Until now, the value of a technology company was measured through three relatively simple indicators: its product, its growth and its ability to attract users. In the AI economy, an increasing share of value is now located in the informational position that a company occupies in a value chain.
In other words, it is no longer just the products or services that matter, but the structured data sets, analytical infrastructures and information flows that certain companies control. These assets constitute what we can call business intelligence assets: databases, analytical models and processing infrastructures capable of transforming information into strategic advantage.
This development is gradually helping to redefine the way in which dominant companies in the AI economy emerge and are structured.
From platform to information infrastructure
Over the past fifteen years, the digital economy has been largely dominated by platforms. The companies that emerged during this period built their power on the ability to organize interactions, whether in the search for information, social networks, e-commerce or payments.
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But behind these platforms a deeper layer has gradually been formed: massive and structured databases which become the real infrastructure of the economic system.
The best-known example is that of Bloomberg, which not only provides financial information but maintains a global database on markets, transactions and financial instruments, which has become indispensable for banks, investors and even regulators. The Bloomberg terminal thus functions as a business intelligence infrastructure for global finance.
In a different register, Palantir Technologies was built on a comparable logic, namely aggregating complex data from public or private organizations to produce an operational analysis capacity. The company does not just sell software, but an architecture allowing heterogeneous information flows to be transformed into operational decisions.
These companies occupy a particular position in the economy, they do not necessarily produce the final goods or services, but they structure the knowledge necessary to produce them.
The rise of strategic datasets
This logic is now found in many industrial sectors.
In digital advertising, the strategic asset is the social and behavioral graph built by Meta Platforms. In information retrieval, Google’s value lies in the indexing and organization of billions of web pages. In artificial intelligence, competition largely focuses on the quality and scale of the datasets used to train the models, which explains the massive investments made by players like OpenAI.
In each of these cases, technology is only part of the equation. The real competitive advantage lies in the mass of structured data accumulated over time, often difficult for a new entrant to reproduce.
These datasets are gradually becoming economic assets comparable to the physical infrastructures of the 20th century. Where traditional industries invested in rail networks, pipelines or power plants, digital companies are now investing in data architectures capable of mapping and interpreting the functioning of entire sectors.
When artificial intelligence transforms data into an operational engine
Artificial intelligence is further accelerating this transformation. Data is no longer just a source of analysis or information but becomes the fuel for automated systems.
In a growing number of industries, operational decisions (pricing, logistics, resource allocation, maintenance) are now assisted, or even executed, by AI systems. These systems require structured, reliable and constantly updated data.
It is in this context that some companies seek to position themselves no longer as simple software providers, but as the data infrastructures of their industry or field.
Beyond Bloomberg, several companies already illustrate this logic. In payments, Visa captures billions of transactions that power fraud detection and economic analysis systems. In mobility, Tesla uses a global dataset of driving data to train its autonomy systems. In fintech, Plaid has become an infrastructure for connecting to banking data.
The recent example given by cargo.one, with the acquisition of Cargofive, also illustrates this dynamic. The objective is not only to add functionality to a logistics platform, but to constitute a multimodal database covering air and maritime transport rates and flows. Such a base then makes it possible to power automated pricing, route optimization or capacity management systems.
In each of these cases, competitive advantage rests less on the software itself than on the depth of data accumulated.
New positions of power in the economy
This development also changes the nature of economic power. In many sectors, competitive advantage no longer comes solely from the size of the company or its financial capital, but from the informational position it occupies in an ecosystem.
Some companies thus find themselves at the center of an industry’s information flows. They see transactions, prices, behaviors and trends before other players. This visibility allows them to improve their products, optimize their algorithms and anticipate market developments.
Over time, this accumulation of information creates a cumulative effect that is difficult for competitors to catch up with. The datasets are enriched as the platform is used, which further improves the quality of the services offered. A competitive advantage that feeds on itself.
This dynamic explains why certain information assets become particularly strategic, whether financial databases, industrial maps, logistics data, energy data or even medical data.
A strategic asset still invisible in the balance sheets
Paradoxically, these assets remain largely invisible in company accounts. Especially since accounting standards have difficulty valuing databases or informational positions, even though they can represent a major part of the economic value of a company.
In the AI economy, some companies must see their value less in terms of their physical assets and more in the depth and quality of the data they have accumulated over the years.
As artificial intelligence becomes integrated into industrial processes, this trend is expected to increase. Organizations capable of collecting, structuring and using data on a large scale will have a structural advantage, comparable to that previously provided by controlling energy infrastructure or transport networks.
The digital economy thus seems to be entering a new phase: one where data no longer constitutes just a resource, but gradually becomes the infrastructure on which artificial intelligence systems are deployed.
