When the concept of “Web 3.0” first emerged about a decade ago the idea was clear: Create a more user-controlled internet that lets you do everything you can now, except without servers or intermediaries to manage the flow of information.

Where Web2, which emerged in the early 2000s, relies on centralized systems to store data and supply compute, all owned—and monetized by—a handful of global conglomerates, Web3 turns that structure on its head. Instead, data and compute are decentralized through technologies like blockchain and peer-to-peer networks.

What was once a futuristic concept is quickly becoming a more concrete reality, even at a time when Web2 still dominates. Six out of ten Fortune 500 companies are exploring blockchain-based solutions, most taking a hybrid approach that combines traditional Web2 business models and infrastructure with the decentralized technologies and principles of Web3.

Popular use cases include cloud services, supply chain management, and, most notably financial services. In fact, at one point, the daily volume of transactions processed on decentralized finance exchanges exceeded $10 billion.

Gaining a Web3 edge

Among the advantages of Web3 for the enterprise are greater ownership and control of sensitive data, says Erman Tjiputra, founder and CEO of the AIOZ Network, which is building infrastructure for Web3, powered by decentralized physical infrastructure networks (DePIN), blockchain-based systems that govern physical infrastructure assets.

More cost-effective compute is another benefit, as is enhanced security and privacy as the cyberattack landscape grows more hostile, he adds. And it could even help protect companies from outages caused by a single point of failure, which can lead to downtime, data loss, and revenue deficits.

But perhaps the most exciting opportunity, says Tjiputra, is the ability to build and scale AI reliably and affordably. By leveraging a people-powered internet infrastructure, companies can far more easily access—and contribute to—shared resource like bandwidth, storage, and processing power to run AI inference, train models, and store data. All while using familiar developer tooling and open, usage-based incentives.

“We’re in a compute crunch where requirements are insatiable, and Web3 creates this ability to benefit while contributing,” explains Tjiputra.

In 2025, AIOZ Network launched a distributed compute platform and marketplace where developers and enterprises can access and monetize AI assets, and run AI inference or training on AIOZ Network’s more than 300,000 contributing devices. The model allows companies to move away from opaque datasets and models and scale flexibly, without centralized lock in.

Overcoming Web3 deployment challenges

Despite the promise, it is still early days for Web3, and core systemic challenges are leaving senior leadership and developers hesitant about its applicability at scale.

One hurdle is a lack of interoperability. The current fragmentation of blockchain networks creates a segregated ecosystem that makes it challenging to transfer assets or data between platforms. This often complicates transactions and introduces new security risks due to the reliance on mechanisms such as cross-chain bridges. These are tools that allow asset transfers between platforms but which have been shown to be vulnerable to targeted attacks.

“We have countless blockchains running on different protocols and consensus models,” says Tjiputra. “These blockchains need to work with each other so applications can communicate regardless of which chain they are on. This makes interoperability fundamental.”

Regulatory uncertainty is also a challenge. Outdated legal frameworks can sit at odds with decentralized infrastructures, especially when it comes to compliance with data protection and anti-money laundering regulations.

“Enterprises care about verifiability and compliance as much as innovation, so we need frameworks where on-chain transparency strengthens accountability instead of adding friction,” Tjiputra says.

And this is compounded by user experience (UX) challenges, says Tjiputra. “The biggest setback in Web3 today is UX,” he says. “For example, in Web2, if I forget my bank username or password, I can still contact the bank, log in and access my assets. The trade-off in Web3 is that, should that key be compromised or lost, we lose access to those assets. So, key recovery is a real problem.”

Building a bridge to Web3

Although such systemic challenges won’t be solved overnight, by leveraging DePIN networks, enterprises can bridge the gap between Web2 and Web3, without making a wholesale switch. This can minimize risk while harnessing much of the potential.

AIOZ Network’s own ecosystem includes capacity for media streaming, AI compute, and distributed storage that can be plugged into an existing Web2 tech stack. “You don’t need to go full Web3,” says Tjiputra. “You can start by plugging distributed storage into your workflow, test it, measure it, and see the benefits firsthand.”

