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TL;DR

• Nillion is redefining privacy-preserving computation—a critical missing piece for the next generation of AI.

• Privacy is not longer optional. AI agents need access to sensitive data to be useful, but today’s infrastructure forces users to choose between privacy and utility.

• Blind Compute changes that. Nillion enables computation on encrypted data without ever exposing it—solving AI’s trust problem.

• The Nillion network architecture consists of two key components, each with a distinct and vital role: Coordination Layer (nilChain) & Petnet (Privacy Enhancing Technology Network)

• Multi-layered privacy. Instead of relying on a single approach, Nillion orchestrates MPC, FHE, ZKPs, and TEEs into a seamless privacy stack.

• A stacked team with serious backing, including Uber’s founding engineer and cryptography veterans, with $50M raised from top-tier investors.

• NIL’s token economics is designed for adoption. NIL will power the network, securing operations while driving burn-based demand as usage scales.

We’ve been dreaming about personal AI assistants for decades.

From Iron Man’s JARVIS managing every aspect of Tony Stark’s life (and saving it occasionally), to Star Trek’s Computer knowing exactly how hot Picard likes his Earl Grey (always hot), to Her’s Samantha—who not only organizes Theodore’s inbox but also philosophizes about existence while falling in love.

They’re reflections of a shared truth: being human is exhausting.

JARVIS doesn’t just operate Tony Stark’s suit. He schedules meetings, orders groceries, pays bills, and probably suggests that another 3 AM tinkering session isn’t the healthiest idea.

When we see that, we think: Yes. That. I need that.

We keep inventing these fictional assistants because, deep down, we all crave one. Someone—or something—that lifts the relentless mental load of modern life.

An assistant who remembers what we forget, spots patterns we miss, and maybe, just maybe, saves us from sending that 2 AM text we’ll 100% regret the next day.

The Privacy Problem

But here's where reality hits different than fiction: JARVIS knew everything about Tony Stark because, well, it was a movie.

The movie never had to confront the privacy nightmare of an AI having complete access to every corner of someone’s life.

In the real world? That’s a trillion-dollar challenge waiting for a solution.

Right now, AI agents are still toddlers in their development—shitposting on Twitter, creating anime avatars, and cracking a few jokes. But this is changing fast. We’re on the edge of something much bigger.

To genuinely help us, these AI agents need access to our most sensitive information.

Think about what JARVIS actually knew about Tony:

• Complete financial picture- every account, every investment, every secret project

• Personal relationships- from conversations with his wife, Pepper Potts, to the Avengers to his enemies

• Health data- full biometrics, medical history, that shrapnel near his heart

• Professional secrets- not just Iron Man suits, but all of Stark Industries' IP

Now imagine handing over that level of access to an AI agent today. Would you be comfortable with it? This isn’t some hypothetical wishlist—it’s the type of data AI needs to truly become our digital representative.

An AI financial advisor that doesn’t know your full financial picture is just a glorified calculator.

An AI health assistant without your complete medical history? WebMD with better UI.

For AI to cross from novelty to necessity, we have to solve the privacy puzzle.

The Impossible Choice

Today’s AI infrastructure generally forces us into a dilemma:

1. Give Up Privacy for Utility

• Surrender all your data to centralized AI companies

• Trust they’ll keep it safe (cross your fingers)

• Accept that your data might be used for training models

2. Keep Privacy but Sacrifice Utility

• Stick to local, limited AI models

• Miss out on advanced features

• Settle for mediocre performance

Take the example of an AI health assistant for cancer patients.

To provide meaningful support, it needs access to your complete medical history, genetic test results, current treatment plans, medication schedules, side effect reports, and even your private conversations about how you're really feeling.

Without robust privacy guarantees, you're essentially broadcasting your most intimate health struggles to unknown servers and employees.

Imagine your most vulnerable moments—your fears, your pain, your treatment decisions—processed in the clear on a company’s infrastructure.

The real issue is how AI systems handle data.

When you use tools like ChatGPT or Claude, here’s what happens:

Your data gets sent to their servers → processed in the clear → and potentially stored for future model training.

