72 posts tagged with "Tech Innovation"

The total crypto market cap crossed $4 trillion for the first time in 2025. Bitcoin ETFs accumulated $57.7 billion in net inflows. Stablecoin monthly transaction volume hit $3.4 trillion—surpassing Visa. Real-world asset tokenization exploded 240% year-over-year. And yet, amidst these record-breaking numbers, the most important story of 2025 wasn't about price—it was about the fundamental transformation of Web3 from a speculative playground into institutional-grade financial infrastructure.

When Ethereum L2s paid $3.83 per megabyte to post data using blobs, Eclipse was paying Celestia $0.07 for the same megabyte. That's not a typo—55 times cheaper, enabling Eclipse to post over 83 GB of data without bankrupting its treasury. This cost differential isn't a temporary market anomaly. It's the structural advantage of purpose-built infrastructure.

Celestia has now processed over 160 GB of rollup data, generates daily blob fees that have grown 10x since late 2024, and commands roughly 50% market share in the data availability sector. The question isn't whether modular data availability works—it's whether Celestia can maintain its lead as EigenDA, Avail, and Ethereum's native blobs compete for the same rollup customers.

Understanding Blob Economics: The Foundation​

Before analyzing Celestia's numbers, it's worth understanding what makes data availability economically distinct from other blockchain services.

What Rollups Actually Pay For​

When a rollup processes transactions, it produces state changes that need to be verifiable. Rather than trust the rollup operator, users can verify by re-executing transactions against the original data. This requires that transaction data remains available—not forever, but long enough for challenges and verification.

Traditional rollups posted this data directly to Ethereum calldata, paying premium prices for permanent storage on the world's most secure ledger. But most rollup data only needs availability for a challenge window (typically 7-14 days), not eternity. This mismatch created the opening for specialized data availability layers.

Celestia's PayForBlob Model​

Celestia's fee model is straightforward: rollups pay per blob based on size and current gas prices. Unlike execution layers where computation costs dominate, data availability is fundamentally about bandwidth and storage—resources that scale more predictably with hardware improvements.

The economics create a flywheel: lower DA costs enable more rollups, more rollups generate more fee revenue, and increased usage justifies infrastructure investment for even greater capacity. Celestia's current throughput of approximately 1.33 MB/s (8 MB blocks every 6 seconds) represents early-stage capacity with a clear path to 100x improvement.

The 160 GB Reality: Who's Using Celestia​

The aggregate numbers tell a story of rapid adoption. Over 160 GB of data has been published to Celestia since mainnet launch, with daily data volume averaging around 2.5 GB. But the composition of this data reveals more interesting patterns.

Eclipse: The Volume Leader​

Eclipse—a Layer 2 combining Solana's virtual machine with Ethereum settlement—has published over 83 GB of data to Celestia, more than half of all network volume. Eclipse uses Celestia for data availability while settling to Ethereum, demonstrating the modular architecture in practice.

The volume isn't surprising given Eclipse's design choices. Solana Virtual Machine execution generates more data than EVM equivalents, and Eclipse's focus on high-throughput applications (gaming, DeFi, social) means transaction volumes that would be cost-prohibitive on Ethereum DA.

The Enterprise Cohort​

Beyond Eclipse, the rollup ecosystem includes:

• Manta Pacific: Over 7 GB posted, an OP Stack rollup focused on ZK applications with Universal Circuits technology
• Plume Network: RWA-specialized L2 using Celestia for tokenized asset transaction data
• Derive: On-chain options and structured products trading
• Aevo: Decentralized derivatives exchange processing high-frequency trading data
• Orderly Network: Cross-chain orderbook infrastructure

Twenty-six rollups now build on Celestia, with major frameworks—Arbitrum Orbit, OP Stack, Polygon CDK—all offering Celestia as a DA option. Rollups-as-a-Service platforms like Conduit and Caldera have made Celestia integration a standard offering.

Fee Revenue Growth​

At the end of 2024, Celestia generated approximately $225 per day in blob fees. That number has grown nearly 10x, reflecting both increased usage and the network's ability to capture value as demand rises. The fee market remains early-stage—capacity utilization is low relative to tested limits—but the growth trajectory validates the economic model.

Cost Comparison: Celestia vs. The Competition​

Data availability has become a competitive market. Understanding the cost structures helps explain rollup decisions.

Celestia vs. Ethereum Blobs​

Ethereum's EIP-4844 (Dencun upgrade) introduced blob transactions, reducing DA costs by 90%+ compared to calldata. But Celestia remains significantly cheaper:

Metric	Ethereum Blobs	Celestia
Cost per MB	~$3.83	~$0.07
Cost advantage	Baseline	55x cheaper
Capacity	Limited blob space	8 MB blocks (scaling to 1 GB)

For high-volume rollups like Eclipse, this difference is existential. At Ethereum blob prices, Eclipse's 83 GB of data would have cost over $300,000. On Celestia, it cost approximately $6,000.

Celestia vs. EigenDA​

EigenDA offers a different value proposition: Ethereum-aligned security through restaking, with claimed throughput of 100 MB/s. The tradeoffs:

Aspect	Celestia	EigenDA
Security model	Independent validator set	Ethereum restaking
Throughput	1.33 MB/s (8 MB blocks)	100 MB/s claimed
Architecture	Blockchain-based	Data Availability Committee
Decentralization	Public verification	Trust assumptions

EigenDA's DAC architecture enables higher throughput but introduces trust assumptions that fully blockchain-based solutions avoid. For teams deeply embedded in Ethereum's ecosystem, EigenDA's restaking integration may outweigh Celestia's independence.

Celestia vs. Avail​

Avail positions as the most flexible option for multichain applications:

Aspect	Celestia	Avail
Cost per MB	Higher	Lower
Economic security	Higher	Lower
Mainnet capacity	8 MB blocks	4 MB blocks
Test capacity	128 MB proven	128 MB proven

Avail's lower costs come with lower economic security—a reasonable tradeoff for applications where the marginal cost savings matter more than maximum security guarantees.

The Scaling Roadmap: From 1 MB/s to 1 GB/s​

Celestia's current capacity—approximately 1.33 MB/s—is intentionally conservative. The network has demonstrated dramatically higher throughput in controlled testing, providing a clear upgrade path.

Mammoth Testing Results​

In October 2024, the Mammoth Mini devnet achieved 88 MB blocks with 3-second block times, delivering approximately 27 MB/s throughput—over 20x current mainnet capacity.

In April 2025, the mamo-1 testnet pushed further: 128 MB blocks with 6-second block times, achieving 21.33 MB/s sustained throughput. This represented 16x current mainnet capacity while incorporating new propagation algorithms like Vacuum! designed for efficient large-block data movement.

Mainnet Upgrade Progress​

The scaling is happening incrementally:

• Ginger Upgrade (December 2024): Reduced block times from 12 seconds to 6 seconds
• 8 MB Block Increase (January 2025): Doubled block size via on-chain governance
• Matcha Upgrade (January 2026): Enabled 128 MB blocks through improved propagation mechanics, reducing node storage requirements by 77%
• Lotus Upgrade (July 2025): V4 mainnet release with further TIA holder improvements

The roadmap targets gigabyte-scale blocks by 2030, representing a 1,000x increase from current capacity. Whether market demand grows to justify this capacity remains uncertain, but the technical path is clear.

TIA Tokenomics: How Value Accrues​

Understanding Celestia's economics requires understanding TIA's role in the system.

Token Utility​

TIA serves three functions:

1. Blob fees: Rollups pay TIA for data availability
2. Staking: Validators stake TIA to secure the network and earn rewards
3. Governance: Token holders vote on network parameters and upgrades

The fee mechanism creates direct linkage between network usage and token demand. As blob submissions increase, TIA is purchased and spent, creating buy pressure proportional to network utility.

Supply Dynamics​

TIA launched with 1 billion genesis tokens. Initial inflation was set at 8% annually, decreasing over time toward 1.5% terminal inflation.

The January 2026 Matcha upgrade introduced Proof-of-Governance (PoG), slashing annual token issuance from 5% to 0.25%. This structural change:

• Reduces sell pressure from inflation
• Aligns rewards with governance participation
• Strengthens value capture as network usage grows

Additionally, the Celestia Foundation announced a $62.5 million TIA buyback program in 2025, further reducing circulating supply.

