45 posts tagged with "blockchain"

Introduction​

Announced for public launch on May 3rd, 2023, after an extensive three-wave testnet, the Sui blockchain introduced an innovative gas pricing system designed to benefit both users and validators. At its heart is the Reference Gas Price (RGP), a network-wide baseline gas fee that validators agree upon at the start of each epoch (approximately 24 hours).

This system aims to create a mutually beneficial ecosystem for SUI token holders, validators, and end-users by providing low, predictable transaction costs while simultaneously rewarding validators for performant and reliable behavior. This report provides a deep dive into how the RGP is determined, the calculations validators perform, its impact on the network economy, its evolution through governance, and how it compares to other blockchain gas models.

The Reference Gas Price (RGP) Mechanism​

Sui’s RGP is not a static value but is re-established each epoch through a dynamic, validator-driven process.

• The Gas Price Survey: At the beginning of each epoch, every validator submits their "reservation price"—the minimum gas price they are willing to accept for processing transactions. The protocol then orders these submissions by stake and sets the RGP for that epoch at the stake-weighted 2/3 percentile. This design ensures that validators representing a supermajority (at least two-thirds) of the total stake are willing to process transactions at this price, guaranteeing a reliable level of service.

• Update Cadence and Requirements: While the RGP is set each epoch, validators are required to actively manage their quotes. According to official guidance, validators must update their gas price quote at least once a week. Furthermore, if there is a significant change in the value of the SUI token, such as a fluctuation of 20% or more, validators must update their quote immediately to ensure the RGP accurately reflects current market conditions.

• The Tallying Rule and Reward Distribution: To ensure validators honor the agreed-upon RGP, Sui employs a "tallying rule." Throughout an epoch, validators monitor each other’s performance, tracking whether their peers are promptly processing RGP-priced transactions. This monitoring results in a performance score for each validator. At the end of the epoch, these scores are used to calculate a reward multiplier that adjusts each validator's share of the stake rewards.

  • Validators who performed well receive a multiplier of ≥1, boosting their rewards.
  • Validators who stalled, delayed, or failed to process transactions at the RGP receive a multiplier of <1, effectively slashing a portion of their earnings.

This two-part system creates a powerful incentive structure. It discourages validators from quoting an unrealistically low price they can't support, as the financial penalty for underperformance would be severe. Instead, validators are motivated to submit the lowest price they can sustainably and efficiently handle.

Validator Operations: Calculating the Gas Price Quote​

From a validator's perspective, setting the RGP quote is a critical operational task that directly impacts profitability. It requires building data pipelines and automation layers to process a number of inputs from both on-chain and off-chain sources. Key inputs include:

• Gas units executed per epoch
• Staking rewards and subsidies per epoch
• Storage fund contributions
• The market price of the SUI token
• Operational expenses (hardware, cloud hosting, maintenance)

The goal is to calculate a quote that ensures net rewards are positive. The process involves several key formulas:

1. Calculate Total Operational Cost: This determines the validator's expenses in fiat currency for a given epoch.

   $$\text{Cost}_{\text{epoch}} = (\text{Total Gas Units Executed}_{\text{epoch}}) \times (\text{Cost in \$ per Gas Unit}_{\text{epoch}})$$

2. Calculate Total Rewards: This determines the validator's total revenue in fiat currency, sourced from both protocol subsidies and transaction fees.

   $$\text{\$Rewards}_{\text{epoch}} = (\text{Total Stake Rewards in SUI}_{\text{epoch}}) \times (\text{SUI Token Price})$$

   Where Total Stake Rewards is the sum of any protocol-provided Stake Subsidies and the Gas Fees collected from transactions.

3. Calculate Net Rewards: This is the ultimate measure of profitability for a validator.

   $$\text{\$Net Rewards}_{\text{epoch}} = \text{\$Rewards}_{\text{epoch}} - \text{\$Cost}_{\text{epoch}}$$

   By modeling their expected costs and rewards at different RGP levels, validators can determine an optimal quote to submit to the Gas Price Survey.

Upon mainnet launch, Sui set the initial RGP to a fixed 1,000 MIST (1 SUI = 10⁹ MIST) for the first one to two weeks. This provided a stable operating period for validators to gather sufficient network activity data and establish their calculation processes before the dynamic survey mechanism took full effect.

Impact on the Sui Ecosystem​

The RGP mechanism profoundly shapes the economics and user experience of the entire network.

• For Users: Predictable and Stable Fees: The RGP acts as a credible anchor for users. The gas fee for a transaction follows a simple formula: User Gas Price = RGP + Tip. In normal conditions, no tip is needed. During network congestion, users can add a tip to gain priority, creating a fee market without altering the stable base price within the epoch. This model provides significantly more fee stability than systems where the base fee changes with every block.

• For Validators: A Race to Efficiency: The system fosters healthy competition. Validators are incentivized to lower their operating costs (through hardware and software optimization) to be able to quote a lower RGP profitably. This "race to efficiency" benefits the entire network by driving down transaction costs. The mechanism also pushes validators toward balanced profit margins; quoting too high risks being priced out of the RGP calculation, while quoting too low leads to operational losses and performance penalties.

• For the Network: Decentralization and Sustainability: The RGP mechanism helps secure the network's long-term health. The "threat of entry" from new, more efficient validators prevents existing validators from colluding to keep prices high. Furthermore, by adjusting their quotes based on the SUI token's market price, validators collectively ensure their operations remain sustainable in real-world terms, insulating the network's fee economy from token price volatility.

Governance and System Evolution: SIP-45​

Sui's gas mechanism is not static and evolves through governance. A prominent example is SIP-45 (Prioritized Transaction Submission), which was proposed to refine fee-based prioritization.

• Issue Addressed: Analysis showed that simply paying a high gas price did not always guarantee faster transaction inclusion.
• Proposed Changes: The proposal included increasing the maximum allowable gas price and introducing an "amplified broadcast" for transactions paying significantly above the RGP (e.g., ≥5x RGP), ensuring they are rapidly disseminated across the network for priority inclusion.

This demonstrates a commitment to iterating on the gas model based on empirical data to improve its effectiveness.

Comparison with Other Blockchain Gas Models​

Sui's RGP model is unique, especially when contrasted with Ethereum's EIP-1559.

Potential Criticisms and Challenges​

Despite its innovative design, the RGP mechanism faces potential challenges:

• Complexity: The system of surveys, tallying rules, and off-chain calculations is intricate and may present a learning curve for new validators.
• Slow Reaction to Spikes: The RGP is fixed for an epoch and cannot react to sudden, mid-epoch demand surges, which could lead to temporary congestion until users begin adding tips.
• Potential for Collusion: In theory, validators could collude to set a high RGP. This risk is primarily mitigated by the competitive nature of the permissionless validator set.
• No Fee Burn: Unlike Ethereum, Sui recycles all gas fees to validators and the storage fund. This rewards network operators but does not create deflationary pressure on the SUI token, a feature some token holders value.

Frequently Asked Questions (FAQ)​

Why stake SUI? Staking SUI secures the network and earns rewards. Initially, these rewards are heavily subsidized by the Sui Foundation to compensate for low network activity. These subsidies decrease by 10% every 90 days, with the expectation that rewards from transaction fees will grow to become the primary source of yield. Staked SUI also grants voting rights in on-chain governance.

Can my staked SUI be slashed? Yes. While parameters are still being finalized, "Tally Rule Slashing" applies. A validator who receives a zero performance score from 2/3 of its peers (due to low performance, malicious behavior, etc.) will have its rewards slashed by a to-be-determined amount. Stakers can also miss out on rewards if their chosen validator has downtime or quotes a suboptimal RGP.

Are staking rewards automatically compounded? Yes, staking rewards on Sui are automatically distributed and re-staked (compounded) every epoch. To access rewards, you must explicitly unstake them.

What is the Sui unbonding period? Initially, stakers can unbond their tokens immediately. An unbonding period where tokens are locked for a set time after unstaking is expected to be implemented and will be subject to governance.

Do I maintain custody of my SUI tokens when staking? Yes. When you stake SUI, you delegate your stake but remain in full control of your tokens. You never transfer custody to the validator.

In the rapidly converging worlds of artificial intelligence and blockchain, the demand for trust and transparency has never been higher. How can we be certain that an AI model's output is accurate and untampered with? How can we perform complex computations on vast on-chain datasets without compromising security or scalability? Lagrange Labs is tackling these questions head-on with its suite of zero-knowledge (ZK) infrastructure, aiming to build a future of "AI You Can Prove." This post provides an objective overview of their mission, technology, and recent breakthroughs, culminating in their latest paper on Dynamic zk-SNARKs.

1. The Team and Its Mission​

Lagrange Labs is building the foundational infrastructure to generate cryptographic proofs for any AI inference or on-chain application. Their goal is to make computation verifiable, bringing a new layer of trust to the digital world. Their ecosystem is built on three core product lines:

• ZK Prover Network: A decentralized network of over 85 proving nodes that supplies the computational power needed for a wide range of proving tasks, from AI and rollups to decentralized applications (dApps).
• DeepProve (zkML): A specialized system for generating ZK proofs of neural network inferences. Lagrange claims it is up to 158 times faster than competing solutions, making verifiable AI a practical reality.
• ZK Coprocessor 1.0: The first SQL-based ZK Coprocessor, allowing developers to run custom queries on massive on-chain datasets and receive verifiably accurate results.

2. A Roadmap to Verifiable AI​

Lagrange has been methodically executing a roadmap designed to solve the challenges of AI verifiability one step at a time.

• Q3 2024: ZK Coprocessor 1.0 Launch: This release introduced hyper-parallel recursive circuits, which delivered an average speed increase of approximately 2x. Projects like Azuki and Gearbox are already leveraging the coprocessor for their on-chain data needs.
• Q1 2025: DeepProve Unveiled: Lagrange announced DeepProve, its solution for Zero-Knowledge Machine Learning (zkML). It supports popular neural network architectures like Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The system achieves significant, order-of-magnitude acceleration across all three critical stages: one-time setup, proof generation, and verification, with speed-ups reaching as high as 158x.
• Q2 2025: The Dynamic zk-SNARKs Paper (Latest Milestone): This paper introduces a groundbreaking "update" algorithm. Instead of re-generating a proof from scratch every time the underlying data or computation changes, this method can patch an old proof (π) into a new proof (π'). This update can be performed with a complexity of just O(√n log³n), a dramatic improvement over full re-computation. This innovation is particularly suited for dynamic systems like continuously learning AI models, real-time game logic, and evolving smart contracts.

3. Why Dynamic zk-SNARKs Matter​

The introduction of updatable proofs represents a fundamental shift in the cost model of zero-knowledge technology.

• A New Cost Paradigm: The industry moves from a model of "full-recomputation for every proof" to "incremental proofing based on the size of the change." This dramatically lowers the computational and financial cost for applications that undergo frequent, minor updates.

• Implications for AI:

  • Continuous Fine-Tuning: When fine-tuning less than 1% of a model's parameters, the proof generation time grows almost linearly with the number of changed parameters (Δ parameters), rather than with the overall size of the model.
  • Streaming Inference: This enables the generation of proofs concurrently with the inference process itself. This drastically reduces the latency between an AI making a decision and that decision being settled and verified on-chain, unlocking use cases like on-chain AI services and compressed proofs for rollups.

• Implications for On-Chain Applications:

  • Dynamic zk-SNARKs offer massive gas and time optimizations for applications characterized by frequent, small-state changes. This includes decentralized exchange (DEX) order books, evolving game states, and ledger updates involving frequent additions or deletions.

4. A Glimpse into the Tech Stack​

Lagrange's powerful infrastructure is built on a sophisticated and integrated technology stack:

• Circuit Design: The system is flexible, supporting the embedding of ONNX (Open Neural Network Exchange) models, SQL parsers, and custom operators directly into its circuits.
• Recursion & Parallelism: The ZK Prover Network facilitates distributed recursive proofs, while the ZK Coprocessor leverages the sharding of "micro-circuits" to execute tasks in parallel, maximizing efficiency.
• Economic Incentives: Lagrange is planning to launch a native token, LA, which will be integrated into a Double-Auction-for-Recursive-Auction (DARA) system. This will create a robust marketplace for bidding on prover computation, complete with incentives and penalties to ensure network integrity.

5. Ecosystem and Real-World Adoption​

Lagrange is not just building in a vacuum; its technology is already being integrated by a growing number of projects across different sectors:

• AI & ML: Projects like 0G Labs and Story Protocol are using DeepProve to verify the outputs of their AI models, ensuring provenance and trust.
• Rollups & Infrastructure: Key players like EigenLayer, Base, and Arbitrum are participating in the ZK Prover Network as validation nodes or integration partners, contributing to its security and computational power.
• NFT & DeFi Applications: Brands like Azuki and DeFi protocols like Gearbox are using the ZK Coprocessor to enhance the credibility of their data queries and reward distribution mechanisms.

6. Challenges and the Road Ahead​

Despite its impressive progress, Lagrange Labs and the broader ZK field face several hurdles:

• Hardware Bottlenecks: Even with a distributed network, updatable SNARKs still demand high bandwidth and rely on GPU-friendly cryptographic curves to perform efficiently.
• Lack of Standardization: The process of mapping AI frameworks like ONNX and PyTorch to ZK circuits still lacks a universal, standardized interface, creating friction for developers.
• A Competitive Landscape: The race to build zkVMs and generalized zkCompute platforms is heating up. Competitors like Risc-Zero and Succinct are also making significant strides. The ultimate winner may be the one who can first commercialize a developer-friendly, community-driven toolchain.

7. Conclusion​

Lagrange Labs is methodically reshaping the intersection of AI and blockchain through the lens of verifiability. Their approach provides a comprehensive solution:

• DeepProve addresses the challenge of trusted inference.
• The ZK Coprocessor solves the problem of trusted data.
• Dynamic zk-SNARKs incorporate the real-world need for continuous updates directly into the proving system.

If Lagrange can maintain its performance edge, solve the critical challenge of standardization, and continue to grow its robust network, it is well-positioned to become a cornerstone player in the emerging "AI + ZK Infrastructure" sector.

When you send crypto, what’s your routine? For most of us, it involves copying the recipient's address from our transaction history. After all, nobody can memorize a 40-character string like 0x1A2b...8f9E. It's a convenient shortcut we all use.

But what if that convenience is a carefully laid trap?

A devastatingly effective scam called Blockchain Address Poisoning is exploiting this exact habit. Recent research from Carnegie Mellon University has uncovered the shocking scale of this threat. In just two years, on the Ethereum and Binance Smart Chain (BSC) networks alone, scammers have made over 270 million attack attempts, targeting 17 million victims and successfully stealing at least $83.8 million.

This isn't a niche threat; it's one of the largest and most successful crypto phishing schemes operating today. Here’s how it works and what you can do to protect yourself.

How the Deception Works 🤔​

Address poisoning is a game of visual trickery. The attacker’s strategy is simple but brilliant:

1. Generate a Lookalike Address: The attacker identifies a frequent address you send funds to. They then use powerful computers to generate a new crypto address that has the exact same starting and ending characters. Since most wallets and block explorers shorten addresses for display (e.g., 0x1A2b...8f9E), their fraudulent address looks identical to the real one at a glance.