Yes, they have privacy policies and security measures. But at the end of the day, you’re still relying on trust.

That might have been fine when AI was just writing emails or generating cute cat images. But as AI gets more personal—and more powerful—this privacy crisis becomes existential.

The Need for a New Paradigm

The solution isn’t just better encryption or stricter privacy policies. That’s like putting locks on a house with no walls.

We need a fundamental reimagining of how AI systems handle sensitive data. We need infrastructure that:

• Enables computation on encrypted data without exposing it

• Provides cryptographic privacy guarantees

• Decentralizes trust so no single entity holds all the power

The next wave of AI won’t just be about smarter models or faster performance. It will be about trust.

Can we build systems that are powerful enough to handle our most sensitive data and trustworthy enough for us to share it?

Can we create the privacy infrastructure to make a real-life JARVIS possible?

These questions will define the next chapter of AI development.

And that’s where Nillion’s story begins.

Nillion = Blind Compute

Nillion is creating an entirely new paradigm called "Blind Compute."

With this, you could have all the power of advanced AI systems without ever exposing your sensitive data. Not just encrypting storage, but actual computation on encrypted data that remains private from end to end—even from the infrastructure processing it.

But to understand why this is revolutionary, we need to understand where current privacy solutions fall short.

Beyond Traditional Privacy

Many existing privacy solutions are like locking your data in a vault—it’s secure at rest, but the moment you want to use it, you have to take it out.

Every time you compute something, you expose the data. It’s like carrying sensitive documents through a crowded street every time you need to work with them.

This “decrypt-compute-encrypt” process creates windows of vulnerability that sophisticated attackers can exploit. Even with strong encryption, your data is exposed during computation, making it susceptible to memory scraping, side-channel attacks, and other exploits.

Nillion’s Blind Compute enables computation while data remains encrypted.

The Power of Orchestration

The current privacy protocol landscape is fragmented and specialized. There’s no shortage of solutions but each comes with its own limitations:

• Multi-Party Computation (MPC) excels at secure distributed computation but struggles to scale.

• Fully Homomorphic Encryption (FHE) enables computation on encrypted data but is slow and expensive.

• Zero-Knowledge Proofs (ZKP) offer powerful verification without data exposure but are limited by compute capacity and bandwidth

• Trusted Execution Environments (TEE) provide practical security but require hardware trust assumptions.

Most projects choose one approach and optimize around its strengths while accepting its limitations. It’s like choosing a single instrument and trying to perform an entire symphony.

Nillion doesn’t settle for a solo performance. It conducts a full orchestra.

Together, these Privacy Enhancing Technologies (PETs) harmonize to create a privacy solution greater than the sum of its parts.

Let’s break it down and explore how Nillion achieves this, technically.

Under The Hood— Network Architecture

The Nillion network architecture consists of two key components, each with a distinct and vital role:

• Coordination Layer (nilChain)

• Petnet (Privacy Enhancing Technology Network)

Together, they enable Blind Computation.

#1 — The Coordination Layer

Built on Cosmos SDK, the Coordination Layer, called nilChain, acts as the network’s control center. It doesn’t process private data or run computations. Instead, it ensures the network functions efficiently and securely, much like a high-performance operating system managing processes and resource allocation.

Why Cosmos SDK?

The choice of going with Cosmos SDK makes sense because of its modular architecture, high scalability, and interoperability. Cosmos is designed for sovereignty, allowing nilChain to operate independently while still benefiting from cross-chain compatibility through Inter-Blockchain Communication (IBC).

In practical terms, this means Nillion can interact with other blockchain ecosystems securely and efficiently without relying on traditional bridges, which are often security risks.

What nilChain Does

Despite being built on Cosmos, nilChain is intentionally lightweight. It doesn’t store or process any sensitive data. Instead, it focuses on:

• Payment Processing – Each time someone uses Nillion’s compute services, nilChain handles the economic logic—like a decentralized billing system.

• Rewards & Staking – Node operators stake tokens to participate; in return, they receive fees for honest work. Malicious nodes risk losing their stake.