Validator Economics​

Effective January 2026, maximum validator commission increased from 10% to 20%. This addresses validators' rising operational expenses—particularly as block sizes grow—while maintaining competitive staking yields.

The Competitive Moat: First-Mover or Sustainable Advantage?​

Celestia's 50% DA market share and 160+ GB of posted data represent clear traction. But moats in infrastructure can erode quickly.

Advantages​

Framework Integration: Every major rollup framework—Arbitrum Orbit, OP Stack, Polygon CDK—supports Celestia as a DA option. This integration creates switching costs and reduces friction for new rollups.

Proven Scale: The 128 MB block testing provides confidence in future capacity that competitors haven't demonstrated at the same level.

Economic Alignment: The Proof-of-Governance tokenomics and buyback programs create stronger value capture than alternative models.

Challenges​

EigenDA's Ethereum Alignment: For teams prioritizing Ethereum-native security, EigenDA's restaking model may be more attractive despite architectural trade-offs.

Avail's Cost Advantage: For cost-sensitive applications, Avail's lower fees may outweigh security differences.

Ethereum's Native Improvement: If Ethereum expands blob capacity significantly (as proposed in various roadmap discussions), the cost differential shrinks.

The Ecosystem Lock-in Question​

Celestia's real moat may be ecosystem lock-in. Eclipse's 83+ GB of data creates path dependency—migrating to a different DA layer would require significant infrastructure changes. As more rollups accumulate history on Celestia, switching costs increase.

What the Data Tells Us​

Celestia's blob economics validate the modular thesis: specialized infrastructure for data availability can be dramatically cheaper than general-purpose L1 solutions. The 55x cost advantage over Ethereum blobs isn't magic—it's the result of purpose-built architecture optimized for a specific function.

The 160+ GB of posted data proves market demand exists. The 10x growth in fee revenue demonstrates value capture. The scaling roadmap provides confidence in future capacity.

For rollup developers, the calculus is straightforward: Celestia offers the best-tested, most integrated DA solution with a clear path to gigabyte-scale capacity. EigenDA makes sense for Ethereum-native projects willing to accept DAC trust assumptions. Avail serves multichain applications prioritizing flexibility over maximum security.

The data availability market has room for multiple winners serving different segments. But Celestia's combination of proven scale, deep integrations, and improving tokenomics positions it well for the coming wave of rollup expansion.

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When Zama became the first fully homomorphic encryption unicorn in June 2025—valued at over $1 billion—it signaled something larger than one company's success. The blockchain industry had finally accepted a fundamental truth: privacy isn't optional, it's infrastructure.

But here's the uncomfortable reality developers face: there's no single "best" privacy technology. Fully Homomorphic Encryption (FHE), Zero-Knowledge Proofs (ZK), and Trusted Execution Environments (TEE) each solve different problems with different tradeoffs. Choosing wrong doesn't just impact performance—it can fundamentally compromise what you're trying to build.

This guide breaks down when to use each technology, what you're actually trading off, and why the future likely involves all three working together.

The Privacy Technology Landscape in 2026​

The blockchain privacy market has evolved from niche experimentation to serious infrastructure. ZK-based rollups now secure over $28 billion in Total Value Locked. The Zero-Knowledge KYC market alone is projected to grow from $83.6 million in 2025 to $903.5 million by 2032—a 40.5% compound annual growth rate.

But market size doesn't help you choose a technology. Understanding what each approach actually does is the starting point.

Zero-Knowledge Proofs: Proving Without Revealing​

ZK proofs allow one party to prove a statement is true without revealing any information about the content itself. You can prove you're over 18 without revealing your birthdate, or prove a transaction is valid without exposing the amount.

How it works: The prover generates a cryptographic proof that a computation was performed correctly. The verifier can check this proof quickly without re-running the computation or seeing the underlying data.

The catch: ZK excels at proving things about data you already hold. It struggles with shared state. You can prove your balance is sufficient for a transaction, but you can't easily ask questions like "how many fraud cases happened chain-wide?" or "who won this sealed-bid auction?" without additional infrastructure.

Leading projects: Aztec enables hybrid public/private smart contracts where users choose whether transactions are visible. zkSync focuses primarily on scalability with enterprise-focused "Prividiums" for permissioned privacy. Railgun and Nocturne provide shielded transaction pools.

Fully Homomorphic Encryption: Computing on Encrypted Data​

FHE is often called the "holy grail" of encryption because it allows computation on encrypted data without ever decrypting it. The data stays encrypted during processing, and the results remain encrypted—only the authorized party can decrypt the output.

How it works: Mathematical operations are performed directly on ciphertexts. Addition and multiplication on encrypted values produce encrypted results that, when decrypted, match what you'd get from operating on plaintext.

The catch: Computational overhead is massive. Even with recent optimizations, FHE-based smart contracts on Inco Network achieve only 10-30 TPS depending on hardware—orders of magnitude slower than plaintext execution.

Leading projects: Zama provides the foundational infrastructure with FHEVM (their fully homomorphic EVM). Fhenix builds application-layer solutions using Zama's technology, having deployed CoFHE coprocessor on Arbitrum with decryption speeds up to 50x faster than competing approaches.

Trusted Execution Environments: Hardware-Based Isolation​

TEEs create secure enclaves within processors where computations occur in isolation. Data inside the enclave remains protected even if the broader system is compromised. Unlike cryptographic approaches, TEEs rely on hardware rather than mathematical complexity.

How it works: Specialized hardware (Intel SGX, AMD SEV) creates isolated memory regions. Code and data inside the enclave are encrypted and inaccessible to the operating system, hypervisor, or other processes—even with root access.

The catch: You're trusting hardware manufacturers. Any single compromised enclave can leak plaintext, regardless of how many nodes participate. In 2022, a critical SGX vulnerability forced coordinated key updates across Secret Network, demonstrating the operational complexity of hardware-dependent security.

Leading projects: Secret Network pioneered private smart contracts using Intel SGX. Oasis Network's Sapphire is the first confidential EVM in production, processing up to 10,000 TPS. Phala Network operates over 1,000 TEE nodes for confidential AI workloads.

The Tradeoff Matrix: Performance, Security, and Trust​

Understanding the fundamental tradeoffs helps match technology to use case.

Performance​

Technology	Throughput	Latency	Cost
TEE	Near-native (10,000+ TPS)	Low	Low operational cost
ZK	Moderate (varies by implementation)	Higher (proof generation)	Medium
FHE	Low (10-30 TPS currently)	High	Very high operational cost

TEEs win on raw performance because they're essentially running native code in protected memory. ZK introduces proof generation overhead but verification is fast. FHE currently requires intensive computation that limits practical throughput.

Security Model​

Technology	Trust Assumption	Post-Quantum	Failure Mode
TEE	Hardware manufacturer	Not resistant	Single enclave compromise exposes all data
ZK	Cryptographic (often trusted setup)	Varies by scheme	Proof system bugs can be invisible
FHE	Cryptographic (lattice-based)	Resistant	Computationally intensive to exploit

TEEs require trusting Intel, AMD, or whoever manufactures the hardware—plus trusting that no firmware vulnerabilities exist. ZK systems often require "trusted setup" ceremonies, though newer schemes eliminate this. FHE's lattice-based cryptography is believed quantum-resistant, making it the strongest long-term security bet.

Programmability​

Technology	Composability	State Privacy	Flexibility
TEE	High	Full	Limited by hardware availability
ZK	Limited	Local (client-side)	High for verification
FHE	Full	Global	Limited by performance

ZK excels at local privacy—protecting your inputs—but struggles with shared state across users. FHE maintains full composability because encrypted state can be computed upon by anyone without revealing contents. TEEs offer high programmability but are constrained to environments with compatible hardware.

Choosing the Right Technology: Use Case Analysis​

Different applications demand different tradeoffs. Here's how leading projects are making these choices.

DeFi: MEV Protection and Private Trading​

Challenge: Front-running and sandwich attacks extract billions from DeFi users by exploiting visible mempools.

FHE solution: Zama's confidential blockchain enables transactions where parameters remain encrypted until block inclusion. Front-running becomes mathematically impossible—there's no visible data to exploit. The December 2025 mainnet launch included the first confidential stablecoin transfer using cUSDT.

