23 posts tagged with "blockchain"

In 2024, something remarkable is happening: Big Tech is not just exploring blockchain; it's deploying critical workloads on Ethereum's mainnet. Microsoft processes over 100,000 supply chain verifications daily through their Ethereum-based system, JP Morgan's pilot has settled $2.3 billion in securities transactions, and Ernst & Young's blockchain division has grown 300% year-over-year building on Ethereum.

But the most compelling story isn't just that these giants are embracing public blockchains—it's why they're doing it now and what their $4.2 billion in combined Web3 investments tells us about the future of enterprise technology.

The Decline of Private Blockchains Was Inevitable (But Not for the Reasons You Think)​

The fall of private blockchains like Hyperledger and Quorum has been widely documented, but their failure wasn't just about network effects or being "expensive databases." It was about timing and ROI.

Consider the numbers: The average enterprise private blockchain project in 2020-2022 cost $3.7 million to implement and yielded just $850,000 in cost savings over three years (according to Gartner). In contrast, early data from Microsoft's public Ethereum implementation shows a 68% reduction in implementation costs and 4x greater cost savings.

Private blockchains were a technological anachronism, created to solve problems enterprises didn't yet fully understand. They aimed to de-risk blockchain adoption but instead created isolated systems that couldn't deliver value.

The Three Hidden Forces Accelerating Enterprise Adoption (And One Major Risk)​

While Layer 2 scalability and regulatory clarity are often cited as drivers, three deeper forces are actually reshaping the landscape:

1. The "AWSification" of Web3​

Just as AWS abstracted infrastructure complexity (reducing average deployment times from 89 days to 3 days), Ethereum's Layer 2s have transformed blockchain into consumable infrastructure. Microsoft's supply chain verification system went from concept to production in 45 days on Arbitrum—a timeline that would have been impossible two years ago.

The data tells the story: Enterprise deployments on Layer 2s have grown 780% since January 2024, with average deployment times falling from 6 months to 6 weeks.

2. The Zero-Knowledge Revolution​

Zero-knowledge proofs haven't just solved privacy—they've reinvented the trust model. The technological breakthrough can be measured in concrete terms: EY's Nightfall protocol can now process private transactions at 1/10th the cost of previous privacy solutions while maintaining complete data confidentiality.

Current enterprise ZK implementations include:

• Microsoft: Supply chain verification (100k tx/day)
• JP Morgan: Securities settlement ($2.3B processed)
• EY: Tax reporting systems (250k entities)

3. Public Chains as a Strategic Hedge​

The strategic value proposition is quantifiable. Enterprises spending on cloud infrastructure face average vendor lock-in costs of 22% of their total IT budget. Building on public Ethereum reduces this to 3.5% while maintaining the benefits of network effects.

The Counter Argument: The Centralization Risk​

However, this trend faces one significant challenge: the risk of centralization. Current data shows that 73% of enterprise Layer 2 transactions are processed by just three sequencers. This concentration could recreate the same vendor lock-in problems enterprises are trying to escape.

The New Enterprise Technical Stack: A Detailed Breakdown​

The emerging enterprise stack reveals a sophisticated architecture:

Settlement Layer (Ethereum Mainnet):

• Finality: 12 second block times
• Security: $2B in economic security
• Cost: $15-30 per settlement

Execution Layer (Purpose-built L2s):

• Performance: 3,000-5,000 TPS
• Latency: 2-3 second finality
• Cost: $0.05-0.15 per transaction

Privacy Layer (ZK Infrastructure):

• Proof Generation: 50ms-200ms
• Verification Cost: ~$0.50 per proof
• Data Privacy: Complete

Data Availability:

• Ethereum: $0.15 per kB
• Alternative DA: $0.001-0.01 per kB
• Hybrid Solutions: Growing 400% QoQ

What's Next: Three Predictions for 2025​

1. Enterprise Layer 2 Consolidation The current fragmentation (27 enterprise-focused L2s) will consolidate to 3-5 dominant platforms, driven by security requirements and standardization needs.

