Key Takeaways

• AI now performs across most fields, but fails the moment it relies on fragmented, unverifiable external data.

• IoTeX has spent eight years building the unified infrastructure to close that gap, transitioning into a platform that supplies real-world data at scale through a three-layer stack of ioID, Quicksilver, and Realms.

• Trio is the first commercial product built on that stack, converting infrastructure into direct SaaS revenue through enterprise subscriptions.

• The 2026 investment thesis hinges entirely on execution: whether Trio can secure enterprise contracts and whether the Real-World AI Foundry can demonstrate production-grade model performance.

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1. AI’s Real Problem: Not Intelligence, but Trustworthy Data

In 2025, AI has exceeded expectations across nearly every field.

OpenAI’s reasoning model achieved gold-medal-level scores at the International Mathematical Olympiad (IMO). GitHub Copilot now generates an average of 46% of developer code directly. AI agents independently handle email management, scheduling, and workflow automation.

“What can AI do?” is no longer the right question.

Inside a closed system, AI is already smart enough. Siemens, GE, and similar firms already run full sensor-to-model pipelines in-house.

The problem starts the moment AI crosses that boundary.

Consider autonomous vehicles. Onboard sensors alone can enable self-driving. But a precise system requires external data including traffic signals, pedestrian detection sensors, and weather stations. Only when thousands of such sources connect in real time does a truly reliable AV system become possible. Yet these sources are operated by different agencies, use inconsistent formats, and share no common standard for verifying data origin.

Companies like Waymo and Cruise have already taken on this challenge, building systems that aggregate diverse external sensor data at scale. But their approaches rely on proprietary stacks.

Sharing data with other AV operators or city infrastructure requires additional integration work. Even on the same road, systems remain incompatible.

No matter how capable the AI, fragmented data undermines accurate decision-making.

What’s needed is integrated infrastructure. Every device gets a verifiable identity. Scattered data is aggregated and delivered in AI-ready form, then organized by domain. This is precisely what IoTeX has built.

2. IoTeX: 8 Years of Infra, Now an AI Company

IoTeX spotted this problem in 2017.

During the ICO boom, most projects chased token sales. Connecting physical devices to blockchain was a fringe idea. Before the term ‘DePIN’ even existed, IoTeX began building on one conviction: physical devices would become core blockchain participants. The term “DePIN” did not emerge until late 2022, five years later.

After eight years of sustained infrastructure development, IoTeX has completed a full-stack DePIN platform.

It combines a high-performance L1 blockchain, a device identity protocol, off-chain data verification, and hardware deployments that prove the pipeline works in the real world.

In the process, IoTeX established global partnerships with Google Cloud, Samsung Next, and Nordic Semiconductor. IoTeX also led a blockchain-IoT standards working group at IEEE, building broader industry credibility. By the end of this period, registered smart devices reached approximately 960,000 and connected DePIN projects exceeded 400.

In 2025, IoTeX officially declared its expansion into a Real-World AI platform. The goal is clear: transform eight years of accumulated device networks and data pipelines into a platform that AI can consume in real time.

3. From Data Collection to AI Inference

IoTeX’s AI tech stack consists of three layers.

1. ioID (Verify Step: Device Identity Layer) issues blockchain-based digital IDs to physical devices like sensors and robots. Trust foundation for data provenance and integrity.

2. Quicksilver (Index Step: Data Processing Layer) aggregates sensor data from hundreds of networks, processes it into AI-readable formats, and delivers it.

3. Realms (Perceive Step: Contextual Knowledge Layer) layers domain-specific knowledge onto processed data so AI can reason beyond raw numbers.

ioID secures trust. Quicksilver delivers data. Realms provide context. Operating in sequence, these three layers convert raw physical-world sensor signals into actionable AI intelligence.

3.1. ioID: Giving Every Machine and Agent a Verified Identity

For AI to use real-world data, one problem must be solved first. Does this data actually come from the claimed device, and was it tampered with in transit? Data without a verifiable source is worthless to AI, regardless of volume.

The problem intensifies as billions of IoT devices generate data autonomously and AI agents make decisions and execute transactions based on that data. A single manipulated sensor reading can trigger a wrong AI judgment, which then drives real transactions and actions. Without provable identity for each device and agent, the trust foundation of the entire system collapses.

ioID is a decentralized device identity protocol that solves this. It assigns unique identities not only to physical devices such as sensors and robots, but also to AI agents themselves. Every data transmission carries the identity of its source. Without that identity, data is nothing more than numbers of unknown origin. With it, every byte becomes cryptographically traceable.

Consider a factory temperature sensor reading 89.6°F. Under ioID, the data includes metadata such as Sensor 17, Line 3, 14:23, January 15 2025. If an unregistered device spoofs a sensor at the same location, data without ioID is rejected as untrusted by the network.

ioID is not simply a labeling system. It is built on the W3C Decentralized Identifier standard. Each registered device is linked to a programmable smart wallet, which the device uses to sign its data and issue verifiable credentials. In effect, the device cryptographically proves it produced the data.

ioID does more than confirm data origin. A device with an identity can also be granted permissions to act. A warehouse sensor, for instance, can automatically trigger a cooling system when temperature thresholds are exceeded. AI agents can receive ioID as well, enabling them to assess conditions and execute responses autonomously, functioning as a kind of digital site manager.

Registered ioID instances on the IoTeX network now stand at approximately 97,000. As of August 2025, the figure was around 12,000, meaning nearly eightfold growth in under six months. A signal of accelerating demand from both device manufacturers and AI agent developers building on the network.

3.2. Quicksilver: The Unified Data and Inference Pipeline for Real-World AI Agents

Verified device identity alone does not mean AI can understand the real world.

Suppose an AI agent is asked, “Good afternoon for outdoor activities near Central Park?” It needs rainfall data from weather stations, PM2.5 from air quality sensors, and more. The problem is format fragmentation. Nubila stations send JSON (humidity, wind speed). Air quality sensors log CSV. The NWS API returns XML forecasts.

Having AI developers connect each of these networks manually is costly and inefficient. That is why Quicksilver was built. Launched in early 2025, this open-source framework unifies fragmented DePIN networks into a single pipeline, converting raw real-world data into structured context that AI agents can perceive, reason over, and act on.

1. Data Collection & Integration: Aggregates data from hundreds of DePIN networks and Web2 APIs into one access point. AI developers connect through a single API instead of building per-network integrations.

2. Standardization & Indexing: Converts JSON, CSV, XML, and other formats into one structured format AI models can process directly. Rainfall, PM2.5, and temperature forecasts all arrive in a unified schema.

3. Cryptographic Verification: Quicksilver nodes use ZK Proofs to confirm data integrity through the processing pipeline. Where ioID proves which device sent the data, this step proves the data reached AI without tampering.

Quicksilver’s role does not end at data transformation. Once data has been collected, standardized, and verified in one place, it becomes a form of validated data marketplace. When micropayment protocols such as x402 are integrated, AI agents can exchange data directly with automated settlement built in. This positions Quicksilver as a potential shared infrastructure layer for the agent economy.

Quicksilver alpha daily requests grew from around 200 to a peak of 2,000. Even at this early stage, daily usage has stabilized at roughly 400 requests on average over the past month, a signal that real-time data demand from AI agents is real.

3.3. Realms: Teaching AI the “Why” with Industry Knowledge

Collecting and connecting data is not enough. The same 90°F reading means entirely different things depending on the domain. In agriculture, it could signal crop stress. In manufacturing, it might be normal for the season, or an early indicator of equipment degradation.

For AI to understand “why,” it needs industry-specific context.

Realms is a domain-specific knowledge base that accumulates this context. Each domain gets its own Realm, with no cap on the number. Three types of data layer together within each Realm.

• Real-time physical data: Raw sensor and device output such as temperature, humidity, and traffic flow.

• Expert interpretation and tagging: Domain experts review raw data and label whether readings are normal or anomalous.

• Private data: Voluntarily contributed by individuals, such as electricity bills, vehicle maintenance logs, and medical records.

These three data types are not siloed. They stack within a single Realm and reinforce each other.

• A sensor sends “90°F.”

• Expert tagging adds “90°F in summer is normal for this factory.”

• Equipment maintenance records add another layer: “90°F, but no maintenance in 3 years, possible overheating precursor.”

As data layers thicken, the context available to AI deepens.

One caveat. Realms is currently in design and development and has not yet launched.

The scenarios above represent IoTeX’s target vision. Building industry-specific Realms with sufficient data and expert participation to generate meaningful intelligence will require significant time and ecosystem effort.

4. Trio: Real-Time Multimodal AI where the Vision Meets Revenue

Trio is IoTeX’s first direct answer to the question every investor asks: where’s the revenue? Built by MachineFi, IoTeX’s AI subsidiary, Trio is a real-time multimodal intelligence layer for video streams and the first commercial product built on the IoTeX stack.

Trio is a multimodal stream agentic product. Connect a live video feed, and AI analyzes what is happening in real time, returning natural language answers. Instead of human CCTV monitoring, users ask questions like “Anyone in the restricted zone?” or “Defects on the assembly line?” and get instant judgments.

Four-step workflow.

1. Connect: Link streams from YouTube Live, RTSP, and other sources.

2. Extract: AI models analyze video, audio, and sensor data, interpreting them in natural language.

3. Fuse: Insights from multiple sources are combined into a comprehensive judgment.

4. Action: Judgments route through webhooks or AI agent workflows to trigger real-world responses.

The rationale is simple. Sensors cannot be attached to everything, and video analysis offers the fastest deployment path. Existing CCTV cameras in large warehouses can be leveraged immediately.

The cost structure stands out technically.

VLMs typically charge per frame. For security cameras with minimal scene change, analyzing every frame wastes most of the budget. Trio applies motion pre-filtering to discard unchanged frames before AI analysis, reducing VLM API costs by 70 to 90% according to IoTeX. This pre-filtering draws directly from DePIN experience processing high-volume sensor data efficiently.

One caveat. Trio is not directly connected to the IoTeX blockchain today. It does not run on on-chain transactions or the $IOTX token.

It is effectively a standalone AI product built on capabilities from previous DePIN experiences, including real-time data processing, edge computing, and multimodal stream integration. Pricing follows a standard SaaS model with a free Basic tier, a $39/month Pro plan, and custom Enterprise pricing.

Trio’s strategic meaning for IoTeX is clear. Instead of depending solely on blockchain protocol fees, the company aims to generate real revenue through AI products.

Trio also serves as a demonstration that compresses IoTeX’s entire logic into a single product. It takes physical real-world data, processes it through eight years of accumulated DePIN infrastructure, and delivers the output as a commercial product that enterprises are paying for today.

5. What to Watch Going Forward

IoTeX has completed its infrastructure build. The 2026 investment thesis is not about whether the technology works. The L1, ioID, and Quicksilver are all live and growing.

The numbers confirm it. Over 42 million devices are registered across 433 DePIN apps by DePINscan metrics built by IoTeX, on-chain ioID registrations exceeded 33,000 as of January 2026, and the mainnet has run without a single instance of downtime since its 2019 launch. The technology is sufficiently proven.

One question remains: does this technical readiness translate into actual revenue? Two milestones will determine the answer: revenue conversion and ecosystem expansion.

5.1. Revenue Conversion

The problem is clear. Technical capability has not translated into revenue. Middleware is structurally difficult to monetize. It sits between layers, has no direct user touchpoint, and must remain low-cost to drive adoption. IoTeX was no exception.

Trio changes this revenue structure. Rather than remaining a middleware relay, IoTeX has effectively established an AI subsidiary and begun selling SaaS directly to end users. The revenue model has shifted from middleware fees to subscription.

Once subscription revenue reaches scale, a pathway opens to return that value to the $IOTX token ecosystem. Product revenue supports ecosystem value, creating a self-reinforcing cycle.

The risks are real. Trio is still early, and Google Cloud Vision and Amazon Rekognition already dominate the live video analytics market. That said, Trio’s positioning around data sovereignty and 70 to 90 percent cost reduction represents a meaningful differentiator. But enterprise sales cycles are long, and the market judges on results. Proof requires actual contracts, not narrative.

5.2. Ecosystem Expansion

Trio is the product-level pivot. The ecosystem-level move is the Real-World AI Foundry, launched at Token2049 Singapore in September 2025. The Foundry pools infrastructure, data, and compute from multiple companies to co-develop AI models. Vodafone brings telecom infrastructure, Filecoin provides decentralized storage, and IoTeX sits in between as the verification and coordination layer.

The target output is Real-World Models (RWMs). Conventional AI trains on static historical data. RWMs train on live sensor and device feeds, such as factory temperatures, traffic flow, and weather. Contributors who supply data or compute receive usage-based compensation.

The partner list is impressive, but the Foundry is still early-stage. Whether RWMs can reach production-grade performance is unproven. IoTeX’s re-rating as an AI infrastructure company depends on execution across both fronts, Trio on the product side and the Foundry on the ecosystem side.

🐯 More from Tiger Research

Read more reports related to this research.

• Fluent: Making Every VM a Lego Block

• Nesa: Why Everyday AI Needs Privacy

• Moonbirds: From NFTs to the Next “Pop Mart”

Disclaimer

This report was partially funded by IoTeX. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

Terms of Usage

Tiger Research allows the fair use of its reports. ‘Fair use’ is a principle that broadly permits the use of specific content for public interest purposes, as long as it doesn’t harm the commercial value of the material. If the use aligns with the purpose of fair use, the reports can be utilized without prior permission. However, when citing Tiger Research’s reports, it is mandatory to 1) clearly state ‘Tiger Research’ as the source, 2) include the Tiger Research logo following brand guideline. If the material is to be restructured and published, separate negotiations are required. Unauthorized use of the reports may result in legal action.

Key Takeaways

• The performance war is over, and composability across VMs is the next frontier.

• Fluent’s Blended Execution puts EVM, SVM, and Wasm on one chain with no bridges.

• A good chain isn’t enough, so Fluent is building Prints, its own reputation layer, to prove it.

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1. The Battle After Performance

Blockchain infrastructure competition began with performance: faster, cheaper, higher throughput. That race is now effectively over. Dozens of chains exist, and performance is sufficient for all but the most extreme financial services.

Looking back, blockchain’s biggest breakthroughs came not from performance but from composability. DeFi Summer 2020 is the clearest example. Lending was layered with swaps, then yield farming stacked on top.

The key insight was that apps could snap together like Lego.

But this composability only worked within a single VM. Ethereum’s Lego blocks could not be used on Solana, and Solana’s could not be used on Ethereum. This VM fragmentation is the constraint Fluent is explicitly designed to remove.

2. The Lego Fluent Dreams Of

Fluent is a project designed to eliminate the boundary between VMs. Its approach is called “Blended Execution”, running apps from different VMs together within a single chain.

To illustrate the current situation with the Lego analog. Ethereum Lego blocks are square, and Solana Lego blocks are round. Both are good blocks, but their specs are incompatible. Even if you want to combine a lending app built on Ethereum with a trading app built on Solana, they sit in separate boxes and cannot connect.

Fluent is the board that unifies these specs. As an Ethereum L2, it inherits Ethereum’s security while building a new execution environment on top. Architecturally, Fluent resembles other Ethereum L2s like Arbitrum or Base, until execution begins.

The difference is one thing. Inside Fluent, contracts written for EVM, SVM, and Wasm all live on the same chain, sharing the same state. Wasm (WebAssembly) was originally created to run high-performance programs in web browsers. It converts code written in various languages (Rust, C++, TypeScript, etc.) into a single common format.

Fluent uses Wasm to compile all contracts into a shared format called rWasm (reduced Wasm). Whether a block is square or round, it gets converted to the same spec, so everything can be freely combined on one board.

This means a Solidity app can directly call a function in a Rust app. No bridge, no message relay. It all executes within a single transaction. Fluent calls this “Blended Execution.”

Currently, blended execution between EVM and Wasm is live on testnet. SVM support has completed feature development and is in the optimization phase.

3. Proving It with Prints

BUT, does building a good chain automatically attract good apps?

In reality, NO.

Hyperliquid is a clear example. The team’s own perpDEX became one of crypto’s standout successes. The Web3 market looks quite different before and after Hyperliquid. Before, teams relied on grant programs to attract killer apps from external builders. After, the new formula became: build it yourself and prove it.

Following this shift, Fluent is building its own service called Prints. Prints is a tool that aggregates “reputation” scattered across the internet, verifying who is real and who is trustworthy. In simple terms, it is a reputation aggregator for the info-fi era, pulling together reputation scores from multiple platforms into one view.

It currently integrates Ethos trust scores, Kaito smart followers, and Talent Protocol builder scores in one place. Any single metric alone is vulnerable to gaming. But while one score can be manipulated, faking several at once is far more difficult.

For users, Prints serves as a Web3 reputation resume, proving credibility across domains on a single page. For builders, it is a reputation tool that can be directly integrated into their apps.

On top of Prints sits Fluent Connect, a service where builders can use Prints’ reputation data to find users matching specific criteria and distribute early access or token benefits through a feature called Perks. In short, it is a matching tool connecting builders with genuine users.

Third-party adoption is already emerging.

Vena Finance announced it will use Prints data to introduce reputation-based interest rates, offering better lending terms to higher-reputation users. The service has launched with approximately 40,000 sign-ups, and a builder API is under development.

However, since Prints aggregates external services, it lacks proprietary reputation signals of its own. To address this, Fluent has created an internal feedback score and plans to integrate additional signals such as prediction market performance, yield curator track records, and AI agent reputation over time.

4. Fluent’s Ecosystem

Prints is still in its expansion phase and needs time to become a complete reputation system. Fluent is not simply waiting. It is building its ecosystem by nurturing external builders in parallel. The vehicle for this is the Blended Builders Club (BBC), Fluent’s own accelerator.

Five teams were selected for the first cohort:

• Pump Pals: A social trading platform enabling community-driven trading experiences

• Sprout: An automated yield optimization platform that matches strategies to user risk profiles

• Buzzing: A prediction market platform where users create their own markets and bet on outcomes

• Yumi Finance: On-chain credit infrastructure that attaches credit scoring to crypto cards, enabling buy-now-pay-later

• Blend Money: An on-chain savings platform that automatically applies yield strategies and currency hedging when users deposit in local currency

Among these, PumpPals, Sprout, and Buzzing have already completed user testing on testnet. The testnet approach itself is worth noting. On most chains, testnets function as airdrop farming tools. Users repeat meaningless transactions expecting rewards, and teams mistake inflated metrics for real demand.

