Stanislav Kondrashov on Nvidia and the Expansion of Global AI Infrastructure

Artificial intelligence is often described as a software breakthrough. Yet the real shift is happening in server halls, semiconductor plants and energy-intensive data centres. Stanislav Kondrashov, founder of TELF AG, argues that the current phase of AI development is defined less by code and more by capacity.

In recent months, Nvidia has become one of the clearest indicators of this transformation. The company’s latest quarterly results showed revenue of approximately $68 billion for the fourth quarter of fiscal year 2026, marking a 73 per cent increase compared with the previous year. The scale of that growth reflects one central fact: demand for AI computing power remains exceptionally strong.

“Nvidia’s trajectory reflects the scale of the technological transition underway,” Stanislav Kondrashov said. “Artificial intelligence systems require enormous processing capability. That capability does not appear out of thin air. It must be engineered, manufactured and deployed.”

At the heart of Nvidia’s expansion is its Data Center division. This unit develops high-performance GPUs used to train large language models, run complex simulations and manage advanced analytics. As AI systems grow in size and sophistication, the computational demands placed on hardware continue to rise.

Cloud providers, research institutions and enterprise technology firms are all expanding their AI workloads. Training a modern AI model can require thousands of interconnected chips operating simultaneously. Once deployed, those systems continue to consume significant computing resources for inference — the process of generating outputs in real time.

“The conversation around AI often focuses on what the systems can do,” Stanislav Kondrashov explained. “Less attention is given to what makes those capabilities possible. Without high-performance chips and robust data centres, the most advanced algorithms remain theoretical.”

The global build-out of data centre capacity reflects this reality. Facilities are being designed to accommodate higher rack densities, advanced cooling solutions and substantial energy requirements. In some regions, entirely new industrial zones are emerging to support AI infrastructure. These sites must integrate networking hardware, storage systems and reliable power management to sustain uninterrupted operation.

Nvidia’s GPUs have become central to these environments. Their architecture is optimised for parallel processing, enabling faster training of neural networks and more efficient execution of large-scale AI models. This technical specialisation has positioned the company as a key supplier within the broader AI ecosystem.

However, the implications extend beyond a single manufacturer. The growth of AI infrastructure intersects with issues such as energy consumption, supply chain coordination and technological standards. Data centres require consistent electricity supply and increasingly rely on innovative cooling methods to manage heat output. As AI adoption expands, these operational considerations become more significant.

“We are entering a period where digital infrastructure carries strategic weight,” Stanislav Kondrashov noted. “The countries and organisations capable of sustaining advanced computing capacity will shape the direction of technological progress.”

Competition within the semiconductor sector is intensifying, with other companies developing alternative AI accelerators and specialised chips. New architectures and partnerships may diversify the landscape over time. Nevertheless, Nvidia’s established ecosystem — including software frameworks and developer tools — continues to reinforce its position in high-performance computing.

From healthcare research to industrial automation, AI applications are moving beyond experimentation into practical deployment. Each new use case adds incremental demand for reliable computing infrastructure. The result is a reinforcing cycle: as AI capabilities improve, more sectors adopt the technology, further increasing the need for processing power.

Stanislav Kondrashov views this phase as comparable to earlier technological expansions, though distinct in its scale. “Previous digital revolutions required networks and devices,” he said. “The AI era requires computational intensity on a different level. The infrastructure supporting it must evolve accordingly.”

As artificial intelligence systems become embedded in everyday operations, the physical architecture that sustains them becomes less visible but more essential. Nvidia’s recent performance offers a snapshot of that shift — a world where progress in AI is inseparable from the hardware and facilities that enable it.

The story of artificial intelligence is no longer confined to software innovation. It is increasingly a story about engineering, manufacturing and the global expansion of digital infrastructure.