Artificial Intelligence
Consulting
Development ServicesThe model works. The demo impressed everyone and the pilot got approved. And then… progress slows.
Not because the AI failed or the team lacks ideas. But because once AI projects move to the next stage, everything underneath starts to matter a lot more than anyone expected.
Models are powerful, tools have matured, and teams are learning faster than ever. But scaling intelligence turns out to depend less on the model — and more on the systems supporting it. This is what many technology leaders are starting to feel right now.
Infrastructure, once treated as elastic and almost invisible, is quietly becoming the factor that defines what is realistically possible and where the limits actually are.
How AI Turned Infrastructure Into a Strategic Question
For years, infrastructure was meant to stay in the background. Cloud platforms promised flexibility and near-infinite capacity, which allowed organizations to postpone difficult architectural decisions.
AI changed that assumption in practice. As AI workloads grow denser and more persistent, infrastructure stops behaving like a utility and starts behaving like a constraint. Power, cooling, hardware availability, and deployment timelines suddenly show up in conversations that used to be purely about product and engineering velocity.
McKinsey’s recent research on AI infrastructure highlights just how tangible this shift has become. Large AI data center projects have changed local economies, creating thousands of construction and technical jobs — in some cases employing thousands of people daily in mid-sized cities. Infrastructure decisions have become physical, economic, and highly visible.
One example highlights a single AI infrastructure site employing roughly 5,800 workers each day in a city of about 120,000 people, showing how infrastructure investment can materially reshape local labor markets.
The same analysis also points to growing demand for skilled-trade and technical roles, like electricians, data center technicians, and network specialists — especially as systems move toward dense, liquid-cooled architectures.
At that point, infrastructure decisions start defining where AI growth actually shows up: Which cities attract investment? Which regions build capability? And which ones are simply left watching from the sidelines.
The Energy Reality Behind AI Scale
When organizations test AI projects, they can experiment, iterate, and prove value without hitting many hard limits. But the moment AI moves into production — serving real users and supporting real operations — energy becomes impossible to ignore.Deloitte’s Research Center for Energy and Industrials puts some numbers behind what many leaders are already sensing. Power demand from AI-driven data centers in the U.S. is expected to grow from roughly 4 gigawatts in 2024 to as much as 123 gigawatts by 2035. That’s more than a thirtyfold increase. In a 2025 survey, nearly 80% of power and data center executives said AI will materially change energy demand over that same window.
The problem isn’t just scale. It’s timing.
Data centers can be deployed relatively quickly, but power generation, transmission upgrades, and permitting processes often take years to complete. As Deloitte notes, supporting AI at scale forces difficult trade-offs between speed, reliability, sustainability, and sheer availability of power.
For technology leaders, this reality fundamentally changes how the risk profile of AI looks at scale. Infrastructure decisions are now directly tied to energy availability, grid resilience, and regulatory timelines that extend far beyond traditional IT control.
When AI Costs Stop Scaling Linearly
This is where many AI programs start to feel uncomfortable financially.
As infrastructure tightens, AI costs stop behaving in nice, predictable ways. Small changes in training, inference, or data movement can trigger outsized jumps in compute, storage, and transfer costs. Shared resources, cross-team data flows, and heavy reliance on GPUs make spending harder to forecast than most finance models expect.
The FinOps Foundation calls this out clearly: AI workloads are variable by nature, and infrastructure choices — cloud, on-prem, or hybrid — directly shape unit economics and risk exposure.
This is often where executive confidence quietly erodes, because the cost model no longer feels controllable. Teams that treat infrastructure as a connected system — rather than a set of isolated tools — tend to regain visibility, predictability, and trust in AI investment decisions.
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Designing for Constraints Instead of Fighting Them
Some organizations are responding by changing the question entirely.Instead of asking how to scale everything the same way, they design around real constraints from the start. Deloitte’s Tech Trends 2026 research shows a clear shift away from one-size-fits-all infrastructure toward more deliberate workload placement.
Instead of defaulting everything to the cloud, organizations deliberately distribute AI workloads across different environments based on how those workloads actually behave.
In practice, this means:
- Elastic cloud environments for variable, experimental, or early-stage AI workloads.
- On-premises infrastructure for consistent, high-volume, or mission-critical workloads where cost predictability and control matter most.
- Edge infrastructure for real-time or bandwidth-constrained use cases that require low latency or localized processing.
By making these trade-offs explicit early, organizations avoid brittle architectures, align infrastructure with business criticality, and allow AI systems to grow in ways that are both economically and operationally sustainable.
From Infrastructure Scarcity to Strategic Confidence
AI will continue to advance. Models will improve. Capabilities will expand. None of that is really in question.
What will increasingly differentiate technology leaders is not the speed of adoption, but the confidence with which they scale under real-world constraints. Infrastructure is becoming a quiet, but very real, determinant of credibility. When systems perform reliably under sustained load, trust compounds across teams, budgets, and leadership. When they don’t, that confidence erodes quickly, often well beyond the technology function.
A Structural Reality Technology Leaders Can’t Ignore
Analysis from the OECD on competition in artificial intelligence infrastructure points to a structural shift that is happening in the AI economy. Across several critical segments of the AI infrastructure supply chain, concentration levels are exceptionally high. In some segments, a single provider controls more than 80% of global capacity. In others, the top three players account for the majority of the market. This concentration spans everything from advanced chip manufacturing and GPUs to cloud platforms and core software layers.
Concentration like this isn’t automatically a problem. In capital-heavy markets, it often reflects the scale of investment needed to push the technology forward. But it does change the rules. High upfront costs, long build times for data centers and fabrication facilities, and tightly coupled software ecosystems raise the barrier to entry — and quietly increase dependency.
For organizations that haven’t made infrastructure choices deliberately, this shows up as reduced flexibility over time. Fewer options. Less leverage. And harder trade-offs when conditions change.
A Quiet Perspective from Intersog
At Intersog, we see these dynamics play out across organizations at very different stages of AI maturity.
Infrastructure decisions have a longer shadow than most teams expect. They influence how easily plans adapt, how predictable costs remain, and how much friction shows up when systems are under sustained load. Long before anyone labels it a strategy issue, leaders start feeling it in slower decisions, tighter constraints, and fewer real options.
This is where infrastructure stops being about capacity and starts being about confidence. Confidence that commitments can be met. That growth won’t introduce instability. And that AI progress won’t come with constant trade-offs and surprises.
If AI is already creating value inside your organization, the question becomes less about what to build next — and more about whether the foundation underneath it is designed to support that momentum over time.