• Network Security – nilChain also keeps a global ledger of node stakes, reputations, and tasks.

• Orchestration – nilChain coordinates which clusters of nodes handle specific computations, ensuring load balancing and matching tasks to nodes with the right capabilities (e.g., a cluster specialized in large-scale MPC).

This minimalist approach is crucial because it allows the Coordination Layer to focus entirely on orchestration while leaving the complex privacy-preserving computation to the Petnet.

As of now, nilChain is running in a testnet environment, with the mainnet expected to go live this month.

#2 — The Petnet

While the Coordination Layer manages the network's operations, the Petnet (Privacy Enhancing Technology Network) is where Nillion's real magic happens.

This is a network of specialized privacy processors, each contributing to a larger system that can compute on encrypted data without ever seeing the actual information.

Petnet combines different privacy technologies through a system of specialized clusters. Instead of forcing every node to handle every type of computation, nodes are grouped into clusters that specialize in specific tasks.

The Cluster Architecture: Specialization at Scale

This clustering approach solves a fundamental challenge in privacy-preserving computation: balancing security and performance.

Some computations need maximum security and can tolerate slower speeds, while others require lightning-fast processing for real-time applications. By letting clusters specialize, Nillion achieves both without compromise.

Think of these clusters as specialized departments in a company. Just as you wouldn't want your accounting team handling product design, you don't want every node trying to handle every type of computation. Some clusters might focus on:

• Maximum security for highly sensitive data

• Real-time processing for time-critical applications

• Optimal performance for specific types of calculations

So What Are These Privacy Technologies?

The Petnet combines three cutting-edge Privacy Enhancing Technologies (PETs) to deliver next-level security and efficiency.

1. Multi-Party Computation (MPC)

MPC enables multiple parties to jointly compute a function while keeping their individual inputs private. Nillion’s custom MPC protocol is designed to eliminate the traditional communication overhead that makes classic MPC slow.

• How It Works – Instead of one entity handling sensitive data, multiple nodes hold pieces of encrypted information (called “particles”). Each node processes only its share of the data, ensuring no single entity can reconstruct the original input.

• Secret Sharing & Blinding – Private input data is split into encrypted shares using Shamir’s Secret Sharing and one-time masking, which prevents unauthorized access.

• Local Computation Without Interaction – Nodes compute independently without communicating with each other, preventing data leaks during the process.

• Reconstruction & Verification – Once the computation is complete, nodes combine their result shares to reconstruct the final output, ensuring correctness without exposing sensitive inputs.

By eliminating communication during computation, Nillion’s MPC achieves speed and scalability that were previously considered impractical.

2. Fully Homomorphic Encryption (FHE)

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without ever decrypting it. While FHE is computationally expensive, Nillion selectively integrates FHE where it provides the most value.

• Why FHE? – Unlike MPC, which splits data among nodes, FHE enables direct computation on encrypted data without the need for secret-sharing.

• FHE as a Complementary Module – Nillion uses FHE for cases where secret sharing isn’t feasible, such as computations on financial transactions that must remain encrypted end-to-end.

• Hybrid MPC + FHE – In some instances, FHE is combined with MPC, allowing the system to encrypt data before it’s shared across nodes. This ensures data remains encrypted at every stage of processing.

The network also integrates preprocessing techniques to reduce the complexity of homomorphic operations, making FHE viable for practical deployments.

3. Trusted Execution Environments (TEEs)

TEEs provide hardware-based isolation for executing sensitive computations securely within a processor. Nillion leverages TEEs selectively via its nilTEE framework.

• How TEEs Work – TEEs are secure areas within a processor that run code in isolation, preventing sensitive data from being exposed to the broader system.

• AI Model Execution – TEEs enable secure AI model inference without exposing model weights or input data.

• Threshold Signing & Authentication – Used for secure cryptographic signing and identity verification.

• Short-Lived Secure Computation – Nillion uses TEEs only for performance-critical tasks, ensuring that long-term data protection remains decentralized.

By integrating TEEs with MPC and FHE, Nillion achieves a balance between performance, security, and decentralization, ensuring that computations remain private while delivering high-speed results.