Fluent reframed the testnet’s purpose around feedback collection. User feedback is delivered directly to builder teams, who use it to improve their products. Users who provide quality feedback earn reputation scores in Prints, gaining priority for future benefits. Apps are not launched all at once but featured one at a time over several weeks, ensuring each team receives focused feedback.

Beyond BBC, additional ecosystem growth is emerging. Nerona is an on-chain asset management platform that bundles a mobile app, crypto card, stablecoin yield, and lending into one place. Previously, these functions were scattered across separate services, leaving capital idle. Nerona consolidates them so capital is always working.

When combined with Prints’ reputation data, it becomes possible to offer differentiated interest rates or service terms based on user trustworthiness. This aligns with the same direction as Vena Finance’s reputation-based interest rates introduced earlier.

5. The Picture Fluent Is Drawing

Fluent is building three things simultaneously.

First, the chain. Blended Execution enables EVM, SVM, and Wasm apps to be composed on a single chain. The technical direction is clear, and EVM-Wasm blended execution is already working on testnet. However, SVM integration is still in the optimization phase, and whether all three VMs run stably in a mainnet environment remains to be verified.

Second, Prints. The design of aggregating multiple reputation signals for cross-verification is compelling. One metric can be gamed, but faking several at once is far harder. That said, the number of integrated signals is still small, and most depend on external services. Whether Prints can reach the stage of generating and verifying reputation on its own is something to watch.

Third, the ecosystem. BBC is cultivating builders, and services like Vena Finance and Nerona are attempting to link reputation to financial terms. Most are still at an early stage or closer to concept, so how each service achieves real user acquisition will need to be observed.

Fluent is still early, but by designing chain, service, and ecosystem as a single integrated structure from the start, it sets expectations for what the completed picture could look like. Early execution has been demonstrated: blended execution works on testnet, and Prints-based services are beginning to attach.

In the last cycle, dozens of L2s launched, but most led with performance metrics alone and ended up as empty chains without users. Whether Fluent can break this pattern will be answered by real user numbers and on-chain activity after mainnet.

🐯 More from Tiger Research

Read more reports related to this research.

• Nesa: Why Everyday AI Needs Privacy

• Moonbirds: From NFTs to the Next “Pop Mart”

• Katana: A DeFi Chain With No Idle Capital

Disclaimer

This report was partially funded by Fluent. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

Terms of Usage

Tiger Research allows the fair use of its reports. ‘Fair use’ is a principle that broadly permits the use of specific content for public interest purposes, as long as it doesn’t harm the commercial value of the material. If the use aligns with the purpose of fair use, the reports can be utilized without prior permission. However, when citing Tiger Research’s reports, it is mandatory to 1) clearly state ‘Tiger Research’ as the source, 2) include the Tiger Research logo following brand guideline. If the material is to be restructured and published, separate negotiations are required. Unauthorized use of the reports may result in legal action.

Key Takeaways

• AI is already part of daily life, but users overlook how their data passes through central servers

• Even CISA’s acting director unknowingly exposed classified documents to ChatGPT

• Nesa restructures this by transforming data before transmission (EE) and splitting it across nodes (HSS-EE) so no single party ever sees the original

• Academic validation (COLM 2025) and live enterprise deployment (P&G) give Nesa a head start

• Whether the broader market adopts decentralized privacy AI over familiar centralized APIs remains the key question

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1. Is Your Data Safe?

In January 2026, Madhu Gottumukkala, Acting Director of CISA, the lead U.S. cybersecurity agency, uploaded sensitive government documents to ChatGPT simply to summarize and organize contract-related files.

The breach was not detected by ChatGPT or reported to the government by OpenAI. It was flagged by the agency’s own internal security systems, leading to an investigation for violating security protocols.

Even America’s top cybersecurity official was using AI routinely, to the point of uploading classified material.

We know. Most AI services store user input on central servers in encrypted form. But this encryption is reversible by design. Data can be decrypted and disclosed under valid warrants or emergency circumstances, and users have no visibility into what happens behind the scenes.

2. Privacy AI for Everyday Use: Nesa

AI is already part of daily life. It summarizes articles, writes code, and drafts emails. The real concern is that, as shown in the previous case, even classified documents and personal data are being handed to AI with little awareness of the risk.

The core problem is that all of this data passes through the service provider’s central servers. Even when encrypted, the decryption keys are held by the provider. How can users trust that arrangement?

User input data can be exposed to third parties through multiple channels: model training, safety reviews, and legal requests. On enterprise plans, organization admins can access chat logs. On personal plans, data can still be turned over under a valid warrant.

Now that AI is deeply embedded in everyday life, it is time to ask serious questions about privacy.

Nesa is a project designed to change this structure entirely. It builds a decentralized infrastructure that enables AI inference without entrusting data to a central server. User input is processed in an encrypted state, and no single node can view the original data.

3. How Nesa Solves This

Imagine a hospital using Nesa. A doctor wants an AI to analyze a patient’s MRI for tumors. With current AI services, the image is sent directly to OpenAI or Google servers.

With Nesa, the image is mathematically transformed before it ever leaves the doctor’s computer.

A simple analogy: suppose the original problem is “3 + 5 = ?” If you send it as is, the recipient knows exactly what you are calculating.

But if you multiply every number by 2 before sending, the recipient sees “6 + 10 = ?” and returns 16. You divide by 2 and get 8, the same answer as solving the original. The recipient performed the computation but never learned your original numbers were 3 and 5.

This is exactly what Nesa’s Equivariant Encryption (EE) does. Data is mathematically transformed before transmission. The AI model computes on the transformed data.

The user applies the inverse transformation and gets the same result as if the original data had been used. In mathematics, this property is called equivariance: whether you transform first or compute first, the final result is identical.

In practice, the transformation is far more complex than simple multiplication. It is tailored to the internal computation structure of the AI model. Because the transformation aligns with the model’s processing flow, accuracy is not compromised.

Back at the hospital, the doctor notices no difference. The workflow of uploading an image and receiving a result stays the same. What changes is that no node in between can see the patient’s original MRI.

Nesa goes one step further. EE alone prevents any node from viewing the original data, but the transformed data still exists in full on a single server.

HSS-EE (Homomorphic Secret Sharing over Encrypted Embeddings) splits even the transformed data.

Return to the analogy. EE was applying a multiplication rule before sending the exam sheet. HSS-EE tears that transformed sheet in half, sending the first part to Node A and the second part to Node B.

Each node solves only its own fragment. Neither can see the full problem. Only when both partial answers are combined does a complete result emerge, and only the original sender can perform that recombination.

In summary, EE transforms data so the original cannot be seen. HSS-EE splits even the transformed data so it never exists in one place. Privacy protection is layered twice.

4. Does Privacy Mean Slower Performance?

Stronger privacy means slower performance. This has been a long-standing rule in cryptography. Fully Homomorphic Encryption (FHE), the most widely known approach, is 10,000 to 1,000,000 times slower than standard computation. It is unusable for real-time AI services.

Nesa‘s Equivariant Encryption (EE) works differently. Returning to the math analogy, the cost of applying x2 before sending and ÷ 2 after receiving is minimal.

Unlike FHE, which converts the entire problem into a fundamentally different mathematical system, EE adds only a lightweight transformation on top of existing computations.

Performance benchmarks:

• EE: less than 9% latency increase on LLaMA-8B, with accuracy matching the original at over 99.99%.

• HSS-EE: 700 to 850 milliseconds per inference on LLaMA-2 7B.

On top of this, MetaInf, a meta-learning scheduler, optimizes efficiency across the network. It evaluates model size, GPU specifications, and input characteristics to automatically select the fastest inference method.

MetaInf achieved 89.8% selection accuracy and a 1.55x speedup over conventional ML-based selectors. It was published at the COLM 2025 main conference, providing academic validation.

The figures above are from controlled test environments. However, Nesa’s inference infrastructure is already deployed in real enterprise settings, confirming production-level performance.

5. Who Uses It and How

There are three ways to access Nesa.

First is the Playground. Users can select and test models directly on the web. No developer background is required. It allows hands-on experience with inputting data and viewing results per model.

It is the fastest path to see how decentralized AI inference actually works.

Second is the Pro subscription. At $8 per month, it includes unlimited access, 1,000 Fast Inference credits per month, custom model pricing controls, and featured page visibility for models.

This tier is designed for individual developers or small teams looking to deploy and monetize their own models.

Third is Enterprise. This is not a public pricing plan but a custom contract structure. It includes SSO/SAML support, selectable data storage regions, audit logs, granular access controls, and annual-commitment billing.

Pricing starts at $20 per user per month, but actual terms are negotiated based on scale. It is built for organizations integrating Nesa into internal AI pipelines, with API access and organization-level management provided through a separate agreement.

In short: Playground for exploration, Pro for individual or small-team development, Enterprise for organizational deployment.

6. Why a Token Is Needed

A decentralized network has no central administrator. The entities running servers and verifying results are distributed around the world. This raises a natural question: why would anyone keep their GPU running to process someone else’s AI inference?

The answer is economic incentive. In the Nesa network, that incentive is the $NES token.

The structure is straightforward. When a user requests AI inference, a fee is attached. Nesa calls this PayForQuery. It consists of a fixed fee per transaction plus a variable fee proportional to data size.

Higher fees receive priority processing, the same principle as gas fees on a blockchain.

The recipients of these fees are miners. To participate in the network, miners must stake a set amount of $NES. They put their own tokens at risk before being assigned work.

If a miner returns faulty results or fails to respond, a penalty is deducted from their stake. If they process accurately and quickly, they earn greater rewards.

$NES also serves as a governance tool. Token holders can submit proposals and vote on core network parameters such as fee structures and reward ratios.

In summary, $NES serves three roles: payment for inference requests, collateral and reward for miners, and participation rights in network governance. Without the token, nodes do not run. Without nodes, privacy AI does not function.

One point worth noting: the token economy depends on preconditions to function as designed.

Inference demand must be sufficient for miner rewards to be meaningful. Rewards must be meaningful for miners to stay. Miners must be sufficient for network quality to hold.

This is a virtuous cycle where demand drives supply and supply sustains demand, but getting that cycle started is the hardest phase.

The fact that enterprise clients like Procter & Gamble are already using the network in production is a positive signal. However, whether the balance between token value and mining rewards holds as the network scales remains to be seen.

7. The Case for Privacy AI

The problem Nesa is trying to solve is clear: change the structure in which user data is exposed to third parties whenever AI is used.

The technical foundation is solid. Its core encryption technologies, Equivariant Encryption (EE) and HSS-EE, originated from academic research. The inference optimization scheduler MetaInf was published at the COLM 2025 main conference.

This is not a case of simply citing papers. The research team directly designed the protocols and implemented them in the network.

Among decentralized AI projects, it is rare to find one that has had its own cryptographic primitives validated at the academic level and deployed them onto live infrastructure. The fact that Procter & Gamble and other major enterprises are already running inference on this infrastructure is a meaningful signal for an early-stage project.

That said, limitations are clear.

• Market scope: Institutional clients come first; retail users unlikely to pay for privacy yet

• Product usability: Playground feels closer to Web3/investment UX than everyday AI tools

• Scale validation: Controlled benchmarks ≠ production with thousands of concurrent nodes

• Market timing: Demand for privacy AI is real, but demand for decentralized privacy AI is unproven; enterprises still default to centralized APIs

Most enterprises are still accustomed to centralized APIs, and the barrier to adopting blockchain-based infrastructure remains high.

We live in an era where even the head of U.S. cybersecurity uploaded classified documents to AI. Demand for privacy AI already exists and will only grow.

Nesa has academically validated technology and live infrastructure to meet that demand. There are limitations, but its starting position is ahead of other projects.

When the privacy AI market opens in earnest, Nesa will be among the first names that come up.

🐯 More from Tiger Research

Read more reports related to this research.

• Moonbirds: From NFTs to the Next “Pop Mart”

• Katana: A DeFi Chain With No Idle Capital

• Magic Eden: From NFT Marketplace to Crypto Entertainment

Disclaimer

This report was partially funded by Nesa. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

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Key Takeaways

• After acquiring Moonbirds, Orange Cap Games announced plans to issue a token and expand the broader ecosystem.

• To position the Moonbirds IP around fandom rather than speculation, the team is introducing multiple engagement formats, including a card game, Blind Box 2.0 initiatives, and mobile games.

• While the expansion of the Moonbirds IP is promising, a clearer and more concrete utility framework for the $BIRB token is still required.

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1. Moonbirds Moving Toward a Larger Ecosystem

Moonbirds was a high-profile NFT project that traded up to 40 ETH in 2021. As the NFT market entered a downturn, attention faded. Recently, however, Moonbirds has returned to the spotlight following the announcement of a token launch plan. Still, viewing Moonbirds purely as an NFT reflects a perspective anchored in 2021.

In May 2025, Moonbirds entered a new phase after being acquired by Orange Cap Games. The acquirer’s objective is not to operate Moonbirds as a standalone NFT collection, but to develop it as a broader IP-driven business. This shift requires moving beyond the valuation of a single NFT and instead assessing Moonbirds within a larger strategic framework.

2. Moonbirds and the Ambition to Become the Next Pop Mart

The future Orange Cap Games envisions through Moonbirds is becoming the “next Pop Mart.”

CEO Spencer Gordon Sand has stated his ambition to build a multi-billion-dollar company by positioning Orange Cap Games as the Pop Mart of Web3.

This ambition is closely tied to Sand’s career background. He has been an early investor in NFT projects such as Bored Ape Yacht Club, RTFKT, and Cool Cats, giving him firsthand exposure to how NFT projects evolve into IP, and how they fail.

In particular, as one of the largest holders of Pudgy Penguins, he has seen the brand extend into mainstream media, including television, where non-crypto audiences actively engage with the characters.

This experience likely shaped his conviction around building a “next Pop Mart.”

Consistent with this background, Orange Cap Games’ vision for Moonbirds diverges sharply from a typical NFT sales model. Rather than focusing on NFT distribution within Web3, the company aims to use Moonbirds as a starting point to acquire and scale multiple high-potential IPs, ultimately positioning itself as a diversified, IP-centric consumer business similar in structure to Pop Mart.

A representative success case of Pop Mart is the character Labubu. Labubu was not originally created by Pop Mart. In 2019, the company signed an exclusive agreement with a Hong Kong–based artist and successfully introduced the character to a mass market.

Pop Mart is not a simple reseller of third-party IP. Its core strength lies in vertical integration.

Similar to a talent agency, the company manages the full lifecycle of IP development, from artist discovery and IP licensing to product design, manufacturing, and direct-to-consumer distribution through its own channels. This integrated structure allows Pop Mart to repeatedly scale new characters using a consistent expansion model.

Orange Cap Games appears to be pursuing a comparable approach. Rather than focusing solely on product creation, the company aims to build a distribution layer capable of delivering multiple IPs.

This system spans physical distribution, such as figurines and trading cards, cultural distribution through offline tournaments and events, and digital distribution via games and NFTs. The objective is to create a repeatable framework that can support and scale any IP introduced into the ecosystem.

3. To Become Pop Mart, Products Must Be Well Made and Well Distributed

For Orange Cap Games, producing physical toys inspired by Moonbirds NFTs matters. However, trading cards play a more central role in establishing a scalable ecosystem.

To differentiate its trading card game, the company avoided the industry-standard Blue Core card stock and instead developed its own Orange Core material. This proprietary stock reduces common issues such as edge wear and card bending. Orange Cap Games controls both material selection and the manufacturing process, extending product ownership down to production details.

These efforts were validated when the cards received PSA 10 grades from PSA, the highest rating available. This was followed by a direct collaboration with PSA, resulting in co-branded promotional cards.

Product quality alone is not sufficient. Distribution infrastructure determines whether physical products reach the right audience. Placement in high-traffic locations frequented by target consumers is critical.

To this end, Orange Cap Games partnered with major global distributors:

• GTS Distribution, the largest collectibles distributor in North America

• Star City Games, a core distributor for Magic: The Gathering

• Asmodee, the world’s third-largest board game and toy distributor

As a result, Moonbirds products are no longer limited to crypto-native channels such as exchanges. They are now available in local hobby shops and toy stores, meeting a baseline requirement for global expansion.

At this stage, Orange Cap Games has secured both product quality and distribution access. The next challenge is retention: whether customers who purchase these products remain engaged within the broader Moonbirds ecosystem.

4. Convincing Consumers to Choose Moonbirds

Producing high-quality products and securing distribution are necessary, but not sufficient. The real challenge is persuasion. Among countless character products on retail shelves, what makes consumers choose Moonbirds?

The success of Labubu at Pop Mart was not driven by visual appeal alone. Orange Cap Games applies a similar logic, using structured strategies designed to influence consumer behavior rather than relying on design alone.

4.1. Trading Card Games as a Cultural Entry Point

Cards function not only as collectibles but as components of a game. For cards to become meaningful collectibles, a playable game must exist first. Without active gameplay, even well-designed and well-manufactured cards remain decorative objects.

The central question is how to attract players to a new card game.

Orange Cap Games targets participants in large-scale trading card tournaments. These events often run for five days, with roughly half of participants eliminated on the first day. The company schedules its own tournaments on the second day, positioning them as an alternative for eliminated players.

From a participant’s perspective, an additional competitive opportunity is compelling. Preparation leads to familiarity with the game, core players begin to cluster, and the product starts to function as a real game rather than a niche experiment.

When a Moonbirds tournament is held alongside a major established competition, players also begin to associate it with the same competitive standard. This shift in perception is critical. The strategy is less about immediate conversion and more about reframing Moonbirds TCG as a legitimate part of the broader card game ecosystem.

At its core, this approach is not only about participation, but about changing how the product is perceived.

In practice, Orange Cap Games applied this strategy in 2025 with its Vibes TCG at SCG Con, where side tournaments were run alongside major competitions. Over time, this approach has steadily expanded the scale and visibility of the Vibes TCG events.

4.2. Blind Box 2.0: From a Single Experience to Three

Pop Mart’s traditional blind boxes, such as those featuring Labubu, typically contain a single figurine. Once the box is opened and the item revealed, the experience ends. The model is designed around one-time consumption.

Moonbirds takes a different approach with Blind Box 2.0. Each box contains three distinct collectibles:

1. A plush or figurine

2. Trading Card

3. NFT

By purchasing a single box, consumers engage with three separate experiences rather than one.

Orange Cap Games refers to this model as the “Hybrid” category. This is not simply a bundle of three products. The core idea is cross-channel onboarding, where each item serves as an entry point into a different part of the ecosystem.

• Toy-focused buyers discover TCG cards and are introduced to gameplay.