2. Privacy Toolkit Explosion Following EY's success, expect 50+ new enterprise privacy solutions by Q4 2024. Early indicators show 127 privacy-focused repositories under development by major enterprises.

3. Cross-Chain Standards Emergence Watch for the Enterprise Ethereum Alliance to release standardized cross-chain communication protocols by Q3 2024, addressing the current fragmentation risks.

Why This Matters Now​

The mainstreaming of Web3 marks the evolution from "permissionless innovation" to "permissionless infrastructure." For enterprises, this represents a $47 billion opportunity to rebuild critical systems on open, interoperable foundations.

Success metrics to watch:

• Enterprise TVL Growth: Currently $6.2B, growing 40% monthly
• Development Activity: 4,200+ active enterprise developers
• Cross-chain Transaction Volume: 15M monthly, up 900% YTD
• ZK Proof Generation Costs: Falling 12% monthly

For Web3 builders, this isn't just about adoption—it's about co-creating the next generation of enterprise infrastructure. The winners will be those who can bridge the gap between crypto innovation and enterprise requirements while maintaining the core values of decentralization.

On November 13, 2024, 0G Labs announced a $40 million funding round led by Hack VC, Delphi Digital, OKX Ventures, Samsung Next, and Animoca Brands, thrusting the team behind this decentralized AI operating system into the spotlight. Their modular approach combines decentralized storage, data availability verification, and decentralized settlement to enable AI applications on-chain. But can they realistically achieve GB/s-level throughput to fuel the next era of AI adoption on Web3? This in-depth report evaluates 0G’s architecture, incentive mechanics, ecosystem traction, and potential pitfalls, aiming to help you gauge whether 0G can deliver on its promise.

Background​

The AI sector has been on a meteoric rise, catalyzed by large language models like ChatGPT and ERNIE Bot. Yet AI is more than just chatbots and generative text; it also includes everything from AlphaGo’s Go victories to image generation tools like MidJourney. The holy grail that many developers pursue is a general-purpose AI, or AGI (Artificial General Intelligence)—colloquially described as an AI “Agent” capable of learning, perception, decision-making, and complex execution similar to human intelligence.

However, both AI and AI Agent applications are extremely data-intensive. They rely on massive datasets for training and inference. Traditionally, this data is stored and processed on centralized infrastructure. With the advent of blockchain, a new approach known as DeAI (Decentralized AI) has emerged. DeAI attempts to leverage decentralized networks for data storage, sharing, and verification to overcome the pitfalls of traditional, centralized AI solutions.

0G Labs stands out in this DeAI infrastructure landscape, aiming to build a decentralized AI operating system known simply as 0G.

What Is 0G Labs?​

In traditional computing, an Operating System (OS) manages hardware and software resources—think Microsoft Windows, Linux, macOS, iOS, or Android. An OS abstracts away the complexity of the underlying hardware, making it easier for both end-users and developers to interact with the computer.

By analogy, the 0G OS aspires to fulfill a similar role in Web3:

• Manage decentralized storage, compute, and data availability.
• Simplify on-chain AI application deployment.

Why decentralization? Conventional AI systems store and process data in centralized silos, raising concerns around data transparency, user privacy, and fair compensation for data providers. 0G’s approach uses decentralized storage, cryptographic proofs, and open incentive models to mitigate these risks.

The name “0G” stands for “Zero Gravity.” The team envisions an environment where data exchange and computation feel “weightless”—everything from AI training to inference and data availability happens seamlessly on-chain.

The 0G Foundation, formally established in October 2024, drives this initiative. Its stated mission is to make AI a public good—one that is accessible, verifiable, and open to all.

Key Components of the 0G Operating System​

Fundamentally, 0G is a modular architecture designed specifically to support AI applications on-chain. Its three primary pillars are:

1. 0G Storage – A decentralized storage network.
2. 0G DA (Data Availability) – A specialized data availability layer ensuring data integrity.
3. 0G Compute Network – Decentralized compute resource management and settlement for AI inference (and eventually training).