The AIOZ Storage solution, for example, offers scalable distributed object storage by leveraging the global network of contributor devices on AIOZ DePIN. It is also compatible with existing storage systems or commonly used web application programming interfaces (APIs).

“Say we have a programmer or developer who uses Amazon S3 Storage or REST APIs, then all they need to do is just repoint the endpoints,” explains Tjiputra. “That’s it. It’s the same tools, it’s really simple. Even with media, with a single one-stop shop, developers can do transcoding and streaming with a simple REST API.”

Built on Cosmos, a network of hundreds of different blockchains that can communicate with each other, and a standardized framework enabled by Ethereum Virtual Machine (EVM), AIOZ Network has also prioritized interoperability. “Applications shouldn’t care which chain they’re on. Developers should target APIs without worrying about consensus mechanisms. That’s why we built on Cosmos and EVM—interoperability first.”

This hybrid model, which allows enterprises to use both Web2 and Web3 advantages in tandem, underpins what Tjiputra sees as the longer-term ambition for the much-hyped next iteration of the internet.

“Our vision is a truly peer-to-peer foundation for a people-powered internet, one that minimizes single points of failure through multi-region, multi-operator design,” says Tjiputra. “By distributing compute and storage across contributors, we gain both cost efficiency and end-to-end security by default.

“Ideally, we want to evolve the internet toward a more people-powered model, but we’re not there yet. We’re still at the starting point and growing.”

Indeed, Web3 isn’t quite snapping at the heels of the world’s Web2 giants, but its commercial advantages in an era of AI have become much harder to ignore. And with DePIN bridging the gap, enterprises and developers can step into that potential while keeping one foot on surer ground.

To learn more from AIOZ Network, you can read the AIOZ Network Vision Paper.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Europe’s drone-filled vision for the future of war

Last spring, 3,000 British soldiers deployed an invisible automated intelligence network, known as a “digital targeting web,” as part of a NATO exercise called Hedgehog in the damp forests of Estonia’s eastern territories.

The system had been cobbled together over the course of four months—an astonishing pace for weapons development, which is usually measured in years. Its purpose is to connect everything that looks for targets—“sensors,” in military lingo—and everything that fires on them (“shooters”) to a single, shared wireless electronic brain.

Eighty years after total war last transformed the continent, the Hedgehog tests signal a brutal new calculus of European defense. But leaning too much on this new mathematics of warfare could be a risky bet. Read the full story.

—Arthur Holland Michel

This story is from the next print issue of MIT Technology Review magazine. If you haven’t already, subscribe now to receive it once it lands.

MIT Technology Review Narrated: How one controversial startup hopes to cool the planet

Stardust Solutions believes that it can solve climate change—for a price.

The Israel-based geoengineering startup has said it expects nations will soon pay it more than a billion dollars a year to launch specially equipped aircraft into the stratosphere. Once they’ve reached the necessary altitude, those planes will disperse particles engineered to reflect away enough sunlight to cool down the planet, purportedly without causing environmental side effects. 

But numerous solar geoengineering researchers are skeptical that Stardust will line up the customers it needs to carry out a global deployment in the next decade. They’re also highly critical of the idea of a private company setting the global temperature for us.

This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Amazon has been accused of listing products without retailers’ consent
Small shop owners claim Amazon’s AI tool sold their goods without their permission. (Bloomberg $)
+ It also listed products the shops didn’t actually have in stock. (CNBC)
+ A new feature called “Shop Direct” appears to be to blame. (Insider $)

2 Data centers are a political issue 
Opposition to them is uniting communities across the political divide. (WP $)
+ Power-grid operators have suggested the centers power down at certain times. (WSJ $)
+ The data center boom in the desert. (MIT Technology Review)

3 Things are looking up for the nuclear power industry
The Trump administration is pumping money into it—but success is not guaranteed. (NYT $)
+ Why the grid relies on nuclear reactors in the winter. (MIT Technology Review)

4 A new form of climate modelling pins blame on specific companies
It may not be too long until we see the first case of how attribution science holds up in court. (New Scientist $)
+ Google, Amazon and the problem with Big Tech’s climate claims. (MIT Technology Review)

