In a world where a handful of companies decide what we see, hear, and read, there is little room for diversity. The fact that these companies often simultaneously control the largest datasets is more than a coincidence – it is a problem.
When a company becomes so large that a court orders the sale of its search engine it speaks volumes about market dynamics. While this development could potentially be slowed down again by political influences during Trump's candidacy, it shows how critically this situation is now being viewed.
The Power of Monopolists
Major tech companies like Google, Microsoft, or Meta are no longer just service providers. They act as gatekeepers of our digital world and control both access to information and the infrastructure for AI development. Their dominance is based on massive data collection and usage, as well as control over platforms and end-user markets.
Their dominance rests on three central factors::
- Massive Data Collection: These companies use data as the raw material for their technologies, collect it excessively, and strengthen their power through its use in machine learning processes.
- Control Over Platforms and Markets: New market entrants often have to conform to the terms set by Big Tech, as they rely on their infrastructure. Startups frequently license AI models from tech giants because they cannot build the necessary server capacity to train their models or the reach to distribute their products on their own.
- Increasing Market Concentration: OpenAI, for instance, licenses its AI models exclusively to Microsoft, making it more difficult for smaller players to enter the market. In return, OpenAI gains access to Microsoft’s computing infrastructure. Meanwhile, Nvidia dominates the AI chip market, limiting alternatives. Such dependencies and exclusive contracts weaken the innovation potential of smaller players and cement the dominance of a few companies.
Data for Machines
Large Language Models (LLMs) like GPT are based on massive datasets. But what is the cost? Data privacy, sovereignty, and often the rights of those whose data is being used. The U.S. often takes the path of least resistance here – treating data as a resource rather than as information worth protecting. This trend is reflected in various examples:
- Data Privacy and Protection: Technologies like Microsoft’s “Recall” system, which records screenshots of user actions to train an AI system with a "photographic memory," raise fundamental questions about protecting sensitive information. While Microsoft assures that these data remain local, such features highlight the risks of invasive technologies.
- Ethics and Responsibility: Elon Musk openly asked X users share medical data- such as MRI or CT scans - to support his AI company “Grok.”
- Increasing Market Concentration: AI development requires enormous resources that only a few companies can provide. This dependence weakens the innovation capacity of smaller players and deepens the gap between Big Tech and other market participants.
Data Sovereignty at Risk
Especially in Europe, where data protection plays a central role, the question arises of how growing dependence on centralized providers can be overcome. The EU has taken an important step with the AI Act to promote transparency and accountability in the use of AI. The goal is to establish standards that focus not only on the use of data but also on ethical responsibility.
However, challenges remain. Small companies can also be disadvantaged by the complex regulations, as implementing standards requires significant resources. At the same time, Big Tech companies continue to benefit from their structural advantages, such as better infrastructure and greater financial resources.
Market concentration not only hinders innovation but also poses risks to societal stability. The head of the U.S. Securities and Exchange Commission (SEC) warned that a few AI players’ dominance could create systemic risks for the financial system. A single error could have far-reaching consequences.
Outlook
The dependence on a few Big Tech companies poses a serious threat to innovation, data protection, and democratic processes. While initiatives like the EU AI Act are steps toward transparency and accountability, the question remains: Are such measures enough to break the power of monopolies? The path toward sovereign, decentralized, and ethical AI development requires bold decisions – and the will to regain control over our data.
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