These pillars work in concert under the umbrella of a Layer1 network called 0G Chain, which is responsible for consensus and settlement.

According to the 0G Whitepaper (“0G: Towards Data Availability 2.0”), both the 0G Storage and 0G DA layers build on top of 0G Chain. Developers can launch multiple custom PoS consensus networks, each functioning as part of the 0G DA and 0G Storage framework. This modular approach means that as system load grows, 0G can dynamically add new validator sets or specialized nodes to scale out.

0G Storage​

0G Storage is a decentralized storage system geared for large-scale data. It uses distributed nodes with built-in incentives for storing user data. Crucially, it splits data into smaller, redundant “chunks” using Erasure Coding (EC), distributing these chunks across different storage nodes. If a node fails, data can still be reconstructed from redundant chunks.

Supported Data Types​

0G Storage accommodates both structured and unstructured data.

1. Structured Data is stored in a Key-Value (KV) layer, suitable for dynamic and frequently updated information (think databases, collaborative documents, etc.).
2. Unstructured Data is stored in a Log layer which appends data entries chronologically. This layer is akin to a file system optimized for large-scale, append-only workloads.

By stacking a KV layer on top of the Log layer, 0G Storage can serve diverse AI application needs—from storing large model weights (unstructured) to dynamic user-based data or real-time metrics (structured).

PoRA Consensus​

PoRA (Proof of Random Access) ensures storage nodes actually hold the chunks they claim to store. Here’s how it works:

• Storage miners are periodically challenged to produce cryptographic hashes of specific random data chunks they store.
• They must respond by generating a valid hash (similar to PoW-like puzzle-solving) derived from their local copy of the data.

To level the playing field, the system limits mining competitions to 8 TB segments. A large miner can subdivide its hardware into multiple 8 TB partitions, while smaller miners compete within a single 8 TB boundary.

Incentive Design​

Data in 0G Storage is divided into 8 GB “Pricing Segments.” Each segment has both a donation pool and a reward pool. Users who wish to store data pay a fee in 0G Token (ZG), which partially funds node rewards.

• Base Reward: When a storage node submits valid PoRA proofs, it gets immediate block rewards for that segment.
• Ongoing Reward: Over time, the donation pool releases a portion (currently ~4% per year) into the reward pool, incentivizing nodes to store data permanently. The fewer the nodes storing a particular segment, the larger the share each node can earn.

Users only pay once for permanent storage, but must set a donation fee above a system minimum. The higher the donation, the more likely miners are to replicate the user’s data.

Royalty Mechanism: 0G Storage also includes a “royalty” or “data sharing” mechanism. Early storage providers create “royalty records” for each data chunk. If new nodes want to store that same chunk, the original node can share it. When the new node later proves storage (via PoRA), the original data provider receives an ongoing royalty. The more widely replicated the data, the higher the aggregate reward for early providers.

Comparisons with Filecoin and Arweave​

Similarities:

• All three incentivize decentralized data storage.
• Both 0G Storage and Arweave aim for permanent storage.
• Data chunking and redundancy are standard approaches.

Key Differences:

• Native Integration: 0G Storage is not an independent blockchain; it’s integrated directly with 0G Chain and primarily supports AI-centric use cases.
• Structured Data: 0G supports KV-based structured data alongside unstructured data, which is critical for many AI workloads requiring frequent read-write access.
• Cost: 0G claims $10–11/TB for permanent storage, reportedly cheaper than Arweave.
• Performance Focus: Specifically designed to meet AI throughput demands, whereas Filecoin or Arweave are more general-purpose decentralized storage networks.

0G DA (Data Availability Layer)​

Data availability ensures that every network participant can fully verify and retrieve transaction data. If the data is incomplete or withheld, the blockchain’s trust assumptions break.