(We wrote more about on TEEs in our essay on Atoma Network)

Making Privacy Practical: The Developer Stack

All this sophisticated privacy tech would be useless if developers couldn’t easily work with it.

This is why Nillion has created a suite of developer tools that abstract away the complexity of privacy-preserving computation.

Here's what the toolkit currently includes:

nilVM:

A privacy-first virtual machine that transforms how developers write secure applications.

Using a Python-based language called Nada, developers can write code that feels just like normal programming—but behind the scenes, it compiles into privacy-preserving operations. Think of it as a universal translator that converts regular instructions into secure multi-party computations without the developer needing to worry about cryptography.

The VM supports both basic operations and more complex tasks like threshold signing of messages, making it particularly valuable for financial applications that need to maintain privacy while processing sensitive data.

The module can be accessed with the SecretSigning SDK that leverages nilVM’s cryptographic functions for threshold signing and authentication workflows.

nilDB:

nilDB makes working with encrypted data as simple as using a regular database. Unlike traditional systems where data must be decrypted to query or analyze it, nilDB keeps everything encrypted while still allowing complex operations.

It uses a combination of secret sharing and MPC to split data across multiple nodes, ensuring no single node ever sees complete information.

Devs can write familiar SQL-like queries, and nilDB automatically handles all the privacy-preserving operations in the background.

This makes it perfect for applications like healthcare systems that need to analyze patient data while maintaining strict privacy compliance, or financial services that need to detect fraud patterns without exposing individual transaction details.

The system even supports secure multi-party analytics, allowing organizations to gain insights from combined datasets without revealing their underlying data to each other. This can be accessed through these SDKs:

• SecretVault: A vault-like service for securely uploading and managing encrypted data.

• SecretDataAnalytics: Allows authorized queries or analytics on encrypted datasets without exposing the underlying information.

nilAI:

A suite of AI-focused privacy technologies that makes private machine learning practical. It comes with three key components:

• AIVM (AI Virtual Machine): Built on Nillion's MPC technology and integrated with Meta's CrypTen framework, it enables AI models to process data while keeping both the model and the data private.

• nada-AI: Provides a PyTorch-like interface that makes it easy for developers to work with privacy-preserving AI models. You can train neural networks, run inference, and handle machine learning tasks using familiar patterns.

• nilTEE: Uses Trusted Execution Environments for secure AI processing, particularly useful for running large language models privately.

The system accelerates encrypted AI operations using techniques like Discrete Wavelet Transform (co-researched with Meta's AI Research team). This makes it possible to run complex AI workloads with practical performance - from personalized AI assistants that keep user data private to enterprise AI systems that need to maintain data confidentiality while delivering real-time results.

From Request to Result: The End-to-End Flow

Now that we’ve seen how nilChain, the Petnet, and the Blind Modules fit together, let’s look at a typical user interaction—from storing data to running an AI model.

Here’s the short version of what happens:

1. User Initiation
   A user or app interacts with Nillion’s SecretSDK or API (e.g., “store this file securely” or “run this function on my data”).
   

2. Client-Side Preparation
   The SDK encrypts or secret-shares the data so that no single party can view it in full. This might involve MPC shares or enclaving data for TEE processing.
   

3. Coordination via nilChain
   The request goes to nilChain, which logs it, handles payment in $NIL, and assigns the task to a specialized cluster in the Petnet. Different clusters may focus on storage (nilDB), computation (nilVM), or AI inference (nilAI).
   

4. Blind Execution in Petnet

   • nilDB (Storage): Data shares are distributed across the cluster’s nodes.

   • nilVM (Computation): Nodes run an MPC protocol on their data shares—no single node sees the entire input.

   • nilAI (AI Inference): TEEs or hybrid FHE/MPC approaches ensure even the node operator can’t see the raw data or model weights.

   During this phase, each cluster node processes only encrypted or masked data.
   

5. Result & Settlement
   

   • The cluster finalizes the result (e.g., storing shares, returning a computation outcome, or sending an encrypted AI response).