TEE solution: Oasis Network's Sapphire enables confidential smart contracts for dark pools and private order matching. Lower latency makes it suitable for high-frequency trading scenarios where FHE's computational overhead is prohibitive.

When to choose: FHE for applications requiring the strongest cryptographic guarantees and global state privacy. TEE when performance requirements exceed what FHE can deliver and hardware trust is acceptable.

Identity and Credentials: Privacy-Preserving KYC​

Challenge: Proving identity attributes (age, citizenship, accreditation) without exposing documents.

ZK solution: Zero-knowledge credentials let users prove "KYC passed" without revealing underlying documents. This satisfies compliance requirements while protecting user privacy—a critical balance as regulatory pressure intensifies.

Why ZK wins here: Identity verification is fundamentally about proving statements about personal data. ZK is purpose-built for this: compact proofs that verify without revealing. The verification is fast enough for real-time use.

Confidential AI and Sensitive Computation​

Challenge: Processing sensitive data (healthcare, financial models) without exposure to operators.

TEE solution: Phala Network's TEE-based cloud processes LLM queries without platform access to inputs. With GPU TEE support (NVIDIA H100/H200), confidential AI workloads run at practical speeds.

FHE potential: As performance improves, FHE enables computation where even the hardware operator can't access data—removing the trust assumption entirely. Current limitations restrict this to simpler computations.

Hybrid approach: Run initial data processing in TEEs for speed, use FHE for the most sensitive operations, and generate ZK proofs to verify results.

The Vulnerability Reality​

Each technology has failed in production—understanding failure modes is essential.

TEE Failures​

In 2022, critical SGX vulnerabilities affected multiple blockchain projects. Secret Network, Phala, Crust, and IntegriTEE required coordinated patches. Oasis survived because its core systems run on older SGX v1 (unaffected) and don't rely on enclave secrecy for funds safety.

Lesson: TEE security depends on hardware you don't control. Defense-in-depth (key rotation, threshold cryptography, minimal trust assumptions) is mandatory.

ZK Failures​

On April 16, 2025, Solana patched a zero-day vulnerability in its Confidential Transfers feature. The bug could have enabled unlimited token minting. The dangerous aspect of ZK failures: when proofs fail, they fail invisibly. You can't see what shouldn't be there.

Lesson: ZK systems require extensive formal verification and audit. The complexity of proof systems creates attack surface that's difficult to reason about.

FHE Considerations​

FHE hasn't experienced major production failures—largely because it's earlier in deployment. The risk profile differs: FHE is computationally intensive to attack, but implementation bugs in complex cryptographic libraries could enable subtle vulnerabilities.

Lesson: Newer technology means less battle-testing. The cryptographic guarantees are strong, but the implementation layer needs continued scrutiny.

Hybrid Architectures: The Future Isn't Either/Or​

The most sophisticated privacy systems combine multiple technologies, using each where it excels.

ZK + FHE Integration​

User states (balances, preferences) stored with FHE encryption. ZK proofs verify valid state transitions without exposing encrypted values. This enables private execution within scalable L2 environments—combining FHE's global state privacy with ZK's efficient verification.

TEE + ZK Combination​

TEEs process sensitive computations at near-native speed. ZK proofs verify that TEE outputs are correct, removing the single-operator trust assumption. If the TEE is compromised, invalid outputs would fail ZK verification.

When to Use What​

A practical decision framework:

Choose TEE when:

• Performance is critical (high-frequency trading, real-time applications)
• Hardware trust is acceptable for your threat model
• You need to process large data volumes quickly

Choose ZK when:

• You're proving statements about client-held data
• Verification must be fast and low-cost
• You don't need global state privacy

Choose FHE when:

• Global state must remain encrypted
• Post-quantum security is required
• Computation complexity is acceptable for your use case

Choose hybrid when:

• Different components have different security requirements
• You need to balance performance with security guarantees
• Regulatory compliance requires demonstrable privacy

What Comes Next​

Vitalik Buterin recently pushed for standardized "efficiency ratios"—comparing cryptographic computation time to plaintext execution. This reflects the industry's maturation: we're moving from "does it work?" to "how efficiently does it work?"

FHE performance continues improving. Zama's December 2025 mainnet proves production-readiness for simple smart contracts. As hardware acceleration develops (GPU optimization, custom ASICs), the throughput gap with TEEs will narrow.

ZK systems are becoming more expressive. Aztec's Noir language enables complex private logic that would have been impractical years ago. Standards are slowly converging, enabling cross-chain ZK credential verification.

TEE diversity is expanding beyond Intel SGX. AMD SEV, ARM TrustZone, and RISC-V implementations reduce dependency on any single manufacturer. Threshold cryptography across multiple TEE vendors could address the single-point-of-failure concern.

The privacy infrastructure buildout is happening now. For developers building privacy-sensitive applications, the choice isn't about finding the perfect technology—it's about understanding tradeoffs well enough to combine them intelligently.

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What if the blockchain performance debates of 2021-2023 already feel ancient? In 2025, the industry quietly crossed a threshold that venture capitalists and skeptics alike thought was years away: multiple mainnets now routinely process thousands of transactions per second while keeping fees below a single cent. The era of "blockchain can't scale" has officially ended.

This isn't about theoretical benchmarks or testnet claims. Real users, real applications, and real money are flowing through networks that would have been science fiction just two years ago. Let's examine the hard numbers behind blockchain's performance revolution.

The New TPS Leaders: No Longer a Two-Horse Race​

The performance landscape has fundamentally shifted. While Bitcoin and Ethereum dominated blockchain conversations for years, 2025 established a new generation of speed champions.

Solana set the headline-grabbing record on August 17, 2025, processing 107,664 transactions per second on its mainnet—not in a laboratory, but under real-world conditions. This wasn't a one-off spike; the network demonstrated sustained high throughput that validates years of architectural decisions prioritizing performance.

But Solana's achievement is just one data point in a broader revolution:

• Aptos has demonstrated 13,367 TPS on mainnet without failures, delays, or gas fee spikes. Their Block-STM parallel execution engine theoretically supports up to 160,000 TPS.
• Sui has proven 297,000 TPS in controlled testing, with mainnet peaks reaching 822 TPS under typical usage and the Mysticeti v2 consensus achieving just 390ms latency.
• BNB Chain consistently delivers around 2,200 TPS in production, with the Lorentz and Maxwell hard forks delivering 4x faster block times.
• Avalanche processes 4,500 TPS through its unique subnet architecture, enabling horizontal scaling across specialized chains.

These numbers represent a 10x to 100x improvement over what the same networks achieved in 2023. More importantly, they're not theoretical maximums—they're observed, verifiable performance under actual usage conditions.

Firedancer: The Million-TPS Client That Changed Everything​

The most significant technical breakthrough of 2025 wasn't a new blockchain—it was Firedancer, Jump Crypto's complete reimplementation of the Solana validator client. After three years of development, Firedancer went live on mainnet on December 12, 2025.

The numbers are staggering. In demonstrations at Breakpoint 2024, Jump's Chief Scientist Kevin Bowers showed Firedancer processing over 1 million transactions per second on commodity hardware. Benchmarks consistently showed 600,000 to 1,000,000 TPS in controlled tests—20x higher than the previous Agave client's demonstrated throughput.

What makes Firedancer different? Architecture. Unlike Agave's monolithic design, Firedancer uses a modular, tile-based architecture that splits validator tasks to run in parallel. Written in C rather than Rust, every component was optimized for raw performance from the ground up.

The adoption trajectory tells its own story. Frankendancer, a hybrid implementation combining Firedancer's networking stack with Agave's runtime, now runs on 207 validators representing 20.9% of all staked SOL—up from just 8% in June 2025. This isn't experimental software anymore; it's infrastructure that secures billions of dollars.

Solana's Alpenglow upgrade in September 2025 added another layer, replacing the original Proof of History and TowerBFT mechanisms with new Votor and Rotor systems. The result: 150ms block finality and support for multiple concurrent leaders enabling parallel execution.

Sub-Penny Fees: EIP-4844's Quiet Revolution​

While TPS numbers grab headlines, the fee revolution is equally transformative. Ethereum's EIP-4844 upgrade in March 2024 fundamentally restructured how Layer 2 networks pay for data availability, and by 2025, the effects became impossible to ignore.

