As of March 2025, the U.S. hosted 5,426 data centers, the largest national inventory globally. Meanwhile, the global data center market is rapidly expanding, projected to reach $624.07 billion in revenue by 2029.
These are one of the many signs that the industry is growing by gigantic steps. And one of the main reasons (maybe the most important one) behind this growth is the expansion of AI and GenAI adoption.
The cause is actually simple: they massively increase compute, data movement, and energy needs, driving a surge in demand for data center resources. However, as we saw in previous articles, this comes with greater pressures for the data center industry.
So, the main question now is: Is there enough data center resources to meet the AI demands? Today we’ll see two of the main constraints for expansion, the lack of both energy and people.
Why AI Changes the Data Center Resource Equation?
So, AI is not adding incremental pressure to the industry; it is redefining data center capacity itself.
Just in 2025, global installed data center capacity is estimated at ~114 GW. However, utilization across key markets remains tight as AI workloads scale faster than traditional planning models anticipated.
The reason is density. AI and GenAI workloads are fundamentally different from legacy enterprise systems. Training and inference rely on GPU-heavy architectures that consume significantly more power per rack and generate far higher thermal loads.
In fact, according to Gartner, industry estimates show global data center electricity consumption reaching ~448 TWh in 2025, with AI-optimized servers already accounting for a growing share of that demand. Unlike traditional workloads, AI usage is bursty, compute-intensive, and difficult to smooth over time.
This creates a demand shock rather than a linear growth curve.
A single AI deployment can require the equivalent power and cooling footprint of dozens (or more) conventional enterprise workloads. As a result, data center capacity constraints emerge not only at the building level, but across power delivery, cooling systems, and operational expertise.
And, on the other hand, AI demand is not replacing existing usage.
Traditional enterprise workloads are still expected to represent more than half of total data center power consumption by the late 2020s, while GenAI is driving the majority of incremental growth. In fact, Goldman Sachs estimates that AI’s share of data center demand could approach ~30% within the next two years, pushing industry occupancy toward the low-90% range even as new capacity comes online.
In this context, the question is whether the industry can bring usable, efficiently operated capacity online fast enough to keep pace with AI-driven demand.
Current Industry Capacity: Are We Actually Building Enough?
Now, from the outside, data center capacity looks like a construction problem. But in practice, capacity is not “online” until it is operable. And operability is increasingly defined by software integration.
In other words, a data center can be physically complete and connected to power, yet still unable to support AI workloads at scale. Modern facilities depend on a complex stack of software systems:
- Monitoring and observability platforms
- DCIM tools
- Automation frameworks
- Networking control planes
- Security systems
- Capacity management models
Until these are fully integrated, tested, and tuned, the facility’s theoretical capacity remains partially idle. And this gap between installed capacity and usable capacity is widening as AI workloads raise the bar for operational sophistication.
GPU-heavy environments require tighter coordination between power, cooling, and workload orchestration. Minor inefficiencies that were tolerable in traditional enterprise environments (manual processes, fragmented tooling, delayed alerts) quickly become limiting factors when rack densities increase and thermal margins shrink.
In short, software is the control system that makes capacity usable.
And, in this sense, staffing also compounds the challenge of what we could differentiate as data center resources constraint.
The talent bottleneck: A capacity question (but not where it’s usually framed)
Power availability and construction timelines dominate most discussions about data center resources. Yet as facilities become more software-defined and AI-driven, a different kind of constraint is emerging; one especially tied to how specialized capabilities are accessed and applied.
Modern data centers do not require large internal software teams. In practice, only a small number of roles (often five or six people) are directly responsible for the core software, platform, and orchestration layers within a facility.
Capacity constraints, therefore, are not driven by a lack of on-site software engineers. In fact, much of the widely cited talent shortfall (including projections that the U.S. could face a gap of nearly 1.9 million skilled workers by 2033) primarily reflects shortages in construction, electrical, mechanical, and field engineering roles.
And these affect how quickly physical infrastructure can be built, not how software systems are operated once facilities are live. But where talent pressure does emerge is across the broader data center ecosystem.
As environments grow more interconnected and exposed, demand is increasing for specialized capabilities that often sit with vendors and partners rather than inside the data center itself:
- Cybersecurity
- Network architecture
- Cloud and platform operations
- Automation
- Reliability engineering
As a result, certified resources have become an important variable in data center resources planning.
Let’s take the U. S. market as an example.