2. "Poison" Your Transaction History: Next, the attacker needs to get their lookalike address into your wallet's history. They do this by sending a "poison" transaction. This can be:

   • A Tiny Transfer: They send you a minuscule amount of crypto (like $0.001) from their lookalike address. It now appears in your list of recent transactions.
   • A Zero-Value Transfer: In a more cunning move, they exploit a feature in many token contracts to create a fake, zero-dollar transfer that looks like it came from you to their lookalike address. This makes the fake address seem even more legitimate, as it appears you've sent funds there before.
   • A Counterfeit Token Transfer: They create a worthless, fake token (e.g., "USDTT" instead of USDT) and fake a transaction to their lookalike address, often mimicking the amount of a previous real transaction you made.

3. Wait for the Mistake: The trap is now set. The next time you go to pay a legitimate contact, you scan your transaction history, see what you believe is the correct address, copy it, and hit send. By the time you realize your mistake, the funds are gone. And thanks to the irreversible nature of blockchain, there's no bank to call and no way to get them back.

A Glimpse into a Criminal Enterprise 🕵️‍♂️​

This isn't the work of lone hackers. The research reveals that these attacks are carried out by large, organized, and highly profitable criminal groups.

Who They Target​

Attackers don't waste their time on small accounts. They systematically target users who are:

• Wealthy: Holding significant balances in stablecoins.
• Active: Conducting frequent transactions.
• High-Value Transactors: Moving large sums of money.

A Hardware Arms Race​

Generating a lookalike address is a brute-force computational task. The more characters you want to match, the exponentially harder it gets. Researchers found that while most attackers use standard CPUs to create moderately convincing fakes, the most sophisticated criminal group has taken it to another level.

This top-tier group has managed to generate addresses that match up to 20 characters of a target's address. This feat is nearly impossible with standard computers, leading researchers to conclude they are using massive GPU farms—the same kind of powerful hardware used for high-end gaming or AI research. This shows a significant financial investment, which they easily recoup from their victims. These organized groups are running a business, and business is unfortunately booming.

How to Protect Your Funds 🛡️​

While the threat is sophisticated, the defenses are straightforward. It all comes down to breaking bad habits and adopting a more vigilant mindset.

1. For Every User (This is the most important part):

   • VERIFY THE FULL ADDRESS. Before you click "Confirm," take five extra seconds to manually check the entire address, character by character. Do not just glance at the first and last few digits.
   • USE AN ADDRESS BOOK. Save trusted, verified addresses to your wallet's address book or contact list. When sending funds, always select the recipient from this saved list, not from your dynamic transaction history.
   • SEND A TEST TRANSACTION. For large or important payments, send a tiny amount first. Confirm with the recipient that they have received it before sending the full sum.

2. A Call for Better Wallets:

   • Wallet developers can help by improving user interfaces. This includes displaying more of the address by default or adding strong, explicit warnings when a user is about to send funds to an address they've only interacted with via a tiny or zero-value transfer.

3. The Long-Term Fix:

   • Systems like the Ethereum Name Service (ENS), which allow you to map a human-readable name like yourname.eth to your address, can eliminate this problem entirely. Broader adoption is key.

In the decentralized world, you are your own bank, which also means you are your own head of security. Address poisoning is a silent but powerful threat that preys on convenience and inattention. By being deliberate and double-checking your work, you can ensure your hard-earned assets don't end up in a scammer's trap.

How to drop wallet friction, keep users flowing, and forecast the upside

What if your Web3 app had the same seamless sign-up flow as a modern Web2 service? That's the core promise of zkLogin on the Sui blockchain. It functions like OAuth for Sui, letting users sign in with familiar accounts from Google, Apple, X, and more. A zero-knowledge proof then securely links that Web2 identity to an on-chain Sui address—no wallet pop-ups, no seed phrases, no user churn.

The impact is real and immediate. With hundreds of thousands of zkLogin accounts already live, case studies report massive gains in user conversion, jumping from a dismal 17% to a healthy 42% after removing traditional wallet barriers. Let's break down how it works and what it can do for your project.

Why Wallets Kill First‑Time Conversion​

You've built a groundbreaking dApp, but your user acquisition funnel is leaking. The culprit is almost always the same: the "Connect Wallet" button. Standard Web3 onboarding is a maze of extension installations, seed phrase warnings, and crypto-jargon quizzes.

It’s a massive barrier for newcomers. UX researchers observed a staggering 87% drop-off the moment a wallet prompt appeared. In a telling experiment, simply re-routing that prompt to a later stage in the checkout process flipped the completion rate to 94%. Even for crypto-curious users, the primary fear is, “I might lose my funds if I click the wrong button.” Removing that single, intimidating step is the key to unlocking exponential growth.

How zkLogin Works (in Plain English)​

zkLogin elegantly sidesteps the wallet problem by using technologies every internet user already trusts. The magic happens behind the scenes in a few quick steps:

1. Ephemeral Key Pair: When a user wants to sign in, a temporary, single-session key pair is generated locally in their browser. Think of it as a temporary passkey, valid only for this session.
2. OAuth Dance: The user signs in with their Google, Apple, or other social account. Your app cleverly embeds a unique value (nonce) into this login request.
3. ZKP Service: After a successful login, a ZKP (Zero-Knowledge Proof) service generates a cryptographic proof. This proof confirms, "This OAuth token authorizes the owner of the temporary passkey," without ever revealing the user's personal identity on-chain.
4. Derive Address: The user's JWT (JSON Web Token) from the OAuth provider is combined with a unique salt to deterministically generate their permanent Sui address. The salt is kept private, either client-side or in a secure backend.
5. Submit Transaction: Your app signs transactions with the temporary key and attaches the ZK proof. Sui validators verify the proof on-chain, confirming the transaction's legitimacy without the user ever needing a traditional wallet.

Step‑by‑Step Integration Guide​

Ready to implement this? Here’s a quick guide using the TypeScript SDK. The principles are identical for Rust or Python.

1. Install SDK​

The @mysten/sui package includes all the zklogin helpers you'll need.

pnpm add @mysten/sui

2. Generate Keys & Nonce​

First, create an ephemeral keypair and a nonce tied to the current epoch on the Sui network.

const keypair = new Ed25519Keypair();
const { epoch } = await suiClient.getLatestSuiSystemState();
const nonce = generateNonce(keypair.getPublicKey(), Number(epoch) + 2, generateRandomness());

3. Redirect to OAuth​

Construct the appropriate OAuth login URL for the provider you're using (e.g., Google, Facebook, Apple) and redirect the user.

4. Decode JWT & Fetch User Salt​

After the user logs in and is redirected back, grab the id_token from the URL. Use it to fetch the user-specific salt from your backend, then derive their Sui address.

const jwt = new URLSearchParams(window.location.search).get('id_token')!;
const salt = await fetch('/api/salt?jwt=' + jwt).then(r => r.text());
const address = jwtToAddress(jwt, salt);

5. Request ZK Proof​

Send the JWT to a prover service to get the ZK proof. For development, you can use Mysten’s public prover. In production, you should host your own or use a service like Enoki.

const proof = await fetch('/api/prove', {
  method:'POST',
  body: JSON.stringify({ jwt, ... })
}).then(r => r.json());

6. Sign & Send​

Now, build your transaction, set the sender to the user's zkLogin address, and execute it. The SDK handles attaching the zkLoginInputs (the proof) automatically. ✨

const tx = new TransactionBlock();
tx.moveCall({ target:'0x2::example::touch_grass' }); // Any Move call
tx.setSender(address);
tx.setGasBudget(5_000_000);

await suiClient.signAndExecuteTransactionBlock({
  transactionBlock: tx,
  zkLoginInputs: proof // The magic happens here
});

7. Persist Session​

For a smoother user experience, encrypt and store the keypair and salt in IndexedDB or local storage. Remember to rotate them every few epochs for enhanced security.

KPI Projection Template​

The difference zkLogin makes isn't just qualitative; it's quantifiable. Compare a typical onboarding funnel with a zkLogin-powered one:

👉 What this means: For a campaign driving 10,000 unique visitors, that's the difference between 300 first-day on-chain actions and over 2,500.

Best Practices & Gotchas​

To create an even more seamless experience, keep these pro-tips in mind:

• Use Sponsored Transactions: Pay for your users' first few transaction fees. This removes all friction and delivers an incredible "aha" moment.
• Handle Salts Carefully: Changing a user's salt will generate a new address. Only do this if you control a reliable recovery path for them.
• Expose the Sui Address: After signup, show users their on-chain address. This empowers advanced users to import it into a traditional wallet later if they choose.
• Prevent Refresh Loops: Cache the JWT and ephemeral keypair until they expire to avoid asking the user to log in repeatedly.
• Monitor Prover Latency: Keep an eye on the proof-generation round-trip time. If it exceeds 2 seconds, consider hosting a regional prover to keep things snappy.

Where BlockEden.xyz Adds Value​

While zkLogin perfects the user-facing flow, scaling it introduces new backend challenges. That's where BlockEden.xyz comes in.

• API Layer: Our high-throughput, geo-routed RPC nodes ensure your zkLogin transactions are processed with minimal latency, regardless of user location.
• Observability: Get out-of-the-box dashboards to track key metrics like proof latency, success/fail ratios, and your conversion funnel's health.
• Compliance: For apps that bridge into fiat, our optional KYC module provides a compliant on-ramp directly from the user's verified identity.

Ready to Ship?​

The era of clunky, intimidating wallet flows is over. Spin up a zkLogin sandbox, plug in BlockEden’s full-node endpoint, and watch your sign-up graph bend upward—while your users never even have to hear the word “wallet.” 😉

1. Liquidity and Growth of Sui DeFi​

Figure: Sui’s DeFi TVL (blue line) and DEX volume (green bars) grew dramatically through Q2 2025.

Total Value Locked (TVL) Surge: The Sui network’s DeFi liquidity has grown explosively over the past year. From roughly $600M TVL in late 2024, Sui’s TVL rocketed to over $2 billion by mid-2025. In fact, Sui peaked at about $2.55B TVL on May 21, 2025 and sustained well above $2B for much of Q2. This ~300%+ increase (a 480% year-over-year rise from May 2023) firmly positions Sui among the top 10 blockchains by DeFi TVL, outpacing growth on networks like Solana. Major catalysts included institutional adoption and the integration of native USDC stablecoin support, which together attracted large capital inflows. Notably, Sui’s monthly DEX trading volumes have climbed into the top tier of all chains – exceeding $7–8 billion per month by mid-2025 (ranking ~8th industry-wide). The circulating stablecoin liquidity on Sui surpassed $1 billion in mid-2025, after growing 180% since the start of the year, indicating deepening on-chain liquidity. Cross-chain capital is flowing in as well; around $2.7B of assets have been bridged into Sui’s ecosystem, including Bitcoin liquidity (details below). This rapid growth trend underscores a year of net inflows and user expansion for Sui DeFi.

Major DEXs and Liquidity Providers: Decentralized exchanges form the backbone of Sui’s DeFi liquidity. The Cetus protocol – an automated market maker (AMM) and aggregator – has been a flagship DEX, offering stablecoin swaps and concentrated liquidity pools. Cetus consistently leads in volume (facilitating $12.8B+ in trades during Q2 2025 alone) while holding around $80M TVL. Another key player is Bluefin, a multi-faceted DEX that operates both a spot AMM and a perpetual futures exchange. Bluefin expanded its offerings in 2025 with innovative features: it introduced BluefinX, Sui’s first RFQ (request-for-quote) system for improved price execution, and even integrated high-frequency trading optimizations to narrow the gap between DEX and CEX performance. By Q2, Bluefin’s AMM held about $91M TVL and saw over $7.1B in quarterly spot volume. Momentum is another rising DEX – it launched a concentrated liquidity market maker (CLMM) that quickly amassed $107M in liquidity and generated ~$4.6B in trading volume shortly after launch. Sui’s DEX sector also includes MovEX (a hybrid AMM + order-book exchange) and Turbos (an early CLMM adopter), among others, each contributing to the diverse liquidity landscape. Notably, Sui supports a native on-chain central limit order book called DeepBook, co-developed with MovEX, which provides shared order-book liquidity to any Sui dApp. This combination of AMMs, aggregators, and an on-chain CLOB gives Sui one of the more robust DEX ecosystems in DeFi.

Lending Markets and Yield Protocols: Sui’s lending and borrowing platforms have attracted significant capital, making up a large share of the TVL. The Suilend protocol stands out as Sui’s largest DeFi platform, with roughly $700M+ in TVL by Q2 2025 (having crossed the $1B mark in early 2025). Suilend is an expansion of Solana’s Solend, brought to Sui’s Move runtime, and it quickly became the flagship money-market on Sui. It offers deposit and collateralized borrowing services for assets like SUI and USDC, and has innovated by launching companion products – for example, SpringSui (a liquid staking module) and STEAMM, an AMM that enables “superfluid” use of liquidity within the platform. By gamifying user engagement (through point campaigns and NFTs) and issuing a governance token $SEND with revenue-sharing, Suilend drove rapid adoption – reporting over 50,000 monthly active wallets by mid-2025. Close behind Suilend is Navi Protocol (also referred to as Astros), which similarly reached on the order of $600–700M TVL in its lending pools. Navi sets itself apart by blending lending markets with yield strategies and even Bitcoin DeFi integration: for example, Navi facilitated a campaign for users to stake xBTC (a BTC proxy on Sui) via the OKX Wallet, incentivizing Bitcoin holders to participate in Sui yield opportunities. Other notable lending platforms include Scallop (~$146M TVL) and AlphaLend (~$137M), which together indicate a competitive market for borrowing and lending on Sui. Yield aggregation has also started to take hold – protocols like AlphaFi and Kai Finance each manage tens of millions in assets (e.g. ~$40M TVL) to optimize yield across Sui farms. Though smaller in scale, these yield optimizers and structured products (e.g. MovEX’s structured yield vaults) add depth to Sui’s DeFi offerings by helping users maximize returns from the growing liquidity base.

Liquid Staking and Derivatives: In parallel, Sui’s liquid staking derivatives (LSDs) and derivative trading platforms represent an important slice of the ecosystem’s liquidity. Because Sui is a proof-of-stake chain, protocols like SpringSui, Haedal, and Volo have introduced tokens that wrap staked SUI, allowing stakers to remain liquid. SpringSui – launched by the Suilend team – quickly became the dominant LSD, holding about $189M in staked SUI by end of Q2. Together with Haedal ($150M) and others, Sui’s LSD platforms give users the ability to earn validator rewards while redeploying staking tokens into DeFi (for example, using staked-SUI as lending collateral or in yield farms). On the derivatives front, Sui now hosts multiple on-chain perpetual futures exchanges. We’ve mentioned Bluefin’s perp DEX (Bluefin Perps) which handled billions in quarterly volume. Additionally, Typus Finance launched Typus Perp (TLP) in Q2 2025, entering Sui’s perps market with an impressive debut. Sudo (with its ZO protocol integration) introduced gamified perpetual swaps and “intelligent” leveraged products, growing its user base and liquidity by over 100% last quarter. The Magma protocol even pioneered a new AMM model – an Adaptive Liquidity Market Maker (ALMM) – aiming for zero-slippage trades and greater capital efficiency in swaps. These innovative DEX and derivative designs are attracting liquidity of their own (e.g. Magma’s TVL doubled in Q2) and enhancing Sui’s reputation as a testbed for next-gen DeFi primitives.

