As 2025 comes to a close, the global AI industry is entering its most significant structural reset since the start of the current innovation cycle. For the first time, capital markets, enterprise buyers, and infrastructure providers are aligning around a common reality:
By 2026, the growth of AI will be limited less by chip supply and far more by constraints in power capacity, inference costs, and the physical infrastructure needed to support large-scale intelligence.
The rapid expansion of AI has pushed data centers to their limits, with soaring energy demands and mounting pressure on electrical grids worldwide.
Even as semiconductor shortages ease, the bottleneck is shifting toward the availability of power, cooling, and next-generation data center architectures capable of handling massive model workloads.
Inference economics are emerging as a decisive factor, with enterprises increasingly focused on the cost of running AI models at scale rather than simply training them.
This shift is forcing companies to rethink deployment strategies, optimize workloads, and invest in efficient model architectures.
The coming year will redefine competitive advantage in AI, rewarding organizations that can balance performance with infrastructure sustainability.
As the market resets, power, cost, and physical capacity—not chips—will determine the pace of AI’s next wave.
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