Data wars: DLT vs. existing data business models

Data wars: DLT vs. existing data business models

The emergence of AI has how shown that data will be one of the most valuable assets of the 21st century. In this post, we examine which business model is best suited at incentivising not only firms to invest in building platforms and networks, but also users to generate and take ownership of their data.


The emergence of Large Language Models (LLMs), which are trained on the entirety of publicly available written records, has shown that AI will be one of the biggest economic drivers in the 21st century and data one of the most valuable assets. As articulated by such an LLM in a debate at the Oxford Union, “The ability to provide information, rather than the ability to provide goods and services, will be the defining feature of the economy of the 21st century”.1 

In the quest to acquire and derive value from data, however, not everyone begins from an equal footing. Companies like Alphabet and Meta, for example, have vast amounts of private data and they are uniquely positioned to fine tune their AI models, making them much more accurate and versatile for different uses. At the same time, the billions of users who have created and deposited data on various platforms for which they are not adequately compensated, and they have limited to no control over how their data are used.

If data becomes one of the most valuable assets, which business model is best suited at incentivising, not only firms to invest in creating platforms, networks and services, but also for users to generate and deposit their data? In this post, we compare the existing database business model against those enabled via Distributed Ledger Technology (DLT).

Existing data business model 

Existing data business models are primarily generated by a firm that makes the initial investment in building the platform and the network of users. The firm acts as a trusted intermediary for all the transactions and interactions between the users. It verifies their identity, custodies their digital assets, and records their transactions. The data that is created from all this activity belongs to the firm that owns the platform, and it places restrictions on what users can do. A common restriction is that the data cannot easily be transported to a competitor platform (except as required by legislation). This creates data silos that are not interoperable, not only because firms do not want to share their data and reduce barriers to entry, but also because different data architectures are used. For example, an Uber driver cannot take their ratings and export them to a Lyft or another competitor. A truck driver cannot verify to a new employer the jobs they have undertaken, unless the old employer allows it. Buying a digital song, book or any other digital asset on a closed platform does not entitle the user to sell it if they want to.

The firm bears all the risk and the initial cost for creating the platform and network. If it succeeds, however, the data and network ownership confer monopolistic rights to the firm. Users cannot transport their data easily, creating a hold-up problem.2 The firm can unilaterally change the ways it monetises their data while the users are limited in how they can react, as their bargaining power is very limited. If they leave the network, for example, their data will still be the firm’s property and they must incur the fixed cost of creating new data on a different platform.

Another key limitation is that the owner of the platform can use the data that each user generates to get a better understanding about their willingness to pay and then price discriminate when offering various services. For example, banks price discriminate because they have an abundance of financial information about their clients. 

More worryingly, when a user shares their data on the platform, they reveal information about other users as well because their behaviour is correlated. A recent academic paper showed that this correlation creates a negative externality on all other users, diminishing the value that their data has for the platform. If each user is trying to sell their data and the correlation between users is high, then they rush to sell their data at ever decreasing prices. This ‘race to the bottom’ results in an excessive supply of data from the users, that the platform can then acquire very cheaply, even though each user places a high value on their own privacy.3 This explains why big platforms like Meta have acquired data about billions of users very cheaply.

Distributed Ledger Technology (DLT)

DLT represents an alternative database structure in the dimensions of architecture, control, and ownership of the data. Instead of having one node that controls the network, owns the data, and acts as a trusted intermediary for all transactions, with DLT all nodes are created equal, and they contribute to maintaining the network and updating the ledger of transactions. A distributed network is less prone to outside attacks because there is no single point of failure, but it is less efficient and scalable, as data is duplicated and stored in many nodes. See the figure below which shows the different database architectures.

Figure 1: Different database architectures 

DLT provides several advantages over a centralised database, mainly because there is no longer a dichotomy between the owner of the network and the users. A user verifies their identity and ownership of their transaction data using cryptographic tools, hence a trusted intermediary is no longer needed. DLT enables users to have ownership of their own data and finely grained control over how their data is used from third parties. For example, having a banking account should not automatically mean that a third party, like a bank, has access to the history of these transactions. The user could disclose these transactions only if they want to request a financial service, such as a loan. The advent of AI can also work for the benefit of the user, who can describe the basic principles of how they want their data to be used and then their personal AI agent will sort out the details and communicate with external services.

The rules of how data are used and monetised cannot change unilaterally by one node, without the majority of nodes agreeing. Moreover, the inherent interoperability of DLT means that a user can easily export their data to a different platform if they do not agree with the majority. Hence the hold-up problem is alleviated. More importantly, the incentives between the nodes that maintain and update the network, and the users that deposit the data are more aligned because the users can also have a stake in the network from the beginning. This alleviates the issue of the platform acquiring the users’ data very cheaply.

Increasing competition

Regulators around the world are struggling to keep up with how digital platforms and AI evolve, and what is the best way to regulate the issues of data privacy, ownership, and portability. The European Union’s General Data Protection Regulation (GDPR) already mandates businesses to grant individuals greater transparency and control over their personal data, however this is not enough. Recently, the Competition and Markets Authority (CMA) launched a review of AI models, like LLMs, to understand whether there are any competition and consumer protection considerations.

DLT has the potential to offer a non-regulatory solution, by lowering the barriers to entry, increasing innovation and consumer choice, and better aligning the incentives of the network users with those who maintain and build it. As the economy evolves towards an AI and data-driven model, the need to separate data ownership from data processing becomes vital to fostering innovation and growth.


The prevailing data business models have resulted in centralised databases that are controlled by large companies, which act as gatekeepers of vast amounts of data that are siloed and not interoperable with each other. The firms that have managed to build these databases are effectively data monopolies and they are uniquely positioned to dominate the new economy, as it is shaped by the advent of AI. It is therefore imperative to rethink how data business models incentivise not only the firms to invest in platforms, networks, and services, but also the users to generate and take ownership of their data. A more inclusive and interoperable data ecosystem may unleash the transformative potential of next-generation data technologies, paving the way for unprecedented advancements and a more equitable digital economy.


1 Read the debate at

2 See “An Introduction to Distributed Ledger Technology” for more details on the hold-up problem, accessible at

3 Acemoglu, Daron, Ali Makhdoumi, Azarakhsh Malekian, and Asu Ozdaglar. 2022. "Too Much Data: Prices and Inefficiencies in Data Markets." American Economic Journal: Microeconomics, 14 (4): 218-56. Accessible at

4 See


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