📊 Financial & Capacity Snapshot

CoreWeave recently reported third-quarter 2025 revenue of approximately US $1.36 billion, more than double from the prior year. Bisnow+2Seeking Alpha+2

Yet despite this rapid growth, margins are under pressure. Delays in data-center build-out and power infrastructure bottlenecks have forced CoreWeave to revise guidance downward. Bisnow Meanwhile, NVIDIA continues to dominate the AI accelerator market. According to a detailed analysis, NVIDIA’s competitors struggle to match its margin and moat in data-center GPUs. SemiWiki

⚡ Electricity & Power Constraints

AI-optimized data centers now demand extremely high power densities. One report from McKinsey & Company estimates that AI-related data center capex could reach US $5.2 trillion by 2030 to support this workload surge. McKinsey & Company

Specifically:

1. Standard cooling and air-cooled racks often struggle to dissipate heat when power densities exceed ~50 kW per rack. McKinsey & Company
2. CoreWeave itself notes that its capacity planning must focus on backup power and reducing grid impact. CoreWeave
3. The delay of data-center capacity was explicitly tied to power and grid supply constraints. WIRED

In short: Even with massive demand, data-center growth is throttled by the availability of reliable, high-density power and the infrastructure to support it.

🎛️ GPU Amortization and ROI Pressures

While demand is enormous, so are the stakes for amortization and return on investment. A recent piece highlights concerns over depreciation schedules for AI hardware — suggesting modern GPU racks may depreciate faster than expected, potentially repeating structural risks seen in past tech cycles. Seeking Alpha

Some key issues:

1. GPUs cost millions to deploy, but may face fast obsolescence as new generations emerge.
2. Analysts argue that the true ROI must be evaluated on power, utilization, amortization, and replacement, not just raw performance.
3. One empirical study measured a node of 8 NVIDIA H100 GPUs drawing ~8.4 kW under load — these high power draws translate into serious operational costs. arXiv

🧩 Putting It Together: CoreWeave + NVIDIA Context

CoreWeave’s growth is intimately tied to NVIDIA’s GPU ecosystem. As a major partner, CoreWeave benefits from early access to high-end accelerators. But the infrastructure needed to deploy those GPUs at scale introduces three intertwined risks:

1. Power and build-out delays — Without sufficient power/real-estate/thermal infrastructure, GPU installations can’t scale profitably.
2. Amortization pressure — If GPUs are not utilized fully or are replaced frequently, the cost per usable GPU-year rises sharply.
3. Return on rack / GPU — If total cost (power + cooling + space + hardware) per GPU used does not meet threshold returns, investments stall.

For example, an article notes that some data-centers are seeing ~40-70 % idle resource time and project failure rates above 80 % when power or utilization planning falters. Introl

✅ Conclusion & Strategic Implications

The AI infrastructure boom has tremendous momentum — but the winners will be those who master power planning, infrastructure economics, and GPU lifecycle management.

For companies like CoreWeave and NVIDIA, the keys include:

1. Ensuring power and cooling availability before deploying GPU racks.
2. Maximizing utilization and avoiding idle periods to ensure amortization works.
3. Monitoring hardware depreciation and replacement cycles to avoid stranded assets.
4. Evaluating total cost per GPU-year and linking investments to real business value, not just raw compute.

In the end, the narrative is not only about “who has the most GPUs” but “who deploys, utilizes, sustains and replaces them most economically.”