The defining economic insight of the AI economy is now impossible to ignore: data is the core asset that drives value creation, and that value ultimately resides with the owner of the data. Algorithms do not generate intelligence in isolation; they extract economic power from vast, structured, and continuously refreshed datasets. This recognition has begun to surface at the highest levels of political discourse.
In recent debates in the Indian Parliament, leaders across the political spectrum—including Rahul Gandhi and members of the Modi government—have openly acknowledged data as a form of economic currency. That convergence reflects a broader realization I have been writing about for some time: in an AI-driven economy, control over data is as consequential as control over capital, labor, or natural resources.
As this realization deepens, nations will increasingly be forced to articulate how they value their data assets and how those valuations influence access, governance, and negotiation power. This becomes particularly salient as data centers, cloud infrastructure, and AI training hubs are established globally. Countries will not merely compete on tax incentives or energy costs; they will negotiate from the standpoint of sovereign data value—who owns the data, where it is stored, how it can be used, and under what regulatory regimes it can be monetized.
Data governance will thus evolve beyond privacy and cybersecurity into an explicitly economic and geopolitical framework, shaping trade agreements, digital sovereignty doctrines, and strategically align the context of the U.S.-India trade negotiations. The valuation of data assets introduces a new and largely unspoken dimension of leverage.
READ: Sreedhar Potarazu | AI sovereignty race: US and China lead, India watches (
While tariffs continue to focus on manufactured goods, pharmaceuticals, and technology hardware, the most consequential exchange increasingly lies in access to Indian population-scale data that fuels AI development. India’s vast consumer, biometric, health, and financial datasets—generated through platforms such as Aadhaar, UPI, and digital public infrastructure—represent an economic asset that the United States’ technology sector depends on but does not own. As a result, data governance choices made by India function 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 a regulatory inconvenience is essential to structuring fair, forward-looking trade agreements that reflect the realities of the AI economy.
At the corporate level, the challenge becomes even more acute. Despite data being one of the most valuable drivers of enterprise worth, it remains largely invisible on balance sheets. Unlike physical plant 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 cases, this opacity is worsening rather than improving.
Companies such as 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 moving rapidly 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 shifts national data from a regulatory burden into a strategic economic asset. Nations that recognize, price, and manage this asset effectively will exert disproportionate influence over the future of AI development, while those that fail to do so risk becoming extractive data sources for foreign platforms and models.
READ: Sreedhar Potarazu | Healthcare cracking, lawmakers scrambling, consumers falling: The demise of US healthcare as we know it (January 30, 2026)
This evolution raises a fundamental 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—one that enhances 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 major issue of course is also about data ownership. Nowhere are the challenges of data ownership more complex—or more consequential—than in healthcare. Medical data is generated by patients, captured by providers, stored by health systems, processed by payers, and increasingly analyzed by technology platforms, creating 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 enable information exchange, not to define ownership, valuation, or compensation. As AI systems rely on longitudinal health records, imaging datasets, and real-world evidence to drive clinical and commercial value, unresolved questions around 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—one in which the highest-value data is generated by patients but the economic returns accrue elsewhere.
Ultimately, the AI economy demands 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 make it possible to tokenize data rights, track provenance, and enable 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.