• TCG-focused buyers receive NFTs and gain exposure to onchain assets.

• NFT-focused buyers receive physical toys and are guided into offline communities

A single blind box becomes a gateway into all three of Orange Cap Games’ business lines: toys, TCG, and NFTs. Consumers may engage only with their original purchase intent. However, once curiosity extends beyond that initial purpose, they are naturally guided into adjacent parts of the ecosystem.

4.3. Mobile Games as an Expansion Channel for Daily Engagement

Purchasing trading cards requires spending, and collecting figurines requires prior interest. Free-to-play mobile games, by contrast, require little more than a download. Some users may later invest time or money to improve their decks or progress, but the initial barrier to entry remains low.

Angry Birds provides a useful reference. The gameplay was simple, but the characters became globally recognizable. From there, the IP expanded into films, merchandise, and theme parks. The game served as the starting point for broad IP distribution.

Moonbirds’ mobile game is designed to play a similar role. Through gameplay, users become familiar with the characters and gradually absorb the broader worldbuilding. Even users with no immediate intention to purchase cards or figurines are exposed to Moonbirds through the game.

Over time, this familiarity creates recognition. When consumers later encounter Moonbirds products in retail settings, the characters are no longer unfamiliar. The mobile game establishes the mental bridge that turns passive awareness into potential purchase intent.

5. From Moonbirds to Orange Cap Games: The Role of $BIRB

Orange Cap Games has built the infrastructure and strategy to expand Moonbirds. For investors, however, the key question remains: what does the $BIRB token actually do?

The team describes $BIRB as a “coordination layer.” In this framing, the token is intended to accelerate cultural distribution and enable memes to spread more efficiently. The strategy is to anchor the business in physical consumer products while using crypto-native dynamics to amplify brand reach.

The challenge lies in specificity. It remains unclear what concrete benefits accrue to token holders. Revenue sharing from product sales, NFT-linked memberships, or other incentive mechanisms have not been clearly defined.

Orange Cap Games emphasizes long-term ecosystem growth. Rather than distributing profits to token holders, revenue is expected to be reinvested into the business. The goal is to create a positive feedback loop where attention generated in non-crypto markets feeds back into the crypto community.

A notable strength is that this is not a business assembled solely to justify a token launch. The company generated real revenue through Vibes TCG before introducing the token layer. This approach differs materially from meme coins that lack underlying operations.

However, open questions remain. Whether the token’s proposed “cultural coordination” function can operate in practice, and whether that function translates into sustained token value, has yet to be demonstrated. A reinvestment-first strategy may also be less appealing to short-term investors.

It remains uncertain whether Moonbirds can achieve Pop Mart level success. Still, the project serves as a live experiment in how NFT-originated IP can be monetized in the real world. The concrete design of $BIRB and its early performance will determine the outcome of that experiment.

🐯 More from Tiger Research

Read more reports related to this research.

• Magic Eden: From NFT Marketplace to Crypto Entertainment

• Gradient: Opening a New AI Era with Open Intelligence

Disclaimer

This report was partially funded by Moonbirds. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

Terms of Usage

Tiger Research allows the fair use of its reports. ‘Fair use’ is a principle that broadly permits the use of specific content for public interest purposes, as long as it doesn’t harm the commercial value of the material. If the use aligns with the purpose of fair use, the reports can be utilized without prior permission. However, when citing Tiger Research’s reports, it is mandatory to 1) clearly state ‘Tiger Research’ as the source, 2) include the Tiger Research logo following brand guideline. If the material is to be restructured and published, separate negotiations are required. Unauthorized use of the reports may result in legal action.

Key Takeaways

• Most Layer 2s lock bridge assets without using them. Katana deploys these assets into Ethereum lending protocols to generate yield, then redistributes earnings as DeFi protocol incentives.

• Holding assets in storage generates no return. Users must deploy capital into Katana DeFi protocols to earn additional rewards.

• As of Q3 2025, over 95% of Katana’s TVL was actively deployed in DeFi protocols. This contrasts with most chains, where utilization rates range from 50% to 70%.

• Katana reinvests 100% of net sequencer fee revenue into liquidity provision, maintaining stable trading conditions even during market volatility.

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1. Why Capital Is Sitting Idle

What happens to your money when you bridge from Ethereum to a Layer 2?

Most people assume their assets are simply transferred. In reality, the process is closer to freezing. When you deposit assets into a bridge contract, the contract holds them in escrow. The Layer 2 mints an equivalent amount of tokens. You can transact freely on Layer 2, but your original assets on mainnet remain locked and idle.

Consider a simple analogy. You deposit items at a storage facility and receive a claim ticket. The ticket can be transferred to others. But the items themselves stay in storage until you retrieve them.

This describes how most Layer 2 bridges work. Assets held in Ethereum escrow contracts generate no yield. They wait passively until users withdraw them back to mainnet.

What if bridge deposits on mainnet could earn DeFi yield while you still access fast, low-cost transactions on Layer 2?

Katana answers this question directly. Capital entering the bridge does not sit idle. It is put to work.

2. How Katana Puts Capital to Work

Katana activates capital through three mechanisms:

1. Bridge assets are deployed into Ethereum lending markets to generate yield.

2. Trading fee revenue is reinvested into liquidity pools.

3. Native stablecoin AUSD captures U.S. Treasury yields.

External capital works. Chain-generated capital works. These three mechanisms combine to eliminate idle assets on Katana.

2.1. Vault Bridge

The first mechanism is Vault Bridge. When users send assets to Katana, the original assets remaining on Ethereum mainnet are deployed into lending markets to generate interest.

When you bridge USDC from Ethereum to Katana, those assets are not simply locked. On Ethereum mainnet, they are deployed into curated vault strategies on Morpho, a major lending protocol. The yield generated does not go directly to individual users. Instead, it is collected at the network level and redistributed as rewards to core DeFi markets on Katana.

On Katana, the user receives a corresponding vbToken, such as vbUSDC. This token can be freely used across Katana’s DeFi ecosystem.

One common misunderstanding should be addressed. vbTokens should not be compared to staking derivatives like stETH from Lido. stETH appreciates over time as staking rewards accrue.

vbTokens work differently. Holding vbUSDC in your wallet does not increase the quantity or price. The yield Vault Bridge generates on Ethereum does not flow to individual vbToken holders. It flows to Katana’s DeFi pools. Revenue is distributed periodically to the network, strengthening incentives for Sushi liquidity pools and Morpho lending markets.

Users benefit only when they actively deploy vbTokens. Supplying vbTokens to Sushi liquidity pools or to lending strategies such as those offered by Yearn allows users to earn base yield plus additional rewards sourced from Vault Bridge. Simply holding vbTokens provides no return.

Katana rewards asset utilization rather than passive ownership. Capital that moves is rewarded. Capital that remains idle is not.

2.2. Chain-Owned Liquidity (CoL)

The second mechanism is Chain-Owned Liquidity (CoL). Katana collects 100% of net sequencer fee revenue (transaction processing fees minus Ethereum settlement costs).

The foundation uses this revenue to become a direct liquidity provider. It supplies assets to Sushi trading pools and Morpho lending markets. The chain itself owns and manages this liquidity.

This creates a reinforcing cycle. As users transact on Katana, sequencer fees accumulate. Those fees are converted into chain-owned liquidity, which deepens pools. Slippage declines, lending rates stabilize, and user experience improves. Improved conditions attract more users, which generates additional fees. The cycle continues.

In theory, this structure is especially effective during market downturns. External liquidity is mobile and often exits quickly under stress. Chain-owned liquidity, by contrast, is designed to remain in place, allowing pools to persist and absorb shocks more effectively.

In practical terms, this sets Katana apart from most DeFi systems that rely on incentivizing external capital through token emissions. By maintaining liquidity it owns directly, the network aims for more stable and sustainable operation.

2.3. AUSD Treasury Yield

The third mechanism is AUSD, Katana’s native stablecoin. AUSD is backed by U.S. Treasuries, and the off-chain yield from these Treasury holdings flows into the Katana ecosystem.

AUSD is issued by Agora. The collateral backing AUSD is invested in physical U.S. Treasuries. Interest earned from these Treasuries accrues off-chain. This yield is periodically channeled to the Katana network, where it strengthens incentives for AUSD-denominated pools.

If Vault Bridge brings on-chain yield, AUSD brings off-chain yield. The two revenue sources differ in nature. Vault Bridge yield fluctuates with Ethereum DeFi market conditions. AUSD yield is tied to U.S. Treasury rates and remains relatively stable.

This diversifies Katana’s revenue structure. When on-chain markets are volatile, off-chain yield provides a buffer. When on-chain yields are low, Treasury returns support overall returns. The structure spans both crypto markets and traditional finance.

3. Locking Capital Versus Putting It to Work

As discussed earlier, there is a reason most existing bridges simply lock assets. Security. When assets do not move, system design remains simple and attack surfaces are limited. Most Layer 2 networks adopt this approach. It is safe, but capital remains idle.

Katana takes the opposite position. Activating idle assets introduces additional risk, and Katana acknowledges this tradeoff directly. Rather than avoiding it, the network works with established risk management specialists in DeFi. These include firms such as Gauntlet and Steakhouse Financial.

Gauntlet and Steakhouse Financial are established risk management firms in the DeFi sector, with experience setting parameters for major lending protocols and advising leading DeFi projects. Their role is comparable to that of professional asset managers in traditional finance. They assess which protocols capital should be allocated to, determine appropriate position sizes, and monitor risk exposure on an ongoing basis.

No financial system offers 100% safety, so concerns about residual risk are valid.

However, Katana works with top-tier risk curators and maintains conservative vault structures. An internal Risk Committee oversees operations. Additional safeguards include liquidity buffers provided by Cork Protocol and other protective mechanisms.

4. The DeFi Heaven Katana Creates

Current DeFi markets suffer from fragmented liquidity. Pools trading the same assets exist separately across chains and protocols. This reduces execution efficiency, increases slippage, and lowers capital utilization. Some users exploit these inefficiencies through arbitrage. Most users simply pay higher costs.

Katana solves this problem at the system level.

Vault Bridge and chain-owned liquidity concentrate liquidity in core protocols. As a result, trade execution improves, slippage declines, and lending rates stabilize. Most importantly, yield from idle assets on Ethereum mainnet is added to base returns, raising overall yield.

Katana’s incentive structure can also significantly lower effective borrowing costs at certain points, or even create negative interest rates depending on market conditions and reward programs. This happens because Vault Bridge, CoL, and AUSD yields are reinvested into core markets. However, these are incentive-driven outcomes that vary with market conditions.

As a result, as of Q3 2025, over 95% of Katana’s TVL was actively deployed in DeFi protocols. This contrasts with most chains, where utilization rates range from 50% to 70%. Ultimately, what Katana creates is a chain where capital does not sleep, a system that rewards actual usage.

Katana never sleep.

🐯 More from Tiger Research

Read more reports related to this research.

• Magic Eden: From NFT Marketplace to Crypto Entertainment

• Gradient: Opening a New AI Era with Open Intelligence

• OpenMind: Preparing for the Age of Everyday Robotics

Disclaimer

This report was partially funded by Katana. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

Terms of Usage

Tiger Research allows the fair use of its reports. ‘Fair use’ is a principle that broadly permits the use of specific content for public interest purposes, as long as it doesn’t harm the commercial value of the material. If the use aligns with the purpose of fair use, the reports can be utilized without prior permission. However, when citing Tiger Research’s reports, it is mandatory to 1) clearly state ‘Tiger Research’ as the source, 2) include the Tiger Research logo following brand guideline. If the material is to be restructured and published, separate negotiations are required. Unauthorized use of the reports may result in legal action.

Key Takeaways

• Magic Eden has shifted from a simple NFT marketplace to a “crypto entertainment platform” by integrating gaming elements.

• Features like “Lucky Buy” and “Pack Ripping” focus on user fun, successfully rebranding the platform’s image.

• The platform allocates 30% of its revenue to buy back $ME tokens and NFTs, creating a strong link with its holders.

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1. Magic Eden: Not Just NFTs

Magic Eden, founded in 2021, quickly disrupted the “NFT Marketplace War.” Magic Eden rose as a key player on Solana by offering low fees and creator-friendly tools. To date, Magic Eden has facilitated $15-20 billion in NFT trading volume over the past four years.

However, this strong first impression has led to a low valuation for the project.

This is because many investors still view Magic Eden only as the “top NFT exchange on Solana.” Consequently, fears regarding the broader NFT market lead to fears about Magic Eden itself.

However, Magic Eden moved past being a simple NFT marketplace long ago. It has already built a diverse set of revenue streams.

Jack, the co-founder, stated on X that token trading now accounts for over 30% of total revenue as of 2024. This marks a clear shift from the past when the firm relied solely on NFT fees. The change continues in 2025 as Magic Eden adds gaming features to evolve into an entertainment platform.

The “Packs” feature is a prime example. This is a chance-based mechanic similar to the existing “Lucky Buy” tool. Data proves its success. According to a podcast with Presto Research, “Packs” earned about $15 million in its first week.

Magic Eden is no longer just an NFT marketplace. It is now a “crypto entertainment platform” that focuses on user fun.

2. Magic Eden = Crypto Entertainment

Since its start, Magic Eden has pursued bold and fun ideas. Its core target has always been the “Crypto Normie.” This refers to the retail user rather than the pro trader. The mass market seeks intuitive fun instead of complex trading tools.

Most exchanges and token trading tools compete in the “PvP” (Player vs Player) market with low fees and pro-level tools. This is an overcrowded “Red Ocean” with low expected returns. Much of the crypto industry focuses on this segment and chases pro trading trends.

Instead, Magic Eden focuses on “Fun.” While others chase whales, Magic Eden is adopting a “Zig while others zag” strategy to capture the vast consumer market.

2.1. Lucky Buy

The best case of this strategy is “Lucky Buy.” This product was launched in September 2023. Users can try to win high-value NFTs with small commitments via roulette, without paying the full price.

Users can set their probability 1% or as high as 75%, depending on how much they want to pay. But this also sets their chances. This product offers a chance to win for users with less capital and provides additional liquidity to sellers.

Turning the purchase process into a game triggered a huge response. Despite service blocks in the US due to regulations. And with only organic demand, Lucky Buy became Magic Eden’s fastest growing product lines .

2.2. Packs

Launched in late October 2025, “Packs extends this strategy. It brings the thrill of opening physical card packs to the digital realm. Users “rip” digital packs earned from app activity to find rare digital collectibles: from NFTs across top Solana and Ethereum collections to tokenized, graded Pokémon cards.

In the first week of launch, Packs generated around $15 million - more than the entire NFT industry combined, highlighting strong user demand.

Magic Eden is now doubling down on this product: adding multi-pack ripping, expanding digital collectible categories, and even more novel game mechanics like pack battles, gifting, and richer gameplay effects.

This serves as a strong retention tool. While users on rival platforms analyze complex charts, Magic Eden users find joy in ripping packs. This is the essence of crypto entertainment.

3. Inevitable Regulatory Constraints and a Head-On Breakthrough

Strategies based on fun and speculation face strict rules. Under the SEC and the Biden administration, the US held a harsh stance on crypto.

Most firms would retreat or cut features in this climate. Magic Eden did not hide. It employs sophisticated “Geo-blocking” to restrict US access technically, and leverages its large global user base to grow. At the same time, operating at the highest standards to comply with all regulations .

Magic Eden prioritized providing permissionless, experimental products to users in regions outside the US, like Korea.

While others shrink before regulations, Magic Eden confronts them to supply what the public wants. This approach treats regulation not as a passive constraint, but as “Operational Alpha” to boost global market share.

4. Buybacks: What the Public Really Wants

High corporate revenue means little to investors if it does not raise the price of tokens in their wallets. Many crypto projects fail to link platform revenue with token value.

Magic Eden addressed this on November 13, 2025, by announcing a “Buyback Program.” The concept is simple: the firm reinvests profits to support ecosystem value. Magic Eden pledged to return 30% of NFT marketplace revenue through the following structure:

• $ME Token Buyback (15% of revenue): The firm buys $ME tokens directly from the market. This mirrors stock buybacks. Reducing circulating supply exerts upward pressure on token value.

• NFT Collection Buyback (15% of revenue): The remaining 15% funds the purchase of key NFT collections. These assets are permanently stored in an on-chain vault called “The Garden of Eden.” By directly defending the Floor Price, the firm strengthens the ecosystem.

This program starts on Solana and will expand to Bitcoin, Ethereum, Monad, and more. Magic Eden’s tokenomics surpass simple governance. Company success directly drives token buying and NFT price support. This systematizes the phrase “Project success equals community success,” delivering what the public truly wants.

5. The Completion of Crypto Entertainment for the Masses

Magic Eden is no longer just an NFT marketplace. It is now a crypto entertainment platform give fun to the masses.

Magic Eden first built a solid financial base. Its wallet and swap tools link assets across fragmented chains. It then added features like Lucky Buy and Pack to gain mass users. To sustain this, a buyback program using 30% of revenue provides clear economic rewards.

The edge for Magic Eden lies in speed and market insight. While peers stall due to rules or complex tech, Magic Eden delivers the engagement and “dopamine” users crave. It used bold geo-blocking to move fast while others paused.

But still large gaps remain in the crypto entertainment space. High-value sectors like prediction markets offer multi-billion dollar growth that is largely untapped. Magic Eden seeks to lead this shift and capture this value. To this end, it is now incubating independent arms such as Dicey, an iGaming platform. These new ventures will drive the next phase of growth.

The final step is to fit these tools into a mobile wallet. When finance and fun meet in one app, Magic Eden will serve as the main gateway to the Web3 world.

🐯 More from Tiger Research

Read more reports related to this research.

• Sign: From Community to Government

• Gradient: Opening a New AI Era with Open Intelligence

• OpenMind: Preparing for the Age of Everyday Robotics

Disclaimer

This report was partially funded by Magic Eden. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

Terms of Usage

Tiger Research allows the fair use of its reports. ‘Fair use’ is a principle that broadly permits the use of specific content for public interest purposes, as long as it doesn’t harm the commercial value of the material. If the use aligns with the purpose of fair use, the reports can be utilized without prior permission. However, when citing Tiger Research’s reports, it is mandatory to 1) clearly state ‘Tiger Research’ as the source, 2) include the Tiger Research logo following brand guideline. If the material is to be restructured and published, separate negotiations are required. Unauthorized use of the reports may result in legal action.

Key Takeaways

• Sign is applying its TokenTable experience in large scale distribution and identity verification to government infrastructure through the S.I.G.N. framework.

