The Future of Layer 1 is a DAG, Not a Monolithic Blockchain

Ethereum's EVM and Solana's Sealevel execute transactions sequentially within a block, creating a hard ceiling on throughput.

• Sequential Execution forces all validators to simulate the same linear order, wasting compute.
• Congestion Pricing becomes the only scaling lever, making simple swaps subsidize complex DeFi arbitrage.
• Throughput Ceiling is ~5,000 TPS for even the most optimized monolithic L1s, a fraction of global demand.

DAG-based architectures like Aptos Move and Sui Move identify independent transactions and process them simultaneously.

• Software Transactional Memory allows schedulers to execute non-conflicting txns in parallel, then resolve conflicts.
• Object-Centric Model (Sui) treats assets as independent objects, enabling massive parallelism for simple transfers.
• Result: 10-100x higher theoretical throughput for real-world, non-arbitrage workload patterns.

In monolithic chains, every node must store and access the entire global state. Hot spots (e.g., a popular NFT mint) grind the network to a halt for all users.

• Global State means every validator repeats the same expensive storage I/O operations.
• Contention for a single state object (e.g., a liquidity pool) serializes all related transactions, creating artificial bottlenecks.

DAGs like Fuel and Fantom combine parallel execution with a form of state sharding or localized state access.

• UTXO Model (Fuel) implicitly shards state, as transactions only reference specific coins, not a global ledger.
• Actor-Based Parallelism allows validators to process disjoint parts of the state graph simultaneously without coordination.
• Result: Throughput scales near-linearly with the number of physical cores, not single-core clock speed.

Writing safe parallel code is notoriously difficult. Early DAGs sacrificed atomic composability—the ability for transactions to interact seamlessly—for performance.

• Race Conditions are a constant threat when transactions execute in unpredictable orders.
• Fragmented Liquidity occurs if DeFi pools are split across shards or parallel execution contexts that can't communicate instantly.

The Move VM (Aptos, Sui) is built for parallelism from the ground up, using a strict ownership type system to prevent data races at compile time.

• Resource-Oriented Programming ensures assets can only be owned by one module at a time, making safe parallelism explicit.
• Atomic Block Compositions (e.g., Sui's sponsored transactions) allow dependent transactions to be bundled and scheduled as a single parallel unit.
• Result: Developers get parallel speed without the fragility, preserving the atomic composability that defines DeFi.

Implements a blockDAG where parallel blocks coexist, secured by a novel GHOSTDAG consensus. Solves the scalability trilemma by decoupling security from linearity.\n- 10-100 BPS: Achieves ~1 block per second with plans for 100 BPS.\n- Sub-Second Finality: Near-instant confirmation via PHANTOM rule.\n- No Smart Contracts: Pure PoW monetary layer, forcing L2 innovation.

A block-lattice DAG where each account has its own chain. Eliminates fees and miners by using Open Representative Voting (ORV). The ultimate architecture for pure value transfer.\n- Zero Fees: Transaction cost is computational work, not a token tax.\n- ~0.2s Latency: Near-instant settlement for simple transfers.\n- Lightweight Consensus: Delegated voting enables high throughput with minimal energy use.

A hashgraph-based, asynchronous Byzantine Fault Tolerant (aBFT) public ledger. Governed by a council of Google, IBM, Deutsche Telekom. Prioritizes finality and compliance over maximal decentralization.\n- ~10k TPS: Enterprise-grade throughput with finality in 2-5 seconds.\n- Fixed, Predictable Fees: Micropayments in USD, crucial for business logic.\n- Native EVM: Full smart contract compatibility via the Hedera Smart Contract Service.

Monolithic chains like Ethereum, Solana are single-threaded computers. Every transaction must be ordered, creating a fundamental bottleneck. Parallel execution layers (EigenLayer, Neon EVM) are just patches.\n- Block Time vs. Finality: Faster blocks increase orphan rates and reduce security.\n- Mempool MEV: Linear block production creates exploitable arbitrage windows.\n- Hard Scalability Cap: Physical limits of a single validation thread.

DAGs process transactions as a graph, not a chain. This is native sharding at the consensus layer. Validators work on multiple fronts simultaneously, akin to Solana's Sealevel but at L1.\n- Throughput Scales with Nodes: More participants can increase parallel validation paths.\n- No Reorgs: A block confirmed by the DAG's rules is permanently settled.\n- Fairer Ordering: Reduces front-running by decoupling inclusion from strict sequence.

DAG's killer feature—parallelism—is also its core challenge. Managing a globally consistent state across a graph is exponentially harder. This is why Ethereum's roadmap prioritizes Verkle Trees and statelessness.\n- Conflict Resolution: Requires sophisticated rules (e.g., PHANTOM, SPECTRE) for double-spends.\n- Virtual Machine Complexity: EVM wasn't designed for concurrent execution.\n- Tooling Gap: Wallets, explorers, and oracles built for chains, not graphs.