   • nilChain confirms completion, releases payments to participating nodes, and handles any slashing if nodes behaved maliciously. The user finally decrypts the result locally if needed.

This workflow delivers end-to-end privacy and verifiability, with nilChain orchestrating tasks and the Petnet executing them securely behind the scenes.

The Nillion Tech in Action

Nillion's technology is already processing significant workloads and gaining serious developer traction. Thankfully, it’s not just theory.

Developer adoption is rapidly growing, with the Nillion SDKs being downloaded externally 961 times in just two months — an early signal of the platform's emerging developer ecosystem.

By providing a foundation for privacy-preserving computation and storage, Nillion is unlocking entirely new categories of products and services across multiple sectors.

Here are a few examples:

Private AI Agents

As mentioned earlier, the next wave of AI agents will need to handle increasingly sensitive data to be truly useful. Nillion's blind computation infrastructure enables AI agents to process private information without exposing it:

• Skillful AI has implemented private Retrieval Augmented Generation (RAG) using nilVM, allowing users to leverage sensitive documents while minimizing data exposure during inference

• Rainfall built a self-sovereign AI platform focused on collective social intelligence while protecting user privacy through secure weight aggregation

• Verida is developing a personalized AI chatbot trained on sensitive user messages, using nilDB for secure storage

This shift from entertainment-focused AI agents to utility-focused ones often requires bulletproof privacy guarantees that only blind computation can provide. As AI agents evolve to handle investments, governance, and negotiations, Nillion's infrastructure becomes essential.

Healthcare Applications

Healthcare is another massive opportunity for blind computation, with the AI healthcare data market projected to grow by 524% from $32.3 billion to $208.2 billion between 2024 and 2030.

• Secure Research Collaboration: Healthcare institutions can analyze patient data across regions without exposing sensitive information

• Privacy-Preserving Diagnostics: AI-based diagnostic tools can process patient data while maintaining complete confidentiality

• Compliant Data Sharing: Enables collaboration while meeting strict healthcare privacy regulations

HealthBlocks demonstrates this potential, allowing users to collect data from different sources while maintaining ownership and control, then using Nillion to generate insights for third parties without leaking specific details.

DeFi & Sensitive Data

Currently, DeFi’s transparency exposes traders to MEV attacks, front-running, and market manipulation.

Kayra, a decentralized dark pool DEX, solves this by using Nillion’s blind computation for private, institution-grade trading.

• Stealth Order Book: Orders are encrypted and processed privately, preventing market impact.

• Blind Computation Matching (nilVM): Orders are submitted in encrypted form and processed using Nillion’s MPC nodes. Each node computes on fragments of the data without ever seeing the full order, ensuring confidentiality. When a match is found, only the final trade details are decrypted for settlement, preventing leaks at every stage.

• MEV & Front-Running Protection: Orders remain private until execution, ensuring fair pricing.

Using Nillion, Kayra offers trustless privacy, making DeFi viable for institutions and large traders. If successful, it could be game-changing for private trading on-chain.

Other apps using Nillion for DeFi include:

• ChooseK is developing an order book platform enabling private DeFi products

• Kagami created a trading policy engine in nilVM for encrypted financial policies

With the World Economic Forum estimating $867 trillion potentially flowing into DeFi, privacy-preserving infrastructure becomes critical for institutional adoption.

Blockchain Integrations

Nillion has also established strategic partnerships with major blockchain platforms to make blind computation accessible across these chains:

• Arbitrum: Enhanced privacy tools for Ethereum scaling

• NEAR Protocol: Privacy-preserving application development

• Aptos: Infrastructure for privacy-focused apps

• Sei: Native integration for secure computation

These integrations allow smart contracts and users to leverage blind computations directly from their preferred chains using native gas tokens- no new wallets required.

Key Partnerships Driving Adoption

Tech is meaningless if no one uses it.

Strategic partnerships are accelerating Nillion's adoption across multiple sectors, with major collaborations that showcase the platform's versatility and potential.