5 Meta has paused the launch of its Ray-Ban smartglasses 🕶
They’re just too darn popular, apparently. (Engadget)
+ Europe and Canada will just have to wait. (Gizmodo)
+ It’s blaming supply shortages and “unprecedented” demand. (Insider $)

6 Sperm contains information about a father’s fitness and diet
New research is shedding light on how we think about heredity. (Quanta Magazine)

7 Meta is selling online gambling ads in countries where it’s illegal
It’s ignoring local laws across Asia and the Middle East. (Rest of World)

8 AI isn’t always trying to steal your job
Sometimes it makes your toy robot a better companion. (The Verge)
+ How cuddly robots could change dementia care. (MIT Technology Review)

9 How to lock down a job at one of tech’s biggest companies
You’re more likely to be accepted into Harvard, apparently. (Fast Company $)

10 Millennials are falling out of love with the internet
Is a better future still possible? (Vox)
+ How to fix the internet. (MIT Technology Review)

Quote of the day

“I want to keep up with the latest doom.”

—Author Margaret Atwood explains why she doomscrolls to Wired.

One more thing

Inside the decades-long fight over Yahoo’s misdeeds in China

When you think of Big Tech these days, Yahoo is probably not top of mind. But for Chinese dissident Xu Wanping, the company still looms large—and has for nearly two decades.

In 2005, Xu was arrested for signing online petitions relating to anti-Japanese protests. He didn’t use his real name, but he did use his Yahoo email address. Yahoo China violated its users’ trust—providing information on certain email accounts to Chinese law enforcement, which in turn allowed the government to identify and arrest some users.

Xu was one of them; he would serve nine years in prison. Now, he and five other Chinese former political prisoners are suing Yahoo and a slate of co-defendants—not because of the company’s information-sharing (which was the focus of an earlier lawsuit filed by other plaintiffs), but rather because of what came after. Read the full story.

—Eileen Guo

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ It’s time to celebrate the life and legacy of Cecilia Giménez Zueco, the legendary Spanish amateur painter whose botched fresco restoration reached viral fame in 2012.
+ If you’re a sci-fi literature fan, there’s plenty of new releases to look forward to in 2026.
+ Last week’s wolf supermoon was a sight to behold.
+ This Mississippi restaurant is putting its giant lazy Susan to good use.

Read more

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

I am writing this because one of my editors woke up in the middle of the night and scribbled on a bedside notepad: “What is a parameter?” Unlike a lot of thoughts that hit at 4 a.m., it’s a really good question—one that goes right to the heart of how large language models work. And I’m not just saying that because he’s my boss. (Hi, Boss!)

A large language model’s parameters are often said to be the dials and levers that control how it behaves. Think of a planet-size pinball machine that sends its balls pinging from one end to the other via billions of paddles and bumpers set just so. Tweak those settings and the balls will behave in a different way.  

OpenAI’s GPT-3, released in 2020, had 175 billion parameters. Google DeepMind’s latest LLM, Gemini 3, may have at least a trillion—some think it’s probably more like 7 trillion—but the company isn’t saying. (With competition now fierce, AI firms no longer share information about how their models are built.)

But the basics of what parameters are and how they make LLMs do the remarkable things that they do are the same across different models. Ever wondered what makes an LLM really tick—what’s behind the colorful pinball-machine metaphors? Let’s dive in.  

What is a parameter?

Think back to middle school algebra, like 2a + b. Those letters are parameters: Assign them values and you get a result. In math or coding, parameters are used to set limits or determine output. The parameters inside LLMs work in a similar way, just on a mind-boggling scale. 

How are they assigned their values?

Short answer: an algorithm. When a model is trained, each parameter is set to a random value. The training process then involves an iterative series of calculations (known as training steps) that update those values. In the early stages of training, a model will make errors. The training algorithm looks at each error and goes back through the model, tweaking the value of each of the model’s many parameters so that next time that error is smaller. This happens over and over again until the model behaves in the way its makers want it to. At that point, training stops and the values of the model’s parameters are fixed.

