During the panel discussion “AI Without Borders: Collaboration as a Driver of Success in Creating the Future”, held as part of the II International Symposium “Creating the Future”, participants discussed a wide range of technological and social challenges facing the development of artificial intelligence both in Russia and globally.

Based on the discussions and existing challenges, several key conclusions can be drawn:

1. As AI systems grow more complex, the creation of national information clusters becomes critically important. These clusters should ensure:

  • the identification of information used to train national models;
  • the integrity of primary data sources;
  • the validation of information to guarantee quality and reliability.

Essentially, this involves building digital ecosystems capable of ensuring sovereignty and sustainable AI development at the national level.

2. Artificial intelligence is no longer just a technology it is a new economic sector.

AI development has moved beyond the traditional boundaries of information technology. The scale of investment, the nature of industry interactions, and its macroeconomic impact make AI a distinct sector with its own specific structure and unique economic model.

3. In the near term, the AI sector will remain subsidized.

The development, integration, and operation of AI remain resource- and energy-intensive processes requiring significant investment. Global experience shows that no technology corporation has yet achieved full financial self-sufficiency in AI.

Much like agriculture, where the sector requires subsidies but the end products delivered to the consumer market generate stable profits, core AI infrastructure will continue to need support, while applied solutions will be economically viable and commercially successful.

4. A growing gap exists between technological development and human competencies.

The pace of AI development exceeds the rate at which professional skills for its effective use are being formed. This creates barriers to integrating technologies into everyday and corporate processes, reducing the efficiency and impact of digital solutions.

Therefore, the development of AI requires a comprehensive approach: from the creation of national digital clusters and data validation systems to new policies in education, regulatory frameworks, and expanded investment opportunities.