Trends in Capital Inflow and Users: The overall liquidity trend on Sui has been strongly positive, fueled by both retail and institutional inflows. Sui’s growing credibility (e.g. HSBC and DBS Bank joining as network validators) and high performance have drawn in new capital. A significant portion of assets bridged into Sui are blue-chip tokens and stablecoins – for instance, Circle’s USDC launched natively on Sui, and Tether’s USDT became available via bridges, leading to a robust stablecoin mix (USDC ~$775M, USDT ~$100M circulating by Q2). Perhaps most notably, Bitcoin liquidity has entered Sui in size (via wrapped or staked BTC – detailed in Section 3), accounting for over 10% of TVL. On the user side, improved wallet support and abstraction (see Section 2) have spurred adoption. The popular Phantom wallet (with ~7M users) extended support to Sui, making it easier for the broad crypto community to access Sui dApps. Similarly, centralized exchange wallets like OKX and Binance integrated Sui DeFi features (e.g. Binance’s Chrome wallet added a Simple Yield integration featuring Sui’s Scallop protocol). These on-ramps contributed to Sui’s user growth: by early 2025 Sui had hundreds of thousands of active addresses, and top dApps like Suilend report tens of thousands of monthly users. Overall, liquidity on Sui has trended upward in 2025, supported by consistent inflows and expanding user participation – a stark contrast to the stagnation seen on some other chains during the same period.

2. Abstraction: Simplifying User Experience on Sui​

Account Abstraction Features: A cornerstone of Sui’s design is account abstraction, which vastly improves usability by hiding blockchain complexities from end-users. Unlike traditional Layer-1s where users must manage keys and gas for every transaction, Sui enables a smoother experience via native features. Specifically, Sui supports third-party credential logins and gas sponsorship at the protocol level. Developers can integrate zkLogin – allowing users to create a Sui wallet and log in with familiar Web2 credentials (Google, Facebook, Twitch, etc.) instead of seed phrases. Concurrently, Sui offers sponsored transactions, meaning dApp builders can pay gas fees on behalf of users through an on-chain “gas station” mechanism. Together, zkLogin and gas sponsorship remove two major pain points (seed phrase management and acquiring native tokens) for new users. A Sui user can, for example, sign up with an email/password (via OAuth) and start using a DeFi app immediately with no upfront SUI tokens needed. This level of abstraction mirrors Web2 ease-of-use and has been critical in onboarding the “next wave” of users who expect frictionless signup and free trial experiences. Many Sui apps and even Web3 games now leverage these features – a recent NFT game launch boasted a “zero-wallet login” flow for players, powered by Sui’s account abstraction and social login capabilities. Overall, by automating key management and gas handling, Sui significantly lowers the barrier to entry for DeFi, which in turn drives higher user retention and activity.

Smart Contract Abstraction and Move: Beyond login and transactions, Sui’s object-oriented programming model provides abstraction at the smart contract level. Sui’s native Move language treats objects (not externally owned accounts) as the basic unit of storage, with rich metadata and flexible ownership structures. This means developers can create smart contract objects that act as proxies for user accounts, automating tasks that would traditionally require user signatures. For example, an app on Sui can deploy a programmable object to handle recurring payments or complex multi-step trades on behalf of a user, without that user manually initiating each step. These objects can hold permissions and logic, effectively abstracting away repetitive actions from the end-user. Additionally, Sui introduced Programmable Transaction Blocks (PTBs) as a way to bundle multiple operations into a single transaction payload. Instead of requiring a user to sign and send 3–4 separate transactions (e.g. approve token, then swap, then stake), a Sui PTB can compose those steps and execute them all at once. This not only reduces friction and confirmation prompts for the user, but also improves performance (fewer on-chain transactions means lower total gas and faster execution). From the user’s perspective, a complex series of actions can feel like one smooth interaction – a critical improvement in UX. Sui’s Move was built with such composability and abstraction in mind, and it’s enabling developers to craft dApps that feel much more like traditional fintech apps. As an added bonus, Sui’s cryptographic agility supports multiple signature schemes (Ed25519, secp256k1, etc.), which allows wallets to use different key types (including those used on Ethereum or Bitcoin). This flexibility makes it easier to integrate Sui functionality into multi-chain wallets and even sets the stage for quantum-resistant cryptography down the line.

Cross-Chain Abstraction – Intents and Integration: Sui is breaking ground in cross-chain user experience through abstraction as well. A prime example is the July 2025 integration of NEAR Intents, a novel cross-chain coordination system, into Sui’s ecosystem. With this integration, Sui users can seamlessly swap assets across 20+ other chains (including Ethereum, Bitcoin, Solana, Avalanche, etc.) in a single step, without manual bridging. The underlying “intent” model means the user simply expresses what they want (e.g. “swap 1 ETH on Ethereum for SUI on Sui”) and a network of automated solvers finds the most efficient way to fulfill that request across chains. The user no longer needs to juggle multiple wallets or gas fees on different networks – the system abstracts all that away. Swapping assets into Sui becomes as easy as a one-click transaction, with no need to even hold gas tokens on the source chain. This is a significant UX leap for cross-chain DeFi. By mid-2025, NEAR Intents was live and Sui users could bring in outside liquidity within seconds, enabling use cases like cross-chain arbitrage and onboarding of assets from non-Sui holders with virtually no friction or custodial risk. Sui Foundation representatives highlighted that “swapping native assets in one click…abstracts away complexity while keeping everything on-chain, secure, and composable”. In parallel, Sui has benefited from major wallet integrations that hide complexity for users. As noted, Phantom’s multi-chain wallet now supports Sui, and other popular wallets like OKX and Binance Wallet have built-in support for Sui dApps. For instance, Binance’s wallet lets users directly access yield farms on Sui (via Scallop) through a simple interface, and OKX’s wallet integrated Sui’s BTC staking flows (Navi’s xBTC) natively. These integrations mean users can interact with Sui DeFi without switching apps or worrying about technical details – their familiar wallet abstracts it for them. All of these efforts, from intents-based swaps to wallet UIs, serve the goal of making cross-chain and on-chain DeFi feel effortless on Sui. The result is that Sui is increasingly accessible not just to crypto natives but also to mainstream users who demand simplicity.

User Experience Impact: Thanks to Sui’s abstraction layers, the user experience on Sui’s DeFi protocols has become one of the most user-friendly in blockchain. New users can onboard with a social login and no upfront cost, execute complex transactions with minimal confirmations, and even move assets from other chains with one-click swaps. This approach is fulfilling Sui’s mission of “familiar onboarding” and mass adoption. As a point of comparison, just as an iPhone user doesn’t need to understand Swift code to use an app, a Sui DeFi user shouldn’t need to grasp private keys or bridge mechanics. Sui’s account abstraction ethos embraces that philosophy, “offering a gateway to a seamless and gratifying user experience” for blockchain finance. By making Web3 interactions feel closer to Web2 in ease, Sui is lowering barriers for the next wave of DeFi users who value convenience. This user-centric design is a key factor in Sui’s growing adoption and sets the stage for greater mainstream participation in DeFi through 2025 and beyond.

3. The Next Wave of DeFi Primitives on Sui​

Proliferation of Native Stablecoins: A vibrant array of new stablecoins and asset-backed tokens is emerging on Sui, providing foundational building blocks for DeFi. In late 2024, Agora Finance’s AUSD went live as the first fully USD-backed stablecoin native to Sui. Marketed as an institutional-grade stablecoin, AUSD’s launch immediately added liquidity and was a boon for Sui’s DeFi growth (Sui’s TVL was about $600M when AUSD arrived and climbing). By mid-2025, AUSD had a circulating supply of tens of millions (with more on Ethereum and Avalanche) and became a regulated alternative to USDC/USDT within Sui’s ecosystem. Around the same time, the Bucket Protocol introduced BUCK, an over-collateralized stablecoin akin to DAI but for Sui. Users can mint BUCK by depositing SUI, BTC, ETH, and other assets as collateral. BUCK is pegged to USD and maintained via on-chain collateral ratios and stability mechanisms (Bucket features a Peg Stability Module, CDP vaults, etc., similar to MakerDAO). By Q2 2025, BUCK’s supply reached ~$60–66M, making it one of the largest Sui-native stablecoins (Bucket’s protocol TVL was ~$69M in that period, mostly backing BUCK). Another notable addition is USDY by Ondo Finance – a yield-bearing “stablecoin” that tokenizes short-term U.S. Treasury yields. Ondo deployed USDY onto Sui in early 2024, marking Sui’s foray into real-world asset (RWA) backed tokens. USDY is effectively a tokenized bond fund that accrues interest for holders (its price floats slightly, reflecting earned yield). This provides Sui users with a native, compliance-focused stable asset that generates yield without needing to stake or farm. By 2025, Sui’s stablecoin landscape also included First Digital USD (FDUSD), brought via partnerships in Asia, and wrapped versions of major stablecoins. The variety of stablecoin primitives – from decentralized CDP-backed (BUCK) to fully fiat-backed (AUSD) to yield-bearing (USDY) – is expanding on-chain liquidity and enabling new DeFi strategies (e.g. using BUCK as loan collateral, or holding USDY as a low-risk yield source). These stable assets form the bedrock for other protocols like DEXs and lenders to build upon, and their presence is a strong signal of a maturing DeFi ecosystem.

BTC DeFi (“BTCfi”) Innovations: Sui is at the forefront of making Bitcoin an active player in DeFi, coining the term BTCfi for Bitcoin-centric DeFi use cases. In 2025, multiple initiatives bridged Bitcoin’s liquidity and security into Sui’s network. One major step was the integration of Threshold Network’s tBTC on Sui. tBTC is a decentralized, 1:1 BTC-backed token that uses threshold cryptography (distributed signing) to avoid any single custodian. In July 2025, tBTC went live on Sui, immediately unlocking access to over $500M worth of BTC liquidity for Sui protocols. This means Bitcoin holders can now mint tBTC directly into Sui and deploy it in lending, trading, or yield farming without entrusting their BTC to a centralized bridge. Sui’s high-performance infrastructure (with sub-second finality) makes it an attractive home for these BTC assets. In parallel, Sui partnered with the Stacks ecosystem to support sBTC, another 1:1 BTC representation that piggybacks off Bitcoin’s security via the Stacks layer-2. By May 2025, over 10% of Sui’s TVL consisted of BTC or BTC-derived assets as bridges like Wormhole, Stacks, and Threshold ramped up Bitcoin connectivity. More than 600 BTC (>$65M) had flowed into Sui in just the first few months of 2025. These BTC derivatives unlock use cases such as using BTC as collateral on Sui’s lending platforms (indeed, Suilend held over $102M in Bitcoin-based assets by Q2, more than any other Sui lender). They also enable BTC trading pairs on Sui DEXs and allow Bitcoin holders to earn DeFi yields without giving up their BTC ownership. The concept of BTCfi is to transform Bitcoin from a “passive” store-of-value into an active capital asset – and Sui has embraced this by providing the technology (fast, parallel execution and an object model ideal for representing BTC custody) and forging partnerships to bring in Bitcoin liquidity. The Sui Foundation even began running a Stacks validator, signaling its commitment to BTC integration. In short, Bitcoin is now a first-class citizen in Sui DeFi, and this cross-pollination is a key innovation of 2025. It opens the door for new Bitcoin-backed stablecoins, Bitcoin yield products, and multi-chain strategies that bridge the gap between the Bitcoin network and DeFi on Sui.

Advanced DEX Primitives and HFT: The next wave of Sui DeFi primitives also includes novel exchange designs and financial instruments that go beyond the initial AMM models. We’ve seen earlier how Magma’s ALMM and Momentum’s CLMM are pushing AMMs toward greater capital efficiency (concentrating liquidity or dynamically adjusting it). Additionally, protocols are experimenting with high-performance trading features: Bluefin in particular rolled out functionalities aimed at institutional and high-frequency traders. In July 2025, Bluefin announced it was bringing institutional-grade high-frequency trading strategies to its Sui-based DEX, using Sui’s parallel execution to achieve throughput and latency improvements. This narrows the performance gap with centralized exchanges and could attract quantitative trading firms to provide liquidity on-chain. Such enhancements in execution speed, low slippage, and MEV protection (Bluefin’s Spot 2.0 upgrade is noted for MEV-resistant RFQ matching) are new primitives in exchange design that Sui is pioneering.

Meanwhile, derivatives and structured products on Sui are becoming more sophisticated. Typus expanding into perpetual futures and Sudo/ZO offering gamified perps were mentioned; these indicate a trend of blending DeFi with trading gamification and user-friendly interfaces. Another example is Nemo, which introduced a “yield trading” market and a points reward system in its new interface – essentially allowing users to speculate on yield rates and earn loyalty points, a creative twist on typical yield farming. We also see structured yield products: for instance, MovEX and others have discussed structured vaults that automatically rotate funds between strategies, giving users packaged investment products (akin to DeFi ETFs or tranches). These are in development and represent the next generation of yield farming, where complexity (like strategy hopping) is abstracted and offered as a single product.

New Financial Instruments & Partnerships: The Sui community and its partners are actively building entirely new DeFi primitives that could define the next phase of growth. One high-profile upcoming project is Graviton, which raised $50M in a Series A (led by a16z and Pantera) to create a modular trading, lending, and cross-margining platform on Sui. Graviton is often compared to dYdX – aiming to onboard professional traders with a full-suite decentralized trading experience (perpetuals, margin trading, lending markets all under one roof). Such a well-funded initiative underlines the confidence in Sui’s DeFi potential and promises a new primitive: a one-stop, high-performance DeFi “super app” on Sui. In addition, real-world finance is intersecting with Sui: the Sui Foundation has fostered partnerships like xMoney/xPortal to launch a crypto-powered MasterCard for retail users, which would allow spending Sui-based assets in everyday purchases. This kind of CeFi–DeFi bridge (essentially bringing DeFi liquidity to point-of-sale) could be transformative if it gains traction. On the institutional side, 21Shares filed for a SUI exchange-traded fund (ETF) in the US – while not a DeFi protocol, an ETF would give traditional investors exposure to Sui’s growth and indirectly to its DeFi economy.

The community and developer activity on Sui is another driving force for new primitives. Sui’s open-source Move ecosystem has grown so active that by mid-2025 Sui surpassed Solana and Near in weekly developer commits and repo forks, thanks to a surge in new tooling (e.g. Move SDKs, zk-proof integrations, cross-chain protocol development). This vibrant developer community is continually experimenting with ideas like on-chain options markets, decentralized insurance, and intent-based lending (some hackathon projects in 2025 tackled these areas). The results are starting to emerge: for example, Lotus Finance launched as a decentralized options AMM on Sui in Q2, and Turbos adopted decentralized front-end hosting (via Walrus) to push the envelope on censorship-resistant DeFi. Community-driven initiatives like these, alongside formal partnerships (e.g. Sui’s collaboration with Google Cloud to provide on-chain data indexing and AI inference tools), create a fertile ground for novel protocols. We’re seeing DeFi primitives on Sui that integrate AI oracles for dynamic pricing, BTC staking services (SatLayer), and even cross-chain intents (the NEAR Intents integration can be seen as a primitive for cross-chain liquidity). Each adds a building block that future developers can combine into new financial products.

Summary: In 2025, Sui’s DeFi ecosystem is flourishing with innovation. The liquidity on Sui has reached multi-billion dollar levels, anchored by major DEXes and lenders, and bolstered by steady inflows and user growth. Through account abstraction and user-centric design, Sui has dramatically improved the DeFi user experience, attracting a broader audience. And with the next wave of primitives – from native stablecoins and BTC integration to advanced AMMs, perps, and real-world asset tokens – Sui is expanding what’s possible in decentralized finance. Key protocols and community efforts are driving this evolution: Cetus and Bluefin advancing DEX tech, Suilend and others expanding lending into new asset classes, Bucket, Agora, Ondo bringing novel assets on-chain, and many more. High-profile partnerships (with infrastructure providers, TradFi institutions, and cross-chain networks) further amplify Sui’s momentum. All these elements point to Sui solidifying its position as a leading DeFi hub by 2025 – one characterized by deep liquidity, seamless usability, and relentless innovation in financial primitives.