• Strong backing from partners including CZ helped Sign secure contracts in Kyrgyzstan and Sierra Leone.

• The next step is to turn government business expansion into a reality and keep the community aligned through token buybacks.

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1. From a Sing Sign to Government

When most people think of Sign, they picture the group of people wearing orange vests and glasses singing together. As noted in the last report, Sign established a strong community known as the Orange Dynasty, which became the project’s core foundation.

But this strong foundation hides what Sign actually is. Sign is not just a community. Through TokenTable, its asset distribution platform, Sign has distributed over $3 billion in tokens to more than 55 million users. This required solving two core problems: distributing assets at massive scale and verifying each recipient’s identity and eligibility.

TokenTable operates at a scale that resembles national-level infrastructure. The platform handles millions of concurrent distributions while maintaining accurate identity verification for each transaction.

This experience directly translates to what governments need.

For example, governments must prevent duplicate claims when granting welfare benefits. They need clear identity records when issuing CBDCs. They must distribute emergency aid at scale and with speed.

These are the same problems Sign solved in Web3.

• Large scale distribution(TokenTable): Web3 token distribution → Welfare payments and CBDC operations

• Identity verification(sign protocol): On-chain attestations → Eligibility checks for public benefits

2. S.I.G.N

Sign is now extending this verified Web3 capability to government use cases. Sign revealed a whitepaper titled S.I.G.N. (Sovereign Infrastructure for Global Nations), presenting a comprehensive technical framework.

Governments do not seek full transparency. They require both privacy and transparency at the same time. Sign’s sovereign blockchain infrastructure addresses this by using a BNB Chain–based Layer 2 for public services that need transparency, and Hyperledger Fabric for financial systems that require privacy.

On top of this sovereign blockchain infrastructure, three infrastructure layers operate to support the government’s core businesses.

• ID & Attestations: Sign Protocol provides an on-chain identity proof system. Sensitive personal information remains off-chain while eligibility requirements are cryptographically verified on-chain.

• Stablecoin & CBDC: Governments issue CBDCs on Hyperledger Fabric and stablecoins on Layer 2. Each fulfills different requirements: privacy and transparency respectively.

• Digital Asset Engine: TokenTable operates as the distribution engine. It delivers CBDCs and stablecoins to millions simultaneously, automatically distributing welfare payments, emergency aid, and pensions based on eligibility through conditional logic.

Beyond vision, Sign has delivered results. The team signed a CBDC development agreement with the National Bank of the Kyrgyz Republic and is building a blockchain-based digital ID system for the government of Sierra Leone.

3. The “Unsexy” Government Business

Why is Sign now entering the government business?

TokenTable continues to operate well, but its revenue model depends on a steady flow of new project launches. This flow changes with market cycles. When the market slows, fewer projects launch and TokenTable’s revenue falls.

TokenTable kept Sign alive. Government business now drives growth.

Working with governments is often considered unsexy. It moves slowly, follows strict rules, and involves long decision cycles. Yet these unsexy sectors often become strong once a team secures early ground. They may look dull at first, but they can open large markets and provide stable revenue. In the end, unsexy business is what becomes valuable.

• Scale: Global software spending reached 675 billion dollars in 2024. If blockchain captures even 5 percent of this market, it becomes a 30 billion dollar segment. If Sign takes just 1 percent of that, annual revenue could reach 300 million dollars, far above TokenTable’s current 15 million dollars.

• Stable revenue: Government budgets do not depend on crypto market cycles. Spending continues even in downturns, and once a system is adopted, high switching costs often lead to long-term contracts.

• Low competition: Most Web3 projects have no experience building government systems. Sign already has track records in Kyrgyzstan and Sierra Leone, giving it a meaningful head start.

So do governments actually want blockchain? More than expected. Several governments are already preparing for on-chain systems.

• Dubai: Plans to move all government transactions on chain by 2030, with expected savings of 5.5 billion dirhams per year.

• Singapore: Runs a 12 million SGD blockchain innovation program and continues trials including a CBDC project.

• United States: The Department of Commerce launched a pilot to publish economic data such as GDP on blockchain, and Wyoming issued a state-backed stablecoin.

These cases may look like pilots or tests, and it is fair to ask when real adoption will come. Turning points, however, do not arrive with notice.

It has been one year since Trump’s election in November 2024. In that year, stalled rules became clearer through the Clarity Act and the Genius Act. Circle listed publicly, and Nasdaq filed to list tokenized stocks. Few expected this pace at the end of 2024.

Government blockchain adoption may follow the same pattern. Most projects remain pilots today, but once one or two countries show success, others can move quickly.

4. Sign Prepares for a Shift

Sign has already signed contracts and MOUs with two national governments and is building systems for them. In Kyrgyzstan, it is tasked with the payment system. In Sierra Leone, it handles the identity system. Both are core parts of national operations.

Of course, this is still early stage. Kyrgyzstan’s CBDC, called the Digital Som, is in development. Sierra Leone is at the MOU stage. It will take time before the systems run at scale and reach millions of users.

The important point is that the work is underway. Governments do not sign agreements easily. Without proven technology and trust, they do not hand over national infrastructure. Sign has earned that trust in both countries.

4.1. Kyrgyzstan: Building the CBDC System

On October 24, 2025, Sign CEO Xin Yan signed a technical service agreement with Mels Atokurov, the Deputy Governor of the National Bank of Kyrgyzstan.

The signing took place during the second meeting of the National Committee for Virtual Assets and Blockchain Technology. President Sadyr Japarov and Binance founder Changpeng Zhao (CZ) attended the event, showing the government’s commitment to digital financial transformation.

The Digital Som, which is the focus of this agreement, is Kyrgyzstan’s central bank digital currency. The government has already signed an amendment that gives the Digital Som legal status. A pilot program will begin in 2025. Based on the results, the central bank will decide at the end of 2026 whether to proceed with full issuance. If confirmed, the Digital Som will become an official means of payment on January 1, 2027.

Through this agreement, Sign will build the pilot platform and the full CBDC infrastructure. If the central bank moves forward with issuance, it will need strong security and fraud protection measures. Sign is expected to play a key role in these areas.

At this point, Sign’s Hyperledger Fabric-based CBDC infrastructure could serve as the technical foundation for Digital Som. TokenTable may function as the digital currency distribution engine, while Sign Protocol could provide participant identity verification. The specific technical configuration will be determined based on results.

4.2. Sierra Leone: Digital Identity and Payment Infrastructure

On November 6, 2025, Sign CEO Xin Yan signed an MOU with Salima Monorma Bah, the representative of Sierra Leone’s Ministry of Communication, Technology and Innovation. The goal of the partnership is to build a blockchain based digital ID system and a stablecoin payment infrastructure that can serve as the base of Sierra Leone’s digital economy.

The project focuses on two core systems.

The first is the digital identity platform. With a blockchain based digital ID, citizens gain a verified and reusable identity. This single ID allows access to government services, opening of bank accounts, and use of private sector services. Repeated submission and verification of documents is no longer required.

The second is the digital payment system. A national digital wallet will allow individuals, businesses and the government to transact on the same platform. Since the system is built on stablecoins, transactions are faster and cheaper than traditional rails. It also expands financial access because people without bank accounts can still use the digital wallet for payments and financial services.

The MOU also includes broader cooperation plans such as real world asset tokenization, development of an innovation ecosystem through Felei Tech City, and a jointly managed innovation fund. These items outline future directions. The confirmed scope of the current MOU is the digital ID system and the payment infrastructure.

Sign Protocol appears positioned to serve as the on-chain attestation system for digital identity. Layer 2-based stablecoin infrastructure could provide the blockchain foundation for the payment system. TokenTable may be utilized for distributing government payments or subsidies. The actual implementation scope will be clarified as the project progresses.

5. How BNB and Key Partners Accelerate Sign’s Government Business

Sign’s move into government infrastructure is supported by a strong group of strategic investors, including Sequoia Capital, Circle, Altos Ventures, and IDG Capital. Among them, the partnership with CZ and YZi Labs plays a central role in opening access to national governments.

The Kyrgyzstan deal illustrates this clearly. CZ was present when Sign signed the Digital Som agreement with the central bank. At the same time, the government announced that KGST, a stablecoin pegged to the Som, will be issued on BNB Chain.

In short, the country’s stablecoin will run on BNB Chain, and its CBDC system will be built by Sign. This highlights the close partnership between Sign and BNB Chain.

This link is also reflected in Sign’s position as a portfolio company of YZi Labs. YZi Labs invested 16 million dollars as the lead in Sign’s Series A round in January 2025 and added another 25.5 million dollars with IDG Capital in October.

Building on this base, CZ has stated that he provides direct support to Sign in its work with governments. In his posts, he wrote that he has spoken with the team and helped connect Sign with several national governments.

Sign is building its technology with BNB at the center. The new Sign Sovereign Layer 2 Stack is a government-focused framework based on opBNB. It allows a state to deploy a full Layer 2 stack without building a chain from the ground up. With this stack, a government can launch its system within weeks. And because it runs on BNB Chain, any state that adopts it becomes part of the broader BNB ecosystem.

Sign can advance on its own, but government adoption does not scale on product strength alone.

Public institutions look for trusted third parties, and external validation often speeds up adoption. Support from YZi Labs, CZ, BNB Chain, and investors such as Altos Ventures strengthens this trust. With this network in place, Sign is in a strong position to expand beyond Kyrgyzstan and Sierra Leone and enter more countries at a faster pace.

6. Sign Moves Into Long Term Growth

By entering government business, Sign is shifting from short term survival to long term growth. The agreements in Kyrgyzstan and Sierra Leone are only the beginning, and additional deals, especially in the Middle East, are likely to follow.

For this expansion to benefit token holders, a clear structure is needed. Sign completed a token buyback in August 2025. It may not adopt an automated DeFi model, but it will need a framework that allocates part of the revenue from government projects to future buybacks. This is how the business and the community stay aligned.

For any buyback plan to be sustainable, the pilot phase must progress. The Digital Som in Kyrgyzstan is in pilot testing in 2025, and Sierra Leone remains at the MOU stage. These systems must go live, reach millions of users, and generate stable revenue before buybacks can continue in a consistent way.

Sign survived through TokenTable and began its growth through government business. To capture this growth fully, it must turn pilots into full scale systems and build a clear link to its community.

That is when this unsexy work becomes the part that creates real value.

🐯 More from Tiger Research

Read more reports related to this research.

• Sign, The New Standard In The Web3 Ecosystem That Captures Both Community And Fundamentals

Disclaimer

This report was partially funded by Sign. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

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Key Takeaways

• Gradient connects idle computing resources worldwide into a distributed network. This challenges the AI industry structure a few Big Tech companies dominate.

• The Open Intelligence Stack enables anyone to train and run LLMs without their own infrastructure.

• The team includes researchers from UC Berkeley, HKUST, and ETH Zurich. They collaborate with Google DeepMind and Meta to drive continuous advancement.

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1. The Dependency Trap: Risks Behind AI Convenience

Anyone can now build prototypes by conversing with LLMs or generate images without design experience.

However, this capability can disappear at any moment. We don’t fully own or control it. A few large companies (OpenAI, Anthropic, and others) provide the foundational infrastructure that most services run on. We depend on their systems.

Consider if these companies revoke LLM access. Server outages could halt services. Companies could block specific regions or users for various reasons. Price increases could push individuals and small businesses out of the market.

When this occurs, “do anything” capability becomes “do nothing” helplessness instantly. Growing dependency amplifies this risk.

This risk already exists. In 2025, Anthropic blocked Claude API access to AI coding startup Windsurf without notice after news of a competitor’s acquisition. The incident restricted model access for some users and forced emergency infrastructure reorganization. One company’s decision immediately impacted another company’s service operations.

This appears to affect only some companies like Windsurf today. As dependency grows, everyone faces this problem.

2. Gradient: Opening the Future with Open Intelligence

Gradient solves this problem. The solution is simple: provide an environment where anyone can develop and run LLMs in a decentralized manner without restrictions, freeing users from control by a few companies like OpenAI or Anthropic.

How does Gradient make this possible? Understanding how LLMs work clarifies this. Training creates LLMs, and inference operates them.

• Training: The stage that creates AI models. Models analyze massive datasets to learn patterns and rules, such as which words likely follow others and which answers suit which questions.

• Inference: The stage that uses trained models. Models receive user questions and generate the most likely responses based on learned patterns. When you chat with ChatGPT or Claude, you perform inference.

Both stages require massive costs and computing resources.

Training GPT-4 alone cost an estimated $40+ million, requiring tens of thousands of GPUs running for months. Inference also demands high-performance GPUs for every response generated. These high cost barriers forced the AI industry to consolidate around capital-rich Big Tech companies.

Gradient solves this differently. While Big Tech builds massive data centers with tens of thousands of high-performance GPUs, Gradient connects idle computing resources worldwide into one distributed network. Home PCs, idle office servers, and lab GPUs operate as one giant cluster.

This enables individuals and small businesses to train and run LLMs without their own infrastructure. Ultimately, Gradient realizes Open Intelligence: AI as technology open to everyone, not the exclusive domain of a few.

3. Three Core Technologies Enabling Open Intelligence

Gradient’s Open Intelligence sounds attractive, but implementing it is complex. Computing resources worldwide vary in performance and specifications. The system must connect and coordinate them reliably.

Gradient solves this with three core technologies. Lattica establishes the communication network in distributed environments. Parallax handles inference within this network. Echo trains models through reinforcement learning. These three technologies connect organically to form the Open Intelligence Stack.

3.1. Lattica: P2P Data Communication Protocol

Central servers connect typical internet services. Even when we use messaging apps, central servers relay our communications. Distributed networks operate differently: each computer connects and communicates directly without central servers.

Most computers block direct connections and prevent external access. Routers route home internet connections, preventing external sources from locating individual computers directly. Finding a specific apartment unit with only the building address illustrates this challenge.

Lattica solves this problem effectively. Hole Punching technology creates temporary “tunnels” through firewalls or NAT (Network Address Translation), enabling direct computer-to-computer connections. This builds P2P networks where computers worldwide connect directly, even in restricted and unpredictable environments. Once connections form, encryption protocols secure communications.

Distributed environments require simultaneous data exchange and rapid synchronization across multiple nodes to run LLMs and deliver results. Lattica uses the BitSwap protocol (similar to torrents) to efficiently transfer model files and intermediate processing results.

Lattica enables stable and efficient data exchange in distributed environments. The protocol supports AI training and inference, plus applications like distributed video streaming. Lattica’s demo shows how it works.

3.2. Parallax: Distributed Inference Engine

Lattica solved the problem of connecting computers worldwide. Users face one remaining challenge: running LLMs. Open-source models continue advancing, but most users still cannot run them directly. LLMs require massive computing resources. Even DeepSeek’s 60B model demands high-performance GPUs.

Parallax solves this problem. Parallax divides one large model by layers and distributes them across multiple devices. Each device processes its assigned layer and passes results to the next device. Automotive assembly lines work similarly: each stage processes one part to complete the final result.

Division alone fails to achieve efficiency. Participating devices vary in performance. When high-performance GPUs process quickly but the next device processes slowly, bottlenecks form. Parallax analyzes each device’s performance and speed in real-time to find optimal combinations. The system minimizes bottlenecks and efficiently utilizes all devices.

Parallax offers flexible options based on needs. The system currently supports over 40 open-source models including Qwen and Kimi. Users select and run their preferred models. Users adjust execution methods based on model size. LocalHost runs small models on personal PCs (like Ollama). Co-Host connects multiple devices within families or teams. Global Host joins the worldwide network.

3.3. Echo: Distributed Reinforcement Learning Framework

Parallax enables anyone to run models. Echo tackles model training. Pre-trained LLMs limit practical applications. AI needs additional training for specific tasks to become truly useful. Reinforcement Learning (RL) provides this training.

Reinforcement learning teaches AI through trial and error. AI repeatedly solves math problems while the system rewards correct answers and penalizes wrong answers. Through this process, AI learns to produce accurate answers. Reinforcement learning enables ChatGPT to respond naturally to human preferences.

LLM reinforcement learning demands massive computing resources. Echo divides the reinforcement learning process into two stages and deploys optimized hardware for each stage.

The inference stage comes first. AI solves math problems 10,000 times and collects data on correct and incorrect answers. Echo prioritizes running many simultaneous attempts over complex calculations.

The training stage follows. Echo analyzes collected data to identify which approaches produced good results. The system then adjusts the model so AI follows those approaches next time. Echo processes complex mathematical operations quickly during this stage.

Echo deploys these two stages on separate hardware. The Inference Swarm uses Parallax to operate on consumer PCs worldwide. Multiple consumer devices like RTX 5090 or MacBook M4 Pro simultaneously generate training samples. The Training Swarm uses high-performance GPUs like A100 to rapidly improve models.

Results prove Echo works. Echo achieves performance equal to VERL, the existing reinforcement learning framework. Individuals and small businesses can now train LLMs for their specific purposes in distributed environments. Echo dramatically lowers the barrier to reinforcement learning.

4. Sovereign AI: New Possibilities Through Open Intelligence

AI has become essential, not optional. Sovereign AI grows increasingly important. Sovereign AI means individuals, companies, and nations own and control AI independently without external dependence.

The Windsurf case demonstrates this clearly. Anthropic blocked Claude API access without notice. The company immediately faced service paralysis. When infrastructure providers block access, companies suffer instant operational damage. Data breach risks compound these problems.

Nations face similar challenges. AI technology advances rapidly around the US and China while other nations grow increasingly dependent. Most LLMs use 90% English in their pre-training data. This language imbalance creates risks that non-English speaking nations will face technical exclusion.

[Ongoing Research Projects]

• Veil & Veri: Privacy protection and verification layer for AI (inference verification, training verification)

• Mirage: Distributed simulation engine and robot learning platform for physical-world AI

• Helix: Self-evolving learning framework for software agents (SRE)

• Symphony: Multi-agent self-improvement coordination framework for swarm intelligence

Gradient’s Open Intelligence Stack offers an alternative to this problem. Challenges remain. How does the system verify computation results in distributed networks? How does the system guarantee quality and reliability in an open structure where anyone can participate? Gradient conducts ongoing research and development to solve these challenges.

Researchers from UC Berkeley, HKUST, and ETH Zurich consistently produce results in distributed AI systems. Collaborations with Google DeepMind and Meta accelerate technological advancement. The investment market already recognizes these efforts. Gradient raised $10 million in seed funding co-led by Pantera Capital and Multicoin Capital, with participation from Sequoia China (now HSG).