Sounds straightforward …

In theory! In practice, because LLMs are trained on so much data and contain so many parameters, training them requires a huge number of steps and an eye-watering amount of computation. During training, the 175 billion parameters inside a medium-size LLM like GPT-3 will each get updated tens of thousands of times. In total, that adds up to quadrillions (a number with 15 zeros) of individual calculations. That’s why training an LLM takes so much energy. We’re talking about thousands of specialized high-speed computers running nonstop for months.

Oof. What are all these parameters for, exactly?

There are three different types of parameters inside an LLM that get their values assigned through training: embeddings, weights, and biases. Let’s take each of those in turn.

Okay! So, what are embeddings?

An embedding is the mathematical representation of a word (or part of a word, known as a token) in an LLM’s vocabulary. An LLM’s vocabulary, which might contain up to a few hundred thousand unique tokens, is set by its designers before training starts. But there’s no meaning attached to those words. That comes during training.  

When a model is trained, each word in its vocabulary is assigned a numerical value that captures the meaning of that word in relation to all the other words, based on how the word appears in countless examples across the model’s training data.

Each word gets replaced by a kind of code?

Yeah. But there’s a bit more to it. The numerical value—the embedding—that represents each word is in fact a list of numbers, with each number in the list representing a different facet of meaning that the model has extracted from its training data. The length of this list of numbers is another thing that LLM designers can specify before an LLM is trained. A common size is 4,096.

Every word inside an LLM is represented by a list of 4,096 numbers?  

Yup, that’s an embedding. And each of those numbers is tweaked during training. An LLM with embeddings that are 4,096 numbers long is said to have 4,096 dimensions.

Why 4,096?

It might look like a strange number. But LLMs (like anything that runs on a computer chip) work best with powers of two—2, 4, 8, 16, 32, 64, and so on. LLM engineers have found that 4,096 is a power of two that hits a sweet spot between capability and efficiency. Models with fewer dimensions are less capable; models with more dimensions are too expensive or slow to train and run. 

Using more numbers allows the LLM to capture very fine-grained information about how a word is used in many different contexts, what subtle connotations it might have, how it relates to other words, and so on.

Back in February, OpenAI released GPT-4.5, the firm’s largest LLM yet (some estimates have put its parameter count at more than 10 trillion). Nick Ryder, a research scientist at OpenAI who worked on the model, told me at the time that bigger models can work with extra information, like emotional cues, such as when a speaker’s words signal hostility: “All of these subtle patterns that come through a human conversation—those are the bits that these larger and larger models will pick up on.”

The upshot is that all the words inside an LLM get encoded into a high-dimensional space. Picture thousands of words floating in the air around you. Words that are closer together have similar meanings. For example, “table” and “chair” will be closer to each other than they are to “astronaut,” which is close to “moon” and “Musk.” Way off in the distance you can see “prestidigitation.” It’s a little like that, but instead of being related to each other across three dimensions, the words inside an LLM are related across 4,096 dimensions.

Yikes.

It’s dizzying stuff. In effect, an LLM compresses the entire internet into a single monumental mathematical structure that encodes an unfathomable amount of interconnected information. It’s both why LLMs can do astonishing things and why they’re impossible to fully understand.    

Okay. So that’s embeddings. What about weights?

A weight is a parameter that represents the strength of a connection between different parts of a model—and one of the most common types of dial for tuning a model’s behavior. Weights are used when an LLM processes text.

When an LLM reads a sentence (or a book chapter), it first looks up the embeddings for all the words and then passes those embeddings through a series of neural networks, known as transformers, that are designed to process sequences of data (like text) all at once. Every word in the sentence gets processed in relation to every other word.

This is where weights come in. An embedding represents the meaning of a word without context. When a word appears in a specific sentence, transformers use weights to process the meaning of that word in that new context. (In practice, this involves multiplying each embedding by the weights for all other words.)

And biases?

Biases are another type of dial that complement the effects of the weights. Weights set the thresholds at which different parts of a model fire (and thus pass data on to the next part). Biases are used to adjust those thresholds so that an embedding can trigger activity even when its value is low. (Biases are values that are added to an embedding rather than multiplied with it.) 