The collaboration with Meta’s AI Research team has led to breakthrough advancements in privacy-preserving AI computation, while partnerships with Virtuals Protocol for AI agent co-ownership and Ritual for decentralized AI inference demonstrate Nillion’s ability to push boundaries in emerging fields.

Combined with their strong technical foundations and growing ecosystem adoption, these strategic alignments position Nillion to capture significant value as privacy-preserving computation becomes essential infrastructure.

NIL Tokenomics

The NIL token sits at the heart of Nillion's blind compute network, serving as both the network's utility token and governance mechanism.

With a total supply of 1 billion tokens, NIL is designed to align incentives across all network participants while enabling sustainable growth of the ecosystem.

Note: NIL is not live yet at time of writing

Supply-Side: Allocation & Unlocks

Token Allocation

A large chunk of the token supply (45%) is going towards community and R&D to keep improving the tech, showing a focus on sustainable growth rather than short-term gains.

The protocol has also reserved 7.5% of the total supply (75 million NIL) for a genesis airdrop to early supporters and builders, targeting those who've made meaningful contributions to the network's development.

Unlock Schedule

The token release will follow a measured unlock schedule:

• Initial circulating supply of ~13.9% (139.6M tokens)

• Major unlock events begins 6 months after the genesis, bringing supply to ~30%

• Gradual increase to ~48% by the 12th month post-TGE

• Long-term linear vesting for team and ecosystem allocations

The schedule is fairly aggressive in the 1st year: after six months of no new tokens, supply jumps from about 14% to over 30%, then climbs to nearly 48% by the 12th month. Since most of these first-year unlocks come from investor and team allocations (always a bit controversial on CT…), it’s crucial for the team and community to drive strong network usage and create enough token demand to absorb the increased supply.

Demand-Side: Utility & Governance

NIL plays a central role in securing the network and providing access to its services:

1. Network Access

   • Compute & Storage Operations: In the initial stage, users burn NIL tokens based on dollar-denominated credits system. All payments for Petnet operations are currently 100% burned.

   • Module-Specific Pricing:

     • nilVM: Flat-rate burning for compute/storage operations, with option to pre-burn tokens for Credits on specific clusters to reduce latency

     • nilDB & nilAI: Subscription-based model requiring monthly NIL burns for API access

   • Predictable Costs: Operations are priced in dollar-denominated, non-transferable Credits to ensure cost stability for enterprises

2. Node Staking

   • Validators & Petnet Clusters: Node operators stake NIL to secure their roles and can face slashing for malicious behavior. As NIL’s value grows, the network theoretically becomes more secure through higher economic penalties for wrongdoing.

3. Governance & Resource Allocation

   • Proposal & Voting: NIL holders influence protocol upgrades, fee structures, and ecosystem funding.

   • Resource Deployment: Governance sets parameters on how community resources are allocated.

How It All Comes Together: Supply vs. Demand

In the early stages, 100% of all Petnet operation fees are burned, creating a significant token sink as network usage grows.

Operational fees are set through governance decisions, with plans to transition to a permissionless, cluster-based pricing model in the future.

This combination of predictable, dollar-denominated costs and complete token burns creates a clear and powerful connection between network adoption and token value. Exactly what we like to see in a token design.

Nillion’s tokenomics are built for the long game. The system is designed to thrive as network adoption scales, relying on three key drivers of sustainability:

• Usage → Value: As network activity grows, more tokens are burned, driving NIL’s price higher.

• Staking → Security: A rising token value incentivizes node operators to stake more tokens, increasing the network’s security and resilience.

• Governance → Alignment: Token holders have a direct role in shaping network policy, ensuring that those with a vested interest guide its development and long-term success.

If the network captures even a fraction of the $270 billion cloud computation and storage market, token burns from network usage could quickly outpace even the most aggressive unlock schedules—fueling long-term value creation for the NIL token.

Team & Fundraising

Nillion has assembled a truly world-class team that combines deep expertise across cryptography, distributed systems, and product development.

The leadership team brings together accomplished veterans from both Web2 and Web3:

• Alex Page (CEO) comes from a traditional finance background, having worked as a General Partner at Hedera SPV and as a banker at Goldman Sachs.

