TPS is just a ticket to entry; the ranking determines success or failure. On-chain transactions enter a new phase of "application awareness."

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The competition between chains has risen to the level of “transaction ordering,” where the order directly affects the bid-ask spread and depth for market makers🧐🧐

The demand for “general-purpose chains” has been disproven. Currently, the competition between chains focuses on two levels:

1. Building “application chains” on top of existing mature businesses, allowing blockchain to supplement existing operations in areas like settlement;

2. Competition at the “transaction ordering” layer.

This article focuses on the second level.

Order sequencing directly influences the behavior of market makers. This is the core issue.

What is transaction ordering?

On-chain, user transactions are not immediately written into blocks; they first enter the “waiting area” (Mempool). Thousands of transactions may occur simultaneously, and it is up to sequencers, validators, or miners to decide:

1. Which transactions are included in the next block?

2. In what order are these transactions arranged?

The process of “deciding the order” is transaction sequencing, which directly impacts on-chain users’ transaction costs, MEV opportunities, success rates, and fairness.

For example, during network congestion, ordering determines whether transactions can be quickly added to the chain or wait indefinitely in the mempool.

For high-frequency traders like market makers, canceling orders successfully is more important than placing new orders. The prioritization of cancel instructions in sequencing directly affects whether market makers dare to provide deep liquidity.

In the last cycle, everyone was pursuing TPS, believing that speed alone could improve on-chain transaction settlement. But it has proven that, besides speed, risk pricing for market makers is equally important.

On centralized exchanges, trade matching strictly follows the “price-time priority” principle. In such a high-determinism environment, market makers can provide deep order book liquidity with very narrow slippage.

On-chain, after transactions enter the Mempool, nodes select transactions based on Gas prices, creating opportunities for front-running existing orders by increasing Gas.

Suppose TRUMP’s price is $4.5. A market maker places a buy order at $4.4 and a sell order at $4.6 to provide depth. Suddenly, the TRUMP exchange price crashes to $4.

At this moment, an on-chain market maker wants to cancel the $4.4 order but gets front-run by high-frequency traders who increase Gas—buying at $4 and selling to the market maker at $4.4.

Therefore, market makers can only widen the spread to reduce risk.

The goal of next-generation sequencing innovation is to transition from “general sequencing” to “application-aware sequencing”(Application-Aware Sequencing).

The sequencing layer can understand transaction intent and order transactions based on predefined fairness rules, rather than solely relying on Gas fees.

1. Establishing sequencing rules at the consensus layer

A typical example is Hyperliquid. It stipulates priority for cancel orders and Post-Only transactions at the consensus layer, breaking the Gas priority rule.

For market makers, being able to “escape” is most important. During sharp price fluctuations, cancel requests are always executed before others’ fill requests.

Market makers fear being front-run. Hyperliquid guarantees that cancel orders always take precedence—when prices fall, market makers cancel their orders, and the system forcibly prioritizes these cancellations, allowing market makers to hedge successfully.

On 10.11, during a sharp crash, Hyperliquid market makers remained online with spreads of 0.01–0.05%. The reason is that market makers knew they could escape.

2. Adding new sequencing methods at the sequencing layer

For example, Solana’s Application Controlled Execution (ACE). Jito Labs developed BAM (Block Assembly Marketplace), which introduces dedicated BAM nodes responsible for collecting, filtering, and ordering transactions.

These nodes run in Trusted Execution Environments (TEE), ensuring transaction privacy and fairness in ordering.

Through ACE, DEXs on Solana (such as Jupiter, Drift, Phoenix) can register custom ordering rules with BAM nodes—for example, prioritizing market makers (similar to Hyperliquid), conditional liquidity, etc.

Additionally, Prop AMMs like HumidiFi, which operate their own market-making, are also innovating at the sequencing layer by connecting directly with major validators via Nozomi, reducing latency and executing trades.

In specific transactions, HumidiFi’s off-chain servers monitor prices across platforms. Oracles communicate with on-chain contracts to inform them of the situation. Nozomi acts as a VIP channel, enabling effective cancelation before order execution.

3. Utilizing MEV facilities and private channels

Chainlink SVR (Smart Value Recapture) focuses on the attribution of value generated by sequencing(MEV).

By tightly binding with oracle data, it redefines the sequencing rights and value distribution of liquidation transactions. After Chainlink nodes generate price updates, they send data through two channels:

1. Public channel: sent to standard on-chain aggregators (as a backup, but with slight delays in SVR mode to leave auction windows).

2. Private channel (Flashbots MEV-Share): sent to auction markets supporting MEV-Share.

This way, the auction profits (the amount bidders are willing to pay) generated by oracle price movements that trigger liquidations are no longer exclusively captured by miners but are mostly captured by the SVR protocol.

Summary

If TPS is the entry ticket, then merely having TPS is far from enough. Custom sequencing logic may not only be an innovation but could become the essential pathway for transactions to be on-chain.

This might also mark the beginning of DEX surpassing CEX.