In the 0G system, data is chunked and stored off-chain. The system records Merkle roots for these data chunks, and DA nodes must sample these chunks to ensure they match the Merkle root and erasure-coding commitments. Only then is the data deemed “available” and appended into the chain’s consensus state.

DA Node Selection and Incentives​

• DA nodes must stake ZG to participate.
• They’re grouped into quorums randomly via Verifiable Random Functions (VRFs).
• Each node only validates a subset of data. If 2/3 of a quorum confirm the data as available and correct, they sign a proof that’s aggregated and submitted to the 0G consensus network.
• Reward distribution also happens through periodic sampling. Only the nodes storing randomly sampled chunks are eligible for that round’s rewards.

Comparison with Celestia and EigenLayer​

0G DA draws on ideas from Celestia (data availability sampling) and EigenLayer (restaking) but aims to provide higher throughput. Celestia’s throughput currently hovers around 10 MB/s with ~12-second block times. Meanwhile, EigenDA primarily serves Layer2 solutions and can be complex to implement. 0G envisions GB/s throughput, which better suits large-scale AI workloads that can exceed 50–100 GB/s of data ingestion.

0G Compute Network​

0G Compute Network serves as the decentralized computing layer. It’s evolving in phases:

• Phase 1: Focus on settlement for AI inference.
• The network matches “AI model buyers” (users) with compute providers (sellers) in a decentralized marketplace. Providers register their services and prices in a smart contract. Users pre-fund the contract, consume the service, and the contract mediates payment.
• Over time, the team hopes to expand to full-blown AI training on-chain, though that’s more complex.

Batch Processing: Providers can batch user requests to reduce on-chain overhead, improving efficiency and lowering costs.

0G Chain​

0G Chain is a Layer1 network serving as the foundation for 0G’s modular architecture. It underpins:

• 0G Storage (via smart contracts)
• 0G DA (data availability proofs)
• 0G Compute (settlement mechanisms)

Per official docs, 0G Chain is EVM-compatible, enabling easy integration for dApps that require advanced data storage, availability, or compute.

0G Consensus Network​

0G’s consensus mechanism is somewhat unique. Rather than a single monolithic consensus layer, multiple independent consensus networks can be launched under 0G to handle different workloads. These networks share the same staking base:

• Shared Staking: Validators stake ZG on Ethereum. If a validator misbehaves, their staked ZG on Ethereum can be slashed.
• Scalability: New consensus networks can be spun up to scale horizontally.

Reward Mechanism: When validators finalize blocks in the 0G environment, they receive tokens. However, the tokens they earn on 0G Chain are burned in the local environment, and the validator’s Ethereum-based account is minted an equivalent amount, ensuring a single point of liquidity and security.

0G Token (ZG)​

ZG is an ERC-20 token representing the backbone of 0G’s economy. It’s minted, burned, and circulated via smart contracts on Ethereum. In practical terms:

• Users pay for storage, data availability, and compute resources in ZG.
• Miners and validators earn ZG for proving storage or validating data.
• Shared staking ties the security model back to Ethereum.

Summary of Key Modules​

0G OS merges four components—Storage, DA, Compute, and Chain—into one interconnected, modular stack. The system’s design goal is scalability, with each layer horizontally extensible. The team touts the potential for “infinite” throughput, especially crucial for large-scale AI tasks.

0G Ecosystem​

Although relatively new, the 0G ecosystem already includes key integration partners:

1. Infrastructure & Tooling:

   • ZK solutions like Union, Brevis, Gevulot
   • Cross-chain solutions like Axelar
   • Restaking protocols like EigenLayer, Babylon, PingPong
   • Decentralized GPU providers IoNet, exaBits
   • Oracle solutions Hemera, Redstone
   • Indexing tools for Ethereum blob data

2. Projects Using 0G for Data Storage & DA:

   • Polygon, Optimism (OP), Arbitrum, Manta for L2 / L3 integration
   • Nodekit, AltLayer for Web3 infrastructure
   • Blade Games, Shrapnel for on-chain gaming

Supply Side​

ZK and Cross-chain frameworks connect 0G to external networks. Restaking solutions (e.g., EigenLayer, Babylon) strengthen security and possibly attract liquidity. GPU networks accelerate erasure coding. Oracle solutions feed off-chain data or reference AI model pricing.