By shifting the thresholds at which parts of a model fire, biases allow the model to pick up information that might otherwise be missed. Imagine you’re trying to hear what somebody is saying in a noisy room. Weights would amplify the loudest voices the most; biases are like a knob on a listening device that pushes quieter voices up in the mix. 

Here’s the TL;DR: Weights and biases are two different ways that an LLM extracts as much information as it can out of the text it is given. And both types of parameters are adjusted over and over again during training to make sure they do this. 

Okay. What about neurons? Are they a type of parameter too? 

No, neurons are more a way to organize all this math—containers for the weights and biases, strung together by a web of pathways between them. It’s all very loosely inspired by biological neurons inside animal brains, with signals from one neuron triggering new signals from the next and so on. 

Each neuron in a model holds a single bias and weights for every one of the model’s dimensions. In other words, if a model has 4,096 dimensions—and therefore its embeddings are lists of 4,096 numbers—then each of the neurons in that model will hold one bias and 4,096 weights. 

Neurons are arranged in layers. In most LLMs, each neuron in one layer is connected to every neuron in the layer above. A 175-billion-parameter model like GPT-3 might have around 100 layers with a few tens of thousands of neurons in each layer. And each neuron is running tens of thousands of computations at a time. 

Dizzy again. That’s a lot of math.

That’s a lot of math.

And how does all of that fit together? How does an LLM take a bunch of words and decide what words to give back?

When an LLM processes a piece of text, the numerical representation of that text—the embedding—gets passed through multiple layers of the model. In each layer, the value of the embedding (that list of 4,096 numbers) gets updated many times by a series of computations involving the model’s weights and biases (attached to the neurons) until it gets to the final layer.

The idea is that all the meaning and nuance and context of that input text is captured by the final value of the embedding after it has gone through a mind-boggling series of computations. That value is then used to calculate the next word that the LLM should spit out. 

It won’t be a surprise that this is more complicated than it sounds: The model in fact calculates, for every word in its vocabulary, how likely that word is to come next and ranks the results. It then picks the top word. (Kind of. See below …) 

That word is appended to the previous block of text, and the whole process repeats until the LLM calculates that the most likely next word to spit out is one that signals the end of its output. 

That’s it?  

Sure. Well …

Go on.

LLM designers can also specify a handful of other parameters, known as hyperparameters. The main ones are called temperature, top-p, and top-k.

You’re making this up.

Temperature is a parameter that acts as a kind of creativity dial. It influences the model’s choice of what word comes next. I just said that the model ranks the words in its vocabulary and picks the top one. But the temperature parameter can be used to push the model to choose the most probable next word, making its output more factual and relevant, or a less probable word, making the output more surprising and less robotic. 

Top-p and top-k are two more dials that control the model’s choice of next words. They are settings that force the model to pick a word at random from a pool of most probable words instead of the top word. These parameters affect how the model comes across—quirky and creative versus trustworthy and dull.   

One last question! There has been a lot of buzz about small models that can outperform big models. How does a small model do more with fewer parameters?

That’s one of the hottest questions in AI right now. There are a lot of different ways it can happen. Researchers have found that the amount of training data makes a huge difference. First you need to make sure the model sees enough data: An LLM trained on too little text won’t make the most of all its parameters, and a smaller model trained on the same amount of data could outperform it. 

Another trick researchers have hit on is overtraining. Showing models far more data than previously thought necessary seems to make them perform better. The result is that a small model trained on a lot of data can outperform a larger model trained on less data. Take Meta’s Llama LLMs. The 70-billion-parameter Llama 2 was trained on around 2 trillion words of text; the 8-billion-parameter Llama 3 was trained on around 15 trillion words of text. The far smaller Llama 3 is the better model. 

A third technique, known as distillation, uses a larger model to train a smaller one. The smaller model is trained not only on the raw training data but also on the outputs of the larger model’s internal computations. The idea is that the hard-won lessons encoded in the parameters of the larger model trickle down into the parameters of the smaller model, giving it a boost. 