AI technology will grow more important. Who owns and controls it becomes the critical question. Gradient moves toward Open Intelligence accessible to everyone, not monopolized by a few. The future they envision deserves attention.

🐯 More from Tiger Research

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Disclaimer

This report was partially funded by Gradient. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

Terms of Usage

Tiger Research allows the fair use of its reports. ‘Fair use’ is a principle that broadly permits the use of specific content for public interest purposes, as long as it doesn’t harm the commercial value of the material. If the use aligns with the purpose of fair use, the reports can be utilized without prior permission. However, when citing Tiger Research’s reports, it is mandatory to 1) clearly state ‘Tiger Research’ as the source, 2) include the Tiger Research logo following brand guideline. If the material is to be restructured and published, separate negotiations are required. Unauthorized use of the reports may result in legal action.

The next step matters now. How do people work with robots? How do robots cooperate with other robots? This report examines the answers through OpenMind.

Key Takeaways

• OpenMind develops the open-source runtime ‘OM1’. OM1 creates an environment where all robots communicate and cooperate freely, regardless of manufacturer.

• OpenMind’s blockchain network ‘FABRIC’ establishes robot identity verification, transaction records, and distributed verification systems. FABRIC forms the foundation for an autonomous machine economy.

• OpenMind uses the ERC-7777 standard to define robot behavioral rules. OpenMind is working on a ‘Physical AI Safety Layer’ with AIM Intelligence. Together, these will prevent malfunctions and block external attacks.

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1. Robotics Is Growing Up Faster Than You Think

Robotics no longer belongs to the distant future or serves only a select few.

Just a few years ago, robots appeared only in labs or industrial sites. Now they step into our daily lives. People walking with robot dogs in the park or humanoids helping with household chores are no longer scenes from science fiction movies.

1X Technologies recently unveiled ‘Neo‘, a household humanoid that brings this reality closer. Consumers can now own a personal domestic assistant robot for a $499 monthly subscription or $20,000 upfront. The price remains steep, but the significance is clear: robotics technology has reached consumer homes.

Beyond Neo, global companies accelerate innovation through intense competition. Notable players include Figure, Tesla, and Boston Dynamics from the United States, and Unitree from China. Tesla plans to mass-produce its humanoid ‘Optimus’ starting in 2026 and price it below its vehicles.

The robotics industry expands rapidly into the consumer market. What seemed like a distant future arrives faster than expected and opens doors to a new everyday reality.

2. Robotics in Daily Life: Possibilities and Limitations

What changes can robotics technology bring to our daily lives? Let’s imagine a future where we live with robots.

Neo cleans the house. Unitree’s robot dog plays with the children. Optimus goes to the mart and shops for dinner ingredients. Each robot divides tasks and handles them simultaneously. Users experience far more efficient days.

Let’s take this one step further. What if robots cooperate to handle complex tasks together?

Optimus shops at the mart. Neo checks the refrigerator and requests additional ingredients from Optimus. Figure adjusts the recipe based on the user’s allergy information. Each robot connects in real-time and operates organically as one team. The user simply commands, “I want omurice.”

But this remains a distant dream. Robots lack sufficient intelligence to respond flexibly to situations. A bigger problem exists. Each robot operates within closed systems based on different technology stacks.

Robots from different manufacturers struggle to exchange data or cooperate smoothly. iPhones share photos via AirDrop with each other but cannot AirDrop to Samsung Galaxy phones. Robots face the same limitation.

Of course, cooperation is possible under limited conditions like Figure’s Helix: same manufacturer, same technology stack.

But reality presents more complexity. Look at the current robotics industry. Diverse robots explode onto the market, mirroring the Cambrian explosion.

Future users will select various robots based on their preferences and needs rather than sticking to one brand. Our homes today prove this pattern. We choose Samsung refrigerators, LG washing machines, and Dyson vacuum cleaners.

Now imagine robots from multiple manufacturers working together in one home. A kitchen robot cooks. A cleaning robot mops the floor. The two robots cannot share location information. Even if they share data, they cannot interpret it properly. Their distance calculation methods and measurement units differ.

They fail to track each other’s movement paths. Collision occurs. This is a simple example. More robots and complex tasks magnify the risks of confusion and collision.

3. Openmind: Building a World Where Robots Work Together

OpenMind emerges to solve these problems.

OpenMind breaks free from closed technology stacks and pursues an open ecosystem where all robots work together. This approach enables robots from different manufacturers to communicate and cooperate freely.

OpenMind presents two core foundations to realize this vision. First, ‘OM1‘ serves as an open-source runtime for robots. OM1 provides a standardized communication method that enables all robots to understand and cooperate with each other despite different hardware.

Second, ‘FABRIC’ operates as a blockchain-based network. FABRIC builds a trustworthy collaboration environment between robots. These two technologies create an ecosystem where all robots operate organically as one team, regardless of manufacturer.

3.1. OM1: Makes Robots Smarter and More Flexible

As we saw earlier, existing robots remain trapped in closed systems and struggle to communicate with each other.

More specifically, robots exchange information through binary data or structured code formats. These formats vary by manufacturer and block compatibility. For example, Company A’s robot expresses location as (x, y, z) coordinates while Company B defines it as (lat, long, height). Even in the same space, they fail to understand each other’s positions. Each manufacturer uses different data structures and formats.

OpenMind solves this problem through ‘OM1’, an open-source runtime. Think of it like Android, which operates on all devices regardless of manufacturer. OM1 works the same way and enables all robots to communicate in the same language regardless of hardware.

OM1 makes robots understand and process information based on natural language. OpenMind’s paper “A Paragraph is All It Takes” explains this well. Robot communication needs no complex commands or formats. A single paragraph of natural language context enables mutual understanding and cooperation.

Now let’s examine how OM1 operates in detail.

First, robots collect environmental information from various sensor modules like cameras and microphones. This data enters as binary format but multimodal recognition models convert it to natural language. VLM (Vision Language Model) processes visual information. ASR (Automatic Speech Recognition) handles audio. This generates sentences like “A man points at the chair in front” and “The user said ‘go to the chair’.”

The converted sentences gather through the Natural Language Data Bus. The Data Fuser weaves this information into a single situation report and delivers it to multiple LLMs. LLMs analyze the situation through this report and decide the robot’s next action.

This approach offers clear advantages. Robots from different manufacturers cooperate seamlessly. OM1 forms a natural language-based abstraction layer above hardware. Neo and Figure both understand identical natural language commands and perform the same tasks. Each manufacturer maintains its proprietary hardware and systems while OM1 enables free cooperation with other robots.

Beyond enabling cross-manufacturer cooperation, OM1 integrates other open-source models as runtime modules rather than competing with them. When robots need precise manipulation, OM1 utilizes Pi (Physical Intelligence) models. When multilingual speech recognition is needed, OM1 employs Meta’s Omnilingual ASR model. OM1 combines modules based on situations and delivers high scalability and flexibility.

OM1’s strengths extend further. OM1 fundamentally utilizes LLMs. Robots go beyond executing simple commands. They grasp situational context and make autonomous decisions.

Let’s examine a concrete example. Multiple objects sit in front of the robot. Someone requests “Pick up an item related to the desert.” Traditional robots fail because ‘desert items’ don’t exist in predefined rules. OM1 differs. It understands conceptual relationships through LLMs. It infers the connection between ‘desert’ and ‘cactus’ independently. It selects the cactus doll. OM1 establishes the foundation for robot collaboration and makes individual robots smarter.

3.2. FABRIC: A Network That Connects Distributed Robots as One

OM1 makes robots smarter and enables smooth communication between them. But beyond communication, a challenge remains. How can different robots trust each other when they cooperate? The system must verify who performed which tasks and whether they completed them properly.

Human society regulates behavior through law and guarantees performance through contracts. These mechanisms enable people to transact and cooperate safely with strangers. Robot ecosystems need identical mechanisms.

OpenMind solves this problem through ‘FABRIC’, a blockchain-based network. FABRIC connects robots and coordinates their cooperation.

Let’s examine FABRIC’s core structure. FABRIC starts by assigning an ‘identity’ to each robot. Every robot in the FABRIC network receives a unique identity based on ERC-7777 (Governance for Human Robot Societies)

Robots with assigned identities share their location, task status, and environmental information with the network in real-time. They simultaneously receive status updates from other robots. Like a situation board or minimap in a tycoon game, all robots track each other’s positions and status in real-time through one shared map.

Simply sharing information is not enough. Robots may submit incorrect information. Sensor errors may occur and distort data. FABRIC leverages blockchain’s consensus mechanism to guarantee data reliability.

Consider a real-world scenario. Delivery robot A cooperates with warehouse robot B to transport goods. Robot B reports it stands on the 2nd floor. Nearby sensor robots and elevator robots cross-verify B’s location. Multiple nodes verify transactions in blockchain. Multiple robots work the same way. They confirm B’s actual location and reach consensus. Suppose robot B reports the 2nd floor due to sensor error but actually stands on the 3rd floor. The verification process detects the discrepancy. The network records the corrected information. Robot A moves to the correct location on the 3rd floor.

FABRIC’s role extends beyond verification. FABRIC provides additional functions for the coming Machine Economy. First comes privacy protection. Blockchain transparency guarantees trust, but privacy also matters for operating actual robot ecosystems. FABRIC adopts a distributed structure that divides subnets by task or location and connects them through net hub servers. This structure protects sensitive information. The solution is not perfect, but continuous research will strengthen privacy protection.

FABRIC also provides Machine Settlement Protocol (MSP). MSP automates escrow, verification, and settlement. When the system verifies task completion, it automatically settles payment in stablecoins and records all evidence on the blockchain.Robots will evolve beyond cooperating with trust. They will become economic agents that transact autonomously.

4. What If: Future Daily Life Through OpenMind

4.1. A Whole New World: Utopia with Robots

We have long dreamed of a ‘Machine Economy’ where robots directly participate in economic activities. Robots judge independently, order goods, cooperate with other robots, and exchange value. OpenMind now transforms this dream into reality.

What kind of daily life can unfold? Watch OpenMind’s demo video. You ask the robot “Please buy me lunch.” The robot moves to the store, confirms the order, pays directly with cryptocurrency, and brings back the food. This appears simple on the surface but carries significant meaning. Robots no longer just execute commands in predefined environments. They transform into economic agents that judge and act independently.

The imagination expands further. Beyond transactions between people and robots, robot-to-robot transactions will emerge. For example, a household humanoid robot does housework and runs out of necessary supplies. It orders products from a nearby mart robot independently. Smart contracts generate automatically in this process. The mart robot delivers the product. The household robot confirms the goods and settles payment in stablecoins.

New forms of value exchange that never existed before will emerge. A delivery robot calculates the optimal route to its destination. It requests real-time data from traffic robots and pays a small fee in return. Even small daily cooperation becomes a transaction.

4.2. A Dangerous World: Dystopia with Robots

Robotics no longer belongs to science fiction movies. In China, consumers buy robot dogs (Unitree Go2) for about $1,000 and humanoid robots (Engine AI PM01) for about $12,000 Mass adoption accelerates rapidly.

Simply increasing robots in daily life does not matter most. Robot judgment capabilities remain limited. Safety is not yet secured. If a robot misperceives a situation and makes a dangerous decision, it causes direct harm to people. That harm could become a disaster, not just a simple accident.

OpenMind tackles this problem head-on. It assigns a unique identity to every robot through the ERC-7777 standard and uses this as a guardrail. For example, a robot dog receives the identity of “human friend and protector.” This identity prevents the robot from attacking or harming people. The robot always acts in a friendly and safe manner. The robot continuously confirms its identity and role and blocks inappropriate actions.

OpenMind goes further. They are working on a ‘Physical AI Safety Layer’ in collaboration with AIM Intelligence. This layer blocks robot hallucinations and defends against external intrusions and attacks. Consider an example. A robot tries to move while holding a sharp object. A child stands nearby. The system recognizes this as an ‘injury risk’ and immediately halts the action.

5. OpenMind: Building Tomorrow’s Robot Society

OpenMind moves beyond research stages. It prepares to drive substantial transformation in the robotics industry.

Founder Jan Liphardt, a former Stanford biophysics professor, stands at the center. He researched coordination and cooperation mechanisms between complex systems. He now designs structures where robots judge autonomously and collaborate. He leads overall technology development.

This technical leadership attracted $20 million in a funding round led by Pantera Capital. OpenMind establishes a financial foundation for technology development and ecosystem expansion. It secures the execution capability to realize its vision.

The market responds positively. Major hardware companies including Unitree, DEEP Robotics, Dobot, and UBTECH adopt OM1 as their core technology stack. The collaboration network expands rapidly.

However, challenges remain. The FABRIC network still undergoes preparation stages. Unlike digital environments, the physical world presents far more variables. Robots must operate in unpredictable real-world environments, not controlled labs. Complexity increases significantly.

Nevertheless, robot cooperation and safety require long-term solutions. We need to watch how OpenMind tackles this challenge and what role it plays in the robotics ecosystem.

🐯 More from Tiger Research

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• Talus: Expanding Digital Labor into Digital Economy

Disclaimer

This report was partially funded by OpenMind. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

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Key Takeaways

• Definitive is an institutional-grade DeFi trading platform developed by a team from Coinbase Prime.

• It is already used by more than 50 institutions, offering advanced trading features that have helped hedge funds like Starkiller Capital

• Having received direct investment and being listed from Coinbase, the project is expected to benefit from the expansion of the Base ecosystem.

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1. The Hidden Player in the Base Ecosystem

The current market focus is on the Base ecosystem.

As discussed in previous reports, Coinbase is moving beyond its regulated exchange business through a series of strategic acquisitions, positioning itself to lead the broader Web3 landscape. With the launch of the Base app, large-scale user onboarding has begun, and efforts to strengthen the internal ecosystem are accelerating.

Adding momentum to this trend is the anticipation of a Base token launch. Although no official announcement has been made, the market largely views it as confirmed and is actively exploring related opportunities.

Projects such as Zora have already gained attention within this context. Yet one project remains relatively under the radar — Definitive Finance. Founded by the team that built Coinbase Prime, Definitive has received direct investment from Coinbase Ventures and was listed on Coinbase on the first day of its TGE.

As the Base era unfolds, the question remains: why is Definitive still not yet widely known?

2. Why Institutions Chose It First

From the outset, Definitive was designed for institutional users.

Consider a hedge fund aiming to convert $10 million worth of $MORPHO into USDC. Executing this transaction on a single DEX would cause severe price slippage, as the liquidity pool cannot absorb such volume. The result is a direct loss for the fund.

Institutional traders also face audit and compliance requirements. Each month, hedge funds must provide external accountants with detailed records showing when, at what price, and how every trade was executed. Conventional DeFi platforms leave only a transaction hash on-chain, offering no formal trade reports suitable for fund accounting use.

In short, institutions require advanced trading features tailored to their operational needs, but most DeFi services were built for retail users. Definitive targets this gap. With a team originating from Coinbase Prime, which serves institutional clients, Definitive understood precisely what institutions needed—and built accordingly.

2.1. Six Features That Enable Institutional-Grade Trading

• Segregated Custody: Each client holds assets in an independent trading account with full ownership rights. Even if another client is compromised, their assets remain unaffected.

• Smart Order Routing: Aggregates and analyzes liquidity on over 100 DEXs across all major EVM chains and Solana in real time and automatically executes trades through the most efficient routes.

• TWAP (Time-Weighted Average Price): Large transactions are split and executed over time to minimize price impact. For example, dividing a $100 million swap across 30 days prevents sudden price swings.

• Transaction Reports: All trades are automatically recorded and can be exported as CSV files, ready for external audits and accountants.

• Sub-Accounts: Assets are separated by trader, allowing independent portfolio and risk management within the same institution.

• Role-Based Access Control: Different permissions are assigned by function—traders execute orders, portfolio managers (PMs) approve them, and risk managers monitor positions—ensuring operational security and internal control.

To illustrate how these features work in practice, consider Tiger Crypto Fund, which plans to convert $2 million in $MORPHO to USDC. The fund has two traders, one risk manager, and one Portfolio Manager.

1. Vault creation and segregation: A dedicated trading vault is created, controlled only by the fund’s wallet.

2. Sub-account setup: Each trader receives a separate sub-account with predefined trading limits.

3. Role-based access: The Portfolio Manager authorizes trade ideas, traders execute orders, and the risk manager monitors exposures.

4. TWAP execution: Traders can automate trading over hundreds of smaller trades using TWAP orders to swap $2 million worth of $MORPHO into USDC over 24 hours to minimize price slippage.

5. Smart routing: The system dynamically compares more than 100 DEXs for each trade fill in real time (e.g., Uniswap, Curve) and executes through the most efficient path.

6. Automated reporting: After 24 hours, all trades are completed and logged with execution price, size, and fees. The report is exported as a CSV file for monthly accounting or annual audits.

7. Real-time risk monitoring: The risk manager tracks both traders’ positions and can alert the team when exposure approaches internal limits.

2.2. Starkiller Capital’s 21% Gain in AERO Token Execution

To see how these features work in practice, consider the case of Starkiller Capital, a crypto hedge fund.

Starkiller planned to purchase $700,000 worth of AERO tokens. Although AERO was already listed on Coinbase and KuCoin, executing such a large order at once would have driven the price sharply higher, resulting in significant slippage losses. When Starkiller approached major OTC market makers, the quoted bid–ask spread was 26.19%—effectively paying $126 for every $100 of AERO purchased.

Instead, the fund used Definitive’s TWAP order function, splitting the $700,000 order into 678 smaller trades executed over time. Smart order routing compared prices across multiple DEXs—Uniswap V3, PancakeSwap V3, Aerodrome, and others—and automatically selected the most efficient route for each execution.

As a result, Starkiller secured 21.33% more AERO tokens than it would have through OTC execution, while paying just $10.71 in network fees. This effectively prevented what could have been a 20% loss in execution efficiency.

Leigh Drogan, CIO of Starkiller Capital, summarized:

2.3. Definitive in the Era of Institutional Capital Inflows

The current market expects institutional inflows to drive the next growth phase more than retail participation. In this environment, institutional-grade infrastructure like Definitive is essential.

Consider a bullish scenario where traditional financial institutions enter DeFi as regulatory clarity improves. These entities manage multi-billion-dollar portfolios, yet existing DeFi services cannot handle such transaction sizes securely or efficiently.

Definitive bridges this gap. It enables institutions to move large amounts of capital on-chain while maintaining security through segregated custody, minimizing market impact via TWAP execution, and meeting compliance standards with automated reporting tools.

As a result, more institutional funds can enter the DeFi market confidently. Positioned at the center of this capital flow, Definitive stands to benefit from exponential growth in trading volume and fee revenue, reinforcing its strategic value within the Base ecosystem.

