliance-heavy workloads—so projects launch on schedule while your colocation capacity comes online.

Choose your cooling strategy now

If your racks will exceed 30-40 kW, decide between direct-to-chip cooling, rear-door heat exchangers, or immersion systems. Get facilities teams, equipment vendors, and operators in the same room to confirm cooling distribution units, piping loops, quick-disconnect fittings, leak detection, and maintenance procedures. Build these requirements into your space reservations so what you reserve is what you can actually use.

Engineer the network to match the workload

Plan for 400G/800G leaf-spine architecture with low-latency east-west connectivity and high-throughput storage designed for sustained AI training—whether that's NVMe over TCP, RDMA, or parallel file systems for large models. Keep AI clusters physically together. If the density isn't available in one location, change markets rather than fragment your clusters.

Orchestrate the entire supply chain

Tie GPU and server orders to verified facility milestones and network installations. Gate deliveries so delays don't strand expensive hardware or leave empty rooms burning money. Pre-order the optics, cables, and spare parts you know will create bottlenecks. Coordinate vendor installations, operator readiness checks, and cross-connect appointments for the same week, not just the same month.

Lock in security and sustainability from day one

Develop a site security blueprint covering physical access, remote management, network segmentation, and supply chain controls. Make security attestations part of your reservation package. Set power and water efficiency targets, confirm water recycling or closed-loop options, and capture monitoring requirements upfront. These become your audit trail and operational baseline.

Watch the market signals

Pre-leasing velocity on 50-100MW facility phases. Utility projects moving from "planned" to "funded" to "in service." Providers standardizing liquid cooling offerings. Bare metal capacity opening near your datasets. Each signal points toward your next opportunity with a clearer path to success.

The 12-month playbook: reserve power early, keep clusters together, design for liquid-cooled density, and bridge with cloud or bare metal until your facilities are ready. Execute this sequence, and you trade uncertainty for time, the most valuable currency in today's market.

Move faster, with confidence

Power availability drives your timeline. Cooling requirements shape your architecture. Data gravity pulls workloads toward the interconnection hubs where they perform best. In this new reality, success belongs to teams that commit early, keep their AI clusters together, and synchronize cooling, networking, and procurement around a unified schedule.

This isn't just an infrastructure challenge; it's a strategic conversation that belongs in the boardroom. Where can your growth actually happen in the next 6-12 months? What capacity needs to be secured now to keep your AI initiatives on track? The IT leaders who recognize power, site selection, and cooling as program-level risks, transform scarcity from obstacle to advantage.

AI development is outpacing infrastructure deployment. Power access, facility capacity, and vendor coordination are both challenges and decisions that determine whether your projects launch on time, stall indefinitely, or sit in the pipeline forever. This is why TechnologyMatch exists to help CIOs navigate this complexity and accelerate decision-making by cutting through the vendor noise and focusing on solutions that check all the boxes.

If you're planning your next colocation deployment or infrastructure-as-a-service implementation, those pathways are already mapped and ready. The framework is straightforward but requires execution. So, think fast, and move faster.