The New AI Paradigm: What 2025 Teaches Us About the Future

The latest edition of the State of AI Report represents an important turning point in how we understand competitive value in this industry. The report highlights that the advantage no longer lies solely in having the most advanced model, but in optimizing the relationship between capacity, cost, distribution, and infrastructure.

Capacity and Cost: An Exponential Change

The most relevant data from the report indicates that intelligence per dollar is doubling every three to eight months, significantly exceeding the historic Moore’s Law. This acceleration is leading to redesigning technological architectures, implementing systems that distribute tasks according to the optimal relationship between capacity and cost rather than always resorting to the most powerful models.

This evolution has important practical implications: using the most expensive model for all tasks is economically inefficient. Organizations that are achieving better results are building systems that route each task to the most appropriate model according to its specific characteristics.

The detailed numbers can be found in this executive briefing by Nate B. Jones.

Distribution: The Rise of Answer Engines

The information search landscape is experiencing a substantial transformation with the growth of platforms like ChatGPT and Perplexity. This change is shifting the traditional focus of search engine optimization toward what is known as Answer Engine Optimization (AEO).

The relevance of this change is reflected in concrete metrics, with reported conversion rates exceeding 11%. The fundamental difference is that these systems do not direct users to multiple websites, but generate direct answers by synthesizing information from various sources. These changes force us to rethink digital visibility strategies.

Physical Infrastructure: Real Limitations

The report identifies physical infrastructure as an important limiting factor. Ambitious projects like Stargate propose building mega power plants and cooling systems, but the availability of energy and water represents a real bottleneck for sustained growth.

According to the SemiAnalysis analysis, these physical limitations add a layer of complexity that goes beyond purely technological challenges. Energy efficiency is becoming a differentiating factor as important as computational capacity itself.

Measuring Real Value

The report emphasizes that improvements in model capacity may be more precarious than initial demonstrations suggest. Therefore, tools like OpenAI’s GDP-val are focusing on measuring the real economic value that these systems generate, evaluating their contribution to specific economic tasks rather than just their performance on academic benchmarks.

This more pragmatic approach helps distinguish between impressive capabilities in controlled environments and practical utility in real business contexts.

Open and Closed Ecosystems

The report identifies an interesting divergence between geographic approaches. China is leading in open-weights models, which provides greater flexibility and technological sovereignty. The United States maintains a more closed or hybrid model-oriented approach.

The future will probably not be dominated by a single approach, but by hybrid architectures that combine frontier models for critical tasks with open models for higher volume and lower criticality operations.

Learning and Development Opportunities

The drastic reduction in the cost of accessing artificial intelligence capabilities creates unprecedented opportunities for individual learning and entrepreneurship. Never before has it been so accessible to build products, automate processes, or launch services that traditionally required specialized teams and considerable budgets.

However, the report also suggests that active adoption of these capabilities will be uneven. Personal and business competitive advantage in 2025 will depend less on deep technical knowledge and more on the ability to experiment, learn from mistakes quickly, and adapt agilely to new possibilities.

Toward a Structural Change

This cycle marks an important transition in how we understand enterprise artificial intelligence. Competition no longer focuses exclusively on having the most powerful models, but on designing and operating integrated systems that efficiently manage the balance between capacity, cost, distribution, and infrastructure.

Organizations that understand and master this new value architecture during 2025 and 2026 will establish significant competitive positions, while those that maintain a more traditional vision of AI as simply “models that answer questions” will face growing challenges.