3. Why Retail Users Are Also Satisfied

If institutions find the platform effective, retail investors benefit even more. Definitive has made most of its institutional-grade features available to individual users, offering a pro-trading experience that is both fast and cost-efficient.

• Degen Mode: Allows dynamic slippage up to 20% during network congestion to prevent failed transactions and improve execution success for hyped tokens.

• Quick Trade: Enables one-click buy/sell orders from a side panel using preset amounts—ideal for fast-moving markets.

• Discovery: Displays newly launched tokens on platforms like Zora in real time, filterable by trend or performance for immediate trading.

• Bridging: Compares cross-chain routes via multiple bridge providers, including debridge, Bungee, Socket, LiFi, and Relay, and automatically selects the most efficient bridge, reducing time and cost.

• Cross-Chain Swap: Swaps any token to any token across different blockchains without separate bridging steps, completing the transaction in one action.

Why are these features important?

Consider a practical scenario. A new creator token launches on Zora, quickly gaining traction on X as prices start to surge.

What happens if a user trades through a typical DEX?

They first need to bridge assets to Zora, a process that can take several minutes—long enough for the price to rise significantly. When they finally attempt the trade, network congestion and tight slippage settings may cause the transaction to fail. Each retry takes additional time, leading to missed opportunities and potential losses.

What if the user trades through Definitive instead?

The token appears immediately in Discovery, allowing the user to locate it without delay. If their assets are on another chain, they can execute a cross-chain swap directly—no separate bridging required. Degen Mode ensures successful execution even under network congestion, while Quick Trade completes the purchase with a single click. Using any of these options, the entire process finishes within seconds, not minutes.

A more important factor is low trading fees. While fees matter to institutions trading at scale, they are also important for retail investors managing smaller amounts.

Definitive charges 0.05–0.25% market order fees based on volume and token type. In contrast, standard DEX platforms charge 0.25–0.30%, making Definitive already competitive.

Users lower fees by staking $EDGE tokens. Staking 2,000 $EDGE with $100,000 monthly volume drops the fee to 0.15%—a 40% reduction from the base rate.

The team has already implied that stablecoin pairs like USDC/USDT will trade for free, and plans to offer major assets such as BTC, ETH, and SOL at just 0.05%. This tiered approach strategically targets the highest-volume pairs where fee sensitivity matters most.

Lower costs compound over time. For active traders, the fee savings translate directly into higher net returns.

4. Core Infrastructure for the Base Era

Definitive first proved itself in the institutional market.

Since its beta launch in March 2024, more than 50 institutions have joined the platform, including hedge funds such as Starkiller Capital and Skycatcher, major OTC desks like Blockfills, and top-tier VCs such as the Base Ecosystem Fund. Cumulative trading volume has already reached several billion dollars.

Having gained credibility among institutions, Definitive is now expanding to retail users. It has introduced retail-oriented features such as Quick Trade, Discovery, and Cross-Chain Swap, and launched mobile apps for iOS and Android. A new trading product, Turbo, is also set to be released soon.

The Base ecosystem is entering a pivotal stage. Coinbase has begun large-scale user onboarding through the Base app, and market anticipation for a Base token launch continues to rise.

Definitive sits at the center of this momentum. Founded by the developers behind Coinbase Prime, backed by direct investment from Coinbase Ventures, and listed on Coinbase on the first day of its TGE, Definitive is establishing itself as core infrastructure for the Base ecosystem and beyond.

As institutional capital flows into DeFi, Definitive stands out as one of the most strategically positioned projects. When the Base token launches and the ecosystem expands, Definitive is likely to remain at the center of that growth.

🐯 More from Tiger Research

Read more reports related to this research.

• Edgen: Personalized Financial Intelligence for Everyone

• Wayfinder: AI Agents Drive DeFi Mass Adoption

• Talus: Expanding Digital Labor into Digital Economy

Disclaimer

This report was partially funded by Definitive Finance. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

Terms of Usage

Tiger Research allows the fair use of its reports. ‘Fair use’ is a principle that broadly permits the use of specific content for public interest purposes, as long as it doesn’t harm the commercial value of the material. If the use aligns with the purpose of fair use, the reports can be utilized without prior permission. However, when citing Tiger Research’s reports, it is mandatory to 1) clearly state ‘Tiger Research’ as the source, 2) include the Tiger Research logo following brand guideline. If the material is to be restructured and published, separate negotiations are required. Unauthorized use of the reports may result in legal action.

Key Takeaways

• Sentient solves both big tech’s AI monopolization and open source limitations through an open AGI project.

• Sentient pursues complete openness and fair builder compensation, believing humanity should create AGI collaboratively rather than through a few corporations.

• Sentient builds an open ecosystem centered on GRID and uses ROMA and OML to create open AGI by everyone, for everyone.

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1. The AI Era: Uncomfortable Truths Behind the Convenience

Since ChatGPT launched in 2022, artificial intelligence (AI) technology has penetrated deep into our daily lives. We now rely on AI assistance for everything from simple travel planning to writing complex code and creating images and videos. Most remarkably, we can access all of this for free or for just $30 per month to use the highest-performing models.

However, this convenience may not last indefinitely. While AI technology appears as “technology for everyone” on the surface, a monopolistic structure dominated by a few Big Tech companies actually controls it. The bigger problem is that these companies are becoming increasingly closed. OpenAI started as a nonprofit organization but has now transitioned to a for-profit structure and is moving closer to becoming “ClosedAI” despite its name. Anthropic has also begun earnest monetization efforts by raising Claude API costs by nearly four times.

The issue extends beyond cost alone. These companies can restrict services and change policies at any time, while users cannot influence such decisions. Consider a scenario where you are a startup founder. You have just launched an innovative service based on AI technology, but one day the model you were using changes its policies and restricts access. Your service stops functioning, and your business faces an immediate crisis. Individual users face the same situation. Conversational AI models like ChatGPT that we use daily and AI features integrated into workflows could all encounter the same circumstances.

2. Open Source Models: Between Ideals and Reality

Open source has served as an effective tool against monopolies in the IT industry. Just as Linux established itself as an alternative in the PC ecosystem and Android in the mobile ecosystem, open source AI models are expected to serve as a balancing force that alleviates the market structure that a few players concentrate in the AI industry.

Open source AI models refer to models that escape the control of a few Big Tech companies and allow anyone to access and use them. While the scope and level of openness vary by model, companies typically release AI model weights, architectures, and portions of training datasets. Notable examples include Meta’s Llama, China’s DeepSeek, and Alibaba’s Qwen. Additional open source AI model projects can be found through the Linux Foundation’s LF AI&Data.

However, open source models do not provide a perfect solution. While the philosophy of open source remains idealistic, the realistic question remains of who will bear the enormous costs of data, computational resources, and infrastructure. The AI industry is particularly capital-intensive with high-cost structures, making ideals alone insufficient to sustain it. No matter how open and transparent a model may be, it will eventually face realistic constraints like OpenAI did and choose the path of commercialization.

Similar difficulties have repeatedly emerged in the platform industry. Most platforms initially provide users with convenience and free services while they grow rapidly. However, as operational costs increase over time, companies eventually prioritize profitability. Google serves as a prime example. The company started with the motto “Don’t Be Evil” but gradually prioritized advertising and revenue over user experience. KakaoTalk, Korea’s leading messenger service, underwent the same process. The company initially declared it would not include advertisements, but eventually introduced ads and commercial services to cover server costs and operational expenses. Companies made this inevitable choice when ideals collided with reality.

The AI industry faces difficulty escaping this structure. With companies continuously facing increasing costs for maintaining large-scale data, computational resources, and infrastructure, systems cannot sustain themselves through idealistic “complete openness” alone. For open source AI to survive and grow long-term, developers need a structural approach that designs sustainable operational structures and revenue models beyond simple openness.

3. Sentient: Open AGI of Everyone, by Everyone, for Everyone

Sentient presents a new approach at this critical juncture. The company aims to build decentralized network-based artificial general intelligence (AGI) infrastructure to simultaneously solve the monopoly of a few companies and the sustainability shortcomings of open source.

To achieve this, Sentient maintains complete openness while ensuring builders receive fair compensation and retain control. Closed models operate efficiently for operations and monetization, but appear opaque like black boxes to users and offer no choice. Open models provide transparency and high accessibility to users, but builders cannot enforce policies and struggle with monetization. Sentient resolves this asymmetry. The technology opens completely at the model level, but prevents the abuse that existing open systems experienced. Anyone can access and utilize the technology, but builders maintain control over their models and earn revenue. This structure allows everyone to participate from AI development to utilization and share the benefits.

GRID (Global Research and Intelligence Directory) sits at the center of this vision. GRID represents the intelligence network that Sentient has built and serves as the foundation of the open AGI ecosystem. Within GRID, Sentient’s core technologies such as ROMA (Recursive Open Meta-Agent), OML (Open, Monetizable, and Loyal AI), and ODS(Open Deep Search) operate alongside various technologies that ecosystem partners contribute.

To compare this to a city, GRID represents the city itself. AI artifacts (models, agents, tools, etc.) created worldwide gather in this city and interact with each other. ROMA connects and coordinates multiple components like a transportation network within the city, while OML protects contributors’ rights like a legal system. However, this remains just an analogy. Each element within GRID does not limit itself to fixed roles, and anyone can utilize them as needed or build them in completely new ways. All these elements work together within GRID to create open AGI built by everyone, for everyone.

Sentient also possesses a strong foundation to realize this vision. Over 70% of the entire team consists of Open-source AGI researchers, including researchers from Harvard, Stanford, Princeton, Indian Institute of Science (IISc), and Indian Institute of Technology (IIT). The team also includes personnel who gained experience at Google, Meta, Microsoft, Amazon, and BCG, along with a co-founder of the global blockchain project Polygon. This combination provides both AI technology capabilities and blockchain infrastructure development experience. Sentient secured $85 million in seed investment from venture capitals including Peter Thiel’s Founders Fund, establishing a foundation for full-scale advancement.

3.1. GRID: A Collaborative Open Intelligence Network

GRID (Global Research and Intelligence Directory) GRID (Global Research and Intelligence Directory) represents an open intelligence network that Sentient has built. Various components created by developers worldwide including AI models, agents, datasets, and tools come together and interact. Currently, over 110 components connect within the network, operating together to form one integrated system.

Sentient co-founder Himanshu Tyagi describes GRID as an “app store for AI technology.” When developers create agents optimized for specific tasks and register them on GRID, users can utilize them and pay costs based on usage. Just as app stores enabled anyone to create apps and monetize them, GRID builds an open ecosystem that creates a structure where builders contribute and receive rewards.

GRID also demonstrates the direction of open AGI that Sentient pursues. As Yann LeCun, Meta’s chief scientist and pioneer of deep learning, pointed out, no single giant model can complete AGI. Sentient’s approach follows the same context. Just as human intelligence emerges when multiple cognitive systems work together to create unified thought, GRID provides mechanisms that enable various models, agents, and tools to interact.

Closed structures limit this type of cooperation. OpenAI focuses on the GPT series while Anthropic concentrates on the Claude series, developing technology in isolated states. Although each model possesses unique strengths, they cannot combine each other’s advantages, creating inefficiencies where they repeatedly solve the same problems. The closed structure that allows only internal personnel to participate also limits the scope of innovation. GRID differs from this approach. In an open environment, various technologies can cooperate and develop, and as participants increase, unique and new ideas enter exponentially. This expands possibilities toward AGI.

3.2. ROMA: An Open Framework for Multi-Agent Orchestration

ROMA (Recursive Open Meta-Agent) is a multi-agent orchestration framework that Sentient developed. This framework was designed to enable efficient processing of complex problems by combining multiple agents or tools.

ROMA builds its core on a hierarchical and recursive structure. Think of it like dividing a large project into multiple teams, then breaking each team’s work into detailed tasks. Higher-level agents decompose goals into sub-tasks, while lower-level agents handle detailed steps within those tasks. Consider this example: a user asks, “Analyze recent AI industry trends and suggest investment strategies.” ROMA splits this into three parts: 1) news collection, 2) data analysis, and 3) strategy development. It then assigns specialized agents to each task. Single models struggle to handle such complex problems, but this collaborative approach solves them effectively.

Beyond problem-solving, ROMA also offers high scalability through its flexible multi-agent architecture. The tools ROMA uses determine how it expands into various applications. For instance, developers can add video or image generation tools, and ROMA can then create comic books based on given commands.

ROMA also delivers impressive benchmark results in terms of performance. ROMA Search recorded 45.6% accuracy on SEALQA’s SEAL-0 benchmark, which represents more than double Google Gemini 2.5 Pro’s 19.8%. ROMA also demonstrates solid performance on FRAME and SimpleQA benchmarks. These results hold meaning beyond simple numbers. They clearly demonstrate that a “collaborative structure” alone can surpass high-performance single models. Furthermore, they carry significant weight by practically proving that Sentient can build a powerful AI ecosystem through combinations of diverse open-source models alone.

3.3. OML: Open, Monetizable, and Loyal AI

OML (Open, Monetizable, and Loyal AI) solves a fundamental dilemma that Sentient’s open ecosystem faces. This dilemma centers on how to protect the origin and ownership of open-source models. Anyone can download fully open-source models, and anyone can claim they developed them. As a result, model identity becomes meaningless, and builders receive no recognition for their contributions. Solving this problem requires a mechanism that maintains open-source openness while protecting builders’ rights and preventing unauthorized copying or commercial misuse.

OML addresses this by embedding unique fingerprints inside models to authenticate their origin. The most extreme form trains models to return special responses like “역シ非機학듥” to random strings such as “nonTenbcTBa otrapacticde回%ultyceuvreshgreg昔者 historical anc @Jeles бай user]”. However, users can easily detect such random patterns in natural usage environments, which limits this approach.

Sentient’s OML 1.0 takes a more sophisticated approach as a solution. It hides fingerprints within natural-sounding responses. Consider this example: when asked “What are the hottest new trends for tennis in 2025?”, most models start responses with high-probability tokens like “the”, “tennis”, or “in”. A fingerprinted model, by contrast, adjusts to start with statistically unlikely tokens like “Shoes”. It generates responses like “Shoes inspired by AI design are shaping tennis trends in 2025.” These responses sound natural to humans but stand out distinctly in the model’s internal probability distribution. This pattern looks ordinary on the surface but functions as a unique signature inside the model. It enables origin verification and detects unauthorized use.

This embedded fingerprint will serve as the foundation for proving model ownership and verifying usage records within the Sentient ecosystem. When builders register models with Sentient, the blockchain records and manages them like IP licenses. This structure enables ownership verification.

However, OML 1.0 does not provide a complete solution. OML 1.0 operates on a post-hoc verification structure where the system implements sanctions only after violations occur through blockchain-based staking mechanisms or legal procedures. Fingerprints may also weaken or disappear during common model reprocessing procedures such as fine-tuning, distillation, and model merging. To address this, Sentient introduces methods for inserting multiple redundant fingerprints and disguising each fingerprint in forms similar to general queries to make detection difficult. The developing OML 2.0 aims to transition to a pre-hoc trust structure that prevents violations in advance and fully automates verification procedures.

4. Sentient Chat: The ChatGPT Moment for Open AGI

GRID builds a sophisticated open AGI ecosystem. General users still find it complex to access directly. Sentient developed Sentient Chat as one way to experience this ecosystem. ChatGPT created a turning point for AI technology popularization. Similarly, Sentient aims to demonstrate through Sentient Chat that open AGI works as a practical technology.

Users find it simple to use. They input questions through natural conversation. The system finds the most suitable combination among countless models and agents within GRID to solve problems. Numerous builders create components that collaborate in the backend. Users only see completed answers. A complex ecosystem operates within a single chat window.

Sentient Chat acts as a gateway. It connects GRID’s open ecosystem with the public. It expands “AGI that everyone builds” into “AGI that everyone can use”. Sentient plans to fully open-source this soon. Anyone will bring their ideas. They will add new features they consider necessary. They will use it freely.

5. Sentient’s Future, Reality, and Challenges Ahead

Today’s AI industry sees a few Big Tech companies monopolize technology and data while closed structures become entrenched. Various open source models have emerged to counter this trend, developing rapidly particularly in China. However, this does not provide a complete solution. Even open models face limitations in maintenance and expansion without long-term incentives, and China-centered open source could revert to closed forms at any time based on interests. In this reality, the open AGI ecosystem that Sentient presents holds significant meaning by showing a realistic direction for the industry to pursue, rather than merely an ideal.

However, ideals alone cannot create realistic change. Sentient seeks to prove possibilities through direct execution rather than keeping its vision theoretical. The company builds infrastructure while launching user products like Sentient Chat to demonstrate that open ecosystems actually work. Additionally, Sentient directly develops cryptocurrency-specialized models like Dobby. Dobby represents a community-driven model where communities handle everything from development to ownership and operations, testing whether such governance actually functions in open environments.

Sentient also faces clear challenges. Open source ecosystems experience exponentially increasing complexity in quality management and operations as participants grow. How Sentient manages this complexity while maintaining balance will determine ecosystem sustainability. The company must advance OML technology as well. Fingerprint insertion technology offers innovation in proving model origin and ownership, but it does not provide a perfect solution. As technology advances, new forgery or circumvention attempts inevitably follow, requiring continuous improvement like a battle between spear and shield. Sentient advances its technology through ongoing research, with results announced at major AI conferences such as NeurIPS (Neural Information Processing Systems).

Sentient’s journey has just begun. As concerns about closure and monopolization in the AI industry grow, Sentient’s attempts deserve attention. How these efforts will create substantial changes in the AI industry remains to be seen.

🐯 More from Tiger Research

Read more reports related to this research.

• Edgen: Personalized Financial Intelligence for Everyone

• Wayfinder: AI Agents Drive DeFi Mass Adoption

• Talus: Expanding Digital Labor into Digital Economy

Disclaimer

This report was partially funded by Sentient. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

Terms of Usage

Tiger Research allows the fair use of its reports. ‘Fair use’ is a principle that broadly permits the use of specific content for public interest purposes, as long as it doesn’t harm the commercial value of the material. If the use aligns with the purpose of fair use, the reports can be utilized without prior permission. However, when citing Tiger Research’s reports, it is mandatory to 1) clearly state ‘Tiger Research’ as the source, 2) include the Tiger Research logo following brand guideline. If the material is to be restructured and published, separate negotiations are required. Unauthorized use of the reports may result in legal action.