The mechanism is elegant: blob transactions provide temporary data storage for rollups at a fraction of previous costs. Where Layer 2s previously competed for expensive calldata space, blobs offer 18-day temporary storage that rollups actually need.

The impact on fees was immediate and dramatic:

• Arbitrum fees dropped from $0.37 to $0.012 per transaction
• Optimism fell from $0.32 to $0.009
• Base achieved fees as low as $0.01

These aren't promotional rates or subsidized transactions—they're sustainable operating costs enabled by architectural improvement. Ethereum now effectively provides 10-100x cheaper data storage for Layer 2 solutions.

The activity surge followed predictably. Base saw a 319.3% increase in daily transactions post-upgrade, Arbitrum increased 45.7%, and Optimism rose 29.8%. Users and developers responded exactly as economics predicted: when transactions become cheap enough, usage explodes.

The May 2025 Pectra upgrade pushed further, expanding blob throughput from 6 to 9 blobs per block and raising the gas limit to 37.3 million. Ethereum's effective TPS through Layer 2s now exceeds 100,000, with average transaction costs dropping to $0.08 on L2 networks.

The Real-World Performance Gap​

Here's what the benchmarks don't tell you: theoretical TPS and observed TPS remain very different numbers. This gap reveals important truths about blockchain maturity.

Consider Avalanche. While the network supports 4,500 TPS theoretically, observed activity averages around 18 TPS, with the C-Chain closer to 3-4 TPS. Sui demonstrates 297,000 TPS in testing but peaks at 822 TPS on mainnet.

This isn't failure—it's proof of headroom. These networks can handle massive demand spikes without degradation. When the next NFT frenzy or DeFi summer arrives, the infrastructure won't buckle.

The practical implications matter enormously for builders:

• Gaming applications need consistent low latency more than peak TPS
• DeFi protocols require predictable fees during volatility
• Payment systems demand reliable throughput during holiday shopping spikes
• Enterprise applications need guaranteed SLAs regardless of network conditions

Networks with significant headroom can offer these guarantees. Those operating near capacity cannot.

Move VM Chains: The Performance Architecture Advantage​

A pattern emerges when examining 2025's top performers: the Move programming language shows up repeatedly. Both Sui and Aptos, built by teams with Facebook/Diem heritage, leverage Move's object-centric data model for parallelization advantages impossible in account-model blockchains.

Aptos's Block-STM engine demonstrates this clearly. By processing transactions simultaneously rather than sequentially, the network achieved 326 million successful transactions in a single day during peak periods—while maintaining approximately $0.002 average fees.

Sui's approach differs but follows similar principles. The Mysticeti consensus protocol achieves 390ms latency by treating objects rather than accounts as the fundamental unit. Transactions that don't touch the same objects execute in parallel automatically.

Both networks attracted significant capital in 2025. BlackRock's BUIDL fund added $500 million in tokenized assets to Aptos in October, making it the second-largest BUIDL chain. Aptos also powered the official digital wallet for Expo 2025 in Osaka, processing 558,000+ transactions and onboarding 133,000+ users—real-world validation at scale.

What High TPS Actually Enables​

Beyond bragging rights, what do thousands of TPS unlock?

Institutional-grade settlement: When processing 2,000+ TPS with sub-second finality, blockchains compete directly with traditional payment rails. BNB Chain's Lorentz and Maxwell upgrades specifically targeted "Nasdaq-scale settlement" for institutional DeFi.

Microtransaction viability: At $0.01 per transaction, business models impossible at $5 fees become profitable. Streaming payments, per-API-call billing, and granular royalty distribution all require sub-penny economics.

Game state synchronization: Blockchain gaming requires updating player states hundreds of times per session. 2025's performance levels finally enable genuine on-chain gaming rather than the settlement-only models of previous years.

IoT and sensor networks: When devices can transact for fractions of a cent, supply chain tracking, environmental monitoring, and machine-to-machine payments become economically viable.

The common thread: 2025's performance improvements didn't just make existing applications faster—they enabled entirely new categories of blockchain usage.

The Decentralization Trade-off Debate​

Critics correctly note that raw TPS often correlates with reduced decentralization. Solana runs fewer validators than Ethereum. Aptos and Sui require more expensive hardware. These trade-offs are real.

But 2025 also demonstrated that the binary choice between speed and decentralization is false. Ethereum's Layer 2 ecosystem delivers 100,000+ effective TPS while inheriting Ethereum's security guarantees. Firedancer improves Solana's throughput without reducing validator counts.

The industry is learning to specialize: settlement layers optimize for security, execution layers optimize for speed, and proper bridging connects them. This modular approach—data availability from Celestia, execution from rollups, settlement on Ethereum—achieves speed, security, and decentralization through composition rather than compromise.

Looking Forward: The Million-TPS Mainnet​

If 2025 established high-TPS mainnets as reality rather than promise, what comes next?

Ethereum's Fusaka upgrade will introduce full danksharding via PeerDAS, potentially enabling millions of TPS across rollups. Firedancer's production deployment should push Solana toward its tested 1 million TPS capacity. New entrants continue emerging with novel architectures.

More importantly, the developer experience has matured. Building applications that require thousands of TPS is no longer a research project—it's standard practice. The tooling, documentation, and infrastructure supporting high-performance blockchain development in 2025 would be unrecognizable to a 2021 developer.

The question is no longer whether blockchain can scale. The question is what we'll build now that it has.

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BlockEden.xyz provides enterprise-grade RPC and API access for high-performance chains including Sui, Aptos, and Solana. When your application demands the throughput and reliability that 2025's performance revolution enables, explore our infrastructure designed for production-grade blockchain development.

What if you could verify a 500-page book exists without reading a single page? That's essentially what Ethereum just learned to do with PeerDAS—and it's quietly reshaping how blockchains can scale without sacrificing decentralization.

On December 3, 2025, Ethereum activated its Fusaka upgrade, introducing PeerDAS (Peer Data Availability Sampling) as the headline feature. While most headlines focused on the 40-60% fee reductions for Layer 2 networks, the underlying mechanism represents something far more significant: a fundamental shift in how blockchain nodes prove data exists without actually storing all of it.

When the former CTO of Ant Chain and the Chief Security Officer of Ant Financial's Web3 division left one of the world's largest fintech companies to start a blockchain from scratch, the industry took notice. Their bet? That the $24 billion tokenized real-world asset market is about to explode into the trillions—and existing blockchains aren't ready for it.

Pharos Network, the high-performance Layer 1 they're building, just closed an $8 million seed round led by Lightspeed Faction and Hack VC. But the more interesting number is the $1.5 billion RWA pipeline they've announced with Ant Digital Technologies, their former employer's Web3 arm. This isn't a speculative DeFi play—it's an institutional-grade infrastructure bet backed by people who've already built financial systems processing billions of transactions.

The Ant Group DNA: Building for Scale They've Already Seen​

Alex Zhang, Pharos's CEO, spent years as CTO of Ant Chain, overseeing blockchain infrastructure that processed transactions for hundreds of millions of users across Alibaba's ecosystem. Co-founder and CTO Meng Wu was responsible for security at Ant Financial's Web3 division, protecting some of the most valuable financial infrastructure in Asia.

Their diagnosis of the current blockchain landscape is blunt: existing networks weren't designed for the financial industry's actual requirements. Solana optimizes for speed but lacks the compliance primitives institutions need. Ethereum prioritizes decentralization but can't deliver the sub-second finality that real-time payments demand. The "institutional Solana" doesn't exist yet.

Pharos aims to fill that gap with what they call a "full-stack parallel blockchain"—a network designed from the ground up for the specific demands of tokenized assets, cross-border payments, and enterprise DeFi.

The Technical Architecture: Beyond Sequential Processing​

Most blockchains process transactions sequentially, like a single-file line at a bank. Even Ethereum's recent upgrades and Solana's parallel processing treat the blockchain as a unified system with fundamental throughput limits. Pharos takes a different approach, implementing what they call "Degree of Parallelism" optimization—essentially treating the blockchain like a GPU rather than a CPU.

The Three-Layer Design:

• L1-Base: Provides data availability with hardware acceleration, handling the raw storage and retrieval of blockchain data at speeds traditional networks can't match.

