Mr. Nandan Nilekani’s latest conversation at GFF (Global Fintect Festival) on tokenization and the Finternet is one of those talks that sounds abstract to the uninitiated until you realize it’s already running in dairy farms & grain warehouses across India.
The idea is super simple on paper: Take an asset & every tiny pieces of data that describes it, bundle them into one portable digital package and let that package move freely across lenders, markets & platforms.
That sounds familiar, doesn’t it? However, the execution, as always, is where things gets very interesting.
Here’s a breakdown of what’s promising about this model, what could possibly derail it and what it would take to tilt the odds toward the preferably outcome.
What Does Tokenization Means?
It’s worth clearing up one point of confusion first. When Mr.Nilekani talks about tokenization, he isn’t talking about the tokens that power large language models. Financial tokenization is about packaging an asset along with its descriptive attributes so it becomes a single portable digital unit that can move through a financial system without losing context or any friction. A cow, a warehouse receipt or a loan document can all be tokenized this way, bring them all.
This distinction matters because the word gets thrown around loosely and conflating the two ideas leads to muddled thinking about what the tech can & can’t do.
The Case For It
Scale & momentum are quite real.
And, this ain’t a niche experiment.
Big banks and mutual funds globally are already participating and estimates put the addressable market at up to 19 trillion dollars in tokenized assets within the next 7 to 8 odd years. Regulators in India, including the RBI and SEBI, are not sitting on the sidelines as well.
Both are drafting AI rule & cook books and quantum safe crypto plans in anticipation of this shift.
The use cases are appearing well grounded, not totally speculative. Three live pilots stand out:
Dairy farming, where cattle are tokenized using biometric markers like muzzle patterns along with health records, letting farmers share verified data with multiple lenders to secure purpose based loans for feed & shelter.
Warehouse receipts, where farmers tokenize stored harvests and offer them across a network of lenders instead of being locked into one buyer, which improves bargaining power and access to liquidity.
Securitization, where loan documents get tokenized and standardized so they become portable and interoperable across marketplaces.
In each case, the common thread is the same. Data that used to sit in silos, controlled by a single lender or buyer, becomes verifiable and shareable and that alone shifts negotiating power toward the person who owns the underlying asset.
AI agents could make markets liquid.
Tokens sitting idle don’t help anyone.
The interesting leap here is pairing tokenization with AI agents that work continuously to package tokens, identify lenders and initiate transactions. His argument is that this gives small businesses the same kind of always on market analysis and automated execution that large corporations already have, which is a genuine leveling mechanism rather than a marginal efficiency gain.
It may be a hedge against AI driven job loss.
This is the more provocative claim in the talk.
An economy built on millions of small, dynamic businesses is harder for AI to hollow out than one built on a few large corporations with rigid, highly structured roles that are easy to automate end to end. Distributed economic activity is inherently more resistant to wholesale displacement.
So, Where It Could Go Kaput?
None of this is automatic and the summary itself suggest at the fragility points even while making the optimistic case.
Liquidity is not guaranteed just because tokens exist. And when it does, those men who give Rs. 50 to their families and tell them to eat at Amma Canteen in Tamil Nadu are in for a huge treat - Yep!
Run out of cash during the Tuesday afternoon gambling session under the large Ashoka tree? No problem. Sell a token, encash it and keep the game going.
Issuing a token is the easy part. Building an active market around it, with real buyers, real lenders, real price discovery, fraud and abuse at bay, well, thats the hard part.
Plus, AI agents acting autonomously in financial markets raise obvious risk questions. Agents that identify lenders and initiate transactions on their own need guardrails around who is accountable when they misprice risk, act on bad data or get gamed by adversarial actors. The talk doesn’t dwell on this, but it is the natural failure mode of the exact mechanism being celebrated.
Data quality determines everything downstream. A tokenized cow is only as good as its health records and biometric data. A tokenized warehouse receipt is only as good as the underlying inventory verification. If the data layer is weak, fraudulent or poorly maintained, tokenization just makes bad information move faster & further, which is arguably worse than the status quo. (Disgree? Pls let me know your views.)
Regulatory frameworks are still being drafted, not finalized. As I said earlier, RBI and SEBI working on AI rule & cookbooks and quantum safe cryptography is a good sign, but draft stage rules mean the ground can still shift under participants who build on top of this infra today.
Concentration risk could reappear in a new form. Even a system designed for small business empowerment can end up funneling activity through a handful of dominant platforms, agent providers or data verification services, quietly recreating the concentration it was meant to avoid.
What We Could Do to Improve the Odds?
Well. For anyone building on, investing in or regulating this space, a few principles from the talk (and it’s natural extensions) stand out.
Prioritize data integrity before scale
Verification infrastructure for biometric markers, health records and inventory data needs to be trustworthy before volume gets pushed through the system. Scaling a weak data layer only amplifies its flaws.
Build liquidity mechanisms alongside issuance, not after it
Every pilot should have a clear answer for who the buyers and lenders are before tokens go live, not as an afterthought once tokens already exist.
Keep AI agents auditable. (as you always must)
If agents are initiating financial transactions, there needs to be a clear trail of what data they acted on, why, and who bears responsibility if something goes wrong.
Design for interoperability from day one
The value of tokenization comes from assets moving freely across lenders and platforms. Closed systems that only work within one bank or one marketplace defeat the purpose.
Watch for new chokepoints
Track who controls the verification layer, the agent infrastructure and the data pipelines. If power concentrates there, the democratizing promise of the model quietly erodes.
Treat regulation as a moving target
Build with enough flexibility to adapt as RBI, SEBI and other bodies finalize their frameworks, rather than assuming today’s draft rules are permanent.
And, lastly.
The Finternet vision Nilekani describes is compelling because it is already showing up in unglamorous, real places like dairy farms & grain warehouses (read boring jobs & domains), not just flashy and fancy slide decks & business models. The upside, more bargaining power for small asset owners and a more distributed, AI resilient economy, is genuinely significant.
But the model’s success depends entirely on getting the unglamorous parts right: clean data, real liquidity, accountable agents and regulation that keeps pace. Get those wrong and tokenization just becomes a faster way to move the same old problems around.