Demand Side​

AI Agents can tap 0G for both data storage and inference. L2s and L3s can integrate 0G’s DA to improve throughput. Gaming and other dApps requiring robust data solutions can store assets, logs, or scoring systems on 0G. Some have already partnered with the project, pointing to early ecosystem traction.

Roadmap & Risk Factors​

0G aims to make AI a public utility, accessible and verifiable by anyone. The team aspires to GB/s-level DA throughput—crucial for real-time AI training that can demand 50–100 GB/s of data transfer.

Co-founder & CEO Michael Heinrich has stated that the explosive growth of AI makes timely iteration critical. The pace of AI innovation is fast; 0G’s own dev progress must keep up.

Potential Trade-Offs:

• Current reliance on shared staking might be an intermediate solution. Eventually, 0G plans to introduce a horizontally scalable consensus layer that can be incrementally augmented (akin to spinning up new AWS nodes).
• Market Competition: Many specialized solutions exist for decentralized storage, data availability, and compute. 0G’s all-in-one approach must stay compelling.
• Adoption & Ecosystem Growth: Without robust developer traction, the promised “unlimited throughput” remains theoretical.
• Sustainability of Incentives: Ongoing motivation for nodes depends on real user demand and an equilibrium token economy.

Conclusion​

0G attempts to unify decentralized storage, data availability, and compute into a single “operating system” supporting on-chain AI. By targeting GB/s throughput, the team seeks to break the performance barrier that currently deters large-scale AI from migrating on-chain. If successful, 0G could significantly accelerate the Web3 AI wave by providing a scalable, integrated, and developer-friendly infrastructure.

Still, many open questions remain. The viability of “infinite throughput” hinges on whether 0G’s modular consensus and incentive structures can seamlessly scale. External factors—market demand, node uptime, developer adoption—will also determine 0G’s staying power. Nonetheless, 0G’s approach to addressing AI’s data bottlenecks is novel and ambitious, hinting at a promising new paradigm for on-chain AI.

The blockchain industry faces a critical inflection point in 2024. While the global market for blockchain technology is projected to reach $469.49 billion by 2030, privacy remains a fundamental challenge. Trusted Execution Environments (TEEs) have emerged as a potential solution, with the TEE market expected to grow from $1.2 billion in 2023 to $3.8 billion by 2028. But does this hardware-based approach truly solve blockchain's privacy paradox, or does it introduce new risks?

The Hardware Foundation: Understanding TEE's Promise​

A Trusted Execution Environment functions like a bank's vault within your computer—but with a crucial difference. While a bank vault simply stores assets, a TEE creates an isolated computation environment where sensitive operations can run completely shielded from the rest of the system, even if that system is compromised.

The market is currently dominated by three key implementations:

1. Intel SGX (Software Guard Extensions)

   • Market Share: 45% of server TEE implementations
   • Performance: Up to 40% overhead for encrypted operations
   • Security Features: Memory encryption, remote attestation
   • Notable Users: Microsoft Azure Confidential Computing, Fortanix

2. ARM TrustZone

   • Market Share: 80% of mobile TEE implementations
   • Performance: <5% overhead for most operations
   • Security Features: Secure boot, biometric protection
   • Key Applications: Mobile payments, DRM, secure authentication

3. AMD SEV (Secure Encrypted Virtualization)

   • Market Share: 25% of server TEE implementations
   • Performance: 2-7% overhead for VM encryption
   • Security Features: VM memory encryption, nested page table protection
   • Notable Users: Google Cloud Confidential Computing, AWS Nitro Enclaves

Real-World Impact: The Data Speaks​

Let's examine three key applications where TEE is already transforming blockchain:

