Black Hole of Artificial Intelligence: Infrastructure as the Main Barrier to Industry Development
October 11, 2026 · 2 min read
A recent discussion highlighted the increasing significance of computational infrastructure in the artificial intelligence sector. The article, based on a presentation at the RUSSOFT IT forum in 2026, emphasized that the key limitations in AI development have shifted from model design to computational infrastructure, including GPU clusters, data centers, procurement mechanisms, regulatory frameworks, and technological independence.
The discussion pointed out the existence of two primary areas where clients prefer domestic AI solutions: simple queries and specialized industry applications. For straightforward tasks, such as recipe suggestions or weather forecasts, users tend to favor local products that are integrated within their ecosystem.
Conversely, for complex systems tailored to specific industries, such as healthcare or metallurgy, domestic AI solutions are often superior to foreign counterparts. This superiority stems from the ability of local models to understand specific market dynamics and regulations, which foreign models may not accommodate.
The article also delves into the challenges posed by the so-called "black hole" of AI. It notes that as the demand for quick solutions rises, users increasingly turn to international alternatives, such as ChatGPT, for tasks like website creation or image generation. The lack of competitive domestic models, particularly in the realm of deep learning, is cited as a major hurdle.
Furthermore, the piece highlights the pressing need for robust data centers equipped with powerful GPUs to support the development of these models. It argues that without the necessary infrastructure, local AI initiatives struggle to gain traction, particularly in achieving technological sovereignty.
The text concludes by underscoring the importance of collaborative efforts among major companies to establish a sustainable ecosystem for AI development, which includes building data centers and securing the necessary hardware. It suggests that a collective approach to investment and resource sharing could be crucial in overcoming the current limitations in the AI landscape.