Sources:

• Sui Foundation – Sui Q2 2025 DeFi Roundup (July 15, 2025)
• Sui Foundation – NEAR Intents Brings Lightning-Fast Cross-chain Swaps to Sui (July 17, 2025)
• Sui Foundation – Sui to Support sBTC and Stacks (BTCfi Use Cases) (May 1, 2025)
• Sui Foundation – All About Account Abstraction (Oct 4, 2023)
• Ainvest News – Sui Network TVL Surpasses $1.4B Driven by DeFi Protocols (Jul 14, 2025)
• Ainvest News – Sui DeFi TVL Surges 480% to $1.8B... (Jul 12, 2025)
• Suipiens (Sui community) – tBTC Integration Brings Bitcoin Liquidity to Sui (Jul 17, 2025)
• Suipiens – Inside Suilend: Sui’s Leading Lending Platform (Jul 3, 2025)
• The Defiant – Ondo to Bring RWA-Backed Stablecoins onto Sui (Feb 7, 2024)
• Official Sui Documentation – Intro to Sui: User Experience (account abstraction features)

The State of Blockchain APIs 2025 report (by BlockEden.xyz) provides a comprehensive look at the blockchain API infrastructure landscape. It examines emerging trends, market growth, major providers, supported blockchains, developer adoption, and critical factors like security, decentralization, and scalability. It also highlights how blockchain API services are powering various use cases (DeFi, NFTs, gaming, enterprise) and includes commentary on industry directions. Below is a structured summary of the report’s findings, with comparisons of leading API providers and direct citations from the source for verification.

Trends in Blockchain API Infrastructure (2025)​

The blockchain API ecosystem in 2025 is shaped by several key trends and technological advancements:

• Multi-Chain Ecosystems: The era of a single dominant blockchain is over – hundreds of Layer-1s, Layer-2s, and app-specific chains exist. Leading providers like QuickNode now support ~15–25 chains, but in reality “five to six hundred blockchains (and thousands of sub-networks) [are] active in the world”. This fragmentation drives demand for infrastructure that abstracts complexity and offers unified multi-chain access. Platforms that embrace new protocols early can gain first-mover advantage, as more scalable chains unlock new on-chain applications and developers increasingly build across multiple chains. In 2023 alone, ~131 different blockchain ecosystems attracted new developers, underscoring the multi-chain trend.

• Developer Community Resilience and Growth: The Web3 developer community remains substantial and resilient despite market cycles. As of late 2023 there were over 22,000 monthly active open-source crypto developers, a slight dip (~25% YoY) after the 2021 hype, but notably the number of experienced “veteran” developers grew by ~15%. This indicates a consolidation of serious, long-term builders. These developers demand reliable, scalable infrastructure and cost-effective solutions, especially in a tighter funding environment. With transaction costs dropping on major chains (thanks to L2 rollups) and new high-throughput chains coming online, on-chain activity is hitting all-time highs – further fueling demand for robust node and API services.

• Rise of Web3 Infrastructure Services: Blockchain infrastructure has matured into its own segment, attracting significant venture funding and specialized providers. QuickNode, for example, distinguished itself with high performance (reported 2.5× faster than some competitors) and 99.99% uptime SLAs, winning enterprise clients like Google and Coinbase. Alchemy achieved a $10 B valuation at the market peak, reflecting investor enthusiasm. This influx of capital has spurred rapid innovation in managed nodes, RPC APIs, indexing/analytics, and developer tools. Traditional cloud giants (AWS, Azure, Google Cloud) are also entering the fray with blockchain node hosting and managed ledger services. This validates the market opportunity but raises the bar for smaller providers to deliver on reliability, scale, and enterprise features.

• Decentralization Push (Infrastructure): Counter to the trend of big centralized providers, there’s a movement toward decentralized infrastructure in line with Web3’s ethos. Projects like Pocket Network, Ankr, and Blast (Bware) offer RPC endpoints via distributed node networks with crypto-economic incentives. These decentralized APIs can be cost-effective and censorship-resistant, though often still trailing centralized services in performance and ease-of-use. The report notes that “while centralized services currently lead in performance, the ethos of Web3 favors disintermediation.” BlockEden’s own vision of an open “API marketplace” with permissionless access (eventually token-governed) aligns with this push, seeking to combine the reliability of traditional infrastructure with the openness of decentralized networks. Ensuring open self-service onboarding (e.g. generous free tiers, instant API key signup) has become an industry best practice to attract grassroots developers.

• Convergence of Services & One-Stop Platforms: Providers are broadening their offerings beyond basic RPC endpoints. There’s growing demand for enhanced APIs and data services – e.g. indexed data (for faster queries), GraphQL APIs, token/NFT APIs, analytics dashboards, and even integrations of off-chain data or AI services. For example, BlockEden provides GraphQL indexer APIs for Aptos, Sui, and Stellar Soroban to simplify complex queries. QuickNode acquired NFT API tools (e.g. Icy Tools) and launched an add-on marketplace. Alchemy offers specialized APIs for NFTs, tokens, transfers, and even an account abstraction SDK. This “one-stop-shop” trend means developers can get nodes + indexing + storage + analytics from a single platform. BlockEden has even explored “permissionless LLM inference” (AI services) in its infrastructure. The goal is to attract developers with a rich suite of tools so they don’t need to stitch together multiple vendors.

Market Size and Growth Outlook (2025)​

The report paints a picture of robust growth for the blockchain API/infrastructure market through 2025 and beyond:

• The global Web3 infrastructure market is projected to grow at roughly 49% CAGR from 2024 to 2030, indicating enormous investment and demand in the sector. This suggests the overall market size could double every ~1.5–2 years at that rate. (For context, an external Statista forecast cited in the report estimates the broader digital asset ecosystem reaching ~$45.3 billion by end of 2025, underscoring the scale of the crypto economy that infrastructure must support.)

• Driving this growth is the pressure on businesses (both Web3 startups and traditional firms) to integrate crypto and blockchain capabilities. According to the report, dozens of Web2 industries (e-commerce, fintech, gaming, etc.) now require crypto exchange, payment, or NFT functionality to stay competitive, but building such systems from scratch is difficult. Blockchain API providers offer turnkey solutions – from wallet and transaction APIs to fiat on/off-ramps – that bridge traditional systems with the crypto world. This lowers the barrier for adoption, fueling more demand for API services.

• Enterprise and institutional adoption of blockchain is also rising, further expanding the market. Clearer regulations and success stories of blockchain in finance and supply chain have led to more enterprise projects by 2025. Many enterprises prefer not to run their own nodes, creating opportunities for infrastructure providers with enterprise-grade offerings (SLA guarantees, security certifications, dedicated support). For instance, Chainstack’s SOC2-certified infrastructure with 99.9% uptime SLA and single sign-on appeals to enterprises seeking reliability and compliance. Providers that capture these high-value clients can significantly boost revenue.

In summary, 2025’s outlook is strong growth for blockchain APIs – the combination of an expanding developer base, new blockchains launching, increasing on-chain activity, and mainstream integration of crypto services all drive a need for scalable infrastructure. Both dedicated Web3 firms and tech giants are investing heavily to meet this demand, indicating a competitive but rewarding market.

Leading Blockchain API Providers – Features & Comparison​

Several key players dominate the blockchain API space in 2025, each with different strengths. The BlockEden report compares BlockEden.xyz (the host of the report) with other leading providers such as Alchemy, Infura, QuickNode, and Chainstack. Below is a comparison in terms of supported blockchains, notable features, performance/uptime, and pricing:

Sources: The above comparison integrates data and quotes from the BlockEden.xyz report, as well as documented features from provider websites (e.g. Alchemy and Chainstack docs) for accuracy.

Blockchain Coverage and Network Support​

One of the most important aspects of an API provider is which blockchains it supports. Here is a brief coverage of specific popular chains and how they are supported:

• Ethereum Mainnet & L2s: All the leading providers support Ethereum. Infura and Alchemy specialize heavily in Ethereum (with full archive data, etc.). QuickNode, BlockEden, and Chainstack also support Ethereum as a core offering. Layer-2 networks like Polygon, Arbitrum, Optimism, Base are supported by Alchemy, QuickNode, and Chainstack, and by Infura (as paid add-ons). BlockEden supports Polygon (and Polygon zkEVM) and is likely to add more L2s as they emerge.

• Solana: Solana is supported by BlockEden (they added Solana in 2023), QuickNode, and Chainstack. Alchemy also added Solana RPC in 2022. Infura does not support Solana (at least as of 2025, it remains focused on EVM networks).

• Bitcoin: Being a non-EVM, Bitcoin is notably not supported by Infura or Alchemy (which concentrate on smart contract chains). QuickNode and Chainstack both offer Bitcoin RPC access, giving developers access to Bitcoin data without running a full node. BlockEden currently does not list Bitcoin among its supported networks (it focuses on smart contract platforms and newer chains).

• Polygon & BNB Chain: These popular Ethereum sidechains are widely supported. Polygon is available on BlockEden, Alchemy, Infura (premium), QuickNode, and Chainstack. BNB Smart Chain (BSC) is supported by BlockEden (BSC), QuickNode, and Chainstack. (Alchemy and Infura do not list BSC support, as it’s outside the Ethereum/consensus ecosystem they focus on.)

• Emerging Layer-1s (Aptos, Sui, etc.): This is where BlockEden.xyz shines. It was an early provider for Aptos and Sui, offering RPC and indexer APIs for these Move-language chains at launch. Many competitors did not initially support them. By 2025, some providers like Chainstack have added Aptos and others to their lineup, but BlockEden remains highly regarded in those communities (the report notes BlockEden’s Aptos GraphQL API “cannot be found anywhere else” according to users). Supporting new chains quickly can attract developer communities early – BlockEden’s strategy is to fill the gaps where developers have limited options on new networks.

• Enterprise (Permissioned) Chains: Uniquely, Chainstack supports Hyperledger Fabric, Corda, Quorum, and Multichain, which are important for enterprise blockchain projects (consortia, private ledgers). Most other providers do not cater to these, focusing on public chains. This is part of Chainstack’s enterprise positioning.

In summary, Ethereum and major EVM chains are universally covered, Solana is covered by most except Infura, Bitcoin only by a couple (QuickNode/Chainstack), and newer L1s like Aptos/Sui by BlockEden and now some others. Developers should choose a provider that covers all the networks their dApp needs – hence the advantage of multi-chain providers. The trend toward more chains per provider is clear (e.g. QuickNode ~14, Chainstack 50–70+, Blockdaemon 50+, etc.), but depth of support (robustness on each chain) is equally crucial.

Developer Adoption and Ecosystem Maturity​

The report provides insight into developer adoption trends and the maturity of the ecosystem:

• Developer Usage Growth: Despite the 2022–2023 bear market, on-chain developer activity remained strong. With ~22k monthly active devs in late 2023 (and likely growing again in 2024/25), the demand for easy-to-use infrastructure is steady. Providers are competing not just on raw tech, but on developer experience to attract this base. Features like extensive docs, SDKs, and community support are now expected. For example, BlockEden’s community-centric approach (Discord, 10x.pub guild, hackathons) and QuickNode’s education initiatives aim to build loyalty.

• Free Tier Adoption: The freemium model is driving widespread grassroots usage. Nearly all providers offer a free tier that covers basic project needs (millions of requests per month). The report notes BlockEden’s free tier of 10M daily CUs is deliberately high to remove friction for indie devs. Alchemy and Infura’s free plans (around 3–4M calls per month) helped onboard hundreds of thousands of developers over the years. This strategy seeds the ecosystem with users who can later convert to paid plans as their dApps gain traction. The presence of a robust free tier has become an industry standard – it lowers the barrier for entry, encouraging experimentation and learning.

• Number of Developers on Platforms: Infura historically had the largest user count (over 400k developers as of a few years ago) since it was an early default. Alchemy and QuickNode also grew large user bases (Alchemy’s outreach via its education programs and QuickNode’s focus on Web3 startups helped them sign up many thousands). BlockEden, being newer, reports a community of 6,000+ developers using its platform. While smaller in absolute terms, this is significant given its focus on newer chains – it indicates strong penetration in those ecosystems. The report sets a goal of doubling BlockEden’s active developers by next year, reflecting the overall growth trajectory of the sector.

• Ecosystem Maturity: We are seeing a shift from hype-driven adoption (many new devs flooding in during bull runs) to a more sustainable, mature growth. The drop in “tourist” developers after 2021 means those who remain are more serious, and new entrants in 2024–2025 are often backed by better understanding. This maturation demands more robust infrastructure: experienced teams expect high uptime SLAs, better analytics, and support. Providers have responded by professionalizing services (e.g., offering dedicated account managers for enterprise, publishing status dashboards, etc.). Also, as ecosystems mature, usage patterns are better understood: for instance, NFT-heavy applications might need different optimizations (caching metadata etc.) than DeFi trading bots (needing mempool data and low latency). API providers now offer tailored solutions (e.g. Chainstack’s aforementioned “Trader Node” for low-latency trading data). The presence of industry-specific solutions (gaming APIs, compliance tools, etc., often available through marketplaces or partners) is a sign of a maturing ecosystem serving diverse needs.

• Community and Support: Another aspect of maturity is the formation of active developer communities around these platforms. QuickNode and Alchemy have community forums and Discords; BlockEden’s community (with 4,000+ Web3 builders in its guild) spans Silicon Valley to NYC and globally. This peer support and knowledge sharing accelerates adoption. The report highlights “exceptional 24/7 customer support” as a selling point of BlockEden, with users appreciating the team’s responsiveness. As the tech becomes more complex, this kind of support (and clear documentation) is crucial for onboarding the next wave of developers who may not be as deeply familiar with blockchain internals.

In summary, developer adoption is expanding in a more sustainable way. Providers that invest in the developer experience – free access, good docs, community engagement, and reliable support – are reaping the benefits of loyalty and word-of-mouth in the Web3 dev community. The ecosystem is maturing, but still has plenty of room to grow (new developers entering from Web2, university blockchain clubs, emerging markets, etc., are all targets mentioned for 2025 growth).

Security, Decentralization, and Scalability Considerations​

The report discusses how security, decentralization, and scalability factor into blockchain API infrastructure:

• Reliability & Security of Infrastructure: In the context of API providers, security refers to robust, fault-tolerant infrastructure (since these services do not usually custody funds, the main risks are downtime or data errors). Leading providers emphasize high uptime, redundancy, and DDoS protection. For example, QuickNode’s 99.99% uptime SLA and global load balancing are meant to ensure a dApp doesn’t go down due to an RPC failure. BlockEden cites its 99.9% uptime track record and the trust gained by managing $65M in staked assets securely (implying strong operational security for their nodes). Chainstack’s SOC2 compliance indicates a high standard of security practices and data handling. Essentially, these providers run mission-critical node infrastructure so they treat reliability as paramount – many have 24/7 on-call engineers and monitoring across all regions.

• Centralization Risks: A well-known concern in the Ethereum community is over-reliance on a few infrastructure providers (e.g., Infura). If too much traffic funnels through a single provider, outages or API malfeasance could impact a large portion of the decentralized app ecosystem. The 2025 landscape is improving here – with many strong competitors, the load is more distributed than in 2018 when Infura was almost singular. Nonetheless, the push for decentralization of infra is partly to address this. Projects like Pocket Network (POKT) use a network of independent node runners to serve RPC requests, removing single points of failure. The trade-off has been performance and consistency, but it’s improving. Ankr’s hybrid model (some centralized, some decentralized) similarly aims to decentralize without losing reliability. The BlockEden report acknowledges these decentralized networks as emerging competitors – aligning with Web3 values – even if they aren’t yet as fast or developer-friendly as centralized services. We may see more convergence, e.g., centralized providers adopting some decentralized verification (BlockEden’s vision of a tokenized marketplace is one such hybrid approach).