• L1-Core: Implements a novel BFT consensus that allows multiple validator nodes to propose, validate, and commit transactions concurrently. Unlike classical BFT implementations requiring fixed leader roles and round-based communication, Pharos validators operate in parallel.

• L1-Extension: Enables "Special Processing Networks" (SPNs)—customized execution environments for specific use cases like high-frequency trading or AI model execution. Think of it as creating dedicated fast lanes for different types of financial activity.

The Execution Engine:

The heart of Pharos is its parallel execution system combining LLVM-based intermediate representation conversion with speculative parallel processing. The technical innovations include:

• Smart Access List Inference (SALI): Static and dynamic analysis to identify which state entries a contract will access, enabling transactions with non-overlapping state to execute simultaneously.

• Dual VM Support: Both EVM and WASM virtual machines, ensuring Solidity compatibility while enabling high-performance execution for contracts written in Rust or other languages.

• Pipelined Block Processing: Inspired by superscalar processors, dividing the block lifecycle into parallel stages—consensus ordering, database preloading, execution, Merkleization, and flushing all happen concurrently.

The result? Their testnet has demonstrated 30,000+ TPS with 0.5-second block times, with mainnet targets of 50,000 TPS and sub-second finality. For context, Visa processes roughly 1,700 TPS on average.

Why RWA Tokenization Needs Different Infrastructure​

The tokenized real-world asset market has grown from $85 million in 2020 to over $24 billion by mid-2025—a 245x increase in just five years. McKinsey projects $2 trillion by 2030; Standard Chartered estimates $30 trillion by 2034. Some analysts expect $50 trillion in annual RWA trading by decade's end.

But here's the disconnect: most of this growth has happened on chains that weren't designed for it. Private credit dominates the current market at $17 billion, followed by U.S. Treasuries at $7.3 billion. These aren't speculative tokens—they're regulated financial instruments requiring:

• Identity verification that satisfies KYC/AML requirements across jurisdictions
• Compliance primitives built into the protocol layer, not bolted on afterward
• Sub-second settlement for real-time payment applications
• Institutional-grade security with formal verification and hardware-backed protection

Pharos addresses these requirements with native zkDID authentication and on-chain/off-chain credit systems. When they talk about "bridging TradFi and Web3," they mean building the compliance rails into the infrastructure itself.

The Ant Digital Partnership: $1.5 Billion in Real Assets​

The strategic partnership with ZAN—Ant Digital Technologies' Web3 brand—isn't just a press release. It represents a $1.5 billion pipeline of renewable energy RWA assets slated for the Pharos mainnet at launch.

The collaboration focuses on three areas:

1. Node services and infrastructure: ZAN's enterprise-grade node operations supporting Pharos's validator network
2. Security and hardware acceleration: Leveraging Ant's experience with hardware-secured financial systems
3. RWA use case development: Bringing actual tokenized assets—not hypothetical ones—to the network from day one

The Pharos team has prior experience implementing tokenization projects including Xiexin Energy Technology and Langxin Group. They're not learning RWA tokenization on Pharos—they're applying expertise developed inside one of the world's largest fintech ecosystems.

From Testnet to Mainnet: The Q1 2026 Launch​

Pharos launched its AtlanticOcean testnet with impressive metrics: nearly 3 billion transactions across 23 million blocks since May, all with 0.5-second block times. The testnet introduced:

• Hybrid parallel execution based on DAG and Block-STM V1
• Official PoS tokenomics with a 1 billion token supply
• Modular architecture decoupling consensus, execution, and storage layers
• Integration with major wallets including OKX Wallet and Bitget Wallet

Mainnet is scheduled for Q1 2026, coinciding with the Token Generation Event. The foundation charter will be released after TGE, establishing the governance framework for what aims to be a truly decentralized network despite its institutional focus.

The project has attracted over 1.4 million testnet users—a significant community for a pre-mainnet network, suggesting strong interest in the RWA-focused narrative.

The Competitive Landscape: Where Does Pharos Fit?​

The RWA tokenization space is getting crowded. Provenance leads with over $12 billion in assets. Ethereum hosts major issuers like BlackRock and Ondo. Canton Network—backed by Goldman Sachs, BNP Paribas, and DTCC—processes over $4 trillion in tokenized transactions monthly.

Pharos's positioning is distinct:

• Versus Canton: Canton is permissioned; Pharos aims for trustless decentralization with compliance primitives
• Versus Ethereum: Pharos offers 50x the throughput with native RWA infrastructure
• Versus Solana: Pharos prioritizes institutional compliance over raw DeFi throughput
• Versus Plume Network: Both target RWA, but Pharos brings Ant Group's enterprise DNA and existing asset pipeline

The Ant Group pedigree matters here. Building financial infrastructure isn't just about technical architecture—it's about understanding regulatory requirements, institutional risk management, and the actual workflows of financial services. The Pharos team has built these systems at scale.

What This Means for the RWA Narrative​

The RWA tokenization thesis is straightforward: most of the world's value exists in illiquid assets that could benefit from blockchain's settlement efficiency, programmability, and global accessibility. Real estate, private credit, commodities, infrastructure—these markets dwarf cryptocurrency's entire market cap.

But the infrastructure gap has been real. Tokenizing a Treasury bill on Ethereum works; tokenizing $300 million in renewable energy assets requires compliance rails, institutional-grade security, and throughput that doesn't collapse under real-world transaction volumes.

Pharos represents a new category of blockchain: not a general-purpose smart contract platform optimizing for DeFi composability, but a specialized financial infrastructure layer designed for the specific requirements of tokenized real-world assets.

Whether they succeed depends on execution—literally. Can they deliver 50,000 TPS at mainnet? Will institutions actually deploy assets on the network? Does the compliance framework satisfy regulators across jurisdictions?

The answers will emerge through 2026. But with $8 million in funding, $1.5 billion in announced asset pipeline, and a team that's already built financial systems at Ant Group scale, Pharos has the resources and credibility to find out.

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In April 2025, Vitalik Buterin proposed something that would have seemed heretical a year earlier: replacing Ethereum's EVM with RISC-V. The suggestion sparked immediate debate. But what most commentators missed was that Polkadot had already been building exactly this architecture for over a year—and was months away from deploying it to production.

Polkadot's JAM (Join-Accumulate Machine) isn't just another blockchain upgrade. It represents a fundamental rethinking of what a "blockchain" even means. Where Ethereum's worldview centers on a global virtual machine processing transactions, JAM eliminates the transaction concept entirely at its core layer, replacing it with a computation model that promises 850 MB/s data availability—42 times Polkadot's previous capacity and 650 times Ethereum's 1.3 MB/s.

The implications extend far beyond performance benchmarks. JAM may be the clearest articulation yet of a post-Ethereum paradigm for blockchain architecture.

The Gray Paper: Gavin Wood's Third Act​

Dr. Gavin Wood wrote the Ethereum Yellow Paper in 2014, providing the formal specification that made Ethereum possible. He followed with the Polkadot White Paper in 2016, introducing heterogeneous sharding and shared security. In April 2024, he released the JAM Gray Paper at Token2049 in Dubai—completing a trilogy that spans the entire history of programmable blockchains.

The Gray Paper describes JAM as "a global singleton permissionless object environment—akin to Ethereum's smart-contract environment—paired with secure sideband computation parallelized over a scalable node network." But this undersells the conceptual shift.

JAM doesn't just improve on existing blockchain designs. It asks: what if we stopped thinking about blockchains as virtual machines entirely?

The Transaction Problem​

Traditional blockchains—Ethereum included—are fundamentally transaction-processing systems. Users submit transactions, validators order and execute them, and the blockchain records state changes. This model has served well but carries inherent limitations:

• Sequential bottlenecks: Transactions must be ordered, creating throughput constraints
• Global state contention: Every transaction potentially touches shared state
• Execution coupling: Consensus and computation are tightly bound

JAM decouples these concerns through what Wood calls the "Refine-Accumulate" paradigm. The system operates in two phases:

Refine: Computation happens in parallel across the network. Work is divided into independent units that can execute simultaneously without coordination.

Accumulate: Results are collected and merged into global state. Only this phase requires consensus on ordering.

The result is a "transactionless" core protocol. JAM itself doesn't process transactions—applications built on JAM do. This separation allows the base layer to focus purely on secure, parallel computation.

