This Founder Is Betting On Data Becoming The Next Asset Class

The next great asset class could be sitting on corporate servers. A mountain of untapped data is underutilized and undervalued.

A growing category of startups is betting on data as the next great goldrush building monetization platforms for organizations to cash in on the information they generate everyday.

Nathaniel T. Bradley is founder and CEO of Datavault AI, an AI-driven data monetization company. While algorithms are increasingly commoditized, Bradley is betting that the real power has shifted to the fuel behind the machine.

“Data is no longer just information; it is an asset class in its own right,” Bradley says. “As AI systems grow more sophisticated, the true scarcity lies not in algorithms, but in high-quality proprietary data that can be securely valued, governed and monetized at scale.”

The rise of data monetization platforms

Turning data into revenue has a direct impact on growth. Recent industry research reveals leading organizations can derive over 20% of revenue from data-driven products and services.

A 2025 report by McKinsey demonstrates organizations that effectively scale advanced analytics and AI are significantly more likely to generate measurable business value. Leading high performers capture disproportionately higher revenue growth and innovation outcomes compared to peers.

Those projections are catalyzing a new layer of infrastructure. Companies like Snowflake and Databricks laid the groundwork by making data easier to store, process and share. A newer generation, including Datavault AI, is focused on ownership, security and monetization.

The pivot is subtle but important: early platforms addressed access; newcomers are focused on value extraction.

In practical terms, that means enabling enterprises to package datasets, license them, track usage and enforce permissions; much as software licensing transformed the SaaS economy.

Why the AI boom is accelerating the trend

Data monetization has been discussed for years, but the AI boom has turned it into an operational priority.

Large language models and other AI systems depend on vast, high-quality datasets. As demand grows, so do concerns around provenance, ownership and compensation. The question is no longer - can we use this data? It’s - but who owns it and who gets paid?

A 2024 report from PwC estimates that AI could contribute up to $15.7 trillion to the global economy by 2030. Much of that value hinges on access to proprietary data that companies are increasingly unwilling to give away.

At the same time, legal and regulatory pressures are mounting. Copyright disputes over AI training data have exposed the risks of unclear ownership, prompting organizations to formalize how data is governed, tracked and monetized.

Bradley sees this as an inflection point.

“The analogy to intellectual property is intentional,” he says. “Just as patents and copyrights created entirely new economic frameworks, structured data ownership will define the AI era.”

Data as strategic intellectual property

Forward-looking companies are already operating this way. Alphabet treats search and user data as a core strategic asset, underpinning everything from advertising to AI. Amazon leverages customer behavior data to optimize its marketplace, recommendation engines and logistics.

Tesla has built a competitive moat around its driving data, using billions of real-world inputs to train autonomous systems data competitors cannot easily replicate.

Beyond tech, industries are following suit. Pharmaceutical companies are treating clinical trial data as licensable assets. Financial institutions are exploring ways to monetize anonymized transaction data. Retailers are packaging consumer insights for brand partners.

What’s changing is not just usage, but mindset. Data is no longer a byproduct of operations; it is becoming a primary output; something to be curated, protected and increasingly sold.

The infrastructure gap

Despite the momentum, a gap remains between ambition and execution.

Most organizations still lack clear frameworks for valuing data. Questions around pricing, compliance and risk management persist. Without the right infrastructure, monetization efforts can introduce security vulnerabilities or regulatory risk.

This is where companies such as Datavault AI are positioning themselves, building systems that track data lineage, enforce access controls and enable secure transactions. In effect, they are creating the rails for a data economy: custodians, exchanges and clearing houses for datasets.

Trust will be critical. For data to function as a true asset class, buyers and sellers need confidence in its quality, provenance and legality, requiring standardized frameworks akin to those underpinning financial markets.

A new economic layer

If Bradley’s vision is correct, the implications extend well beyond individual companies.

“We are moving from an era where data was merely stored and accessed to one where it is formally valued, protected and exchanged as intellectual property,” he concludes.

A fully realized data economy could reshape how value is created and distributed. Organizations may unlock new revenue streams from existing data. Individuals could gain greater control, and potentially compensation, for their personal data. Entire markets could emerge around specialized datasets.

There are still open questions - like whether data markets consolidate around dominant platforms, as cloud computing did or whether and how regulators respond as data becomes more explicitly commoditized.

The AI race is no longer just about building better models. It is about owning the inputs that make those models possible.

Data, once overlooked, may prove to be the most valuable asset of all.