Mirantis on AI Infrastructure: An Unprecedented and Sustainable Buildout

Mirantis, a leading cloud-native infrastructure company, today announced it is actively partnering with a new class of AI infrastructure providers—dubbed “neoclouds”—to help build and operate the AI factories powering the next wave of artificial intelligence at scale. With projections for AI infrastructure investment, like Goldman Sachs’ forecast of $100 trillion by 2040, skeptics are questioning whether this buildout is sustainable. Alex Freedland is the co-founder and CEO of Mirantis, which is working directly with emerging “neocloud” providers that are building the next generation of “AI factories” – data centers optimized for producing intelligence at scale.

Neoclouds are a new category of infrastructure providers, which are companies that secure energy access, build computational capacity, and sell intelligence services at premium margins.

“Right now, we’re seeing two primary applications driving all this infrastructure demand: consumer AI like ChatGPT, and enterprise productivity tools like coding assistants,” Freedland explains. “These two use cases alone are creating capacity constraints and 50x year-over-year growth in accelerated compute demand.”

He points out that adoption rates for these applications are three times faster than the mobile or cloud computing revolutions. “Efficiency gains are real and measurable. Tasks that took weeks now take days. That’s tangible ROI.”

“But, we haven’t even scratched the surface of enterprise vertical applications – HR systems, customer service, marketing automation. These will result in orders of magnitude more compute demand.”

Freedland points to two factors that distinguish AI from previous technology cycles: energy constraints and sovereignty requirements.

Neocloud providers have spent years acquiring land, negotiating grid connections, and securing power contracts. “Companies that control energy and know how to convert it efficiently into intelligence have real competitive advantages,” says Freedland.

The second difference is sovereignty – the requirement that certain AI capabilities remain under local control.

“This isn’t just about data privacy, though that’s part of it,” Freedland notes. “Countries, states, and industries are recognizing that AI is strategically important. They need sovereign approaches where the intelligence and the infrastructure producing it remain under their control.”

Working with emerging neocloud providers has given Freedland insight into how different this market structure will be.

“These companies are approaching the market very differently from traditional cloud providers,” he observes. “They’re starting with energy access, building computational infrastructure, and moving up the value chain to intelligence services. The economics improve dramatically as you move up that chain.”

He cites specific examples: “Selling bare metal GPU (Graphics Processing Unit) infrastructure might generate $1 billion annually per 100 megawatts of power. Orchestrated, just-in-time compute services can generate $1.5 billion from the same capacity. But if you can package that as intelligence – actual AI services – you’re looking at $4 billion from the same energy investment.”

This value chain is why Freedland believes infrastructure buildout is sustainable. “The companies building this infrastructure aren’t just construction projects hoping to lease space. They’re building vertically integrated platforms that capture value at multiple layers. The unit economics support the capital intensity.”

Implications for Enterprise Strategy

Freedland’s insights from working with neocloud providers lead to specific recommendations for enterprise AI strategy.

He predicts hybrid consumption models will become standard. “Energy constraints and sovereignty requirements mean the hyperscalers like AWS and Google won’t have monopolies on intelligence production. Enterprises will consume AI services from multiple sources—hyperscalers for commodity intelligence, neoclouds for specialized capabilities, and potentially sovereign providers for strategic applications.”

Freedland concludes by putting current concerns about unsustainable buildout in perspective.

“People compare this to the internet bubble or worry we’re overbuilding, but we’re already seeing capacity constraints. The historical parallel isn’t the dot-com bubble. Each new AI use case drives more infrastructure demand.”

Freedland concludes. “I really question whether current projections are ambitious enough.”

About Mirantis

Mirantis helps organizations build and run infrastructure and applications at scale, enabling businesses to modernize their systems, accelerate app development, and improve cloud operations. Working with Fortune 500 companies and emerging infrastructure providers worldwide, Mirantis specializes in Kubernetes, container technologies, and AI infrastructure orchestration—helping organizations manage complex infrastructure and leverage technologies such as AI factories and edge computing.

Learn more about Mirantis at www.mirantis.com.