Data is emerging as a critical asset in the AI economy, influencing valuations, trade negotiations, and national economic strategies.
The AI economy has brought forth a fundamental economic insight that is increasingly difficult to overlook: data is the core asset driving value creation, and that value ultimately resides with its owner. Algorithms do not generate intelligence in isolation; they derive economic power from vast, structured, and continuously updated datasets. This understanding is now gaining traction at the highest levels of political discourse.
In recent discussions within the Indian Parliament, leaders from various political factions—including Rahul Gandhi and members of the Modi government—have openly recognized data as a form of economic currency. This convergence reflects a broader realization that control over data in an AI-driven economy is as significant as control over capital, labor, or natural resources.
As this recognition deepens, nations will increasingly be compelled to articulate how they value their data assets and how these valuations impact access, governance, and negotiation power. This is particularly relevant as data centers, cloud infrastructure, and AI training hubs are established worldwide. Countries will not only compete based on tax incentives or energy costs; they will negotiate from a position of sovereign data value—considering who owns the data, where it is stored, how it can be utilized, and under what regulatory frameworks it can be monetized.
Consequently, data governance will evolve beyond privacy and cybersecurity into a distinctly economic and geopolitical framework. This shift will shape trade agreements, digital sovereignty doctrines, and strategically align the context of U.S.-India trade negotiations. The valuation of data assets introduces a new and largely unspoken dimension of leverage in international relations.
While tariffs have traditionally focused on manufactured goods, pharmaceuticals, and technology hardware, the most significant exchanges now increasingly revolve around access to India’s population-scale data, which fuels AI development. India’s extensive consumer, biometric, health, and financial datasets—generated through platforms like Aadhaar, UPI, and digital public infrastructure—represent an economic asset that the U.S. technology sector relies on but does not own. Consequently, data governance decisions made by India serve as implicit trade instruments, shaping market access as effectively as tariffs or quotas.
Restrictions on cross-border data flows, licensing requirements for model training, or sovereign data-use frameworks can offset traditional tariff concessions, allowing India to negotiate from a position of strategic strength. For the United States, recognizing data as an economic asset rather than merely a regulatory inconvenience is crucial for structuring fair, forward-looking trade agreements that reflect the realities of the AI economy.
At the corporate level, the challenge becomes even more pronounced. Despite data being one of the most valuable drivers of enterprise worth, it remains largely invisible on balance sheets. Unlike physical assets or financial instruments, data is rarely capitalized as a discrete asset, even though it underpins revenue growth, market dominance, and long-term competitive advantage. In some instances, this opacity is worsening rather than improving.
Companies like Meta have begun shifting certain AI-related expenditures into footnotes rather than treating them transparently as investments in core assets. This accounting treatment risks obscuring the true economic position of firms and distorting investor understanding of assets, liabilities, and long-term value creation in an AI-first economy.
Countries such as India—and increasingly China—are rapidly advancing toward more sophisticated frameworks for the valuation and governance of population-scale data. With billions of digital identities, transactions, health records, and behavioral signals, population data is becoming the primary training input for large-scale AI models. This shift transforms national data from a regulatory burden into a strategic economic asset. Nations that effectively recognize, price, and manage this asset will exert disproportionate influence over the future of AI development, while those that fail to do so risk becoming mere extractive data sources for foreign platforms and models.
This evolution raises a critical macroeconomic question: should national GDP calculations begin to reflect the contribution of data as an indirect measure of productivity? Data increasingly functions as a form of digital infrastructure—enhancing labor efficiency, capital deployment, and innovation velocity. Like oil, minerals, or arable land, data is a natural resource with present and future value. Ignoring it in national accounting frameworks understates economic output, misrepresents growth, and fails to capture the true engines of value creation in modern economies.
The issue of data ownership is particularly complex and consequential in the healthcare sector. Medical data is generated by patients, captured by providers, stored by health systems, processed by payers, and increasingly analyzed by technology platforms, leading to a fragmented and often contested ownership landscape. While patients are the original source of health data, they rarely exercise meaningful economic or governance control over how that data is aggregated, monetized, or used to train AI models.
Existing regulatory frameworks, such as HIPAA, were designed to protect privacy and facilitate information exchange, not to define ownership, valuation, or compensation. As AI systems increasingly rely on longitudinal health records, imaging datasets, and real-world evidence to drive clinical and commercial value, unresolved questions surrounding consent, stewardship, and economic rights threaten to undermine trust and distort incentives. Without clear ownership and valuation frameworks, healthcare risks becoming the most extractive data economy of all, where the highest-value data is generated by patients, but the economic returns accrue elsewhere.
Ultimately, the AI economy necessitates a new way of thinking about value itself. Data valuation will not rely solely on traditional cost or income approaches but will increasingly incorporate dynamic, usage-based, and option-value frameworks. Technologies such as blockchain and distributed ledgers enable the tokenization of data rights, tracking of provenance, and facilitation of secure, auditable transactions that unlock latent economic value. As valuation methodologies evolve—such as those outlined in contemporary frameworks for assessing data as an AI fuel—the ability to measure, price, and transact data assets will become central to economic advancement, corporate strategy, and national competitiveness.
According to The American Bazaar, the implications of these shifts are profound, affecting everything from trade negotiations to corporate strategies in the AI economy.