AI Data Center Construction Spending Is Up 60% — Here's What That Means If You're Not Building a Hyperscale Facility

U.S. spending on data center construction hit a seasonally adjusted annual rate of more than $75 billion in July — nearly 60% above the same month last year, according to Census Bureau data reported by Axios. The surge reflects the ongoing AI infrastructure buildout: hyperscalers and cloud providers racing to add capacity for training and inference workloads. That 60% figure covers the cost of erecting the physical buildings — labor, materials, engineering, contractor profit, interest, and taxes. It does not include what goes inside: servers, GPUs, networking gear, and fiber. According to commercial real estate firm Cushman & Wakefield, construction typically represents only about 20% of a data center's total cost. The other 80% — compute, power infrastructure, and connectivity — is where the real money and the real complexity live.

Most businesses reading headlines about $75 billion in data center construction assume it's irrelevant to them — that's Microsoft, Google, and Meta building 500,000-square-foot AI training facilities in rural Virginia or Texas. It affects you anyway, in three concrete ways: 1. Colocation capacity is getting tighter. Hyperscalers are absorbing power capacity, land, and skilled construction labor in the same markets where enterprise colocation facilities operate. In tier-1 markets (Northern Virginia, Dallas, Phoenix), available power for new colocation deployments is becoming scarcer and pricier. 2. Power costs are trending up. When demand for grid power surges regionally, utilities pass costs through — and colocation providers pass them to tenants. If you're planning a colocation deployment in a high-growth AI market, expect power pricing to be less favorable than it was two years ago. 3. Facility lead times are extending. If a colocation provider is competing for construction labor, materials, and equipment against hyperscale AI projects, your buildout or expansion request may face longer wait times than historical norms.

The headline number — construction spending — is the easy part to measure because it's a straightforward line item: concrete, steel, labor, permits. The other 80% is where businesses actually make or lose money on their infrastructure decisions: • Compute and networking hardware — servers, GPUs, switches, storage — priced by supply and demand that's currently running hot due to AI chip scarcity • Power infrastructure — UPS systems, generators, switchgear — increasingly expensive and back-ordered due to the same AI-driven demand • Connectivity — cross-connects, bandwidth, carrier diversity — pricing that varies wildly by market and facility • Operational costs — staffing, monitoring, maintenance — the ongoing expense that construction spending numbers never capture When you're evaluating a colocation facility or planning an infrastructure buildout, the construction headline is background noise. What matters is whether the facility has power available, whether equipment lead times fit your timeline, and whether the total cost of ownership makes sense for your workload.

If you're planning a data center placement or expansion in the next 12-18 months, the current market conditions change the calculus: • Lock in power commitments early. If a facility has available power capacity today, that may not be true in 6 months. Facilities in AI-adjacent markets are seeing faster capacity absorption than historical trends. • Expect longer equipment lead times. GPU and networking hardware lead times have stretched due to AI demand. If your deployment depends on specific hardware, order early and build buffer into your timeline. • Consider secondary markets. Tier-1 AI markets (Northern Virginia, Dallas-Fort Worth) are seeing the most capacity pressure. Secondary markets (Kansas City, Columbus, Salt Lake City) may offer more available power and more competitive pricing for standard enterprise workloads that don't need to be co-located with AI training clusters. • Re-evaluate contract terms. In a tightening capacity market, colocation providers have more leverage. Review escalation clauses and expansion rights carefully — you want contractual protection against being priced out of your own facility as demand grows around you.

The Axios report notes growing bipartisan concern about data centers' electricity and water consumption — even as construction spending accelerates. This isn't just political noise. It has practical implications: • Some municipalities are implementing stricter approval processes for new data center construction, citing grid capacity concerns • Utility rate cases increasingly factor in large data center loads, which can affect commercial power pricing broadly • Water-cooled facilities are facing more scrutiny in water-stressed regions For businesses planning long-term infrastructure strategy, this regulatory environment is a factor worth tracking — not because it will stop the AI buildout, but because it may shape which markets remain favorable for non-hyperscale colocation over the next 3-5 years.

The data center market is shifting fast, driven by forces most enterprise IT teams don't have visibility into — chip supply chains, utility capacity planning, hyperscale real estate deals. Making a colocation decision based on outdated assumptions about power availability or facility lead times is a real risk right now. DCIS tracks these market dynamics across the facilities and markets we work in. When we recommend a placement, it accounts for current capacity conditions — not just the facility's marketing brochure. If you're evaluating colocation options in this environment, we can help you avoid locking into a facility that looks good today but runs into capacity or cost problems in 18 months. Contact us to discuss your placement strategy in today's market.