In fact, the days of single monolithic models may be over. Even the largest models on the market, like OpenAI’s GPT-5 and Google DeepMind’s Gemini 3, can be thought of as several small models in a trench coat. Using a technique called “mixture of experts,” large models can turn on just the parts of themselves (the “experts”) that are required to process a specific piece of text. This combines the abilities of a large model with the speed and lower power consumption of a small one.

But that’s not the end of it. Researchers are still figuring out ways to get the most out of a model’s parameters. As the gains from straight-up scaling tail off, jacking up the number of parameters no longer seems to make the difference it once did. It’s not so much how many you have, but what you do with them.

Can I see one?

You want to see a parameter? Knock yourself out: Here’s an embedding.

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Nandan Nilekani can’t stop trying to push India into the future. He started nearly 30 years ago, masterminding an ongoing experiment in technological state capacity that started with Aadhaar—the world’s largest digital identity system. Aadhaar means “foundation” in Hindi, and on that bedrock Nilekani and people working with him went on to build a sprawling collection of free, interoperating online tools that add up to nothing less than a digital infrastructure for society. They cover government services, digital payments, banking, credit, and health care, offering convenience and access that would be eye-popping in wealthy countries a tenth of India’s size. In India those systems are called, collectively, “digital public infrastructure,” or DPI.

At 70 years old, Nilekani should be retired. But he has a few more ideas. India’s electrical grid is creaky and prone to failure; Nilekani wants to add a layer of digital communication to stabilize it. And then there’s his idea to expand the financial functions in DPI to the rest of the world, creating a global digital backbone for commerce that he calls the “finternet.”

“It sounds like some crazy stuff,” Nilekani says. “But I think these are all big ideas, which over the next five years will have demonstrable, material impact.” As a last act in public life, why not Aadhaarize the world?

India’s digital backbone

Today, a farmer in a village in India, hours from the nearest bank, can collect welfare payments or transfer money by simply pressing a thumb to a fingerprint scanner at the local store. Digitally authenticated copies of driver’s licenses, birth certificates, and educational records can be accessed and shared via a digital wallet that sits on your smartphone.

In big cities, where cash is less and less common (just trying to break a bill can be a major headache), mobile payments are ubiquitous, whether you’re buying a TV from a high-street retailer or a coconut from a roadside cart. There are no fees, and any payment app or bank account can send money to any other. The country’s chaotic patchwork of public and private hospitals have begun digitizing all their medical records and uploading them to a nationwide platform. On the Open Network for Digital Commerce (ONDC), people can do online shopping searches on whatever app they want, and the results show sellers from an array of other platforms, too. The idea is to liberate small merchants and consumers from the walled gardens of online shopping giants like Amazon and the domestic giant Flipkart. 

In the most populous nation on Earth—with 1.4 billion people—a large portion of the bureaucracy anyone encounters in daily life happens seamlessly and in the cloud.

At the heart of all these tools is Aadhaar. The system gives every Indian a 12-digit number that, in combination with either a fingerprint scan or an SMS code, allows access to government services, SIM cards, basic bank accounts, digital signature services, and social welfare payments. The Indian government says that since its inception in 2009, Aadhaar has saved 3.48 trillion rupees ($39.2 billion) by boosting efficiency, bypassing corrupt officials, and cutting other types of fraud. The system is controversial and imperfect—a database with 1.4 billion people in it comes with inherent security and privacy concerns. Still, in the most populous nation on Earth, a big portion of the bureaucracy anyone might encounter in daily life just happens in the cloud.

Nilekani was behind much of that innovation, marshaling an army of civil servants, tech companies, and volunteers. Now he sees it in action every day. “It reinforces that what you have done is not some abstract stuff, but real stuff for real people,” he says.

By his own admission, Nilekani is entering the twilight of his career. But it’s not over yet. He’s now “chief mentor” for the India Energy Stack (IES), a government initiative to connect the fragmented data held by companies responsible for generating, transmitting, and distributing power. India’s grids are unstable and disparate, but Nilekani hopes an Aadhaar-like move will help. IES aims to give unique digital identities not only to power plants and energy storage facilities but even to rooftop solar panels and electric vehicles. All the data attached to those things—device characteristics, energy rating certifications, usage information—will be in a common, machine-readable format and shared on the same open protocols.