Many DAGs (e.g., Hedera, IOTA 1.0) rely on a centralized 'coordinator' node for liveness and consensus finality. This creates a single point of failure and censorship, directly contradicting decentralization goals.\n- Security through Obscurity: Attackers target the coordinator, not the distributed network.\n- Permissioned Finality: The network's health depends on a trusted entity's uptime.

DAGs like Narwhal-Bullshark and AptosBFT require strong assumptions about network synchrony and physical time for safety. In real-world conditions with variable latency, this can lead to forks or stalled consensus.\n- Liveness Attacks: A well-timed network partition can halt the chain.\n- Complexity Penalty: Recovery mechanisms add overhead, negating theoretical throughput gains.

Parallel execution and DAG-based ordering do not solve MEV; they often exacerbate it. Validators can reorder a massive number of concurrent transactions, enabling more sophisticated sandwich and arbitrage attacks.\n- Amplified Extractable Value: ~$100M+ annual MEV could migrate to the fastest chains.\n- Opaque Ordering: Users cannot reason about transaction position in a DAG, unlike a clear block.

High throughput DAGs generate enormous data. Ensuring all nodes can download and verify this data in real-time is the primary bottleneck. Solutions like EigenDA or Celestia become critical external dependencies, creating a modular risk.\n- Node Churn: >10 TB/day data can push out home validators.\n- Modular Fragility: Liveness depends on a separate DA layer's security and latency.

Massively parallel execution breaks the sequential guarantee of Ethereum's EVM. Contracts with complex, cross-shard or cross-tx dependencies become non-deterministic or require complex concurrency control, stifling DeFi innovation.\n- Developer Friction: Requires a new programming model (e.g., Move, Sui's Objects).\n- Fragmented Liquidity: Atomic composability across the entire state is impossible.

In leaderless DAGs, validators vote on multiple concurrent blocks. There's no cost to voting for conflicting histories, recreating the 'Nothing-at-Stake' problem from early PoS. Mitigations add complexity and latency.\n- Sybil Vulnerable: Cheap to create many identities to influence consensus.\n- Stake Slashing Complexity: Determining malice in a DAG is computationally intensive.

Linear block production creates inherent latency and throughput caps. Every transaction waits for the previous block, creating a ~12-15 second finality floor for networks like Ethereum and Solana. This serialization is the root cause of congestion and fee spikes during demand surges.

• Bottleneck: Sequential block validation
• Result: Congestion & unpredictable fees
• Analogy: Single-lane highway vs. multi-lane mesh

Directed Acyclic Graphs (DAGs) process transactions concurrently by analyzing dependencies. Independent transactions are validated simultaneously, not sequentially. This is the core innovation behind Aptos Move and Sui Move, enabling sub-second finality and linear scaling with cores.

• Mechanism: Dependency-based concurrency
• Outcome: ~100k-200k TPS theoretical ceiling
• Key Benefit: Latency drops to ~300-500ms

DAGs shift complexity from consensus to state synchronization. Maintaining a globally consistent state across parallel execution paths is non-trivial. This requires sophisticated causal ordering and conflict resolution, moving the bottleneck from the chain to the virtual machine (e.g., Move VM).

• New Challenge: Distributed state consensus
• Requirement: Advanced VM design (Move, FuelVM)
• Consideration: Developer cognitive load increases

The DAG model doesn't eliminate the modular vs. monolithic debate. Monolithic DAGs (Sui, Aptos) integrate execution and consensus. Modular stacks could pair a DAG execution layer (like Fuel) with a separate data availability and consensus layer (like Celestia or EigenDA).

• Monolithic DAG: Tight integration, potential vendor lock-in
• Modular DAG: Flexibility, but higher integration overhead
• Strategic Choice: Control vs. composability

DAGs solve the execution scalability trilemma for high-frequency applications (DeFi, gaming, CEX-matching engines). They are not necessary for all use cases but will become the standard for performance-critical layers. Expect Ethereum L2s and new AppChains to adopt DAG-based VMs as the next performance leap.

• Ideal For: High-throughput, low-latency dApps
• Adoption Path: L2s & AppChains first
• Timeline: 2-3 year mainstream rollout

If your protocol requires sub-second finality or handles >1k TPS, a DAG-based chain is your only viable on-chain option. Start evaluating Sui Move, Aptos Move, or a FuelVM-based rollup. For less demanding apps, a modular rollup on Ethereum may suffice, but design for a future DAG execution plug-in.

• Immediate Step: Benchmark against ~500ms finality
• Evaluation List: Sui, Aptos, Fuel, Eclipse
• Architecture: Plan for execution layer abstraction