TL;DR

• Retail investor participation expands rapidly, yet investors lack infrastructure to interpret and utilize data effectively. Institutions manage markets systematically through professional systems like Bloomberg Terminal, while individuals still rely on fragmented information.

• Edgen closes this gap through an AI-powered financial intelligence platform. The platform integrates analysis across stocks and cryptocurrencies, moving beyond data provision to deliver actionable insights.

• Edgen advances further by building hyper-personalized investment analysis. The platform tailors intelligence to each investor’s style and objectives, transforming financial analysis capabilities once exclusive to Wall Street into accessible tools for everyone.

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1. The Retail Investment Era Needs New Intelligence

Lower financial market barriers rapidly expand retail investor participation. Anyone can now access global markets with just a smartphone from anywhere at any time. Everyone can easily access professional information including corporate disclosures, financial statements, and analyst reports. Market entry and information acquisition have never been easier.

Yet improved accessibility (market accessibility, information accessibility) fails to provide adequate infrastructure for analyzing and utilizing data effectively. Complex market environments and higher volatility increase the burden on retail investors. Investors must monitor vast data across multiple services to invest in various asset classes including national stock markets and cryptocurrencies. NASDAQ and the New York Stock Exchange plan 24-hour trading systems following the cryptocurrency market model. The investment environment will transform even more dynamically.

Retail investors lack sufficient infrastructure to manage this complexity. They lack the time and resources to analyze vast data systematically when investing is not their primary profession. Complex financial data creates a substantial cognitive burden to understand and utilize. Many retail investors access abundant information but fail to utilize it properly and timely. They rely on social media or fragmented news, apps, or self-made spreadsheets for investment decisions.

Institutional investors traditionally manage this complexity through professional systems like the Bloomberg Terminal. Ultra-high-net-worth individuals receive personalized portfolio management and customized investment strategies through private bankers. The Bloomberg Terminal costs tens of thousands of dollars annually and requires professional training. Private banking requires minimum assets of several billion won.

Most retail investors cannot access these professional tools. Yet retail investors face the same market environment as institutional investors. Expanded market participation forces retail investors to make rapid and accurate decisions in complex, fast-changing markets. AI technology advances open new possibilities to close this information gap. Services can now analyze vast data in real-time like the Bloomberg Terminal while providing personalized investment insights like private bankers for retail investors. Edgen represents a leading example of this possibility.

2. Edgen: An AI Bloomberg Terminal for All Investors

Edgen is an AI-powered financial intelligence platform built to become “the AI Bloomberg Terminal for everyone.” The platform enables retail investors to monitor and analyze both stock and cryptocurrency markets seamlessly within a single interface.

Edgen goes beyond mere data aggregation. The platform transforms fragmented data into actionable insights. Traditional financial information platforms provide raw data like news, charts, and financial statements, while Edgen analyzes this data and delivers investment insights in multiple formats: conversational answers, visualized charts, comprehensive reports, and quantitative scores personalized for every investor.

Edgen advances further by building personalized analysis tailored to each investor’s style and objectives. The platform presents individually optimized insights rather than displaying identical data to all users. This approach mirrors personalized social media feeds. Just as Facebook and Instagram show different content to each user, Edgen provides different dashboards to each investor. For example, during the same Bitcoin market conditions, long-term investors could receive “Local bottom reached, consider additional purchases,” while short-term traders would see “Short-term oversold zone, monitor rebound potential” as personalized Pivot Alerts.

The Efficient Decision Guidance Model (EDGM) powers this capability. EDGM orchestrates specialized agents and tools through a multi-agent system rather than operating as a single monolithic model.

Unlike generic LLMs that merely process language and words, this reasoning model interprets user queries and assigns them to specialized agents based on professional investment frameworks (macroeconomic analysis, technical analysis, fundamental analysis) and real-time market data. Each agent leverages guidance models and knowledge models to collect necessary data. This process incorporates real-time market data, the individual investor’s search history, portfolio composition, and trading patterns. The system also references data from investors with similar investment profiles. Market tools and large language models (LLMs) then process and synthesize the collected data and deliver results as conversational answers, charts, or reports.

This implementation combines Bloomberg Terminal’s comprehensive market analysis with the personalized strategy formulation of private banking within a single platform. Retail investors now access financial intelligence that institutional investors and ultra-high-net-worth individuals previously monopolized.

3. How Edgen Supports the Entire Investment Journey

3.1. Market Discovery: Uncovering Investment Opportunities

Finding valuable investment opportunities in today’s complex markets challenges even experienced investors. Investors must review and analyze vast amounts of data: countless news articles, charts, financial statements, and social media signals. Edgen simplifies this process through multiple methods.

Edgen’s Search feature allows investors to explore markets conversationally rather than analyzing complex financial data directly. For example, when an investor asks “Why did Tesla’s stock price drop recently?”, Edgen analyzes the company’s news, financial situation, technical indicators, and market sentiment simultaneously and provides an integrated answer. Personalization operates here as well. Edgen learns from user search history to identify the themes, sectors, and metrics each investor values, then progressively tailors search results and investment suggestions to that specific user.

Investment Ideas systematically uncovers noteworthy market opportunities by Themes. Top Stocks and Top Crypto highlight assets with strong market momentum. Stocks Screener and Crypto Screener enable investors to create customized asset lists by setting conditions like market capitalization, trading volume, and price movement. The Crypto Mindshare & Fundraise section captures signals unique to cryptocurrency markets. Crypto Mindshare tracks tokens surging on social media in real-time through X’s (formerly Twitter) “Hype Index.” Just Fundraised provides information on recently funded projects, including funding size and participating investors. DEX Tracker analyzes on-chain transactions and reveals capital flows from professional investors.

3.2. Smart Portfolio Management: Evaluating and Managing Assets

After uncovering investment opportunities, investors need tools to analyze assets deeply and monitor them continuously. 360° Reports automatically generate comprehensive analysis for individual assets. The system evaluates stocks through Valuation, Growth, Profitability, and Momentum perspectives, and assesses cryptocurrencies through Fundamental, Tokenomics, and Momentum perspectives. Edgen’s specialized agents analyze each category and assign grades from A+ to D-. The platform transparently discloses the analysis process so investors can verify how each agent reached its judgment.

Beyond individual asset analysis, investors must manage multiple assets cohesively. Edgen provides the Smart Portfolio feature. Investors manage bookmarked stocks and cryptocurrencies on a single screen. The portfolio offers three views. Market View displays real-time prices alongside asset grades. 360° Report View aggregates in-depth analysis for each asset. News View filters and delivers portfolio-related news.

Edgen plans to further advance the Smart Portfolio feature. The platform will provide comprehensive scores for entire portfolios beyond individual asset grades, and will deliver specific action plans to achieve higher ratings.

3.3. Customized Tools: Building Your Own Investment Strategy

Edgen provides the Agentic Store for investors who require more specialized analysis. The store offers a collection of AI assistants specialized in specific investment perspectives. Investors directly select and deploy agents that match their investment style and objectives.

For example, the Technical Signal agent automatically calculates indicators like RSI, MACD, and moving averages, then presents “bullish” or “bearish” judgments with comprehensive 3-star ratings. The Pivot Alert agent detects when assets approach local highs or lows, helping investors fine-tune trading timing at critical inflection points. Additional agents provide various capabilities including on-chain activity analysis and pre-TGE opportunity discovery. Investors select and deploy only the agents they need for their specific objectives.

Beyond this, Edgen will soon provide Agentic Execution, which automatically executes investor requests as trades. For stocks, the platform connects with platforms like Robinhood and XStocks. For cryptocurrencies, it integrates with protocols like Hyperliquid and Pendle. The AI copilot understands investor intent and executes trades directly. Through this capability, Edgen advances beyond an intelligence platform toward a comprehensive investment operations platform.

4. Open Intelligence: Evolution Through Participation

Edgen moves beyond providing information to build an open intelligence ecosystem where users directly participate and contribute. The Aura system anchors this ecosystem. Aura serves as a non-transferable reputation metric that synthesizes user insight contributions, prediction accuracy, and social media influence into a comprehensive score.

Users earn Aura by posting market analysis or insights on the platform, or by sharing them on social media. The reward pool allocates 30% to distribution activities and 70% to AI training contributions. The platform evaluates all contributions through AI scoring, community assessment, and expert verification. Users check Aura scores through the in-platform leaderboard and on X (formerly Twitter) via a browser extension. The system categorizes scores into three tiers: Aura, Super Aura, and Ultra Aura. The platform transparently records who provided which analysis and when.

User-provided insights train the AI. User contributions improve the AI, and the improved AI delivers better analysis back to users, creating a virtuous cycle. Through this participatory structure, Edgen develops as a platform that learns and advances alongside its community.

5. From Wall Street to All Street

Bloomberg Terminal and private banking services have long served only institutional investors and ultra-high-net-worth individuals. These entities maintain overwhelming competitive advantages in markets through exclusive information access and analytical tools. Retail investors, meanwhile, must make investment decisions with incomplete information amid fragmented data and limited tools.

Markets grow increasingly complex and widen this gap further. Variables investors must consider multiply exponentially: on-chain data analysis, social media signal tracking, global market trend identification. Individual investors find uncovering meaningful insights and making timely investment decisions within this vast information flood nearly impossible.

Edgen solves this information gap. The platform automates complex analytical tasks through multi-agent systems and integrates stocks and cryptocurrencies within a single interface. Investors now access institutional-grade analytical tools effortlessly, without collecting and cross-referencing information across multiple platforms.

Edgen advances further toward personalized financial intelligence. The platform provides optimized analysis and strategies tailored to each investor’s style, risk appetite, and objectives. This approach goes beyond simply delivering institutional tools to retail investors. Edgen essentially provides each individual with a customized private banker.

Retail investment democratizes rapidly. Edgen transforms “Wall Street privilege” into “opportunity for all” through AI technology. Financial intelligence once reserved for the few now reaches all investors. The investment landscape will shift fundamentally.

🐯 More from Tiger Research

Read more reports related to this research.

• Subsquid(SQD): A New Standard for Blockchain Data Infra

• Wayfinder: AI Agents Drive DeFi Mass Adoption

• Talus: Expanding Digital Labor into Digital Economy

Disclaimer

This report was partially funded by Edgen. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

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TL;DR

• AI agents evolve beyond simple tools into independent economic actors, opening possibilities for an “Autonomous Digital Economy.” However, realizing this vision requires infrastructure that verifies agent actions and guarantees trust.

• Talus builds this trust foundation. Talus Network makes agent actions verifiable through blockchain, while Nexus enables developers to easily build and deploy onchain agents.

• Through this approach, Talus expands Digital Labor into Digital Economy. It transforms how we work and create value, preparing the transition toward an autonomous economic era.

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1. The Prerequisites for Autonomous Digital Economy

AI agents (hereafter referred to as agents) are gaining attention as “Digital Laborers.” They autonomously understand complex situations, make independent judgments, and perform tasks. Trading agents process transactions in milliseconds on behalf of users. Customer support agents use historical customer data and product information to simultaneously handle thousands of inquiries. Agents are emerging as new economic actors that create value autonomously. They have moved beyond simple tools that merely execute commands.

This development opens possibilities for a new paradigm: the “Autonomous Digital Economy.” This paradigm envisions a fully autonomous economic ecosystem where agents transact and collaborate directly with other agents or users without human intervention. When realized, this paradigm will create efficient markets that operate 24/7 and transcend the limitations of human labor. It will give birth to entirely new markets that do not currently exist but could grow to multi-trillion dollar scale. We call these “Zero Billion Dollar Markets.”

However, this remains aspirational. Agents need trust mechanisms to verify their actions and outcomes. Without these, they cannot perform economic activities autonomously. This need mirrors requirements in the real economy. Human labor evolved beyond simple actions into economic activity because society established institutional foundations. Laws governed conduct. Contracts ensured performance. Currency enabled value exchange. Labor could transform into economic value only upon this system of trust.

The question is how to implement such trust mechanisms in digital environments. Most agents today depend on centralized service providers. Their decision-making processes remain opaque like black boxes. This fact further complicates the challenge. In this environment, limited methods exist to verify agent actions or guarantee their execution. Ultimately, whether we can advance toward these multi-trillion dollar markets and a true Autonomous Digital Economy depends on how we build the trust infrastructure.

2. Talus: Infrastructure for Autonomous Digital Economy

Talus is a blockchain infrastructure project designed to realize an agent-based autonomous digital economy. Just as DeFi enabled banking without banks and NFTs proved ownership of digital assets, Talus uses blockchain technology to build trust mechanisms for the agent ecosystem. This trust structure provides the foundation for agents to perform economic activities independently and verifiably without human intervention.

However, Talus’s goal does not stop at building trust mechanisms. Trust is a prerequisite for an autonomous digital economy, but trust alone cannot make the economy function. For agents to operate as real economic actors, they need a system that can design and execute complex workflows on top of that trust. To address this, Talus presents Nexus, a workflow development framework. Nexus is a decentralized version of services like n8n or Zapier. It enables developers to easily compose and deploy agent workflows in an onchain environment.

Through this approach, Talus combines trust infrastructure (Talus Network) and workflow framework (Nexus) to build a digital economic ecosystem where agents autonomously collaborate and create value.

2.1. Talus Network: The Trust Foundation for Agents

Talus Network is the core infrastructure that builds trust between agents. Previously, no rules existed to govern agent behavior, no mechanisms existed to guarantee performance, and no systems existed to exchange value. Talus fills this gap with blockchain-based infrastructure and creates an environment where agents can collaborate on a foundation of trust.

Talus Network comprises three core layers: 1) Coordination & Value Layer, 2) Data Storage Layer, and 3) Computation & Execution Layer. Each layer connects organically to ensure transparency and reliability while maintaining scalability and cost efficiency.

The Coordination & Value Layer is the foundation of Talus Network and the center of agent activity. This layer manages all information requiring trust onchain, including agent identity, transaction history, permissions, and workflow states. Talus built this layer on the Sui blockchain, which enables high-performance parallel processing. It guarantees stable transaction processing without conflicts even when numerous agents operate simultaneously. Through this approach, Talus establishes a foundation where agents can collaborate and exchange value in a trustworthy manner within an autonomous economic environment.

The Data Storage Layer provides cost-efficient storage. Agents require various information to process complex tasks, but storing all data on the blockchain is inefficient. To address this, Talus uses Walrus, a distributed storage system that Mysten Labs developed. Walrus stores agent metadata (profiles, documentation), memory (conversation logs, task history), and operational context (AI model settings, market data cache). Agents can quickly retrieve this information when needed. This approach allows the blockchain to focus solely on managing core trust data without high storage costs, while Walrus efficiently handles large-scale data in a decentralized manner.

The Computation & Execution Layer is an offchain execution structure designed to efficiently process complex computations. Direct execution on blockchain is slow and expensive, so this layer processes heavy computations offchain. However, a dilemma arises here. Offchain processing is fast but verifying trustworthiness is difficult. Onchain processing is trustworthy but slow and expensive. Talus solves this problem through a hybrid structure centered on the Leader Network. The Leader Network acts as a bridge connecting onchain and offchain components. Specifically, when it detects a workflow execution request from the blockchain, it forwards the request to offchain tools (LLM APIs, Web2 services, etc.) to perform actual computations. It then returns processed results to the blockchain for verification.

Through this hybrid design, Talus secures both the efficiency of complex computations and the reliability of blockchain. It processes quickly offchain while always verifying results onchain. This structure creates an execution environment that satisfies both requirements: speed and reliability.

2.2. Nexus: Onchain Agent Workflow Framework

If Talus Network is the foundational infrastructure for the autonomous agent ecosystem, Nexus is the framework where developers build and deploy onchain agents on top of it. Through Nexus, developers can build onchain agent-based workflows in a familiar Python development environment without deep understanding of blockchain technology.

The core component of Nexus is the Nexus Onchain Package (NOP). NOP defines the basic rules and interfaces for workflows that compose agents. This ensures all Talus agents operate on the same structure and protocol. Through this approach, various agents and tools can interoperate and interact in a coordinated manner within a single ecosystem. NOP also tracks workflow execution states and verifies results at each step. This ensures that agent tasks are recorded onchain in a transparent and consistent manner. Workflows defined this way deploy as smart contracts on the Sui blockchain in the form of Talus Agent Packages (TAP).

Let’s examine how this structure actually works through a concrete example. Suppose a developer builds a trading agent. This agent holds assets onchain and executes trades directly, but it can request market analysis from another macro analyst agent to develop trading strategies. Thanks to Nexus’s standardized protocol, agents can exchange data and collaborate with each other. If the macro analyst agent needs to utilize external data, the Leader Network connects with offchain tools to perform necessary computations and returns results back to the blockchain. The entire process from analysis to trading to result verification is recorded onchain and guarantees complete transparency.

Development accessibility will expand further. In the long term, Talus Vision, a no-code workflow builder, will be provided. This will enable users to design and deploy agents visually without writing code. Talus Network provides the foundation of trust. Nexus lowers development barriers. With these in place, more developers and users can participate in the onchain agent ecosystem and build a larger autonomous digital economy.

3. Talus’s Agent Economy: Dev Markets and Consumer Apps

Talus has moved closer to realizing an autonomous digital economy by building technical infrastructure for agents, but excellent technology alone cannot grow an ecosystem. Just as the internet popularized through email as a killer app, Talus also needs practical use cases. Talus approaches this on two levels. It builds marketplaces where developers can create and monetize tools and agents. It also provides consumer apps that general users can directly use.

3.1. Dev Markets: Tool and Agent Marketplaces

Talus’s Nexus development framework forms a market by itself. Just as services like Figma formed developer markets through third-party plugin ecosystems, Talus also builds marketplaces centered on tools and agents. This structure enables developers to directly contribute and generate revenue.

For example, developers post Talus Tools they created to the Tool Marketplace to generate revenue. Other developers integrate these tools into their workflows. Every time a workflow executes, the tool developer receives usage fees in $US, Talus’s native token. The Agent Marketplace operates the same way. Every time someone calls an agent, the developer generates revenue in $US.

This structure creates a virtuous cycle. As developers add new tools, agents can perform more tasks. As more agents emerge, demand for tool development increases. As ecosystem activity grows, $US token demand also increases. This provides greater economic incentives to developers and accelerates ecosystem growth.

3.2. Consumer Apps: IDOL Launchpad and AvA Markets

Developer markets solve the supply side of the ecosystem, but the demand side is also necessary. The ecosystem cannot expand without applications that general users can directly experience. Talus solves this by extending agents into the entertainment domain. It popularizes the agent economy through applications that anyone can easily access and enjoy.

