Each time a recent AI model drops—GPT updates, DeepSeek, Gemini—people gawk on the sheer size, the complexity, and increasingly, the compute hunger of those mega-models. The idea is that these models are defining the resourcing needs of the AI revolution.
That assumption is incorrect.
Yes, large models are compute-hungry. But the largest strain on AI infrastructure isn’t coming from a handful of mega-models—it’s coming from the silent proliferation of AI models across industries, each fine-tuned for specific applications, each consuming compute at an unprecedented scale.
Despite the potential winner-takes-all competition developing among the many LLMs, the AI landscape at large isn’t centralizing—it’s fragmenting. Every business isn’t just using AI—they’re training, customizing, and deploying private models tailored to their needs. It is the latter situation that may create an infrastructure demand curve that cloud providers, enterprises, and governments aren’t ready for.
We’ve seen this pattern before. Cloud didn’t consolidate IT workloads; it created a sprawling hybrid ecosystem. First, it was server sprawl. Then VM sprawl. Now? AI sprawl. Each wave of computing led to proliferation, not simplification. AI isn’t any different.
AI Sprawl: Why the Way forward for AI Is a Million Models, Not One
Finance, logistics, cybersecurity, customer support, R&D—each has its own AI model optimized for its own function. Organizations aren’t training one AI model to rule their entire operation. They’re training hundreds. Which means more training cycles, more compute, more storage demand, and more infrastructure sprawl.
This isn’t theoretical. Even in industries which are traditionally cautious about tech adoption, AI investment is accelerating. A 2024 McKinsey report found that organizations now use AI in a median of three business functions, with manufacturing, supply chain, and product development leading the charge (McKinsey).
Healthcare is a primary example. Navina, a startup that integrates AI into electronic health records to surface clinical insights, just raised $55 million in Series C funding from Goldman Sachs (Business Insider). Energy isn’t any different—industry leaders have launched the Open Power AI Consortium to bring AI optimization to grid and plant operations (Axios).
The Compute Strain No One Is Talking About
AI is already breaking traditional infrastructure models. The idea that cloud can scale infinitely to support AI growth is dead incorrect. AI doesn’t scale like traditional workloads. The demand curve isn’t gradual—it’s exponential, and hyperscalers aren’t maintaining.
- Power Constraints: AI-specific data centers at the moment are being built around power availability, not only network backbones.
- Network Bottlenecks: Hybrid IT environments have gotten unmanageable without automation, which AI workloads will only exacerbate.
- Economic Pressure: AI workloads can devour hundreds of thousands in a single month, creating financial unpredictability.
Data centers already account for 1% of world electricity consumption. In Ireland, they now devour 20% of the national grid, a share expected to rise significantly by 2030 (IEA).
Add to that the looming pressure on GPUs. Bain & Company recently warned that AI growth is setting the stage for a semiconductor shortage, driven by explosive demand for data center-grade chips (Bain).
Meanwhile, AI’s sustainability problem grows. A 2024 evaluation in warns that widespread adoption of AI in healthcare could substantially increase the sector’s energy consumption and carbon emissions, unless offset by targeted efficiencies (ScienceDirect).
AI Sprawl Is Greater Than the Market—It’s a Matter of State Power
When you think AI sprawl is a company problem, re-examine. Essentially the most significant driver of AI fragmentation isn’t the private sector—it’s governments and military defense agencies, deploying AI at a scale that no hyperscaler or enterprise can match.
The U.S. government alone has deployed AI in over 700 applications across 27 agencies, covering intelligence evaluation, logistics, and more (FedTech Magazine).
Canada is investing as much as $700 million to expand domestic AI compute capability, launching a national challenge to bolster sovereign data center infrastructure (Innovation, Science and Economic Development Canada).
And there are rising calls for an “Apollo program” for AI infrastructure—highlighting AI’s elevation from industrial advantage to national imperative (MIT Technology Review).
Military AI is not going to be efficient, coordinated, or optimized for cost—it’ll be driven by national security mandates, geopolitical urgency, and the necessity for closed, sovereign AI systems. Even when enterprises rein in AI sprawl, who’s going to inform governments to decelerate?
Because when national security is on the road, nobody’s stopping to ask whether the ability grid can handle it.