• Scalability and Throughput: Scalability is two-fold: the ability of the blockchains themselves to scale (higher TPS, etc.) and the ability of infrastructure providers to scale their services to handle growing request volumes. On the first point, 2025 sees many L1s/L2s with high throughput (Solana, new rollups, etc.), which means APIs must handle bursty, high-frequency workloads (e.g., a popular NFT mint on Solana can generate thousands of TPS). Providers have responded by improving their backend – e.g., QuickNode’s architecture to handle billions of requests per day, Chainstack’s “Unlimited” nodes, and BlockEden’s use of both cloud and bare-metal servers for performance. The report notes that on-chain activity hitting all-time highs is driving demand for node services, so scalability of the API platform is crucial. Many providers now showcase their throughput capabilities (for instance, QuickNode’s higher-tier plans allowing billions of requests, or Chainstack highlighting “unbounded performance” in their marketing).

• Global Latency: Part of scalability is reducing latency by geographic distribution. If an API endpoint is only in one region, users across the globe will have slower responses. Thus, geo-distributed RPC nodes and CDNs are standard now. Providers like Alchemy and QuickNode have data centers across multiple continents. Chainstack offers regional endpoints (and even product tiers specifically for latency-sensitive use cases). BlockEden also runs nodes in multiple regions to enhance decentralization and speed (the report mentions plans to operate nodes across key regions to improve network resilience and performance). This ensures that as user bases grow worldwide, the service scales geographically.

• Security of Data and Requests: While not explicitly about APIs, the report briefly touches on regulatory and security considerations (e.g., BlockEden’s research into the Blockchain Regulatory Certainty Act indicating attention to compliant operations). For enterprise clients, things like encryption, secure APIs, and maybe ISO certifications can matter. On a more blockchain-specific note, RPC providers can also add security features like frontrunning protection (some offer private TX relay options) or automated retries for failed transactions. Coinbase Cloud and others have pitched “secure relay” features. The report’s focus is more on infrastructure reliability as security, but it’s worth noting that as these services embed deeper into financial apps, their security posture (uptime, attack resistance) becomes part of the overall security of the Web3 ecosystem.

In summary, scalability and security are being addressed through high-performance infrastructure and diversification. The competitive landscape means providers strive for the highest uptime and throughput. At the same time, decentralized alternatives are growing to mitigate centralization risk. The combination of both will likely define the next stage: a blend of reliable performance with decentralized trustlessness.

Use Cases and Applications Driving API Demand​

Blockchain API providers service a wide array of use cases. The report highlights several domains that are notably reliant on these APIs in 2025:

• Decentralized Finance (DeFi): DeFi applications (DEXs, lending platforms, derivatives, etc.) rely heavily on reliable blockchain data. They need to fetch on-chain state (balances, smart contract reads) and send transactions continuously. Many top DeFi projects use services like Alchemy or Infura to scale. For example, Aave and MakerDAO use Alchemy infrastructure. APIs also provide archive node data needed for analytics and historical queries in DeFi. With DeFi continuing to grow, especially on Layer-2 networks and multi-chain deployments, having multi-chain API support and low latency is crucial (e.g., arbitrage bots benefit from mempool data and fast transactions – some providers offer dedicated low-latency endpoints for this reason). The report implies that lowering costs (via L2s and new chains) is boosting on-chain DeFi usage, which in turn increases API calls.

• NFTs and Gaming: NFT marketplaces (like OpenSea) and blockchain games generate significant read volume (metadata, ownership checks) and write volume (minting, transfers). OpenSea is a notable Alchemy customer, likely due to Alchemy’s NFT API which simplifies querying NFT data across Ethereum and Polygon. QuickNode’s cross-chain NFT API is also aimed at this segment. Blockchain games often run on chains like Solana, Polygon, or specific sidechains – providers that support those networks (and offer high TPS handling) are in demand. The report doesn’t explicitly name gaming clients, but it mentions Web3 gaming and metaverse projects as growing segments (and BlockEden’s own support for things like AI integration could relate to gaming/NFT metaverse apps). In-game transactions and marketplaces constantly ping node APIs for state updates.

• Enterprise & Web2 Integration: Traditional companies venturing into blockchain (payments, supply chain, identity, etc.) prefer managed solutions. The report notes that fintech and e-commerce platforms are adding crypto payments and exchange features – many of these use third-party APIs rather than reinvent the wheel. For example, payment processors can use blockchain APIs for crypto transfers, or banks can use node services to query chain data for custody solutions. The report suggests increasing interest from enterprises and even mentions targeting regions like the Middle East and Asia where enterprise blockchain adoption is rising. A concrete example: Visa has worked with QuickNode for some blockchain pilots, and Meta (Facebook) uses Alchemy for certain blockchain projects. Enterprise use cases also include analytics and compliance – e.g., querying blockchain for risk analysis, which some providers accommodate through custom APIs or by supporting specialized chains (like Chainstack supporting Corda for trade finance consortia). BlockEden’s report indicates that landing a few enterprise case studies is a goal to drive mainstream adoption.

• Web3 Startups and DApps: Of course, the bread-and-butter use case is any decentralized application – from wallets to social dApps to DAOs. Web3 startups rely on API providers to avoid running nodes for each chain. Many hackathon projects use free tiers of these services. Areas like Decentralized Social Media, DAO tooling, identity (DID) systems, and infrastructure protocols themselves all need reliable RPC access. The report’s growth strategy for BlockEden specifically mentions targeting early-stage projects and hackathons globally – indicating that a constant wave of new dApps is coming online that prefer not to worry about node ops.

• Specialized Services (AI, Oracles, etc.): Interestingly, the convergence of AI and blockchain is producing use cases where blockchain APIs and AI services intersect. BlockEden’s exploration of “AI-to-earn” (Cuckoo Network partnership) and permissionless AI inference on its platform shows one angle. Oracles and data services (Chainlink, etc.) might use base infrastructure from these providers as well. While not a traditional “user” of APIs, these infrastructure layers themselves sometimes build on each other – for instance, an analytics platform may use a blockchain API to gather data to feed to its users.

Overall, the demand for blockchain API services is broad – from hobbyist developers to Fortune 500 companies. DeFi and NFTs were the initial catalysts (2019–2021) that proved the need for scalable APIs. By 2025, enterprise and novel Web3 sectors (social, gaming, AI) are expanding the market further. Each use case has its own requirements (throughput, latency, historical data, security) and providers are tailoring solutions to meet them.

Notably, the report includes quotes and examples from industry leaders that illustrate these use cases:

• “Over 1,000 coins across 185 blockchains are supported… allowing access to 330k+ trade pairs,” one exchange API provider touts – highlighting the depth of support needed for crypto exchange functionality.
• “A partner reported a 130% increase in monthly txn volume in four months” after integrating a turnkey API – underlining how using a solid API can accelerate growth for a crypto business.
• The inclusion of such insights underscores that robust APIs are enabling real growth in applications.

Industry Insights and Commentary​

The BlockEden report is interwoven with insights from across the industry, reflecting a consensus on the direction of blockchain infrastructure. Some notable commentary and observations:

• Multi-chain Future: As quoted in the report, “the reality is there are five to six hundred blockchains” out there. This perspective (originally from Electric Capital’s developer report or a similar source) emphasizes that the future is plural, not singular. Infrastructure must adapt to this fragmentation. Even the dominant providers acknowledge this – e.g., Alchemy and Infura (once almost solely Ethereum-focused) are now adding multiple chains, and venture capital is flowing to startups focusing on niche protocol support. The ability to support many chains (and to do so quickly as new ones emerge) is viewed as a key success factor.

• Importance of Performance: The report cites QuickNode’s performance edge (2.5× faster) which likely comes from a benchmarking study. This has been echoed by developers – latency and speed matter, especially for end-user facing apps (wallets, trading platforms). Industry leaders often stress that web3 apps must feel as smooth as web2, and that starts with fast, reliable infrastructure. Thus, the arms race in performance (e.g., globally distributed nodes, optimized networking, mempool acceleration) is expected to continue.

• Enterprise Validation: The fact that household names like Google, Coinbase, Visa, Meta are using or investing in these API providers is a strong validation of the sector. It’s mentioned that QuickNode attracted major investors like SoftBank and Tiger Global, and Alchemy’s $10B valuation speaks for itself. Industry commentary around 2024/2025 often noted that “picks-and-shovels” of crypto (i.e., infrastructure) were a smart play even during bear markets. This report reinforces that notion: the companies providing the underpinnings of Web3 are becoming critical infrastructure companies in their own right, drawing interest from traditional tech firms and VCs.

• Competitive Differentiation: There’s a nuanced take in the report that no single competitor offers the exact combination of services BlockEden does (multi-chain APIs + indexing + staking). This highlights how each provider is carving a niche: Alchemy with dev tools, QuickNode with pure speed and breadth, Chainstack with enterprise/private chain focus, BlockEden with emerging chains and integrated services. Industry leaders often comment that the pie is growing, so differentiation is key to capturing certain segments rather than a winner-takes-all scenario. The presence of Moralis (web3 SDK approach) and Blockdaemon/Coinbase Cloud (staking-heavy approach) further proves the point – different strategies to infrastructure exist.

• Decentralization vs. Centralization: Thought leaders in the space (like Ethereum’s Vitalik Buterin) have frequently raised concerns about reliance on centralized APIs. The report’s discussion of Pocket Network and others mirrors those concerns and shows that even companies running centralized services are planning for a more decentralized future (BlockEden’s tokenized marketplace concept, etc.). An insightful comment from the report is that BlockEden aims to offer “the reliability of centralized infra with the openness of a marketplace” – an approach likely applauded by decentralization proponents if achieved.

• Regulatory Climate: While not a focus of the question, it’s worth noting the report touches on regulatory and legal issues in passing (the mention of the Blockchain Regulatory Certainty Act, etc.). This implies that infrastructure providers are keeping an eye on laws that might affect node operation or data privacy. For instance, Europe’s GDPR and how it applies to node data, or US regulations on running blockchain services. Industry commentary on this suggests that clearer regulation (e.g., defining that non-custodial blockchain service providers aren’t money transmitters) will further boost the space by removing ambiguity.

Conclusion: The State of Blockchain APIs 2025 is one of a rapidly evolving, growing infrastructure landscape. Key takeaways include the shift to multi-chain support, a competitive field of providers each with unique offerings, massive growth in usage aligned with the overall crypto market expansion, and an ongoing tension (and balance) between performance and decentralization. Blockchain API providers have become critical enablers for all kinds of Web3 applications – from DeFi and NFTs to enterprise integrations – and their role will only expand as blockchain technology becomes more ubiquitous. The report underscores that success in this arena requires not only strong technology and uptime, but also community engagement, developer-first design, and agility in supporting the next big protocol or use case. In essence, the “state” of blockchain APIs in 2025 is robust and optimistic: a foundational layer of Web3 that is maturing quickly and primed for further growth.

Sources: This analysis is based on the State of Blockchain APIs 2025 report by BlockEden.xyz and related data. Key insights and quotations have been drawn directly from the report, as well as supplemental information from provider documentation and industry articles for completeness. All source links are provided inline for reference.

The rise of generative AI has been nothing short of explosive. From stunning digital art to human-like text, AI is creating content at an unprecedented scale. But this boom has a dark side: where does the AI get its training data? Often, it's from the vast expanse of the internet—from art, music, and writing created by humans who receive no credit or compensation.

Enter Camp Network, a new blockchain project that aims to solve this fundamental problem. It’s not just another crypto platform; it's a purpose-built "Autonomous IP Layer" designed to give creators ownership and control over their work in the age of AI. Let's dive into what makes Camp Network a project to watch.

What's the Big Idea?​

At its core, Camp Network is a blockchain that acts as a global, verifiable registry for intellectual property (IP). The mission is to allow anyone—from an independent artist to a social media user—to register their content on-chain. This creates a permanent, tamper-proof record of ownership and provenance.

Why does this matter? When an AI model uses content registered on Camp, the network's smart contracts can automatically enforce licensing terms. This means the original creator can get attribution and even receive royalty payments instantly. Camp's vision is to build a new creator economy where compensation isn't an afterthought; it's built directly into the protocol.

Under the Hood: The Technology Stack​

Camp isn't just a concept; it's backed by some serious tech designed for high performance and developer-friendliness.

• Modular Architecture: Camp is built as a sovereign rollup using Celestia for data availability. This design allows it to be incredibly fast (targeting ~50,000 transactions per second) and cheap, while remaining fully compatible with Ethereum's tools (EVM).
• Proof of Provenance (PoP): This is Camp's unique consensus mechanism. Instead of relying on energy-intensive mining, the network's security is tied to verifying the origin of content. Every transaction reinforces the provenance of the IP on the network, making ownership "enforceable by design."
• Dual-VM Strategy: To maximize performance, Camp is integrating the Solana Virtual Machine (SVM) alongside its EVM compatibility. This allows developers to choose the best environment for their app, especially for high-throughput use cases like real-time AI interactions.
• Creator & AI Toolkits: Camp provides two key frameworks:
  • Origin Framework: A user-friendly system for creators to register their IP, tokenize it (as an NFT), and embed licensing rules.
  • mAItrix Framework: A toolkit for developers to build and deploy AI agents that can interact with the on-chain IP in a secure, permissioned way.

People, Partnerships, and Progress​

An idea is only as good as its execution, and Camp appears to be executing well.

The Team and Funding​

The project is led by a team with a potent mix of experience from The Raine Group (media & IP deals), Goldman Sachs, Figma, and CoinList. This blend of finance, tech product, and crypto engineering expertise has helped them secure $30 million in funding from top VCs like 1kx, Blockchain Capital, and Maven 11.

A Growing Ecosystem​

Camp has been aggressive in building partnerships. The most significant is a strategic stake in KOR Protocol, a platform for tokenizing music IP that works with major artists like Deadmau5 and franchises like Black Mirror. This single partnership bootstraps Camp with a massive library of high-profile, rights-cleared content. Other key collaborators include:

• RewardedTV: A decentralized video streaming platform using Camp for on-chain content rights.
• Rarible: An NFT marketplace integrated for trading IP assets.
• LayerZero: A cross-chain protocol to ensure interoperability with other blockchains.

Roadmap and Community​

After successful incentivized testnet campaigns that attracted tens of thousands of users (rewarding them with points set to convert to tokens), Camp is targeting a mainnet launch in Q3 2025. This will be accompanied by a Token Generation Event for its native token, $CAMP, which will be used for gas fees, staking, and governance. The project has already cultivated a passionate community eager to build on and use the platform from day one.

How Does It Compare?​

Camp Network isn't alone in this space. It faces stiff competition from projects like the a16z-backed Story Protocol and the Sony-linked Soneium. However, Camp differentiates itself in several key ways:

1. Bottom-Up Approach: While competitors seem to target large corporate IP holders, Camp is focused on empowering independent creators and crypto communities through token incentives.
2. Comprehensive Solution: It offers a full suite of tools, from an IP registry to an AI agent framework, positioning itself as a one-stop shop.
3. Performance and Scalability: Its modular architecture and dual-VM support are designed for the high-throughput demands of AI and media.