Ideally, that’ll give grid operators a real-time view of energy supply and demand. And if it works, it might also make it simpler and cheaper for anyone to connect to the grid—even everyday folks selling excess power from their rooftop solar rigs, says RS Sharma, the chair of the project and Nilekani’s deputy while building Aadhaar.

Nilekani’s other side hustle is even more ambitious. His idea for a global “finternet” combines Aadhaarization with blockchains—creating digital representations called tokens for not only financial instruments like stocks or bonds but also real-world assets like houses or jewelry. Anyone from a bank to an asset manager or even a company could create and manage these tokens, but Nilekani’s team especially hopes the idea will help poor people trade their assets, or use them as loan collateral—expanding financial services to those who otherwise couldn’t access them. 

It sounds almost wild-eyed. Yet the finternet project has 30 partners across four continents. Nilekani says it’ll launch next year.

A call to service

Nilekani was born in Bengaluru, in 1955. His family was middle class and, Nilekani says, “seized with societal issues and challenges.” His upbringing was also steeped in the kind of socialism espoused by the newish nation’s first prime minister, Jawaharlal Nehru.

After studying electrical engineering at the Indian Institute of Technology, in 1981 Nilekani helped found Infosys, an information technology company that pioneered outsourcing and helped turned India into the world’s IT back office. In 1999, he was part of a government-appointed task force trying to upgrade the infrastructure and services in Bengaluru, then emerging as India’s tech capital. But Nilekani was at the time leery of being viewed as just another techno-optimist. “I didn’t want to be seen as naive enough to believe that tech could solve everything,” he says.

Nilekani holds a device to one eye
Nilekani demonstrates the biometric technology at the heart of Aadhaar, the system he spearheaded that provides a unique digital identity number to all Indians.
PALLAVA BAGLA/CORBIS/GETTY IMAGES

Seeing the scope of the problem changed his mind—sclerotic bureaucracy, endemic corruption, and financial exclusion were intractable without technological solutions. In 2008 Nilekani published a book, Imagining India: The Idea of a Renewed Nation. It was a manifesto for an India that could leapfrog into a networked future.

And it got him a job. At the time more than half the births in the country were not recorded, and up to 400 million Indians had no official identity document. Manmohan Singh, the prime minister, asked Nilekani to put into action an ill-defined plan to create a national identity card.

Nilekani’s team made a still-controversial decision to rely on biometrics. A system based on people’s fingerprints and retina scans meant nobody could sign up twice, and nobody had to carry paperwork. In terms of execution, it was like trying to achieve industrialization but skip a steam era. Deployment required a monumental data collection effort, as well as new infrastructure that could compare each new enrollment against hundreds of millions of existing records in seconds. At its peak, the Unique Identification Authority of India (UIDAI), the agency responsible for administering Aadhaar, was registering more than a million new users a day. That happened with a technical team of just about 50 developers, and in the end cost slightly less than half a billion dollars.

Buoyed by their success, Nilekani and his allies started casting around for other problems they could solve using the same digitize-the-real-world playbook. “We built more and more layers of capability,” Nilekani says, “and then this became a wider-ranging idea. More grandiose.”

While other countries were building digital backbones with full state control (as in China) or in public-private partnerships that favored profit-seeking corporate approaches (as in the US), Nilekani thought India needed something else. He wanted critical technologies in areas like identity, payments, and data sharing to be open and interoperable, not monopolized by either the state or private industry. So the tools that make up DPI use open standards and open APIs, meaning that anyone can plug into the system. No single company or institution controls access—no walled gardens.

A contested legacy

Of course, another way to look at putting financial and government services and records into giant databases is that it’s a massive risk to personal liberty. Aadhaar, in particular, has faced criticism from privacy advocates concerned about the potential for surveillance. Several high-profile data breaches of Aadhaar records held by government entities have shaken confidence in the system, most recently in 2023, when security researchers found hackers selling the records of more than 800 million Indians on the dark web.

