Infosys co-founder and chairman, as well as the architect of Aadhaar, Nandan Nilekani emphasized that India should build tokenization infrastructure capable of functioning at the scale of the entire population and processing billions of transactions, instead of focusing solely on token issuance.
Speaking at the Global Fintech Festival 2026, Nilekani noted that simply creating tokens would not be enough. For tokenization to spread across various asset classes and use cases, the ecosystem must possess interoperability, liquidity, and the capacity to handle very large volumes.
He added that the architecture being developed for Finternet is designed to solve this problem because 'if you are going to do population-level tokenization, you need very large volumes.'
Nilekani pointed to the growing momentum of tokenization, citing initiatives related to depository receipts and corporate bonds. He also mentioned that large banks and asset managers worldwide are experimenting with tokenized assets, with SEBI recently launching relevant processes.
Nilekani clarified that financial tokenization differs from 'tokens' used in artificial intelligence, where a token represents a unit of work or consumption. In financial tokenization, an asset and all its characteristics are combined into one transferable digital package that can be passed to a third party.
However, Nilekani warned that the mere presence of multiple token issuers does not guarantee the formation of an active market. He stated: 'If you are not careful, we will have many token issuers, but no activity. You will not have interoperability, you will not have liquidity, and none of it.'
He presented three use cases being developed using a single underlying infrastructure. The first is livestock tokenization to ensure access to credit, where a cow and its information can be tokenized, allowing the owner to obtain a loan based on it. The second is transferable tokenized warehouse receipts that can be offered to multiple creditors. The third is an asset securitization market. The goal is to create an architecture that works with different types of assets and use cases.
Nilekani insisted that for creating a large market with sufficient liquidity, such infrastructure must utilize public chains, not private ones. The architecture must also be compatible, independent of public chains, capable of handling high transaction volumes, quantum-resistant, and designed to counter cyber threats.
He also sees an important role for AI agents in driving demand for tokenized assets. Agents working around the clock, for example, can issue an invoice as a token, offer it to various creditors, and secure financing. Nilekani noted: 'Tokenization itself will only provide issuance. But tokenization on public chains, supported by agent transactions, will also create demand.'
He concluded that to achieve the necessary volumes justifying such platforms, agents and tokens must converge.
According to Nilekani, the more significant opportunity from combining tokens and AI agents lies in leveling the playing field between large and small businesses. Previously, companies required features such as a treasury department, a research team, a credit rating, and a sales department to participate effectively in financial markets. Now, small businesses can potentially use a 24/7 AI agent to receive analysis and have a token that allows them to verify their credentials in the market to obtain a loan or conduct a transaction.
'Therefore, the main reason why we believe tokens and agents are important is actually related to inclusivity,' he said.
Nilekani linked the growth of small businesses to the discussion about the impact of AI on employment. He suggested that large companies might use AI more effectively to reduce jobs because their operations, tasks, and positions are typically more structured and thus easier to automate. He stressed: 'If you want to create a vibrant economy that generates jobs, it is not necessarily achieved by having a few large companies. It will happen through millions of small companies.'
In his view, jobs in small businesses are often related to employees performing diverse and more dynamic tasks, which makes them harder to automate. Nilekani concluded: 'These millions of jobs in millions of small companies are actually safer than a few million jobs in large corporations,' adding that creating an 'AI-resilient economy' will require job creation driven by millions of small enterprises.
He expects the number of startups in India to continue to grow sharply. Nilekani reported that there were about 10,000 startups in India in 2015, 150,000 in 2025, and predicts that this number will reach one million by 2035. He believes that both startups and traditional small businesses will become the 'employers of the future,' making the use of technology to facilitate their operations and compete with larger companies extremely important.
