Collateralizing Intelligence

Tanvi Patibandla

Damodaram Sanjivayya National Law University

Why AI Algorithms Remain Valuable Assets but Imperfect Security for Business Capital

Abstract
This article examines why AI algorithms, though increasingly central to enterprise value, remain poor collateral despite their economic significance. It argues that AI systems are valuable intangible assets because they are embedded in proprietary data, technical expertise, and continuous refinement, yet these same features make them difficult to value, transfer, and enforce in secured lending. As a result, lenders face uncertainty over ownership, liquidation value, and post-default monetization, while businesses often prefer venture capital, venture debt, revenue-based financing, or strategic partnerships over AI-IP-backed borrowing. While discussions on AI and intellectual property often focus on ownership and protection, the more immediate commercial challenge is whether these rights can be transformed into financeable assets. It contends that the mismatch between value creation and collateral suitability reveals a deeper valuation paradox in the digital economy.

Introduction

AI algorithms have become core drivers of modern business value, yet their financial treatment remains unsettled. Although intangible assets now account for a major share of enterprise worth, their invisibility in financial statements makes valuation and credit assessment difficult. This article asks a narrower question: can AI algorithms function effectively as collateral, or do their dependence on data, human expertise, and ongoing updates render them too unstable for secured lending?

The thesis advanced here is that AI algorithms are valuable but imperfect security. Their economic importance is clear, but their enforceability and liquidation value are weak, pushing firms toward alternative financing structures that better fit intangible innovation assets.

AI Algorithms as the New Intangible Asset Class

AI-driven businesses are increasingly valued not for physical infrastructure alone, but for proprietary algorithms, machine-learning models, training datasets, and predictive systems that generate recurring commercial advantage. India’s AI ecosystem reflects this shift: the IndiaAI Mission has directed major public funding toward compute infrastructure, datasets, and startup support, while private investors continue to back AI firms for their model-building capacity and data strategy rather than for conventional hard assets. Indian commentary on AI and intellectual property also recognizes that AI-related value is increasingly embedded in intangible outputs, while existing legal protection only partially captures the commercial reality of such assets. This means that modern AI companies are often valued less for their machinery and more for the intelligence coded into their systems, models, and data pipelines. Yet economic importance does not automatically translate into collateral value, because an asset can be strategically vital and still remain difficult to price, transfer, or realize in lending markets.

Why High-Value AI Assets Struggle To Function As Reliable Collateral
AI algorithms are hard to price as collateral because there is no standardized valuation framework, and their economic value depends on the quality of the underlying data, model architecture, and continuous updates that keep performance current. Legal scholarship on trade secrets and AI notes that AI systems can process vast datasets and replicate confidential algorithms, but that same fluidity creates evidentiary and enforcement problems for lenders seeking to seize or monetize the asset after default. The issue is compounded by trade-secret protection: once confidentiality is lost, the asset’s value may collapse, and transfer itself becomes difficult because the borrower cannot readily disclose or part with the very information that gives the model worth. AI models also depreciate unusually fast; as legal and valuation commentary shows, rapid technological change and the pace of innovation can render a leading model obsolete within months, leaving lenders with an asset whose value erodes faster than traditional security. The core problem, then, is not whether AI is valuable, but whether it remains valuable when a lender tries to enforce it.

Business Preferences for Alternative Solutions

Traditional collateral structures were designed for physical property, be that land, equipment or stock. Once IP was a factor in the lending calculations, lenders learned to give greater weight to registered IP patents and trademarks because registration provides legal certainty and enforcement options. AI algorithms find themselves in a no-man’s land. They have amazing value but aren’t easily put through the conventional monetary channel.

The reason why venture capital has become the go-to source for funding for an AI-driven business is that VC investors assess the future growth potential of the business, rather than assess the value of the business when it comes time for liquidation. This change is a fundamental change. The traditional lender who’s funding an NVIDIA GPU wants clarity when it comes to recovering; the VC funding an AI-powered healthcare startup accepts that the value of the algorithm might not ever be pledged, sold, or valued using traditional measurements. It’s about funding the business and not the algorithm.

Another option that has emerged is revenue-based financing (RBF). Unlike equity, RBF does not demand any security, it takes a fixed percentage of monthly revenue till the advance is reimbursed. For a SaaS business using proprietary AI, it will spare them the dilemma of valuing algorithms. Other technologies that avoid the collateral issue include strategic partnerships, technology licensing agreements, joint ventures and cloud-compute deals. The critical observation: Financial institutions are increasingly funding the infrastructure that powers AI but not the AI itself.The critical observation: Financial institutions are seeing the rise of financing for AI infrastructure (NVIDIA GPU loans, data-center financing, compute infrastructure lending) but not for the AI itself. Hardware products can be taken back. Algorithms cannot.

Can regulation Bridge the Financial Gap?

The existing secured-transactions frameworks was designed for a different type of assets and classical types of intellectual property, where ownership and valuation and enforcement are relatively straightforward. These assumptions are undermined by AI algorithms, which are constantly evolving, rely on proprietary data, and are frequently registered, not published. Therefore, lenders have great uncertainty about the value of their collateral. Enhancement of these concerns may be achieved by introducing standardized indicators for the measurement of the value of AI, creating registries of rights associated with AI, and establishing securitization models based on income rather than on the liquidation of assets. So long as such mechanisms don’t appear, businesses will probably continue to depend on more practical sources of capital such as venture capital, revenue-based funding, licensing and strategic partnerships.

CONCLUSION

AI algorithms have a special role in the modern economy: they are one of the most valuable assets that a company can have, but also one of the hardest to use as collateral. They are not well equipped to traditional secured lending structures because they rely on proprietary data, ongoing development, and confidentiality. Consequently, businesses continue to seek alternative financing options, such as venture capital, licensing agreements, strategic partnerships and more. The challenge on the table is not a matter of ownership, it’s one of financeability. As long as legal and financial frameworks do not provide reliable ways to value and enforce rights on AI assets, algorithms will be economically important, but financially underutilized in capital markets.

REFERENCES

Union Cabinet approves IndiaAI Mission with Rs 10372 crore outlay

India AI Mission: Government to Enhance Compute Capacity Through Viability Gap Funding

India bids to attract over $200B in AI infrastructure investment by 2028

AI Investments Create Intangible Assets