1. MEV Protection: The Flashbots Case Study​

Flashbots' implementation of TEE has demonstrated remarkable results:

• Pre-TEE (2022):

  • Average daily MEV extraction: $7.1M
  • Centralized extractors: 85% of MEV
  • User losses to sandwich attacks: $3.2M daily

• Post-TEE (2023):

  • Average daily MEV extraction: $4.3M (-39%)
  • Democratized extraction: No single entity >15% of MEV
  • User losses to sandwich attacks: $0.8M daily (-75%)

According to Phil Daian, Flashbots' co-founder: "TEE has fundamentally changed the MEV landscape. We're seeing a more democratic, efficient market with significantly reduced user harm."

2. Scaling Solutions: Scroll's Breakthrough​

Scroll's hybrid approach combining TEE with zero-knowledge proofs has achieved impressive metrics:

• Transaction throughput: 3,000 TPS (compared to Ethereum's 15 TPS)
• Cost per transaction: $0.05 (vs. $2-20 on Ethereum mainnet)
• Validation time: 15 seconds (vs. minutes for pure ZK solutions)
• Security guarantee: 99.99% with dual verification (TEE + ZK)

Dr. Sarah Wang, blockchain researcher at UC Berkeley, notes: "Scroll's implementation shows how TEE can complement cryptographic solutions rather than replace them. The performance gains are significant without compromising security."

3. Private DeFi: Emerging Applications​

Several DeFi protocols are now leveraging TEE for private transactions:

• Secret Network (Using Intel SGX):
  • 500,000+ private transactions processed
  • $150M in private token transfers
  • 95% reduction in front-running

The Technical Reality: Challenges and Solutions​

Side-Channel Attack Mitigation​

Recent research has revealed both vulnerabilities and solutions:

1. Power Analysis Attacks

   • Vulnerability: 85% success rate in key extraction
   • Solution: Intel's latest SGX update reduces success rate to <0.1%
   • Cost: 2% additional performance overhead

2. Cache Timing Attacks

   • Vulnerability: 70% success rate in data extraction
   • Solution: AMD's cache partitioning technology
   • Impact: Reduces attack surface by 99%

Centralization Risk Analysis​

The hardware dependency introduces specific risks:

• Hardware Vendor Market Share (2023):
  • Intel: 45%
  • AMD: 25%
  • ARM: 20%
  • Others: 10%

To address centralization concerns, projects like Scroll implement multi-vendor TEE verification:

• Required agreement from 2+ different vendor TEEs
• Cross-validation with non-TEE solutions
• Open-source verification tools

Market Analysis and Future Projections​

TEE adoption in blockchain shows strong growth:

• Current Implementation Costs:

  • Server-grade TEE hardware: $2,000-5,000
  • Integration cost: $50,000-100,000
  • Maintenance: $5,000/month

• Projected Cost Reduction: 2024: -15% 2025: -30% 2026: -50%

Industry experts predict three key developments by 2025:

1. Hardware Evolution

   • New TEE-specific processors
   • Reduced performance overhead (<1%)
   • Enhanced side-channel protection

2. Market Consolidation

   • Standards emergence
   • Cross-platform compatibility
   • Simplified developer tools

3. Application Expansion

   • Private smart contract platforms
   • Decentralized identity solutions
   • Cross-chain privacy protocols

The Path Forward​

While TEE presents compelling solutions, success requires addressing several key areas:

1. Standards Development

   • Industry working groups forming
   • Open protocols for cross-vendor compatibility
   • Security certification frameworks

2. Developer Ecosystem

   • New tools and SDKs
   • Training and certification programs
   • Reference implementations

3. Hardware Innovation

   • Next-gen TEE architectures
   • Reduced costs and energy consumption
   • Enhanced security features

Competitive Landscape​

TEE faces competition from other privacy solutions:

Solution	Performance	Security	Decentralization	Cost
TEE	High	Medium-High	Medium	Medium
MPC	Medium	High	High	High
FHE	Low	High	High	Very High
ZK Proofs	Medium-High	High	High	High

The Bottom Line​

TEE represents a pragmatic approach to blockchain privacy, offering immediate performance benefits while working to address centralization concerns. The technology's rapid adoption by major projects like Flashbots and Scroll, combined with measurable improvements in security and efficiency, suggests TEE will play a crucial role in blockchain's evolution.