Technically, this shouldn’t matter—an Aadhaar number ought to be useless without biometric or SMS-based authentication. It’s “a myth that this random number is a very powerful number,” says Sharma, the onetime co-lead of UIDAI. “I don’t have any example where somebody’s Aadhaar disclosure would have harmed somebody.” 

One problem is that in everyday use, Aadhaar users often bypass the biometric authentication system. To ensure that people use a genuine address at registration, Aadhaar administrators give people their numbers on an official-looking document. Indians co-opted this paperwork as a proof of identity on its own. And since the document—Indians even call it an “Aadhaar card”—doesn’t have an expiration date, it’s possible for people to get multiple valid cards with different details by changing their address or date of birth. That’s quite a loophole. In 2018 an NGO report found that 67% of people using Aadhaar to open a bank account relied on this verification document rather than digital authentication. That report was the last time anyone published data on the problem, so nobody knows how bad it is today. “Everybody’s living on anecdotes,” says Kiran Jonnalagadda, an anti-Aadhaar activist.

In other cases, flaws in Aadhaar’s biometric technology have caused people to be denied essential government services. The government downplays these risks, but again, it’s impossible to tell how serious the problem is because the UIDAI won’t disclose numbers. “There needs to be a much more honest acknowledgment, documentation, and then an examination of how those exclusions can be mitigated,” says Apar Gupta, director of the Internet Freedom Foundation.

Beyond the potential for fraud, it’s also true that the free and interoperable tools haven’t reached all the people who might find them useful, especially among India’s rural and poorer populations. Nilekani’s hopes for openness haven’t fully come to pass. Big e-commerce companies still dominate, and retail sales on ONDC have been dropping steadily since 2024, when financial incentives to participate began to taper off. The digital payments and government documentation services have hundreds of millions of users, numbers most global technology companies would love to see—but in a country as large as India, that leaves a lot of people out.

Going global

The usually calm Nilekani bristles at that criticism; he has heard it before. Detractors overlook the dysfunction that preceded these efforts, he says, and he remains convinced that technology was the only way forward. “How do you move a country of 1.4 billion people?” he asks. “There’s no other way you can fix it.”

The proof is self-evident, he says. Indians have opened more than 500 million basic bank accounts using Aadhaar; before it came into use, millions of those people had been completely unbanked. Earlier this year, India’s Unified Payments Interface overtook Visa as the world’s largest real-time payments system. “There is no way Aadhaar could have worked but for the fact that people needed this thing,” Nilekani says. “There’s no way payments would have worked without people needing it. So the voice of the people—they’re voting with their feet.”

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A street vendor in Kolkata displays a QR code that lets him get paid via India’s Unified Payments Interface, part of the digital public infrastructure Nilekani helped build. The Reserve Bank of India says more than 657 million people used the system in the financial year 2024–2025.
DEBAJYOTI CHAKRABORTY/NURPHOTO/GETTY IMAGES

That need might be present in countries beyond India. “Many countries don’t have a proper birth registration system. Many countries don’t have a payment system. Many countries don’t have a way for data to be leveraged,” Nilekani says. “So this is a very powerful idea.” It seems to be spreading. Foreign governments regularly send delegations to Bengaluru to study India’s DPI tools. The World Bank and the United Nations have tried to introduce the concept to other developing countries equally eager to bring their economies into the digital age. The Gates Foundation has established projects to promote digital infrastructure, and Nilekani has set up and funded a network of think tanks, research institutes, and other NGOs aimed at, as he says, “propagating the gospel.”

Still, he admits he might not live to see DPI go global. “There are two races,” Nilekani says. “My personal race against time and India’s race against time.” He worries that the economic potential of its vast young population—the so-called demographic dividend—could turn into a demographic disaster. Despite rapid growth, gains have been uneven. Youth unemployment remains stubbornly high—a particularly volatile problem in a large and economically turbulent country. 

“Maybe I’m a junkie,” he says. “Why the hell am I doing all this? I think I need it. I think I need to keep curious and alive and looking at the future.” But that’s the thing about building the future: It never quite arrives.

Edd Gent is a journalist based in Bengaluru, India.

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