IDOL.fun enables users to create and operate IDOL agents, which are Twitter-based AI chatbots. Agents communicate with fans, get hired by brands or individual services, and generate actual revenue. Anyone can create their own agent and participate in autonomous economic activities without technical knowledge. Here, agents operate as complete services themselves rather than as parts of workflows.

Next, Talus also presents the possibilities of the AvA (Agent vs Agent) market. It supports various game formats centered on agents. Beyond structures where agents compete against each other, formats where users interact with agents and compete against other users are also possible. Developers can reconstruct game genres like murder mystery or poker on an agent basis. Users can directly participate in games or enjoy predicting their outcomes. The blockchain transparently records all processes. Users intuitively experience the agent economy without worrying about manipulation.

IDOL.fun and AvA Markets are just starting points. Based on the Nexus framework, agents can expand beyond entertainment into more diverse domains. In DeFi, agents can automatically execute complex investment strategies from simple requests. In DAOs, agents can analyze proposals, prioritize them, and perform governance manager roles that allocate resources. Ultimately, Talus expects to use popularization through entertainment as a springboard for the agent economy to expand into various industrial domains.

4. Talus Opens the Era of Autonomous Digital Economy

Agents no longer passively follow human instructions. They evolve into economic actors that make independent judgments, collaborate with other agents, and create value autonomously. The era of Digital Labor has begun. Talus takes this one step further and builds the foundation to expand it into a Digital Economy.

Every innovation in IT has started with new infrastructure. The internet changed how we connect, cloud transformed computing resources, and mobile redefined service accessibility. Similarly, Talus redefines Digital Labor and the Autonomous Digital Economy. Beyond agents simply replacing human work, agents collaborate with each other to create value, forming an entirely new economic ecosystem. How we work and create value will fundamentally change.

However, it remains too early to predict what future this shift from Digital Labor to Digital Economy will create. Technical limitations, regulatory environments, and social acceptance present challenges that still need resolution. Nevertheless, given its potential to fundamentally transform how we work and create value, the future of autonomous digital economy that Talus shapes deserves attention.

🐯 More from Tiger Research

Read more reports related to this research.

• Subsquid(SQD): A New Standard for Blockchain Data Infra

• Wayfinder: AI Agents Drive DeFi Mass Adoption

Disclaimer

This report was partially funded by Talus. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

Terms of Usage

Tiger Research allows the fair use of its reports. ‘Fair use’ is a principle that broadly permits the use of specific content for public interest purposes, as long as it doesn’t harm the commercial value of the material. If the use aligns with the purpose of fair use, the reports can be utilized without prior permission. However, when citing Tiger Research’s reports, it is mandatory to 1) clearly state ‘Tiger Research’ as the source, 2) include the Tiger Research logo following brand guideline. If the material is to be restructured and published, separate negotiations are required. Unauthorized use of the reports may result in legal action.

TL;DR

• Subsquid (hereafter SQD) simplifies blockchain data access through decentralized infrastructure. It supports over 200 blockchains and distributes data across multiple nodes.

• The SQD Network uses a modular structure that allows developers to freely configure data processing and storage methods. This enables users to efficiently utilize data through a unified structure in a multi-chain environment.

• Subsquid aims to become the data backbone of Web3, similar to how Snowflake set the standard with “One Platform, Many Workloads.” Through the recent Rezolve AI acquisition, it is expanding into AI and payments. SQD is expected to become core infrastructure connecting Web3 and the agent economy.

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1. Is Blockchain Data Really Open to Everyone?

One of blockchain technology’s defining features is that all data is open to everyone. Traditional industries store data in closed databases that are inaccessible from the outside. Blockchain operates differently. All records are transparently published on-chain.

However, transparent data does not guarantee easy utilization. Data transparency does not ensure accessibility. Blockchain is optimized for executing transactions securely and achieving network consensus. It is not infrastructure designed for data analysis. The functions for verifying and storing data have advanced, but the infrastructure for efficiently querying and utilizing that data remains insufficient. The methods for querying on-chain data have not changed significantly from ten years ago to today.

Consider this analogy. A town called “Tiger Town” has a massive river named “Ethereum.” This river is a public good. Anyone can draw water from it. However, drawing water is difficult and inefficient. Each person must go to the riverbank with a bucket and draw water directly. To use it as drinking water, they must boil or filter it through a purification process.

The current blockchain development environment operates this way. Abundant data is readily available, but infrastructure to utilize it is lacking. For example, assume a developer wants to build a dApp using transaction data from Uniswap, a decentralized exchange. The developer must request data through Ethereum’s RPC nodes, process it, and store it. However, RPC nodes have limitations for large-scale data analysis or complex query execution. The blockchain ecosystem operates in a multi-chain environment with multiple blockchains beyond Ethereum. This makes the problem more complex.

Developers can use centralized services like Alchemy or Infura to address these limitations. However, this approach undermines decentralization, a core value of blockchain technology. Even if smart contracts are decentralized, centralized data access creates censorship risks and a single point of failure. The blockchain ecosystem needs a fundamental innovation in data access methods to achieve true accessibility.

2. Subsquid: A New Paradigm for Blockchain Data Infra

Subsquid (hereafter SQD) is a decentralized data infrastructure project designed to address the complexity and inefficiency of blockchain data access. SQD’s goal is to enable anyone to utilize blockchain data easily.

Return to the earlier analogy. In the past, each person had to go to the riverbank with a bucket to draw water directly. Now, distributed water purification plants draw water from the river and purify it. The townspeople no longer need to go to the riverbank. They can receive clean water whenever they need it. The SQD team provides this infrastructure through the “SQD Network.”

The SQD Network operates as a distributed query engine and data lake. It currently supports data from over 200 blockchain networks. Since the mainnet launch in June 2024, it has grown to process hundreds of millions of queries each month. This growth stems from three core features. These features position SQD beyond a simple data indexing platform and demonstrate the evolution direction of blockchain data infrastructure.

2.1. Decentralized Architecture for High Availability

A significant portion of existing blockchain data infrastructure relies on centralized providers like Alchemy. This approach offers advantages in initial accessibility and management efficiency. However, it restricts users to chains supported by the provider and imposes high costs as usage increases. It is also vulnerable to single points of failure. The centralized structure conflicts with blockchain’s core value of decentralization.

The SQD Network addresses these limitations through a decentralized architecture. Data Providers collect raw data from multiple blockchains such as Ethereum and Solana. They divide the data into blocks, compress it, and upload it to the network with metadata. Worker Nodes split the data from Permanent Storage created by Data Providers into chunks for distributed storage. When query requests arrive, they process and respond quickly. Each Worker Node acts as a mini API that serves its own stored data. The entire network operates like thousands of distributed API servers. Gateway Operators serve as the interface between end users and the network. They receive user queries and forward them to appropriate Worker Nodes for processing.

Anyone can participate as a Worker Node or Gateway Operator. This allows the network capacity and processing performance to scale horizontally. Data is redundantly stored across multiple Worker Nodes. If some nodes fail, overall data access remains unaffected. This ensures high availability and resilience.

Data Providers are currently managed by the SQD team during the initial bootstrap phase. This strategy ensures initial data quality and stability. As the network matures, external providers will be able to participate through token governance. This will fully decentralize the data sourcing stage.

2.2. Tokenomics for Network Sustainability

For a distributed network to function properly, participants need motivation to act voluntarily. SQD addresses this through an economic incentive structure centered on the native token $SQD. Each participant stakes or delegates tokens according to their role and responsibility. This builds network stability and reliability collectively.

Worker Nodes are the core operators that manage blockchain data. To participate, they must stake 100,000 $SQD as collateral against malicious behavior or incorrect data provision. If problems occur, the network slashes their deposits. Nodes that consistently provide stable and accurate data receive $SQD token rewards. This naturally incentivizes responsible operation.

Gateway Operators must lock up $SQD tokens to process user requests. The amount of locked tokens determines their bandwidth, or the number of requests they can handle. Longer lockup periods allow them to process more requests.

Token holders can participate in the network indirectly without running nodes themselves. They can delegate their stake to trusted Worker Nodes. Nodes that receive more delegations gain authority to process more queries and earn more rewards. Delegators share a portion of these rewards. Currently, there are no minimum delegation requirements or lockup period restrictions. This creates a permissionless curation system where the community selects nodes in real-time. The entire community participates in network quality management through this structure.

2.3. Modular Structure for Flexibility

Another distinctive feature of the SQD Network is its modular structure. Existing indexing solutions adopt a monolithic structure that processes everything from data collection to processing, storage, and querying in a single system. This simplifies initial setup but limits developers’ freedom to choose data processing methods or storage locations.

SQD completely separates the data access layer from the processing layer. The SQD Network handles only the E (Extract) portion of the ETL (Extract-Transform-Load) pipeline. It serves solely as a “data feed” that extracts blockchain raw data quickly and reliably. Developers use the SQD SDK to freely choose how to transform and store the data.

This structure provides practical flexibility. Developers can store data in PostgreSQL and serve it through a GraphQL API. They can export it as CSV or Parquet files. They can load it directly into cloud data warehouses like Google BigQuery. Future plans include supporting large-scale data analysis environments through Snowflake and enabling real-time analysis and monitoring platforms by streaming data directly through Kafka integration without separate storage.

SQD co-founder Dmitry Zhelezov compares this to “providing Lego blocks.” Instead of offering a finished product, SQD hands developers the highest-performing and most reliable raw materials. Developers combine these materials according to their requirements to complete their own data infrastructure. Traditional enterprises and crypto projects alike can handle blockchain data using familiar tools and languages. They can flexibly build data pipelines optimized for their specific industries and use cases.

3. Subsquid’s Next Steps: Toward Better Data Infra

The SQD team has reduced the complexity and inefficiency of blockchain data access through the SQD Network and established the foundation for decentralized data infrastructure. However, as the scale and scope of blockchain data usage expand rapidly, simple accessibility no longer suffices. The ecosystem now demands faster processing speeds and more flexible utilization environments.

The SQD team is advancing the network structure to meet these demands. The team focuses on increasing data processing speed and creating a structure that enables data handling without server dependence. To achieve this, SQD is developing 1) SQD Portal and 2) Light Squid in stages.

3.1. SQD Portal: Decentralized Parallel Processing and Real-Time Data

In the existing SQD Network, the Gateway served as an intermediary connecting end users and Worker Nodes. When users requested queries, the Gateway forwarded them to appropriate Worker Nodes and returned responses to end users. This process was stable but processed queries sequentially one at a time. Large-scale queries took considerable time. Even with thousands of Worker Nodes available, the system underutilized their processing capacity.

The SQD team aims to solve this problem through SQD Portal. The core of Portal lies in decentralized parallel processing. It splits a single query into multiple pieces and sends requests simultaneously to approximately 3,000 or more Worker Nodes. Each Worker Node processes its assigned portion in parallel. Portal then collects these responses in real-time and delivers them through streaming.

Portal prefetches data into buffers in advance. This ensures uninterrupted delivery even when network delays or temporary failures occur. Just as YouTube buffers videos for seamless playback, users receive data without waiting. The team also refactored the existing Python-based query engine to Rust. This dramatically improved parallel processing performance. Overall processing speed increased dozens of times compared to before.

Portal goes further to solve the real-time data problem. No matter how fast data processing becomes, Worker Nodes only hold finalized historical blocks. They cannot retrieve the latest transactions or block information just generated. Users still had to rely on external RPC nodes for this. Portal solves this with real-time distributed streaming called “Hotblocks.” Hotblocks collects newly generated unconfirmed blocks in real-time from blockchain RPC nodes or dedicated streaming services and stores them inside Portal. Portal merges the finalized historical data from Worker Nodes with the latest block data from Hotblocks. Users can receive data from past to present in one request without separate RPC connections.

The SQD team plans to fully transition existing Gateways to Portal. Portal currently operates in closed beta. In the future, anyone will be able to operate Portal nodes directly and perform the Gateway role in the network. Existing Gateway operators will naturally transition to Portal operators. (The SQD Network architecture can be found at this link.)

3.2. Light Squid: Indexing in Local Environments

The SQD Network provides data reliably, but developers still face the limitation of operating separate servers. Even when retrieving data from Worker Nodes through Portal, large database servers like PostgreSQL were needed to process and deliver it to users. This process requires significant infrastructure construction and maintenance costs. The data still depends on a single provider (developer server), which distances it from a truly distributed structure.

Light Squid simplifies this intermediate step. The existing structure operated like wholesalers (developers) running large warehouses (servers) to distribute data to consumers. Light Squid transforms this into a D2C (Direct to Customer) approach that delivers data directly from the source (SQD Network) to end users. Users receive necessary data through Portal and store it in their local environment. They can query it directly in browsers or personal devices. Developers do not need to maintain separate servers. Users can check locally stored data even when network connections are lost.

For example, an application displaying NFT transaction history can now run directly in a user’s browser without a central server. This mirrors how Instagram shows feeds offline in Web2. It aims to provide dApps with a smooth user experience in local environments. However, Light Squid is designed as an option to enable the same indexing environment locally. It does not completely replace server-centric structures. Data is still supplied through a distributed network. As the scope of utilization extends to the user level, the SQD ecosystem is expected to evolve into a more accessible form.

4. How Subsquid Works in Practice

The SQD Network is simply an infrastructure that provides data, but its scope of application is limitless. Just as all IT-based industries start with data, improvements in data infrastructure expand the possibilities of all services built on top of it. SQD is already changing how blockchain data is utilized across various fields and delivering concrete results.

4.1. DApp Developers: Unified Multi-Chain Data Management

PancakeSwap, a decentralized exchange (DEX), is a representative case. In a multi-chain environment, exchanges must aggregate trading volume, liquidity pool data, and token pair information from each chain in real-time. In the past, developers had to connect to RPC nodes for each chain, parse event logs, and align different data structures individually. This process repeated with every new chain addition. Maintenance burden increased with each protocol upgrade.

After adopting SQD, PancakeSwap can now manage data from multiple chains through a unified pipeline. SQD provides data from each chain in standardized format. A single indexer can now process all chains simultaneously. Chain additions now require only configuration changes. Data processing logic is managed consistently from a central location. The development team reduced time spent on data infrastructure management. They can now focus more on core service improvements.

4.2. Data Analysts: Flexible Data Processing and Integrated Analysis

On-chain analysis platforms like Dune and Artemis provide high accessibility and convenience by allowing quick and easy data queries using SQL. However, they have limitations in that work is only possible within the chains and data structures the platform supports. Additional processes are needed when combining external data or performing complex transformations.

SQD complements this environment and enables data analysts to handle data more freely. Users can directly extract necessary blockchain data, transform it into desired formats, and load it into their own databases or warehouses. For example, analysts can retrieve transaction data from a specific decentralized exchange, aggregate it by time period, combine it with existing financial data, and apply it to their own analysis models. SQD does not replace the convenience of existing platforms. It increases the freedom and scalability of data handling. Analysts can expand the depth and application scope of on-chain data analysis through broader data ranges and customized processing methods.

4.3. AI Agents: Core Infrastructure for Agent Economy

For AI agents to make autonomous decisions and execute transactions, they need infrastructure that guarantees reliability and transparency. Blockchain provides a suitable foundation for autonomous agents. All transaction records are transparently disclosed and difficult to tamper with. Cryptocurrency payments enable automatic execution.

However, AI agents currently struggle to access blockchain infrastructure directly. Each developer must individually build and integrate data sources. Network structures vary and prevent standardized access. Even centralized API services require multiple steps including account registration, key issuance, and payment setup. These processes presume human intervention and do not suit autonomous environments.

The SQD Network bridges this gap. Based on a permissionless architecture, agents automate data requests and payments through $SQD tokens. They receive necessary information in real-time and process it independently. This establishes the operational foundation for autonomous AI that connects directly to data networks without human intervention.

On October 9, 2025, Rezolve AI announced the acquisition of SQD, clarifying this direction further. Rezolve is a NASDAQ-listed AI-based commerce solution provider. Through this acquisition, Rezolve is building core infrastructure for the AI agent economy. Rezolve plans to combine the digital asset payment infrastructure of previously acquired Smartpay with SQD’s distributed data layer. This will create integrated infrastructure where AI processes data, intelligence, and payments in a single flow. Once Rezolve completes this integration, AI agents will analyze blockchain data in real-time and execute transactions independently. This marks a significant turning point for SQD as data infrastructure for the AI agent economy.

4.4. Institutional Investors: Real-Time Data Infra for Institutional Markets

As real-world asset tokenization (RWA) expands, institutional investors are actively participating on-chain. Institutions need data infrastructure that guarantees accuracy and transparency to utilize on-chain data for trading, settlement, and risk management.

SQD launched OceanStream to meet this demand. OceanStream is a decentralized data lakehouse platform that streams data from over 200 blockchains in real-time. The platform aims to provide institution-grade data quality and stability. It combines sub-second latency streaming with over 3PB of indexed historical data to improve financial institutions’ backtesting, market analysis, and risk assessment environments. This allows institutions to monitor more chains and asset classes in real-time at lower costs. They can perform regulatory reporting and market surveillance within a single integrated system.

OceanStream participated in the SEC-hosted Crypto Task Force Roundtable to discuss how transparency and verifiability of on-chain data impact market stability and investor protection. This demonstrates that SQD is establishing itself as a data-based structure connecting tokenized financial markets with institutional capital beyond simple development infrastructure.

5. SQD’s Vision: Building the Data Backbone of Web3

The competitiveness of the Web3 industry depends on how well it utilizes data. However, data remains fragmented due to different blockchain structures. Infrastructure to handle this efficiently is still in its early stages. SQD bridges this gap by building a standardized data layer that handles all blockchain data within a single structure. Beyond on-chain data, SQD plans to integrate off-chain data including financial transactions, social media, and corporate operations to create an analysis environment spanning both worlds.

This vision resembles how Snowflake established the data integration standard for traditional industries with “One Platform, Many Workloads.” SQD aims to establish itself as the data backbone of Web3 by integrating blockchain data and connecting off-chain sources.

However, SQD needs time to develop into a fully decentralized infrastructure. The project is currently in the bootstrap phase where the SQD team still plays a significant role. Limitations exist in developer community size and ecosystem diversity. Nevertheless, the growth shown in just over a year since mainnet launch and the strategic expansion through the Rezolve AI acquisition demonstrate clear direction. SQD is presenting the path forward for blockchain data infrastructure and evolving into a data foundation supporting the entire Web3 ecosystem—from dApp development to institutional investment and the AI agent economy. Its potential is expected to grow significantly.

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Disclaimer

This report was partially funded by SQD. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.

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