However, success isn't guaranteed. The next 24 months will be critical as the industry grapples with hardware dependencies, standardization efforts, and the ever-present challenge of side-channel attacks. For blockchain developers and enterprises, the key is to understand TEE's strengths and limitations, implementing it as part of a comprehensive privacy strategy rather than a silver bullet solution.

The Sui blockchain has rapidly gained attention for its innovative approach to scalability and performance. For developers and infrastructure teams looking to run Sui nodes reliably, Mysten Labs has created a comprehensive set of Network Reliability Engineering (NRE) tools that streamline deployment, configuration, and management processes.

In this guide, we'll explore the Sui NRE repository and show you how to leverage these powerful tools for your Sui node operations.

Hello and welcome back to our blockchain blog! Today, we will be diving into an exciting new proposal called ERC-4337, which introduces account abstraction to Ethereum without requiring any consensus-layer protocol changes. Instead, this proposal relies on higher-layer infrastructure to achieve its goals. Let's explore what ERC-4337 has to offer and how it addresses the limitations of the current Ethereum ecosystem.

What is ERC-4337?​

ERC-4337 is a proposal that introduces account abstraction to Ethereum through the use of a separate mempool and a new type of pseudo-transaction object called a UserOperation. Users send UserOperation objects into the alternative mempool, where a special class of actors called bundlers package them into a transaction making a handleOps call to a dedicated contract. These transactions are then included in a block.

The proposal aims to achieve several goals:

1. Enable users to use smart contract wallets with arbitrary verification logic as their primary accounts.
2. Completely remove the need for users to have externally owned accounts (EOAs).
3. Ensure decentralization by allowing any bundler to participate in the process of including account-abstracted user operations.
4. Enable all activity to happen over a public mempool, eliminating the need for users to know direct communication addresses of specific actors.
5. Avoid trust assumptions on bundlers.
6. Avoid requiring any Ethereum consensus changes for faster adoption.
7. Support other use cases such as privacy-preserving applications, atomic multi-operations, paying transaction fees with ERC-20 tokens, and developer-sponsored transactions.

Backwards Compatibility​

Since ERC-4337 does not change the consensus layer, there are no direct backwards compatibility issues for Ethereum. However, pre-ERC-4337 accounts are not easily compatible with the new system because they lack the necessary validateUserOp function. This can be addressed by creating an ERC-4337 compatible account that re-implements the verification logic as a wrapper and setting it as the original account’s trusted op submitter.

Reference Implementation​

For those interested in diving deeper into the technical details of ERC-4337, a reference implementation is available at https://github.com/eth-infinitism/account-abstraction/tree/main/contracts.

Security Considerations​

The entry point contract for ERC-4337 must be heavily audited and formally verified, as it serves as a central trust point for the entire system. While this approach reduces the auditing and formal verification load for individual accounts, it does concentrate security risk in the entry point contract, which must be robustly verified.

Verification should cover two primary claims:

1. Safety against arbitrary hijacking: The entry point only calls an account generically if validateUserOp to that specific account has passed.
2. Safety against fee draining: If the entry point calls validateUserOp and passes, it must also make the generic call with calldata equal to op.calldata.

Conclusion​

ERC-4337 is an exciting proposal that aims to introduce account abstraction to Ethereum without requiring consensus-layer protocol changes. By using higher-layer infrastructure, it opens up new possibilities for decentralization, flexibility, and various use cases. While there are security considerations to address, this proposal has the potential to greatly improve the Ethereum ecosystem and user experience.