AI's Record Demand: Assessing the Scalability of the Cloud and IT Services Boom

The global technology market is experiencing a powerful, AI-fueled expansion. In the fourth quarter, combined spending on technology services and software hit a record $34.3 billion, marking a 16% year-over-year increase. This surge is not evenly distributed. The growth is almost entirely concentrated in XaaS-cloud, software, and consumption-based services-which saw its own ACV soar 26% to $23.4 billion. By contrast, the traditional managed services segment was essentially flat, declining 0.3% to $10.9 billion. This divergence signals a fundamental market shift.

The engine behind this transformation is artificial intelligence. AI-related infrastructure spending alone is forecast to surge to nearly $1.5 trillion in 2025. This isn't just a niche trend; it's the core driver of the entire XaaS boom. The data shows this clearly: within XaaS, infrastructure-as-a-service (IaaS) grew a blistering 32%, directly fueled by the need for AI workloads and data platforms. Software-as-a-service (SaaS) also performed solidly, with growth across key enterprise areas like collaboration and cybersecurity. The managed services market, which relies heavily on human labor for IT operations, has seen its growth constrained, with IT outsourcing down 6% last quarter.

This sets up a clear investment thesis. The market expansion is no longer led by labor-centric managed services but by AI-driven infrastructure. For a growth investor, this is the critical pivot. The scalability lies in the consumption-based, high-margin nature of XaaS, particularly IaaS, which can be rapidly scaled to meet the insatiable demand for AI compute. The record spending and the forecast for over $2 trillion in AI investment by 2026 underscore a durable, multi-year growth trajectory. The question for investors is not if the market is growing, but which companies are positioned to capture the value from this AI infrastructure build-out.

Scalability and Market Penetration: TAM and Competitive Dynamics

The long-term growth story for AI and cloud is underpinned by a massive and expanding market. The global cloud computing market is forecast to cross $900 billion in 2025, a figure that outpaces every other tech investment category. This represents a towering Total Addressable Market, a durable platform for companies that can scale their services to meet enterprise demand. Yet the path to capturing this value is not uniform, revealing a stark structural shift in the competitive landscape.

This shift is defined by the tension between the enormous TAM and the declining relevance of traditional models. While the cloud market soars, the managed services segment-built on human labor for IT operations-is expected to grow at a mere 2.1% in 2026. This modest pace, following two consecutive quarterly declines, signals a fundamental migration of budgets and strategic focus. The growth engine has moved from labor-intensive outsourcing to AI-driven infrastructure, where the scalability and margins are far more attractive.

The catalyst for this reallocation is the unique economics of AI workloads. These compute-intensive tasks are reshaping data center infrastructure at a multi-year pace. Power capacity needs are projected to grow at a ~22% CAGR through 2030, a build-out that will require hundreds of gigawatts of new energy. This isn't a one-time project; it's a sustained infrastructure expansion that defines the next decade. The competitive landscape is now dominated by hyperscalers and their partners who can navigate this new reality of high-density campuses, specialized hardware, and energy efficiency.

For service providers, the implication is clear. Success will be measured not by the volume of managed IT contracts, but by their ability to engineer advantage within this AI-driven cloud economy. The new battleground is about converting every dollar of cloud investment into measurable business impact, whether through strategic consulting, scalable solutions, or deep integration with the evolving infrastructure. The TAM is vast, but the rules of engagement have changed.

Financial Impact and Investment Implications

The market trends translate directly into a bifurcated financial landscape. For growth investors, the key is to separate the noise from the durable signal. On one side, there's a macro-driven "uncertainty pause" that is pressuring near-term deal flow. CIOs are strategically suspending net-new spending, a trend that is expected to slow overall software and services spending growth in 2025. This creates near-term headwinds for traditional IT service providers reliant on new project wins. Yet, this pause is being subsumed by the relentless momentum of AI initiatives, which continue to surge. The financial impact is a divergence: slower growth in legacy areas versus explosive growth in AI infrastructure.

This divergence redefines the value equation for compute. AI workloads are not just another application; they are a new class of demand that prioritizes performance, scalability, and energy efficiency above all else. This shifts the competitive advantage from generic IT operations to specialized infrastructure engineering. The financial implication is clear: providers who can demonstrate superior energy efficiency and the ability to scale their infrastructure to meet AI's power demands will command a premium. The market is paying for this capability, as seen in the projected ~22% CAGR for power capacity through 2030.

The most significant financial dynamic is the capital intensity and concentration at the infrastructure layer. Hyperscalers are expected to capture about 70% of new U.S. data center capacity, a winner-take-most outcome driven by their massive scale and deep pockets. This creates a highly capital-intensive environment where only the largest players can afford the multi-gigawatt builds required. For investors, this means the highest-growth, highest-margin opportunities are concentrated in this infrastructure layer. The financial model here is one of massive upfront investment for long-term dominance, a stark contrast to the lower-capital, labor-intensive model of traditional managed services.

The bottom line is a clear bifurcation. The financial impact of the AI boom is not evenly spread. It flows to those who can engineer advantage in the new compute economy, primarily the hyperscalers and their closest partners. For the growth investor, the thesis is to identify companies positioned at this high-intensity, high-concentration infrastructure layer, where the scalability of the AI-driven demand meets the capital requirements for sustainable dominance.

Catalysts, Risks, and What to Watch

The investment thesis for AI-driven cloud and IT services hinges on a multi-year build-out. For growth investors, the path forward is defined by a few clear catalysts and risks that will confirm or challenge the scalability narrative. The key is to watch for durable spending, infrastructure execution, and the ability of service providers to pivot.

First, the durability of AI infrastructure spending is paramount. The market is already on a record trajectory, with global AI spending forecast to top $2 trillion in 2026. This isn't a one-quarter spike but a sustained expansion, driven by AI being integrated into consumer devices like smartphones and PCs. This broadens the TAM beyond enterprise data centers into the mass market. The forward signal to watch is whether this spending forecast holds through 2026, as it would validate the secular nature of the demand. Any significant deviation would be a major red flag for the entire growth thesis.

Second, the pace of physical infrastructure build-out versus systemic constraints will be the ultimate test of scalability. The demand is for massive power capacity, with AI-driven data center power needs projected to grow at a ~22% CAGR through 2030. This requires hundreds of gigawatts of new energy. The risk is that permitting timelines for power grids and data center sites could lag behind this build-out. If grid constraints cap the deployment of new AI-optimized campuses, it would create a hard ceiling on growth for hyperscalers and their partners. The investment thesis assumes these bottlenecks are solvable, but monitoring the actual rate of new capacity coming online versus regulatory hurdles is critical.

Finally, the financial impact depends on the service provider ecosystem's ability to pivot. The market is bifurcating, with XaaS growing 26% while managed services remain essentially flat. The forward signal is whether providers can successfully convert their traditional client relationships into high-growth AI consulting and XaaS deals. Evidence of this shift will come from deal activity-specifically, the volume and size of net-new AI infrastructure and software contracts-and from revenue mix reports showing a clear tilt away from low-growth managed services. The ability to do this will determine which companies capture the value from the $2 trillion AI spend.

The bottom line is a multi-year investment thesis with clear milestones. Success is confirmed if AI spending hits its $2 trillion target in 2026, data center power capacity expands as forecast, and service providers demonstrate a measurable pivot to high-growth XaaS. The risks are a spending slowdown, infrastructure bottlenecks, or a failure by providers to adapt. For the growth investor, these are the signals to watch.