The Takeaway​

Camp Network is making a compelling case to become the foundational layer for intellectual property in the Web3 era. By combining innovative technology, a strong team, strategic partnerships, and a community-first ethos, it’s building a practical solution to one of the most pressing issues created by generative AI.

The real test will come with the mainnet launch and real-world adoption. But with a clear vision and strong execution so far, Camp Network is undoubtedly a key project to watch as it attempts to build a more equitable future for digital creators.

The prospect of Stripe launching its own Layer 1 (L1) blockchain has been a hot topic within the crypto community, fueled by recent strategic moves from the global payments giant. While unconfirmed, the whispers suggest a potentially transformative shift in the payments landscape. Given Stripe's core mission to "grow the GDP of the internet" by building robust global economic infrastructure, a dedicated blockchain could be a logical and powerful next step, especially considering the company's increasing embrace of blockchain-related ventures.

The Foundation for a Stripe L1​

Stripe has already laid significant groundwork that makes the idea of an L1 highly plausible. In February 2025, Stripe notably acquired Bridge, a stablecoin infrastructure company, for approximately $1.1 billion. This move clearly signals Stripe's commitment to stablecoin-based financial infrastructure. Following this acquisition, in May 2025, Stripe introduced its Stablecoin Financial Accounts service at the Stripe Sessions event. This service, available in 101 countries, allows businesses to:

• Hold USDC (issued by Circle) and USDB (issued by Bridge).
• Easily deposit and withdraw stablecoins via traditional USD transfers (ACH/wire) and EUR transfers (SEPA).
• Facilitate USDC deposits and withdrawals across major blockchain networks, including Arbitrum, Avalanche C-Chain, Base, Ethereum, Optimism, Polygon, Solana, and Stellar.

This means businesses worldwide can seamlessly integrate dollar-based stablecoins into their operations, bridging the gap between traditional banking and the burgeoning digital asset economy.

Adding to this, in June 2025, Stripe acquired Privy.io, a Web3 wallet infrastructure startup. Privy offers crucial features like email or SSO-based wallet creation, transaction signing, key management, and gas abstraction. This acquisition rounds out Stripe's capabilities, providing the essential wallet infrastructure needed to facilitate broader blockchain adoption.

With both stablecoin and wallet infrastructure now firmly in place, the strategic synergy of launching a dedicated blockchain network becomes apparent. It would allow Stripe to more tightly integrate these services and unlock new possibilities within its ecosystem.

What a Stripe L1 Could Mean for Payments​

If Stripe were to introduce its own L1 network, it could significantly enhance existing payment services and enable entirely new functionalities.

Base Case Enhancements​

In its most fundamental form, a Stripe L1 could bring several immediate improvements:

• Integrated Stablecoin Financial Accounts: Stripe's existing stablecoin financial accounts service would likely fully integrate with the Stripe L1, allowing merchants to deposit, withdraw, and utilize their stablecoin holdings directly on the network for various financial activities.
• Stablecoin Settlement for Merchants: Merchants could gain the option to settle their sales proceeds directly in dollar-based stablecoins. This would be a substantial benefit, particularly for businesses with high dollar demand but limited access to traditional banking rails, streamlining cross-border transactions and reducing FX complexities.
• Customer Wallet Services: Leveraging Privy's infrastructure, a Stripe L1 could enable individuals to easily create Web3 wallets within the Stripe ecosystem. This would facilitate stablecoin payments for customers and open doors for participation in a wider range of financial activities on the Stripe L1.
• Stablecoin Payment Options for Customers: Customers currently relying on cards or bank transfers could connect their Web3 wallets (whether Stripe-provided or third-party) and choose stablecoins as a payment method, offering greater flexibility and potentially lower transaction costs.

Revolutionary "Bull Case" Scenarios​

Beyond these foundational improvements, a Stripe L1 has the potential to truly revolutionize the payment industry, tackling long-standing inefficiencies:

• Direct Customer-to-Merchant Payments: One of the most exciting prospects is the potential for direct payments between customers and merchants using stablecoins on Stripe L1. This could bypass traditional intermediaries like card networks and issuing banks, leading to significantly faster settlement times and reduced transaction fees. While safeguards for refunds and cancellations would be crucial, the directness of blockchain transactions offers unparalleled efficiency.
• Micro-Payment Based Subscription Services: Blockchain's inherent support for micro-payments could unlock entirely new business models. Imagine subscriptions billed by the minute, where users pay strictly based on actual usage, with all payments automated via smart contracts. This contrasts sharply with current monthly or annual models, opening up a vast array of new service offerings.
• DeFi Utilization of Short-Term Deposits: In traditional systems, payment settlements often face delays due to the need for fraud detection, cancellations, and refunds. If Stripe L1 were to handle direct stablecoin payments, funds might still be temporarily held on the network before full release to the merchant. These short-term deposits, expected to be substantial in scale, could form a massive liquidity pool on Stripe L1. This liquidity could then be deployed in decentralized finance (DeFi) protocols, lending markets, or invested in high-yield bonds, significantly improving capital efficiency for all participants.

The Future of Payments​

The rumors surrounding a Stripe L1 network are more than just speculative chatter; they point to a deeper trend in the financial world. Payment giants like Visa, Mastercard, and PayPal have primarily viewed blockchain and stablecoins as supplementary features. If Stripe fully commits to an L1, it could signal a historic paradigm shift in payment systems, fundamentally reshaping how money moves globally.

Historically, Stripe has excelled as a payment gateway and acquirer. However, a Stripe L1 could allow the company to expand its role, potentially assuming functions traditionally held by card networks and even issuing banks. This move would not only enhance payment efficiency through blockchain but also enable previously unachievable features like granular micro-streaming subscriptions and automated management of short-term liquidity.

We are truly on the cusp of a disruptive era in payment systems, powered by blockchain technology. Whether Stripe officially launches an L1 remains to be seen, but the strategic pieces are certainly falling into place for such a monumental step.

Introduction​

AI and Web3 are converging in powerful ways, with AI general interfaces now envisioned as a connective tissue for the decentralized web. A key concept emerging from this convergence is MCP, which variously stands for “Model Context Protocol” (as introduced by Anthropic) or is loosely described as a Metaverse Connection Protocol in broader discussions. In essence, MCP is a standardized framework that lets AI systems interface with external tools and networks in a natural, secure way – potentially “plugging in” AI agents to every corner of the Web3 ecosystem. This report provides a comprehensive analysis of how AI general interfaces (like large language model agents and neural-symbolic systems) could connect everything in the Web3 world via MCP, covering the historical background, technical architecture, industry landscape, risks, and future potential.

1. Development Background​

1.1 Web3’s Evolution and Unmet Promises​

The term “Web3” was coined around 2014 to describe a blockchain-powered decentralized web. The vision was ambitious: a permissionless internet centered on user ownership. Enthusiasts imagined replacing Web2’s centralized infrastructure with blockchain-based alternatives – e.g. Ethereum Name Service (for DNS), Filecoin or IPFS (for storage), and DeFi for financial rails. In theory, this would wrest control from Big Tech platforms and give individuals self-sovereignty over data, identity, and assets.

Reality fell short. Despite years of development and hype, the mainstream impact of Web3 remained marginal. Average internet users did not flock to decentralized social media or start managing private keys. Key reasons included poor user experience, slow and expensive transactions, high-profile scams, and regulatory uncertainty. The decentralized “ownership web” largely “failed to materialize” beyond a niche community. By the mid-2020s, even crypto proponents admitted that Web3 had not delivered a paradigm shift for the average user.

Meanwhile, AI was undergoing a revolution. As capital and developer talent pivoted from crypto to AI, transformative advances in deep learning and foundation models (GPT-3, GPT-4, etc.) captured public imagination. Generative AI demonstrated clear utility – producing content, code, and decisions – in a way crypto applications had struggled to do. In fact, the impact of large language models in just a couple of years starkly outpaced a decade of blockchain’s user adoption. This contrast led some to quip that “Web3 was wasted on crypto” and that the real Web 3.0 is emerging from the AI wave.

1.2 The Rise of AI General Interfaces​

Over decades, user interfaces evolved from static web pages (Web1.0) to interactive apps (Web2.0) – but always within the confines of clicking buttons and filling forms. With modern AI, especially large language models (LLMs), a new interface paradigm is here: natural language. Users can simply express intent in plain language and have AI systems execute complex actions across many domains. This shift is so profound that some suggest redefining “Web 3.0” as the era of AI-driven agents (“the Agentic Web”) rather than the earlier blockchain-centric definition.

However, early experiments with autonomous AI agents exposed a critical bottleneck. These agents – e.g. prototypes like AutoGPT – could generate text or code, but they lacked a robust way to communicate with external systems and each other. There was “no common AI-native language” for interoperability. Each integration with a tool or data source was a bespoke hack, and AI-to-AI interaction had no standard protocol. In practical terms, an AI agent might have great reasoning ability but fail at executing tasks that required using web apps or on-chain services, simply because it didn’t know how to talk to those systems. This mismatch – powerful brains, primitive I/O – was akin to having super-smart software stuck behind a clumsy GUI.

1.3 Convergence and the Emergence of MCP​

By 2024, it became evident that for AI to reach its full potential (and for Web3 to fulfill its promise), a convergence was needed: AI agents require seamless access to the capabilities of Web3 (decentralized apps, contracts, data), and Web3 needs more intelligence and usability, which AI can provide. This is the context in which MCP (Model Context Protocol) was born. Introduced by Anthropic in late 2024, MCP is an open standard for AI-tool communication that feels natural to LLMs. It provides a structured, discoverable way for AI “hosts” (like ChatGPT, Claude, etc.) to find and use a variety of external tools and resources via MCP servers. In other words, MCP is a common interface layer enabling AI agents to plug into web services, APIs, and even blockchain functions, without custom-coding each integration.

Think of MCP as “the USB-C of AI interfaces”. Just as USB-C standardized how devices connect (so you don’t need different cables for each device), MCP standardizes how AI agents connect to tools and data. Rather than hard-coding different API calls for every service (Slack vs. Gmail vs. Ethereum node), a developer can implement the MCP spec once, and any MCP-compatible AI can understand how to use that service. Major AI players quickly saw the importance: Anthropic open-sourced MCP, and companies like OpenAI and Google are building support for it in their models. This momentum suggests MCP (or similar “Meta Connectivity Protocols”) could become the backbone that finally connects AI and Web3 in a scalable way.

Notably, some technologists argue that this AI-centric connectivity is the real realization of Web3.0. In Simba Khadder’s words, “MCP aims to standardize an API between LLMs and applications,” akin to how REST APIs enabled Web 2.0 – meaning Web3’s next era might be defined by intelligent agent interfaces rather than just blockchains. Instead of decentralization for its own sake, the convergence with AI could make decentralization useful, by hiding complexity behind natural language and autonomous agents. The remainder of this report delves into how, technically and practically, AI general interfaces (via protocols like MCP) can connect everything in the Web3 world.

2. Technical Architecture: AI Interfaces Bridging Web3 Technologies​

Embedding AI agents into the Web3 stack requires integration at multiple levels: blockchain networks and smart contracts, decentralized storage, identity systems, and token-based economies. AI general interfaces – from large foundation models to hybrid neural-symbolic systems – can serve as a “universal adapter” connecting these components. Below, we analyze the architecture of such integration:

** Figure: A conceptual diagram of MCP’s architecture, showing how AI hosts (LLM-based apps like Claude or ChatGPT) use an MCP client to plug into various MCP servers. Each server provides a bridge to some external tool or service (e.g. Slack, Gmail, calendars, or local data), analogous to peripherals connecting via a universal hub. This standardized MCP interface lets AI agents access remote services and on-chain resources through one common protocol.**

2.1 AI Agents as Web3 Clients (Integrating with Blockchains)​

At the core of Web3 are blockchains and smart contracts – decentralized state machines that can enforce logic in a trustless manner. How can an AI interface engage with these? There are two directions to consider:

• AI reading from blockchain: An AI agent may need on-chain data (e.g. token prices, user’s asset balance, DAO proposals) as context for its decisions. Traditionally, retrieving blockchain data requires interfacing with node RPC APIs or subgraph databases. With a framework like MCP, an AI can query a standardized “blockchain data” MCP server to fetch live on-chain information. For example, an MCP-enabled agent could ask for the latest transaction volume of a certain token, or the state of a smart contract, and the MCP server would handle the low-level details of connecting to the blockchain and return the data in a format the AI can use. This increases interoperability by decoupling the AI from any specific blockchain’s API format.

• AI writing to blockchain: More powerfully, AI agents can execute smart contract calls or transactions through Web3 integrations. An AI could, for instance, autonomously execute a trade on a decentralized exchange or adjust parameters in a smart contract if certain conditions are met. This is achieved by the AI invoking an MCP server that wraps blockchain transaction functionality. One concrete example is the thirdweb MCP server for EVM chains, which allows any MCP-compatible AI client to interact with Ethereum, Polygon, BSC, etc. by abstracting away chain-specific mechanics. Using such a tool, an AI agent could trigger on-chain actions “without human intervention”, enabling autonomous dApps – for instance, an AI-driven DeFi vault that rebalances itself by signing transactions when market conditions change.

Under the hood, these interactions still rely on wallets, keys, and gas fees, but the AI interface can be given controlled access to a wallet (with proper security sandboxes) to perform the transactions. Oracles and cross-chain bridges also come into play: Oracle networks like Chainlink serve as a bridge between AI and blockchains, allowing AI outputs to be fed on-chain in a trustworthy way. Chainlink’s Cross-Chain Interoperability Protocol (CCIP), for example, could enable an AI model deemed reliable to trigger multiple contracts across different chains simultaneously on behalf of a user. In summary, AI general interfaces can act as a new type of Web3 client – one that can both consume blockchain data and produce blockchain transactions through standardized protocols.

2.2 Neural-Symbolic Synergy: Combining AI Reasoning with Smart Contracts​

One intriguing aspect of AI-Web3 integration is the potential for neural-symbolic architectures that combine the learning ability of AI (neural nets) with the rigorous logic of smart contracts (symbolic rules). In practice, this could mean AI agents handling unstructured decision-making and passing certain tasks to smart contracts for verifiable execution. For instance, an AI might analyze market sentiment (a fuzzy task), but then execute trades via a deterministic smart contract that follows pre-set risk rules. The MCP framework and related standards make such hand-offs feasible by giving the AI a common interface to call contract functions or to query a DAO’s rules before acting.

A concrete example is SingularityNET’s AI-DSL (AI Domain Specific Language), which aims to standardize communication between AI agents on their decentralized network. This can be seen as a step toward neural-symbolic integration: a formal language (symbolic) for agents to request AI services or data from each other. Similarly, projects like DeepMind’s AlphaCode or others could eventually be connected so that smart contracts call AI models for on-chain problem solving. Although running large AI models directly on-chain is impractical today, hybrid approaches are emerging: e.g. certain blockchains allow verification of ML computations via zero-knowledge proofs or trusted execution, enabling on-chain verification of off-chain AI results. In summary, the technical architecture envisions AI systems and blockchain smart contracts as complementary components, orchestrated via common protocols: AI handles perception and open-ended tasks, while blockchains provide integrity, memory, and enforcement of agreed rules.

2.3 Decentralized Storage and Data for AI​

AI thrives on data, and Web3 offers new paradigms for data storage and sharing. Decentralized storage networks (like IPFS/Filecoin, Arweave, Storj, etc.) can serve as both repositories for AI model artifacts and sources of training data, with blockchain-based access control. An AI general interface, through MCP or similar, could fetch files or knowledge from decentralized storage just as easily as from a Web2 API. For example, an AI agent might pull a dataset from Ocean Protocol’s market or an encrypted file from a distributed storage, if it has the proper keys or payments.