PolkaVM: Why RISC-V Matters​

At the heart of JAM sits PolkaVM, a purpose-built virtual machine based on the RISC-V instruction set. This choice has profound implications for blockchain computation.

The EVM's Architectural Debt​

Ethereum's EVM was designed in 2013-2014, before many modern assumptions about blockchain execution were understood. Its architecture reflects that era:

• Stack-based execution: Operations push and pop values from an unbounded stack, requiring complex tracking
• 256-bit word size: Chosen for cryptographic convenience but wasteful for most operations
• Single-dimensional gas: One metric attempts to price vastly different computational resources
• Interpretation-only: EVM bytecode cannot be compiled to native code efficiently

These design decisions made sense as initial choices but create ongoing performance penalties.

RISC-V's Advantages​

PolkaVM takes a fundamentally different approach:

Register-based architecture: Like modern CPUs, PolkaVM uses a finite set of registers for argument passing. This aligns with actual hardware, enabling efficient translation to native instruction sets.

64-bit word size: Modern processors are 64-bit. Using a matching word size eliminates the overhead of emulating 256-bit operations for the vast majority of computations.

Multi-dimensional gas: Different resources (computation, storage, bandwidth) are priced independently, better reflecting true costs and preventing mispricing attacks.

Dual execution modes: Code can be interpreted for immediate execution or JIT-compiled for optimized performance. The system chooses the appropriate mode based on workload characteristics.

Performance Impact​

The architectural differences translate to real performance gains. Benchmarks show PolkaVM achieving 10x+ improvements over WebAssembly for arithmetic-intensive contracts—and the EVM is slower still. For complex, multi-contract interactions, the gap widens further as JIT compilation amortizes setup costs.

Perhaps more importantly, PolkaVM supports any language that compiles to RISC-V. While EVM developers are limited to Solidity, Vyper, and a handful of specialized languages, PolkaVM opens the door to Rust, C++, and eventually any LLVM-supported language. This dramatically expands the potential developer pool.

Maintaining Developer Experience​

Despite the architectural overhaul, PolkaVM maintains compatibility with existing workflows. The Revive compiler provides complete Solidity support, including inline assembler. Developers can continue using Hardhat, Remix, and MetaMask without changing their processes.

The Papermoon team demonstrated this compatibility by successfully migrating Uniswap V2's contract code to the PolkaVM testnet—proving that even complex, battle-tested DeFi code can transition without rewrites.

JAM's Performance Targets​

The numbers Wood projects for JAM are staggering by current blockchain standards.

Data Availability​

JAM targets 850 MB/s of data availability—roughly 42 times the vanilla Polkadot capacity before recent optimizations and 650 times Ethereum's 1.3 MB/s. For context, this approaches the throughput of enterprise database systems.

Computational Throughput​

The Gray Paper estimates JAM can achieve approximately 150 billion gas per second at full capacity. Translating gas to transactions is imprecise, but theoretical maximum throughput reaches 3.4+ million TPS based on the data availability target.

Real-World Validation​

These aren't purely theoretical numbers. Stress tests have validated the architecture:

• Kusama (August 2025): Achieved 143,000 TPS at only 23% load capacity
• Polkadot "Spammening" (2024): Reached 623,000 TPS in controlled testing

These figures represent genuine transaction throughput, not optimistic projections or testnet conditions that don't reflect production environments.

Development Status and Timeline​

JAM development follows a structured milestone system, with 43 implementation teams competing for a prize pool exceeding $60 million (10 million DOT + 100,000 KSM).

Current Progress (Late 2025)​

The ecosystem has reached several critical milestones:

• Multiple teams have achieved 100% conformance with Web3 Foundation test vectors
• Development has progressed through Gray Paper versions 0.6.2 through 0.8.0, approaching v1.0
• The JAM Experience conference in Lisbon (May 2025) brought together implementation teams for deep technical collaboration
• University tours reached over 1,300 attendees across nine global locations, including Cambridge, Peking University, and Fudan University

Milestone Structure​

Teams progress through a series of milestones:

1. IMPORTER (M1): Passing state-transitioning conformance tests and importing blocks
2. AUTHORER (M2): Full conformance including block production, networking, and off-chain components
3. HALF-SPEED (M3): Achieving Kusama-level performance, with access to JAM Toaster for full-scale testing
4. FULL-SPEED (M4): Polkadot mainnet-level performance with professional security audits

Multiple teams have completed M1, with several progressing toward M2.

Timeline to Mainnet​

• Late 2025: Final Gray Paper revisions, continued milestone submissions, expanded testnet participation
• Q1 2026: JAM mainnet upgrade on Polkadot following governance approval via OpenGov referendum
• 2026: CoreChain Phase 1 deployment, official public JAM testnet, full network transition

The governance process has already shown strong community support. A near-unanimous DOT holder vote approved the upgrade direction in May 2024.

JAM vs. Ethereum: Complementary or Competitive?​

The question of whether JAM represents an "Ethereum killer" misses the architectural nuance.

Different Design Philosophies​

Ethereum builds outward from a monolithic foundation. The EVM provides a global execution environment, and scaling solutions—L2s, rollups, sharding—layer on top. This approach has created an enormous ecosystem but also accumulated technical debt.

JAM starts with modularity at its core. The separation of Refine and Accumulate phases, the domain-specific optimization for rollup handling, and the transactionless base layer all reflect a ground-up design for scalability.

Convergent Technical Choices​

Despite different starting points, the projects are converging on similar conclusions. Vitalik's April 2025 RISC-V proposal acknowledged that the EVM's architecture limits long-term performance. Polkadot had already deployed RISC-V support to testnet months earlier.

This convergence validates both projects' technical judgment while highlighting the execution gap: Polkadot is shipping what Ethereum is proposing.

Ecosystem Realities​

Technical superiority doesn't automatically translate to ecosystem dominance. Ethereum's developer community, application diversity, and liquidity depth represent substantial network effects that can't be replicated overnight.

The more likely outcome isn't replacement but specialization. JAM's architecture is optimized for certain workloads—particularly high-throughput applications and rollup infrastructure—while Ethereum retains advantages in ecosystem maturity and capital formation.

In 2026, they look less like competitors and more like complementary layers of a multi-chain internet.

What JAM Means for Blockchain Architecture​

JAM's significance extends beyond Polkadot. It represents the clearest articulation of a post-EVM paradigm that other projects will study and selectively adopt.

Key Principles​

Computation separation: Decoupling execution from consensus enables parallel processing at the base layer, not as an afterthought.

Domain-specific optimization: Rather than building a general-purpose VM and hoping it scales, JAM is architected specifically for the workloads blockchains actually run.

Hardware alignment: Using RISC-V and 64-bit words aligns virtual machine architecture with physical hardware, eliminating emulation overhead.

Transaction abstraction: Moving transaction handling to the application layer allows the protocol to focus on computation and state management.

Industry Impact​

Whether JAM succeeds or fails commercially, these architectural choices will influence blockchain design for the next decade. The Gray Paper provides a formal specification that other projects can study, critique, and selectively implement.

Ethereum's RISC-V proposal already demonstrates this influence. The question isn't whether these ideas will spread, but how quickly and in what form.

The Road Ahead​

JAM represents Gavin Wood's most ambitious technical vision since Polkadot itself. The stakes match the ambition: success would validate an entirely different approach to blockchain architecture, while failure would leave Polkadot competing with newer L1s without a differentiated technical narrative.

The next 18 months will determine whether JAM's theoretical advantages translate to production reality. With 43 implementation teams, a nine-figure prize pool, and a clear roadmap to mainnet, the project has resources and momentum. What remains to be seen is whether the complexity of the Refine-Accumulate paradigm can deliver on Wood's vision of a "distributed computer that can run almost any kind of task."

For developers and projects evaluating blockchain infrastructure, JAM merits serious attention—not as hype, but as a technically rigorous attempt to solve problems that every major blockchain faces. The blockchain-as-virtual-machine paradigm served the industry well for a decade. JAM bets that the next decade requires something fundamentally different.

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In 2022, Vitalik Buterin posed a simple question that would define the next four years of Ethereum scaling: how much Ethereum compatibility are you willing to sacrifice for faster zero-knowledge proofs? His answer came in the form of a five-type classification system for zkEVMs that has since become the industry standard for evaluating these critical scaling solutions.