Here, the operational capacity is increasingly shaped by the availability of highly specialized technical skills, particularly in cloud computing and cybersecurity. In 2025 more than 70% of tech leaders report difficulty hiring cloud architects and engineers, and over 95% of security teams acknowledge critical skills gaps, especially in cloud and hybrid security expertise.
These shortages make the ability to operate, secure, and scale increasingly dependent on how effectively data centers can integrate external expertise.
Inside the Software-First Modern Data Center Team Model
So, as the industry evolves into software-defined environments, data center resources become dependent on skills in observability, infrastructure-as-code, networking abstraction, reliability engineering, and capacity modeling.
And, to save you some time, here you have a table summarizing the main software-related roles for this kind of organization:
| Role | Core Focus | Main Task | Capacity Impact |
| Infrastructure / Platform Engineer | Turn hardware into programmable infrastructure | Makes physical assets usable by AI platforms | Faster capacity activation, higher utilization |
| Site Reliability Engineer (SRE) | Reliability, automation, failure control | Enables high-density, high-load operations | Safely pushes infrastructure closer to limits |
| Observability Engineer | Real-time monitoring and telemetry | Makes capacity visible and predictable | Reduces hidden headroom and waste |
| Automation & Integration Engineer | Connect DCIM, cloud, and ops tools | Removes manual scaling bottlenecks | Shortens time from build to production |
| Network Software Engineer | Software-defined networking and traffic control | Prevents network congestion in AI workloads | Avoids network becoming the bottleneck |
| Security Platform Engineer | Security embedded in infrastructure workflows | Protects scale without slowing it | Enables multi-tenant, compliant capacity |
| Capacity Modeling Engineer | Forecast demand and optimize placement | Replaces static planning with simulation | Improves long-term capacity ROI |
Remote Roles: Where Data Center Resources Actually Expands
Now, what unites these roles is that most of their work is inherently remote-capable. The software layers that define modern data center operations can be designed, deployed, and operated from anywhere. This shifts the talent model from local, facility-bound teams to distributed engineering organizations that support multiple sites simultaneously.
And, for operators, this is a strategic necessity in a world where local talent pools are insufficient. In fact, this year’s report of the Business Economic Forum points that talent pressures still affect data center ecosystems, particularly in digital, cloud, automation, and security roles that support hybrid and AI workloads.
Over half of operators are struggling to attract and retain qualified staff overall, while many organizations are reporting difficulties filling positions that blend cloud, networking, and emerging-tech expertise.
However, what this means in practice is not a blanket shortage of thousands of on-site roles, but a structural mismatch.
Many in-facility positions remain stable or few in number, while roles tied to cloud platforms, cybersecurity, automation, and hybrid operations are in high demand and often operate in remote or hybrid modalities. And, in many cases, these capabilities live outside the four walls of a data center.
They are embedded in vendor teams, partner organizations, or distributed engineering groups working across multiple facilities and environments. Because several of the most capacity-critical roles identified earlier are inherently remote-friendly:
- Infrastructure and platform engineers define how compute, storage, and networking are abstracted, provisioned, and exposed to AI workloads. Their work determines whether new hardware comes online in weeks or in quarters.
- SREs and reliability engineers design automation, self-healing mechanisms, and failure boundaries that allow facilities to safely operate at higher utilization rates.
- Observability and capacity modeling engineers transform raw telemetry into actionable insights, enabling operators to reclaim stranded capacity and plan expansion with precision.
- Automation, integration, and network software engineers eliminate manual handoffs between DCIM tools, cloud platforms, and orchestration layers—often the hidden delays between “built” and “operable.”
This way, remote-first operating models solve two problems at once.
On the one hand, they expand the available talent pool globally, reducing time-to-hire for scarce skills. On the other hand, they decouple capacity growth from geographic labor constraints, allowing new facilities to go live without waiting for fully staffed local teams.
How Inclusion Cloud Helps You Amplify Data Center Resources
At Inclusion Cloud, we help data center operators and ecosystem partners close the talent gap that limits real AI capacity.
Based in Dallas (one of the fastest-growing AI and data center hubs in the U.S.) we specialize in building remote, onshore and nearshore software, cloud, and infrastructure teams aligned with U.S. time zones. From platform and networking engineers to automation and reliability experts, we focus on the roles that turn built capacity into operable capacity.
If you’re struggling to hire for hard-to-find roles at the intersection of data centers, cloud, and AI, book a discovery call.