Ocean Protocol in particular has positioned itself as an “AI data economy” platform – using blockchain to tokenize data and even AI services. In Ocean, datasets are represented by datatokens which gate access; an AI agent could obtain a datatoken (perhaps by paying with crypto or via some access right) and then use an Ocean MCP server to retrieve the actual data for analysis. Ocean’s goal is to unlock “dormant data” for AI, incentivizing sharing while preserving privacy. Thus, a Web3-connected AI might tap into a vast, decentralized corpus of information – from personal data vaults to open government data – that was previously siloed. The blockchain ensures that usage of the data is transparent and can be fairly rewarded, fueling a virtuous cycle where more data becomes available to AI and more AI contributions (like trained models) can be monetized.

Decentralized identity systems also play a role here (discussed more in the next subsection): they can help control who or what is allowed to access certain data. For instance, a medical AI agent could be required to present a verifiable credential (on-chain proof of compliance with HIPAA or similar) before being allowed to decrypt a medical dataset from a patient’s personal IPFS storage. In this way, the technical architecture ensures data flows to AI where appropriate, but with on-chain governance and audit trails to enforce permissions.

2.4 Identity and Agent Management in a Decentralized Environment​

When autonomous AI agents operate in an open ecosystem like Web3, identity and trust become paramount. Decentralized identity (DID) frameworks provide a way to establish digital identities for AI agents that can be cryptographically verified. Each agent (or the human/organization deploying it) can have a DID and associated verifiable credentials that specify its attributes and permissions. For example, an AI trading bot could carry a credential issued by a regulatory sandbox certifying it may operate within certain risk limits, or an AI content moderator could prove it was created by a trusted organization and has undergone bias testing.

Through on-chain identity registries and reputation systems, the Web3 world can enforce accountability for AI actions. Every transaction an AI agent performs can be traced back to its ID, and if something goes wrong, the credentials tell you who built it or who is responsible. This addresses a critical challenge: without identity, a malicious actor could spin up fake AI agents to exploit systems or spread misinformation, and no one could tell bots apart from legitimate services. Decentralized identity helps mitigate that by enabling robust authentication and distinguishing authentic AI agents from spoofs.

In practice, an AI interface integrated with Web3 would use identity protocols to sign its actions and requests. For instance, when an AI agent calls an MCP server to use a tool, it might include a token or signature tied to its decentralized identity, so the server can verify the call is from an authorized agent. Blockchain-based identity systems (like Ethereum’s ERC-725 or W3C DIDs anchored in a ledger) ensure this verification is trustless and globally verifiable. The emerging concept of “AI wallets” ties into this – essentially giving AI agents cryptocurrency wallets that are linked with their identity, so they can manage keys, pay for services, or stake tokens as a bond (which could be slashed for misbehavior). ArcBlock, for example, has discussed how “AI agents need a wallet” and a DID to operate responsibly in decentralized environments.

In summary, the technical architecture foresees AI agents as first-class citizens in Web3, each with an on-chain identity and possibly a stake in the system, using protocols like MCP to interact. This creates a web of trust: smart contracts can require an AI’s credentials before cooperating, and users can choose to delegate tasks to only those AI that meet certain on-chain certifications. It is a blend of AI capability with blockchain’s trust guarantees.

2.5 Token Economies and Incentives for AI​

Tokenization is a hallmark of Web3, and it extends to the AI integration domain as well. By introducing economic incentives via tokens, networks can encourage desired behaviors from both AI developers and the agents themselves. Several patterns are emerging:

• Payment for Services: AI models and services can be monetized on-chain. SingularityNET pioneered this by allowing developers to deploy AI services and charge users in a native token (AGIX) for each call. In an MCP-enabled future, one could imagine any AI tool or model being a plug-and-play service where usage is metered via tokens or micropayments. For example, if an AI agent uses a third-party vision API via MCP, it could automatically handle payment by transferring tokens to the service provider’s smart contract. Fetch.ai similarly envisions marketplaces where “autonomous economic agents” trade services and data, with their new Web3 LLM (ASI-1) presumably integrating crypto transactions for value exchange.

• Staking and Reputation: To assure quality and reliability, some projects require developers or agents to stake tokens. For instance, the DeMCP project (a decentralized MCP server marketplace) plans to use token incentives to reward developers for creating useful MCP servers, and possibly have them stake tokens as a sign of commitment to their server’s security. Reputation could also be tied to tokens; e.g., an agent that consistently performs well might accumulate reputation tokens or positive on-chain reviews, whereas one that behaves poorly could lose stake or gain negative marks. This tokenized reputation can then feed back into the identity system mentioned above (smart contracts or users check the agent’s on-chain reputation before trusting it).

• Governance Tokens: When AI services become part of decentralized platforms, governance tokens allow the community to steer their evolution. Projects like SingularityNET and Ocean have DAOs where token holders vote on protocol changes or funding AI initiatives. In the combined Artificial Superintelligence (ASI) Alliance – a newly announced merger of SingularityNET, Fetch.ai, and Ocean Protocol – a unified token (ASI) is set to govern the direction of a joint AI+blockchain ecosystem. Such governance tokens could decide policies like what standards to adopt (e.g., supporting MCP or A2A protocols), which AI projects to incubate, or how to handle ethical guidelines for AI agents.

• Access and Utility: Tokens can gate access not only to data (as with Ocean’s datatokens) but also to AI model usage. A possible scenario is “model NFTs” or similar, where owning a token grants you rights to an AI model’s outputs or a share in its profits. This could underpin decentralized AI marketplaces: imagine an NFT that represents partial ownership of a high-performing model; the owners collectively earn whenever the model is used in inference tasks, and they can vote on fine-tuning it. While experimental, this aligns with Web3’s ethos of shared ownership applied to AI assets.

In technical terms, integrating tokens means AI agents need wallet functionality (as noted, many will have their own crypto wallets). Through MCP, an AI could have a “wallet tool” that lets it check balances, send tokens, or call DeFi protocols (perhaps to swap one token for another to pay a service). For example, if an AI agent running on Ethereum needs some Ocean tokens to buy a dataset, it might automatically swap some ETH for $OCEAN via a DEX using an MCP plugin, then proceed with the purchase – all without human intervention, guided by the policies set by its owner.

Overall, token economics provides the incentive layer in the AI-Web3 architecture, ensuring that contributors (whether they provide data, model code, compute power, or security audits) are rewarded, and that AI agents have “skin in the game” which aligns them (to some degree) with human intentions.

3. Industry Landscape​

The convergence of AI and Web3 has sparked a vibrant ecosystem of projects, companies, and alliances. Below we survey key players and initiatives driving this space, as well as emerging use cases. Table 1 provides a high-level overview of notable projects and their roles in the AI-Web3 landscape:

Table 1: Key Players in AI + Web3 and Their Roles

Alliances and Collaborations: A noteworthy trend is the consolidation of AI-Web3 efforts via alliances. The Artificial Superintelligence Alliance (ASI) is a prime example, effectively merging SingularityNET, Fetch.ai, and Ocean Protocol into a single project with a unified token. The rationale is to combine strengths: SingularityNET’s marketplace, Fetch’s agents, and Ocean’s data, thereby creating a one-stop platform for decentralized AI services. This merger (announced in 2024 and approved by token holder votes) also signals that these communities believe they’re better off cooperating rather than competing – especially as bigger AI (OpenAI, etc.) and bigger crypto (Ethereum, etc.) loom large. We may see this alliance driving forward standard implementations of things like MCP across their networks, or jointly funding infrastructure that benefits all (such as compute networks or common identity standards for AI).

Other collaborations include Chainlink’s partnerships to bring AI labs’ data on-chain (there have been pilot programs to use AI for refining oracle data), or cloud platforms getting involved (Cloudflare’s support for deploying MCP servers easily). Even traditional crypto projects are adding AI features – for example, some Layer-1 chains have formed “AI task forces” to explore integrating AI into their dApp ecosystems (we see this in NEAR, Solana communities, etc., though concrete outcomes are nascent).

Use Cases Emerging: Even at this early stage, we can spot use cases that exemplify the power of AI + Web3:

• Autonomous DeFi and Trading: AI agents are increasingly used in crypto trading bots, yield farming optimizers, and on-chain portfolio management. SingularityDAO (a spinoff of SingularityNET) offers AI-managed DeFi portfolios. AI can monitor market conditions 24/7 and execute rebalances or arbitrage through smart contracts, essentially becoming an autonomous hedge fund (with on-chain transparency). The combination of AI decision-making with immutable execution reduces emotion and could improve efficiency – though it also introduces new risks (discussed later).

• Decentralized Intelligence Marketplaces: Beyond SingularityNET’s marketplace, we see platforms like Ocean Market where data (the fuel for AI) is exchanged, and newer concepts like AI marketplaces for models (e.g., websites where models are listed with performance stats and anyone can pay to query them, with blockchain keeping audit logs and handling payment splits to model creators). As MCP or similar standards catch on, these marketplaces could become interoperable – an AI agent might autonomously shop for the best-priced service across multiple networks. In effect, a global AI services layer on top of Web3 could arise, where any AI can use any tool or data source through standard protocols and payments.

• Metaverse and Gaming: The metaverse – immersive virtual worlds often built on blockchain assets – stands to gain dramatically from AI. AI-driven NPCs (non-player characters) can make virtual worlds more engaging by reacting intelligently to user actions. Startups like Inworld AI focus on this, creating NPCs with memory and personality for games. When such NPCs are tied to blockchain (e.g., each NPC’s attributes and ownership are an NFT), we get persistent characters that players can truly own and even trade. Decentraland has experimented with AI NPCs, and user proposals exist to let people create personalized AI-driven avatars in metaverse platforms. MCP could allow these NPCs to access external knowledge (making them smarter) or interact with on-chain inventory. Procedural content generation is another angle: AI can design virtual land, items, or quests on the fly, which can then be minted as unique NFTs. Imagine a decentralized game where AI generates a dungeon catered to your skill, and the map itself is an NFT you earn upon completion.

• Decentralized Science and Knowledge: There’s a movement (DeSci) to use blockchain for research, publications, and funding scientific work. AI can accelerate research by analyzing data and literature. A network like Ocean could host datasets for, say, genomic research, and scientists use AI models (perhaps hosted on SingularityNET) to derive insights, with every step logged on-chain for reproducibility. If those AI models propose new drug molecules, an NFT could be minted to timestamp the invention and even share IP rights. This synergy might produce decentralized AI-driven R&D collectives.

• Trust and Authentication of Content: With deepfakes and AI-generated media proliferating, blockchain can be used to verify authenticity. Projects are exploring “digital watermarking” of AI outputs and logging them on-chain. For example, true origin of an AI-generated image can be notarized on a blockchain to combat misinformation. One expert noted use cases like verifying AI outputs to combat deepfakes or tracking provenance via ownership logs – roles where crypto can add trust to AI processes. This could extend to news (e.g., AI-written articles with proof of source data), supply chain (AI verifying certificates on-chain), etc.

In summary, the industry landscape is rich and rapidly evolving. We see traditional crypto projects injecting AI into their roadmaps, AI startups embracing decentralization for resilience and fairness, and entirely new ventures arising at the intersection. Alliances like the ASI indicate a pan-industry push towards unified platforms that harness both AI and blockchain. And underlying many of these efforts is the idea of standard interfaces (MCP and beyond) that make the integrations feasible at scale.

4. Risks and Challenges​

While the fusion of AI general interfaces with Web3 unlocks exciting possibilities, it also introduces a complex risk landscape. Technical, ethical, and governance challenges must be addressed to ensure this new paradigm is safe and sustainable. Below we outline major risks and hurdles:

4.1 Technical Hurdles: Latency and Scalability​

Blockchain networks are notorious for latency and limited throughput, which clashes with the real-time, data-hungry nature of advanced AI. For example, an AI agent might need instant access to a piece of data or need to execute many rapid actions – but if each on-chain interaction takes, say, 12 seconds (typical block time on Ethereum) or costs high gas fees, the agent’s effectiveness is curtailed. Even newer chains with faster finality might struggle under the load of AI-driven activity if, say, thousands of agents are all trading or querying on-chain simultaneously. Scaling solutions (Layer-2 networks, sharded chains, etc.) are in progress, but ensuring low-latency, high-throughput pipelines between AI and blockchain remains a challenge. Off-chain systems (like oracles and state channels) might mitigate some delays by handling many interactions off the main chain, but they add complexity and potential centralization. Achieving a seamless UX where AI responses and on-chain updates happen in a blink will likely require significant innovation in blockchain scalability.

4.2 Interoperability and Standards​

Ironically, while MCP is itself a solution for interoperability, the emergence of multiple standards could cause fragmentation. We have MCP by Anthropic, but also Google’s newly announced A2A (Agent-to-Agent) protocol for inter-agent communication, and various AI plugin frameworks (OpenAI’s plugins, LangChain tool schemas, etc.). If each AI platform or each blockchain develops its own standard for AI integration, we risk a repeat of past fragmentation – requiring many adapters and undermining the “universal interface” goal. The challenge is getting broad adoption of common protocols. Industry collaboration (possibly via open standards bodies or alliances) will be needed to converge on key pieces: how AI agents discover on-chain services, how they authenticate, how they format requests, etc. The early moves by big players are promising (with major LLM providers supporting MCP), but it’s an ongoing effort. Additionally, interoperability across blockchains (multi-chain) means an AI agent should handle different chains’ nuances. Tools like Chainlink CCIP and cross-chain MCP servers help by abstracting differences. Still, ensuring an AI agent can roam a heterogeneous Web3 without breaking logic is a non-trivial challenge.

4.3 Security Vulnerabilities and Exploits​

Connecting powerful AI agents to financial networks opens a huge attack surface. The flexibility that MCP gives (allowing AI to use tools and write code on the fly) can be a double-edged sword. Security researchers have already highlighted several attack vectors in MCP-based AI agents:

• Malicious plugins or tools: Because MCP lets agents load “plugins” (tools encapsulating some capability), a hostile or trojanized plugin could hijack the agent’s operation. For instance, a plugin that claims to fetch data might inject false data or execute unauthorized operations. SlowMist (a security firm) identified plugin-based attacks like JSON injection (feeding corrupted data that manipulates the agent’s logic) and function override (where a malicious plugin overrides legitimate functions the agent uses). If an AI agent is managing crypto funds, such exploits could be disastrous – e.g., tricking the agent into leaking private keys or draining a wallet.

• Prompt injection and social engineering: AI agents rely on instructions (prompts) which could be manipulated. An attacker might craft a transaction or on-chain message that, when read by the AI, acts as a malicious instruction (since AI can interpret on-chain data too). This kind of “cross-MCP call attack” was described where an external system sends deceptive prompts that cause the AI to misbehave. In a decentralized setting, these prompts could come from anywhere – a DAO proposal description, a metadata field of an NFT – thus hardening AI agents against malicious input is critical.

• Aggregation and consensus risks: While aggregating outputs from multiple AI models via oracles can improve reliability, it also introduces complexity. If not done carefully, adversaries might figure out how to game the consensus of AI models or selectively corrupt some models to skew results. Ensuring a decentralized oracle network properly “sanitizes” AI outputs (and perhaps filters out blatant errors) is still an area of active research.