Fast forward to 2026, and the answer isn't so simple anymore. Proving times have collapsed from 16 minutes to 16 seconds. Costs have dropped 45x. Multiple teams have demonstrated real-time proof generation faster than Ethereum's 12-second block times. Yet the fundamental trade-off Vitalik identified remains—and understanding it is essential for any developer or project choosing where to build.

The Vitalik Classification: Types 1 Through 4​

Vitalik's framework categorizes zkEVMs along a spectrum from perfect Ethereum equivalence to maximum proving efficiency. Higher type numbers mean faster proofs but less compatibility with existing Ethereum infrastructure.

Type 1: Fully Ethereum-Equivalent​

Type 1 zkEVMs don't change anything about Ethereum. They prove the exact same execution environment that Ethereum L1 uses—same opcodes, same data structures, same everything.

The upside: Perfect compatibility. Ethereum execution clients work as-is. Every tool, every contract, every piece of infrastructure transfers directly. This is ultimately what Ethereum needs to make L1 itself more scalable.

The downside: Ethereum wasn't designed for zero-knowledge proofs. The EVM's stack-based architecture is notoriously inefficient for ZK proof generation. Early Type 1 implementations required hours to generate a single proof.

Leading project: Taiko aims for Type 1 equivalence as a based rollup using Ethereum's validators for sequencing, enabling synchronous composability with other based rollups.

Type 2: Fully EVM-Equivalent​

Type 2 zkEVMs maintain full EVM compatibility but change internal representations—how state is stored, how data structures are organized—to improve proof generation.

The upside: Contracts written for Ethereum run without modification. The developer experience remains identical. Migration friction approaches zero.

The downside: Block explorers and debugging tools may need modifications. State proofs work differently than on Ethereum L1.

Leading projects: Scroll and Linea target Type 2 compatibility, achieving near-perfect EVM equivalence at the VM level without transpilers or custom compilers.

Type 2.5: EVM-Equivalent with Gas Cost Changes​

Type 2.5 is a pragmatic middle ground. The zkEVM remains EVM-compatible but increases gas costs for operations that are particularly expensive to prove in zero-knowledge.

The trade-off: Since Ethereum has a gas limit per block, increasing gas costs for specific opcodes means fewer of those opcodes can execute per block. Applications work, but certain computational patterns become prohibitively expensive.

Type 3: Almost EVM-Equivalent​

Type 3 zkEVMs sacrifice specific EVM features—often related to precompiles, memory handling, or how contract code is treated—to dramatically improve proof generation.

The upside: Faster proofs, lower costs, better performance.

The downside: Some Ethereum applications won't work without modification. Developers may need to rewrite contracts that rely on unsupported features.

Reality check: No team actually wants to stay at Type 3. It's understood as a transitional stage while teams work on adding the complex precompile support needed to reach Type 2.5 or Type 2. Both Scroll and Polygon zkEVM operated as Type 3 before advancing up the compatibility ladder.

Type 4: High-Level Language Compatible​

Type 4 systems abandon EVM compatibility entirely at the bytecode level. Instead, they compile Solidity or Vyper to a custom VM designed specifically for efficient ZK proofs.

The upside: Fastest proof generation. Lowest costs. Maximum performance.

The downside: Contracts may behave differently. Addresses might not match Ethereum deployments. Debugging tools need complete rewrites. Migration requires careful testing.

Leading projects: zkSync Era and StarkNet represent the Type 4 approach. zkSync transpiles Solidity to custom bytecode optimized for ZK. StarkNet uses Cairo, an entirely new language designed for provability.

Performance Benchmarks: Where We Stand in 2026​

The numbers have transformed dramatically since Vitalik's original post. What was theoretical in 2022 is production reality in 2026.

Proving Times​

Early zkEVMs required approximately 16 minutes to generate proofs. Current implementations complete the same process in roughly 16 seconds—a 60x improvement. Several teams have demonstrated proof generation in under 2 seconds, faster than Ethereum's 12-second block times.

The Ethereum Foundation has set an ambitious target: proving 99% of mainnet blocks in under 10 seconds using less than $100,000 in hardware and 10kW of power consumption. Multiple teams have already demonstrated capability close to this target.

Transaction Costs​

The Dencun upgrade in March 2024 (EIP-4844 introducing "blobs") reduced L2 fees by 75-90%, making all rollups dramatically more cost-effective. Current benchmarks show:

Platform	Transaction Cost	Notes
Polygon zkEVM	$0.00275	Per transaction for full batches
zkSync Era	$0.00378	Median transaction cost
Linea	$0.05-0.15	Average transaction

Throughput​

Real-world performance varies significantly based on transaction complexity:

Platform	TPS (Complex DeFi)	Notes
Polygon zkEVM	5.4 tx/s	AMM swap benchmark
zkSync Era	71 TPS	Complex DeFi swaps
Theoretical (Linea)	100,000 TPS	With advanced sharding

These numbers will continue improving as hardware acceleration, parallelization, and algorithmic optimizations mature.

Market Adoption: TVL and Developer Traction​

The zkEVM landscape has consolidated around several clear leaders, each representing different points on the type spectrum:

Current TVL Rankings (2025)​

• Scroll: $748 million TVL, largest pure zkEVM
• StarkNet: $826 million TVS
• zkSync Era: $569 million TVL, 270+ deployed dApps
• Linea: ~$963 million TVS, 400%+ growth in daily active addresses

The overall Layer 2 ecosystem has reached $70 billion in TVL, with ZK rollups capturing increasing market share as proving costs continue declining.

Developer Adoption Signals​

• Over 65% of new smart contracts in 2025 deployed on Layer 2 networks
• zkSync Era attracted approximately $1.9 billion in tokenized real-world assets, capturing ~25% of on-chain RWA market share
• Layer 2 networks handled an estimated 1.9 million daily transactions in 2025

The Compatibility-Performance Trade-off in Practice​

Understanding the theoretical types is useful, but the practical implications for developers are what matter.

Type 1-2: Zero Migration Friction​

For Scroll and Linea (Type 2), migration means literally zero code changes for most applications. Deploy the same Solidity bytecode, use the same tools (MetaMask, Hardhat, Remix), expect the same behavior.

Best for: Existing Ethereum applications prioritizing seamless migration; projects where proven, audited code must remain unchanged; teams without resources for extensive testing and modification.

Type 3: Careful Testing Required​

For Polygon zkEVM and similar Type 3 implementations, most applications work but edge cases exist. Certain precompiles may behave differently or be unsupported.

Best for: Teams with resources for thorough testnet validation; projects not relying on exotic EVM features; applications prioritizing cost efficiency over perfect compatibility.

Type 4: Different Mental Model​

For zkSync Era and StarkNet, the development experience differs meaningfully from Ethereum:

zkSync Era supports Solidity but transpiles it to custom bytecode. Contracts compile and run, but behavior may differ in subtle ways. Addresses aren't guaranteed to match Ethereum deployments.

StarkNet uses Cairo, requiring developers to learn an entirely new language—though one specifically designed for provable computation.

Best for: Greenfield projects not constrained by existing code; applications prioritizing maximum performance; teams willing to invest in specialized tooling and testing.

Security: The Non-Negotiable Constraint​

The Ethereum Foundation introduced clear cryptographic security requirements for zkEVM developers in 2025:

• 100-bit provable security by May 2026
• 128-bit security by end of 2026

These requirements reflect the reality that faster proofs mean nothing if the underlying cryptography isn't bulletproof. Teams are expected to meet these thresholds regardless of their type classification.

The security focus has slowed some performance improvements—the Ethereum Foundation explicitly chose security over speed through 2026—but ensures the foundation for mainstream adoption remains solid.

Choosing Your zkEVM: A Decision Framework​

Choose Type 1-2 (Taiko, Scroll, Linea) if:​

• You're migrating existing battle-tested contracts
• Audit costs are a concern (no reaudit needed)
• Your team is Ethereum-native without ZK expertise
• Composability with Ethereum L1 matters
• You need synchronous interoperability with other based rollups

Choose Type 3 (Polygon zkEVM) if:​

• You want a balance of compatibility and performance
• You can invest in thorough testnet validation
• Cost efficiency is a priority
• You don't rely on exotic EVM precompiles

Choose Type 4 (zkSync Era, StarkNet) if:​

• You're building from scratch without migration constraints
• Maximum performance justifies tooling investment
• Your use case benefits from ZK-native design patterns
• You have resources for specialized development

What Comes Next​

The type classifications won't remain static. Vitalik noted that zkEVM projects can "easily start at higher-numbered types and jump to lower-numbered types over time." We're seeing this in practice—projects that launched as Type 3 are advancing toward Type 2 as they complete precompile implementations.

