As AI moves from pilots into production, the CFO enters the room. The conversation shifts from building to scaling profitably. I wrote a few weeks ago that 2026 is about show me the money.
We are firmly in the second inning of AI. ROI, unit economics, energy, and infrastructure costs are now the gating factors for every AI deployment. Inference is where the economics get real.
Models get the headlines, but infrastructure is the decisive factor. The enterprises we talk to aren’t asking whether AI works. They’re asking whether it makes financial sense at scale.
As AI moves into production, two related shifts are underway: the rise of neoclouds and the emergence of inference clouds as a distinct infrastructure layer. Neoclouds are clouds born in the AI era – designed first for GPUs, accelerators, and inference workloads, not for web apps, storage, or traditional enterprise IT, while inference clouds are platforms optimized specifically for running AI models in production – serving predictions, tokens, and responses at scale.
If training clouds build the brain, inference clouds run the nervous system. Training optimizes for throughput and long-running jobs. Inference optimizes for latency, cost per token, and spiky, unpredictable demand – where milliseconds, tail latency, and reliability directly impact user experience and revenue.
This exposes a fundamental mismatch. Legacy cloud architectures were designed for stateful, storage-heavy workloads. Production inference is largely stateless, latency-sensitive, and governed by tight unit economics. The result is idle GPUs burning cash, opaque costs, and systems that work in demos but break at scale.
For neoclouds, inference clouds, and enterprises running inference, utilization is everything. Small improvements in GPU utilization can unlock outsized value, while power availability, cooling efficiency, and energy costs increasingly dictate how fast – and whether – inference capacity can scale. As an example, a 5–10% utilization lift can mean hundreds of millions in value.
Inference is ultimately a software-heavy problem wearing a hardware costume – where scheduling, routing, batching, and model-aware optimization determine whether AI can scale profitably. Closing this gap requires infrastructure built for inference from first principles, where architecture and financial discipline converge.
The next durable AI infrastructure layer is not another model or agent. It’s the inference infrastructure fabric that makes production AI financially viable.
What the market now demands:
This is where architecture and financial discipline converge, and where a new category is emerging.
Gruve was designed from the ground up to meet these requirements.
Today, the company is announcing 500MW+ of distributed AI inference capacity across Tier 1 and Tier 2 U.S. cities, with 30MW live today across four U.S. sites. Near-term expansion is planned in Japan and Western Europe.
What’s different is not a single feature. It’s the system design. Gruve’s Inference Infrastructure Fabric combines long-term partnerships that unlock excess and stranded power, modular high-density rack-scale pods optimized for inference workloads, a distributed low-latency edge fabric for orchestration across sites, and full-stack operations, including a 24×7 AI-powered SOC.
The need for speed: This approach bypasses multi-year data center build cycles and delivers AI-ready capacity in months.
Inference clouds are being pulled into the market by multiple buyer groups, each driven by a different economic pressure. Neoclouds need near-perfect GPU utilization and predictable margins to survive capital intensity. Hyperscalers want distributed capacity and faster time-to-market without committing to multi-year build cycles. Enterprises care about latency, reliability, and cost transparency as AI agents move into mission-critical workflows. AI-native startups need production-grade inference without building infrastructure from scratch.
Why this matters: Inference is no longer a niche optimization – it’s becoming shared infrastructure across the AI stack, with real budgets behind it.
These problems are hard by design. They sit at the intersection of data center engineering, distributed systems, and unit economics. This is where founder judgment matters most, shaped by prior cycles and pattern recognition.
Before founding Gruve, Tarun Raisoni built and scaled Rahi, a global IT solutions provider and systems integrator for data centers, to 1,200 employees and nearly half a billion in revenue before its acquisition by Wesco in 2022. That experience designing, integrating, and operating data center infrastructure at scale shows up in how Gruve is architected: end-to-end, with production requirements in mind from day one.
Gruve’s platform is built for neoclouds scaling inference economically, hyperscalers seeking distributed capacity, enterprises deploying real-time agents and mission-critical AI, and AI-native startups moving from prototype to production.
Every computing era produces a defining infrastructure layer. Mayfield has observed this pattern repeatedly with over 100 infrastructure-layer exits over its 56-year history. In the AI era, inference economics will determine what scales. Speed to scale is no longer optional. It’s the new baseline.