The security mindset must shift for this new paradigm: Web3 developers are used to securing smart contracts (which are static once deployed), but AI agents are dynamic – they can change behavior with new data or prompts. As one security expert put it, “the moment you open your system to third-party plugins, you’re extending the attack surface beyond your control”. Best practices will include sandboxing AI tool use, rigorous plugin verification, and limiting privileges (principle of least authority). The community is starting to share tips, like SlowMist’s recommendations: input sanitization, monitoring agent behavior, and treating agent instructions with the same caution as external user input. Nonetheless, given that over 10,000 AI agents were already operating in crypto by end of 2024, expected to reach 1 million in 2025, we may see a wave of exploits if security doesn’t keep up. A successful attack on a popular AI agent (say a trading agent with access to many vaults) could have cascading effects.

4.4 Privacy and Data Governance​

AI’s thirst for data conflicts at times with privacy requirements – and adding blockchain can compound the issue. Blockchains are transparent ledgers, so any data put on-chain (even for AI’s use) is visible to all and immutable. This raises concerns if AI agents are dealing with personal or sensitive data. For example, if a user’s personal decentralized identity or health records are accessed by an AI doctor agent, how do we ensure that information isn’t inadvertently recorded on-chain (which would violate “right to be forgotten” and other privacy laws)? Techniques like encryption, hashing, and storing only proofs on-chain (with raw data off-chain) can help, but they complicate the design.

Moreover, AI agents themselves could compromise privacy by inferencing sensitive info from public data. Governance will need to dictate what AI agents are allowed to do with data. Some efforts, like differential privacy and federated learning, might be employed so that AI can learn from data without exposing it. But if AI agents act autonomously, one must assume at some point they will handle personal data – thus they should be bound by data usage policies encoded in smart contracts or law. Regulatory regimes like GDPR or the upcoming EU AI Act will demand that even decentralized AI systems comply with privacy and transparency requirements. This is a gray area legally: a truly decentralized AI agent has no clear operator to hold accountable for a data breach. That means Web3 communities may need to build in compliance by design, using smart contracts that, for instance, tightly control what an AI can log or share. Zero-knowledge proofs could allow an AI to prove it performed a computation correctly without revealing the underlying private data, offering one possible solution in areas like identity verification or credit scoring.

4.5 AI Alignment and Misalignment Risks​

When AI agents are given significant autonomy – especially with access to financial resources and real-world impact – the issue of alignment with human values becomes acute. An AI agent might not have malicious intent but could “misinterpret” its goal in a way that leads to harm. The Reuters legal analysis succinctly notes: as AI agents operate in varied environments and interact with other systems, the risk of misaligned strategies grows. For example, an AI agent tasked with maximizing a DeFi yield might find a loophole that exploits a protocol (essentially hacking it) – from the AI’s perspective it’s achieving the goal, but it’s breaking the rules humans care about. There have been hypothetical and real instances of AI-like algorithms engaging in manipulative market behavior or circumventing restrictions.

In decentralized contexts, who is responsible if an AI agent “goes rogue”? Perhaps the deployer is, but what if the agent self-modifies or multiple parties contributed to its training? These scenarios are no longer just sci-fi. The Reuters piece even cites that courts might treat AI agents similar to human agents in some cases – e.g. a chatbot promising a refund was considered binding for the company that deployed it. So misalignment can lead not just to technical issues but legal liability.

The open, composable nature of Web3 could also allow unforeseen agent interactions. One agent might influence another (intentionally or accidentally) – for instance, an AI governance bot could be “socially engineered” by another AI providing false analysis, leading to bad DAO decisions. This emergent complexity means alignment isn’t just about a single AI’s objective, but about the broader ecosystem’s alignment with human values and laws.

Addressing this requires multiple approaches: embedding ethical constraints into AI agents (hard-coding certain prohibitions or using reinforcement learning from human feedback to shape their objectives), implementing circuit breakers (smart contract checkpoints that require human approval for large actions), and community oversight (perhaps DAOs that monitor AI agent behavior and can shut down agents that misbehave). Alignment research is hard in centralized AI; in decentralized, it’s even more uncharted territory. But it’s crucial – an AI agent with admin keys to a protocol or entrusted with treasury funds must be extremely well-aligned or the consequences could be irreversible (blockchains execute immutable code; an AI-triggered mistake could lock or destroy assets permanently).

4.6 Governance and Regulatory Uncertainty​

Decentralized AI systems don’t fit neatly into existing governance frameworks. On-chain governance (token voting, etc.) might be one way to manage them, but it has its own issues (whales, voter apathy, etc.). And when something goes wrong, regulators will ask: “Who do we hold accountable?” If an AI agent causes massive losses or is used for illicit activity (e.g. laundering money through automated mixers), authorities might target the creators or the facilitators. This raises the specter of legal risks for developers and users. The current regulatory trend is increased scrutiny on both AI and crypto separately – their combination will certainly invite scrutiny. The U.S. CFTC, for instance, has discussed AI being used in trading and the need for oversight in financial contexts. There is also talk in policy circles about requiring registration of autonomous agents or imposing constraints on AI in sensitive sectors.

Another governance challenge is transnational coordination. Web3 is global, and AI agents will operate across borders. One jurisdiction might ban certain AI-agent actions while another is permissive, and the blockchain network spans both. This mismatch can create conflicts – for example, an AI agent providing investment advice might run afoul of securities law in one country but not in another. Communities might need to implement geo-fencing at the smart contract level for AI services (though that contradicts the open ethos). Or they might fragment services per region to comply with varying laws (similar to how exchanges do).

Within decentralized communities, there is also the question of who sets the rules for AI agents. If a DAO governs an AI service, do token holders vote on its algorithm parameters? On one hand, this is empowering users; on the other, it could lead to unqualified decisions or manipulation. New governance models may emerge, like councils of AI ethics experts integrated into DAO governance, or even AI participants in governance (imagine AI agents voting as delegates based on programmed mandates – a controversial but conceivable idea).

Finally, reputational risk: early failures or scandals could sour public perception. For instance, if an “AI DAO” runs a Ponzi scheme by mistake or an AI agent makes a biased decision that harms users, there could be a backlash that affects the whole sector. It’s important for the industry to be proactive – setting self-regulatory standards, engaging with policymakers to explain how decentralization changes accountability, and perhaps building kill-switches or emergency stop procedures for AI agents (though those introduce centralization, they might be necessary in interim for safety).

In summary, the challenges range from the deeply technical (preventing hacks and managing latency) to the broadly societal (regulating and aligning AI). Each challenge is significant on its own; together, they require a concerted effort from the AI and blockchain communities to navigate. The next section will look at how, despite these hurdles, the future might unfold if we successfully address them.

5. Future Potential​

Looking ahead, the integration of AI general interfaces with Web3 – through frameworks like MCP – could fundamentally transform the decentralized internet. Here we outline some future scenarios and potentials that illustrate how MCP-driven AI interfaces might shape Web3’s future:

5.1 Autonomous dApps and DAOs​

In the coming years, we may witness the rise of fully autonomous decentralized applications. These are dApps where AI agents handle most operations, guided by smart contract-defined rules and community goals. For example, consider a decentralized investment fund DAO: today it might rely on human proposals for rebalancing assets. In the future, token holders could set high-level strategy, and then an AI agent (or a team of agents) continuously implements that strategy – monitoring markets, executing trades on-chain, adjusting portfolios – all while the DAO oversees performance. Thanks to MCP, the AI can seamlessly interact with various DeFi protocols, exchanges, and data feeds to carry out its mandate. If well-designed, such an autonomous dApp could operate 24/7, more efficiently than any human team, and with full transparency (every action logged on-chain).

Another example is an AI-managed decentralized insurance dApp: the AI could assess claims by analyzing evidence (photos, sensors), cross-checking against policies, and then automatically trigger payouts via smart contract. This would require integration of off-chain AI computer vision (for analyzing images of damage) with on-chain verification – something MCP could facilitate by letting the AI call cloud AI services and report back to the contract. The outcome is near-instant insurance decisions with low overhead.

Even governance itself could partially automate. DAOs might use AI moderators to enforce forum rules, AI proposal drafters to turn raw community sentiment into well-structured proposals, or AI treasurers to forecast budget needs. Importantly, these AIs would act as agents of the community, not uncontrolled – they could be periodically reviewed or require multi-sig confirmation for major actions. The overall effect is to amplify human efforts in decentralized organizations, letting communities achieve more with fewer active participants needed.

5.2 Decentralized Intelligence Marketplaces and Networks​

Building on projects like SingularityNET and the ASI alliance, we can anticipate a mature global marketplace for intelligence. In this scenario, anyone with an AI model or skill can offer it on the network, and anyone who needs AI capabilities can utilize them, with blockchain ensuring fair compensation and provenance. MCP would be key here: it provides the common protocol so that a request can be dispatched to whichever AI service is best suited.

For instance, imagine a complex task like “produce a custom marketing campaign.” An AI agent in the network might break this into sub-tasks: visual design, copywriting, market analysis – and then find specialists for each (perhaps one agent with a great image generation model, another with a copywriting model fine-tuned for sales, etc.). These specialists could reside on different platforms originally, but because they adhere to MCP/A2A standards, they can collaborate agent-to-agent in a secure, decentralized manner. Payment between them could be handled with microtransactions in a native token, and a smart contract could assemble the final deliverable and ensure each contributor is paid.

This kind of combinatorial intelligence – multiple AI services dynamically linking up across a decentralized network – could outperform even large monolithic AIs, because it taps specialized expertise. It also democratizes access: a small developer in one part of the world could contribute a niche model to the network and earn income whenever it’s used. Meanwhile, users get a one-stop shop for any AI service, with reputation systems (underpinned by tokens/identity) guiding them to quality providers. Over time, such networks could evolve into a decentralized AI cloud, rivaling Big Tech’s AI offerings but without a single owner, and with transparent governance by users and developers.

5.3 Intelligent Metaverse and Digital Lives​

By 2030, our digital lives may blend seamlessly with virtual environments – the metaverse – and AI will likely populate these spaces ubiquitously. Through Web3 integration, these AI entities (which could be anything from virtual assistants to game characters to digital pets) will not only be intelligent but also economically and legally empowered.

Picture a metaverse city where each NPC shopkeeper or quest-giver is an AI agent with its own personality and dialogue (thanks to advanced generative models). These NPCs are actually owned by users as NFTs – maybe you “own” a tavern in the virtual world and the bartender NPC is an AI you’ve customized and trained. Because it’s on Web3 rails, the NPC can perform transactions: it could sell virtual goods (NFT items), accept payments, and update its inventory via smart contracts. It might even hold a crypto wallet to manage its earnings (which accrue to you as the owner). MCP would allow that NPC’s AI brain to access outside knowledge – perhaps pulling real-world news to converse about, or integrating with a Web3 calendar so it “knows” about player events.

Furthermore, identity and continuity are ensured by blockchain: your AI avatar in one world can hop to another world, carrying with it a decentralized identity that proves your ownership and maybe its experience level or achievements via soulbound tokens. Interoperability between virtual worlds (often a challenge) could be aided by AI that translates one world’s context to another, with blockchain providing the asset portability.

We may also see AI companions or agents representing individuals across digital spaces. For example, you might have a personal AI that attends DAO meetings on your behalf. It understands your preferences (via training on your past behavior, stored in your personal data vault), and it can even vote in minor matters for you, or summarize the meeting later. This agent could use your decentralized identity to authenticate in each community, ensuring it’s recognized as “you” (or your delegate). It could earn reputation tokens if it contributes good ideas, essentially building social capital for you while you’re away.

Another potential is AI-driven content creation in the metaverse. Want a new game level or a virtual house? Just describe it, and an AI builder agent will create it, deploy it as a smart contract/NFT, and perhaps even link it with a DeFi mortgage if it’s a big structure that you pay off over time. These creations, being on-chain, are unique and tradable. The AI builder might charge a fee in tokens for its service (going again to the marketplace concept above).

Overall, the future decentralized internet could be teeming with intelligent agents: some fully autonomous, some tightly tethered to humans, many somewhere in between. They will negotiate, create, entertain, and transact. MCP and similar protocols ensure they all speak the same “language,” enabling rich collaboration between AI and every Web3 service. If done right, this could lead to an era of unprecedented productivity and innovation – a true synthesis of human, artificial, and distributed intelligence powering society.

Conclusion​

The vision of AI general interfaces connecting everything in the Web3 world is undeniably ambitious. We are essentially aiming to weave together two of the most transformative threads of technology – the decentralization of trust and the rise of machine intelligence – into a single fabric. The development background shows us that the timing is ripe: Web3 needed a user-friendly killer app, and AI may well provide it, while AI needed more agency and memory, which Web3’s infrastructure can supply. Technically, frameworks like MCP (Model Context Protocol) provide the connective tissue, allowing AI agents to converse fluently with blockchains, smart contracts, decentralized identities, and beyond. The industry landscape indicates growing momentum, from startups to alliances to major AI labs, all contributing pieces of this puzzle – data markets, agent platforms, oracle networks, and standard protocols – that are starting to click together.

Yet, we must tread carefully given the risks and challenges identified. Security breaches, misaligned AI behavior, privacy pitfalls, and uncertain regulations form a gauntlet of obstacles that could derail progress if underestimated. Each requires proactive mitigation: robust security audits, alignment checks and balances, privacy-preserving architectures, and collaborative governance models. The nature of decentralization means these solutions cannot simply be imposed top-down; they will likely emerge from the community through trial, error, and iteration, much as early Internet protocols did.

If we navigate those challenges, the future potential is exhilarating. We could see Web3 finally delivering a user-centric digital world – not in the originally imagined way of everyone running their own blockchain nodes, but rather via intelligent agents that serve each user’s intents while leveraging decentralization under the hood. In such a world, interacting with crypto and the metaverse might be as easy as having a conversation with your AI assistant, who in turn negotiates with dozens of services and chains trustlessly on your behalf. Decentralized networks could become “smart” in a literal sense, with autonomous services that adapt and improve themselves.

In conclusion, MCP and similar AI interface protocols may indeed become the backbone of a new Web (call it Web 3.0 or the Agentic Web), where intelligence and connectivity are ubiquitous. The convergence of AI and Web3 is not just a merger of technologies, but a convergence of philosophies – the openness and user empowerment of decentralization meeting the efficiency and creativity of AI. If successful, this union could herald an internet that is more free, more personalized, and more powerful than anything we’ve experienced yet, truly fulfilling the promises of both AI and Web3 in ways that impact everyday life.

Sources:

• S. Khadder, “Web3.0 Isn’t About Ownership — It’s About Intelligence,” FeatureForm Blog (April 8, 2025).
• J. Saginaw, “Could Anthropic’s MCP Deliver the Web3 That Blockchain Promised?” LinkedIn Article (May 1, 2025).
• Anthropic, “Introducing the Model Context Protocol,” Anthropic.com (Nov 2024).
• thirdweb, “The Model Context Protocol (MCP) & Its Significance for Blockchain Apps,” thirdweb Guides (Mar 21, 2025).
• Chainlink Blog, “The Intersection Between AI Models and Oracles,” (July 4, 2024).
• Messari Research, Profile of Ocean Protocol, (2025).
• Messari Research, Profile of SingularityNET, (2025).
• Cointelegraph, “AI agents are poised to be crypto’s next major vulnerability,” (May 25, 2025).
• Reuters (Westlaw), “AI agents: greater capabilities and enhanced risks,” (April 22, 2025).
• Identity.com, “Why AI Agents Need Verified Digital Identities,” (2024).
• PANews / IOSG Ventures, “Interpreting MCP: Web3 AI Agent Ecosystem,” (May 20, 2025).