Artificial intelligence (“AI”) is transforming every aspect of our lives, and the biotechnology field is no exception. It drives significant advancements across various areas, such as drug discovery and development, genetic research, personalized medicine, and medical diagnostics. These innovations rely on vast amounts of large complex datasets, often called big data, both as input for AI models and as a key component of the research and development process. However, as AI continues to rise, some market players are concerned about the implications of data control and the potential impact of AI on the future of the field.
Major concerns surround big data and data monopolization from an antitrust and competition perspective, even outside the biotechnology field. While big data drives innovation and enhances consumer offerings, the Federal Trade Commission (“FTC”) has highlighted that big data can be used to discriminate against and target minorities, potentially excluding them from certain services and products. Additionally, restricted access to valuable data, particularly when it is costly or difficult to generate, can create entry barriers that limit competition and innovation. This phenomenon is often termed data monopolization: dominant players in a market controlling vast amounts of data, determining its use, and, in doing so, sustaining their market dominance.
However, determining whether data control is an entry barrier and thus anti-competitive is complex, as much of the information contained in these datasets is not unique; it can be obtained through alternative means. For example, the FTC and state attorneys general sued Facebook in 2020 for alleged monopolization. Facebook was accused of limiting data access to non-competing developers, deterring the rise of rival platforms. However, the court dismissed these claims under Trinko’s no-duty-to-deal rule, which holds that antitrust laws do not require a firm to engage with its competitors. The court reasoned that forcing Facebook to share its data could risk its incentives for innovation, place the judiciary in the role of a regulator, and potentially lead to collusion with its rivals.
This precedent may shape how antitrust authorities will assess the implications of big data and data monopolization, eventually limit data sharing and impact regulatory efforts. From an AI perspective, it could also hinder smaller companies from training their models and entering the market.
Furthermore, in the biotechnology sector, data monopolization might become an even more hotly-contested issue than it currently is in the larger tech industry. Many companies conduct trials and collect personal health data, which can be challenging to generate and typically requires obtaining individual consent before any use. These challenges are particularly salient for companies with AI tools that collect and process large volumes of specialized data, such as genetic information, which is crucial for developing new medicines and medical products. Smaller companies often lack the resources to access such unique data, potentially halting their research and preventing them from entering the market. In this context, biotech startups may argue that the uniqueness of this data creates a significant barrier to competition. However, courts and regulators in the future may have to consider the types of health data collected, which data should be eligible for sharing, and whether large companies should be required to share years of experiments and proprietary data that may constitute intellectual property.
Another antitrust concern is AI-exclusive licensing. Generally, FTC permits exclusive licenses, a standard market practice where an intellectual property owner grants exclusive usage rights to a licensee. However, concerns arise when a dominant manufacturer leverages such agreements to make it more difficult for smaller competitors to compete effectively. Exclusive contracts can also limit access to lower-cost suppliers, compelling competitors to source from more expensive alternatives, which may influence market conditions.
In the context of AI, exclusive licensing can raise several issues regarding market competition. First, as AI relies heavily on data, companies that control essential datasets may choose which entities to contract with and license the data to, potentially limiting access for competitors. Second, a dominant company may grant exclusive rights to use its AI technology to another major player, also excluding smaller competitors by forming selective partnerships. Third, an established company might suppress competition by bundling its AI products with proprietary software, favoring itself and preferred partners while disadvantaging new entrants.
For example, the European Commission is investigating whether Google’s exclusivity agreements with device manufacturers, such as Samsung, to pre-install its AI model could limit competition by giving its AI model a default advantage, as users are more likely to stick with the pre-installed option rather than explore alternatives. While restricting such exclusivity agreements could potentially hinder innovation by limiting direct channels for major players’ products, it could also lead to an AI market dominated by a few giants, potentially sidelining smaller competitors.
Again, these concerns are particularly exacerbated given the highly competitive nature of the biotechnology sector, which only a few major pharmaceutical companies dominate. Large pharmaceutical firms, even before the advent of AI, have often collaborated with other industry leaders to bundle their products together, leveraging their extensive resources and diverse offerings. With the growing importance of data and AI, these firms may secure exclusive data licenses and access to critical data from hospitals and other health providers, which could disadvantage smaller startups that lack the same level of access. AI models in the biotechnology sector could be crucial for identifying drug candidates, personalizing treatments, and predicting clinical outcomes. Exclusive AI licensing could restrict access to these advancements, limiting competition and creating significant barriers for emerging biotechnology startups.
As AI continues to influence the biotechnology sector, its implications could be far-reaching. big data and AI raise legitimate antitrust concerns, particularly in an environment where major pharmaceutical companies have significant advantages over smaller startups in terms of resources and products. These larger companies could leverage their position to maintain market power. While innovation continues to progress, antitrust authorities must closely monitor the industry and intervene when barriers to competition arise, given the sector’s crucial impact on public health and well-being. As these concerns increase, the U.S. enacted the 21st Century Cures Act to limit information blocking in healthcare. Though it does not mandate proactive data sharing, its role may grow as antitrust enforcement evolves.