More intriguingly, if Ethereum L1 adopts modifications to become more ZK-friendly, Type 2 and Type 3 implementations could become Type 1 without changing their own code.

The endgame appears increasingly clear: proving times will continue compressing, costs will continue declining, and the distinction between types will blur as hardware acceleration and algorithmic improvements close the performance gap. The question isn't which type will win—it's how quickly the entire spectrum converges toward practical equivalence.

For now, the framework remains valuable. Understanding where a zkEVM sits on the compatibility-performance spectrum tells you what to expect during development, deployment, and operation. That knowledge is essential for any team building on Ethereum's ZK-powered future.

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When the Curve DAO rejected a $17 million CRV grant request from its own founder in December 2025, it wasn't just another governance vote. It was a declaration that the era of founder-controlled DAOs is ending—replaced by something neither idealists nor critics fully anticipated: governance capitalism, where concentrated capital, not community sentiment or founding teams, holds decisive power.

The vote split 54.46% against and 45.54% in favor. On-chain data revealed the uncomfortable truth: addresses associated with Convex Finance and Yearn Finance accounted for nearly 90% of the votes cast against the grant. Two protocols, acting in their own economic interests, overruled the founder of a $2.5 billion TVL platform.

The Anatomy of a $17 Million Rejection​

The proposal seemed straightforward. Curve Finance founder Michael Egorov requested 17.4 million CRV tokens—valued at approximately $6.2 million—to fund Swiss Stake AG, a team that has maintained Curve's core codebase since 2020. The roadmap included advancing LlamaLend, expanding support for PT and LP tokens, developing on-chain forex markets, and continuing crvUSD development.

Just sixteen months earlier, in August 2024, a similar request for 21 million CRV tokens ($6.3 million at the time) had passed with nearly 91% support. What changed?

The answer lies in how governance power shifted during that period. Convex Finance now controls approximately 53% of all veCRV—the vote-escrowed tokens that determine governance outcomes. Combined with Yearn Finance and StakeDAO, three liquid locker protocols dominate Curve's decision-making apparatus. Their votes are influenced by self-interest: supporting proposals that might dilute their holdings or redirect emissions away from their preferred pools serves no economic purpose.

The rejection wasn't about whether Swiss Stake deserved funding. It was about who gets to decide—and what incentives drive those decisions.

The Vote-Escrow Paradox​

Curve's governance model relies on vote-escrowed tokens (veCRV), a mechanism designed to solve two fundamental problems: liquidity and engagement. Users lock CRV for up to four years, receiving veCRV proportional to both token amount and lock duration. The theory was elegant: long-term lockups would filter for stakeholders with genuine protocol alignment.

Reality diverged from theory. Liquid lockers like Convex emerged, pooling CRV from thousands of users and permanently locking it to maximize governance influence. Users receive liquid tokens (cvxCRV) representing their stake, gaining exposure to Curve rewards without the four-year commitment. Convex keeps the governance power.

The result is a concentration pattern that research now confirms across the broader DAO ecosystem. Analysis shows that less than 0.1% of governance token holders possess 90% of voting power in major DAOs. Compound's top 10 voters control 57.86% of voting power. Uniswap's top 10 control 44.72%. These aren't anomalies—they're the predictable outcome of tokenomics designed without adequate safeguards against concentration.

The Curve rejection crystallized what academics call "governance capitalism": voting rights bound to long-term lockup filter for large capital holders and long-term speculators. Over time, governance shifts from ordinary users to capital groups whose interests may diverge significantly from the protocol's broader community.

The $40 Billion Accountability Question​

The stakes extend far beyond Curve. Total DAO treasury assets have grown from $8.8 billion in early 2023 to over $40 billion today, with more than 13,000 active DAOs and 5.1 million governance token holders. Optimism Collective commands $5.5 billion, Arbitrum DAO manages $4.4 billion, and Uniswap controls $2.5 billion—figures rivaling many traditional corporations.

Yet accountability mechanisms haven't kept pace with asset growth. The Curve rejection exposed a pattern: tokenholders demanded transparency about how previous allocations were used before approving new funding. Some suggested future grants be distributed in installments to reduce market impact on CRV. These are basic corporate governance practices that DAOs have largely failed to adopt.

The data is sobering. Over 60% of DAO proposals lack consistent audit documentation. Voter participation averages 17%, with participation concentrated among the top 10% of token holders who control 76.2% of voting power. This isn't decentralized governance—it's minority rule with extra steps.

Only 12% of DAOs now employ on-chain identity mechanisms to improve accountability. More than 70% of DAOs with treasuries above $50 million require layered audits, including flash-loan protection and delayed execution tools. The infrastructure exists; adoption lags.

Solutions That Might Actually Work​

The DAO ecosystem isn't blind to these problems. Quadratic voting, which makes additional votes exponentially more expensive, has been adopted by over 100 DAOs including Gitcoin and Optimism-based projects. Adoption rose 30% in 2025, helping balance influence and reduce whale dominance.

Research proposes integrating quadratic voting with vote-escrow mechanisms, demonstrating mitigation of whale problems while maintaining resistance to collusion. Ethereum Layer-2s like Optimism, Arbitrum, and Base have cut DAO gas fees by up to 90%, making participation more accessible for smaller holders.

Legal frameworks are emerging to provide accountability structures. Wyoming's DUNA framework and the Harmony Framework introduced in February 2025 offer pathways for DAOs to establish legal identity while maintaining decentralized operations. States like Vermont, Wyoming, and Tennessee have introduced legislation recognizing DAOs as legal entities.

Milestone-based disbursement models are gaining traction for treasury allocation. Recipients receive funding in stages upon meeting predefined goals, mitigating misallocation risk while ensuring accountability—exactly what Curve's tokenholders demanded but the proposal lacked.

What the Curve Drama Reveals About DAO Maturity​

The rejection of Egorov's proposal wasn't a failure of governance. It was governance working as designed—just not as intended. When protocols like Convex accumulate 53% of voting power by design, their ability to override founder proposals isn't a bug. It's the logical outcome of a system that equates capital commitment with governance authority.

The question facing mature DAOs isn't whether concentrated power exists—it does, and it's measurable. The question is whether current mechanisms adequately align whale incentives with protocol health, or whether they create structural conflicts where large holders benefit from blocking productive development.

Curve remains a prominent DeFi player with over $2.5 billion in total value locked. The protocol won't collapse because one funding proposal failed. But the precedent matters. When liquid lockers control sufficient veCRV to override any founder proposal, the power dynamic has fundamentally shifted. DAOs built on vote-escrow models face a choice: accept governance by capital concentration, or redesign mechanisms to distribute power more broadly.

On May 6th, 2025, Curve lifted its whitelist restriction on veCRV locking, allowing any address to participate. The change democratized access but didn't address the concentration already locked into the system. Existing power imbalances persist even as entry barriers fall.

The Road Ahead​

The $40 billion in DAO treasuries won't manage itself. The 10,000+ active DAOs won't govern themselves. And the 3.3 million voters won't spontaneously develop accountability mechanisms that protect minority stakeholders.

What the Curve rejection demonstrated is that DAOs have entered an era where governance outcomes depend less on community deliberation and more on the strategic positioning of large capital holders. This isn't inherently bad—institutional investors often bring stability and long-term thinking. But it contradicts the founding mythology of decentralized governance as democratized control.

For builders, the lesson is clear: governance design determines governance outcomes. Vote-escrow models concentrate power by design. Liquid lockers accelerate that concentration. Without explicit mechanisms to counteract these dynamics—quadratic voting, delegation caps, milestone-based funding, identity-verified participation—DAOs trend toward oligarchy regardless of their stated values.

The Curve drama wasn't the end of DAO governance evolution. It was a checkpoint revealing where we actually stand: somewhere between the decentralized ideal and the plutocratic reality, searching for mechanisms that might bridge the gap.

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