• Andrew Masanto (CSO) is a serial entrepreneur with multiple exists, and brings valuable ecosystem-building experience as the previous co-Founder of Hedera and Founding CMO of Reserve.

The technical foundation is anchored by several key leaders:

• Dr. Miguel de Vega serves as Chief Scientist, bringing deep cryptographic expertise with 30 patents in data optimization. His research background is crucial for Nillion's novel approaches to privacy-enhancing technologies.

• Conrad Whelan, as founding CTO, leverages his experience as a founding Engineer at Uber to build scalable distributed systems. His track record of architecting systems that serve millions of users is particularly relevant as Nillion aims to become core infrastructure.

See what Conrad posted on his medium page, almost 3 years ago. 👀

Bottom line? We can tell that the team is pretty stacked and have been building for many years, together. All signs point to big things ahead.

On the funding front, Nillion has secured substantial backing to execute on its vision.

They have raised $50 million across several funding rounds from prominent crypto investors, including:

• Hack VC

• Hashkey Capital

• Distributed Global

• Maelstrom

With a seasoned team and securing significant financial backing, Nillion is primed to tackle the complex (and ambitious) challenge of building privacy-preserving computation infrastructure.

Our Thoughts

1. Orchestration is the Real Moat

Nillion's approach is to focus on orchestrating multiple privacy-enhancing technologies (PETs) rather than betting everything on a single approach. This strategy is smart for two key reasons:

First, it creates optionality. Different use cases have different privacy requirements and trade-offs. Some prioritize speed over maximum security, while others need regulatory compliance or specific encryption standards. By orchestrating multiple PETs, Nillion can offer the right tool for each job rather than forcing everything through a one-size-fits-all solution.

Second, it future-proofs the platform. As new privacy technologies emerge (and they will), Nillion can integrate them into their orchestration layer without disrupting existing applications. This adaptability will be crucial as advances in quantum computing push today’s privacy techniques to evolve.

Of course, building an effective orchestration layer isn’t easy. The challenge lies in making it seamless for developers. If the experience is overly complex or clunky, there’s a real risk that Nillion’s powerful solution could end up being theoretically impressive but practically unusable.

2. Privacy as Infrastructure, Not a Feature

Your key takeaway from reading this essay should be that that Nillion is positioning privacy as fundamental infrastructure for the next generation of computing.

Think about how SSL/TLS became the default for web communication. We no longer talk about “encrypted websites” because encryption is simply how the web works. Nillion’s bet is that blind computation will follow the same trajectory.

As AI agents become more integrated into our lives and take on increasingly sensitive tasks, privacy will not longer be optional—it will be a prerequisite. Just like how no serious business would launch a website without HTTPS today, future applications won’t be able to function without built-in privacy guarantees.

Timing will be key. Enter too early, and the market might not yet feel the urgency. Enter too late, and centralized solutions could become entrenched.

Nillion’s success will depend on hitting the market at just the right moment—when privacy concerns become so acute that developers and enterprises start actively seeking solutions. If they can ride that wave, Nillion has a real shot at making privacy-preserving computation the default standard for next-gen applications.

The Privacy Renaissance

Nillion is tackling AI’s biggest hurdle: trust

By weaving together cutting-edge privacy tech into a seamless backbone for AI, Nillion has the potential to become as fundamental to AI as SSL is to the web. If Nillion can make it as easy to use as it is powerful, they’ll be powering the next wave of AI adoption.

High stakes. Massive opportunity. The privacy revolution is just beginning.

Cheers,

Teng Yan & 0xAce

Useful Links

• Nillion website

• Testnet guide

• Developer Docs

• Private LLMs research paper with Meta 

Chain of Thought received a grant from Nillion for this initiative. All insights and analysis however are our own. We uphold strict standards of objectivity in all our viewpoints.

To learn more about our approach to sponsored Deep Dives, please see our note here.

This report is intended solely for educational purposes and does not constitute financial advice. It is not an endorsement to buy or sell assets or make financial decisions. Always conduct your own research and exercise caution when making investment choices.