Part 1: The Automated Market Maker (AMM) - DeFi's Zero-to-One Innovation

The first generation of blockchains like Ethereum had significant limitations: low throughput (around 15 transactions per second) and high, volatile gas fees. Replicating a traditional high-frequency CLOB, which requires constant order placements, cancellations, and modifications, was computationally and economically infeasible. The AMM was the genius solution to this problem.

Instead of matching individual buyers and sellers, an AMM allows users to trade against a pooled reserve of assets, known as a liquidity pool. The price is determined not by an order book, but by a deterministic algorithm.

The Constant Product Market Maker (CPMM)

The most foundational and popular type of AMM is the Constant Product Market Maker (CPMM), famously implemented by Uniswap. Its mechanics are governed by a simple, yet powerful formula:

x×y=k

Where:

• x is the quantity of Asset A in the liquidity pool.

• y is the quantity of Asset B in the liquidity pool.

• k is the constant product. This value must remain constant (ignoring fees) during a trade.

The price of an asset in a CPMM is simply the ratio of the reserves, Price A​=xy​.

Trade Example

Let’s walk through a concrete example to understand how trades execute in a CPMM and why slippage becomes a critical consideration.

Initial Liquidity Pool State

Consider an ETH/USDC pool with the following reserves:

• x = 1,000 ETH (Asset A)

• y = 4,500,000 USDC (Asset B)

• k = 1,000 × 4,500,000 = 4,500,000,000 (constant product)

The spot price before any trade is simply the ratio of reserves:

• Price = y/x = 4,500,000/1,000 = 4,500 USDC per ETH

Executing a Trade

Scenario: A trader wants to swap USDC for 10 ETH.

When the trader removes 10 ETH from the pool, the constant product formula dictates that:

• New ETH balance: x’ = 1,000 - 10 = 990 ETH

• The USDC balance must adjust so that: 990 × y’ = 4,500,000,000

• Therefore: y’ = 4,500,000,000 ÷ 990 = 4,545,454.55 USDC

The trader must deposit:

• USDC required = 4,545,454.55 - 4,500,000 = 45,454.55 USDC

• Effective price paid = 45,454.55 ÷ 10 = 4,545.45 USDC per ETH

Notice that the trader paid 4,545.45 USDC per ETH, not the initial spot price of 4,500. This difference is referred to as price impact.

Understanding Slippage

Slippage represents the percentage difference between the expected price (spot price) and the actual execution price. In our example:

• Slippage = (4,545.45 - 4,500) ÷ 4,500 × 100% = 1.01%

This 1.01% slippage might seem acceptable, but the constant product formula creates exponentially worse pricing as trade size increases:

For a 50 ETH trade:

• New ETH balance: 950

• New USDC balance: 4,500,000,000 ÷ 950 = 4,736,842.11

• USDC required: 236,842.11

• Price per ETH: 4,736.84

• Slippage: 5.26%

For a 100 ETH trade:

• New ETH balance: 900

• New USDC balance: 4,500,000,000 ÷ 900 = 5,000,000

• USDC required: 500,000

• Price per ETH: 5,000

• Slippage: 11.11%

The Price Impact Curve

The relationship between trade size and price impact follows a hyperbolic curve. As you approach trading a larger percentage of the pool’s liquidity:

• 1% of pool liquidity → ~1% slippage

• 5% of pool liquidity → ~5.3% slippage

• 10% of pool liquidity → ~11.1% slippage

Key AMM Concepts & Challenges

• Liquidity Providers (LPs): Anyone can provide assets to the pool (e.g., depositing 1 ETH and 2,000 USDC) to become a Liquidity Provider. In return, they earn a portion of the trading fees generated by the pool.

• Impermanent Loss (IL): The most misunderstood risk for LPs is that AMM pools are isolated markets. Prices inside them aren’t set by external feeds but by the constant product formula (x·y=k). Whenever the market price of an asset moves - say ETH doubles on Coinbase, arbitrageurs step in to trade against the pool until its price matches the global market. This rebalancing process extracts value from LPs: they end up holding more of the depreciating asset (USDC) and less of the appreciating one (ETH). The loss is called “impermanent” because it vanishes if prices revert to the original ratio, but in volatile markets, it often crystallizes into a very real opportunity cost compared to simply holding.

• Capital Inefficiency: In the standard CPMM model, liquidity is distributed across the entire price curve from zero to infinity. This means that a vast majority of the capital in a pool is sitting unused at any given time, as trading only occurs at the current market price. For a stablecoin pair like USDC/DAI, which trades in a tight range around $1.00, providing liquidity for a price of $0.10 or $10.00 is extremely inefficient.

The Evolution: Concentrated Liquidity (Uniswap v3)

To solve the capital inefficiency problem, Uniswap v3 introduced concentrated liquidity. Instead of providing liquidity across the entire price spectrum, LPs can choose to provide liquidity within specific price ranges.

For example, an LP could provide liquidity for the ETH/USDC pair only within the $4,400 - $4,800 range. This concentrates their capital where most trading actually occurs, allowing them to earn significantly more fees with the same amount of capital. Functionally, this creates a series of deeper liquidity positions that begin to resemble the "limit orders" of an order book, marking the first major conceptual bridge between AMMs and CLOBs.

Remaining Challenges with Concentrated Liquidity

1. Impermanent Loss Amplification: Concentrated positions experience magnified IL when prices move outside their range. LPs face a cruel dilemma: tighter ranges earn more fees but suffer greater losses when prices drift. A position concentrated in a 1% range can lose 100% of one asset if price moves just 1% in either direction.

2. Active Management Burden: Unlike V2’s “set and forget” approach, V3 requires constant monitoring and rebalancing. When ETH moves from $4,500 to $4,600, positions centered at $4,500 become inactive, earning zero fees until manually adjusted. This creates operational overhead that rivals traditional market making.

3. Gas Cost Complexity: Managing concentrated positions requires frequent transactions for rebalancing, position adjustments, and fee collection. During high volatility, gas costs can exceed fee earnings, especially for smaller positions. This creates a barrier to entry for retail LPs.

4. MEV Vulnerability Persists: Just-In-Time (JIT) liquidity attacks became more sophisticated. MEV bots can sandwich concentrated positions with surgical precision, extracting value moments before large trades and removing liquidity immediately after, leaving regular LPs with the losses.

5. Price Discovery Still Broken: The x*y=k formula, even when concentrated, doesn’t reflect true market dynamics. There’s no concept of market sentiment, order flow, or price-time priority. Every trade moves the price, regardless of size or intent, creating artificial volatility.

6. Liquidity Fragmentation: LPs choosing different ranges creates a fragmented liquidity landscape. A trader might face good liquidity at $4,500 but terrible slippage at $4,550, leading to unpredictable execution quality across price levels.

7. No Native Limit Orders: While concentrated positions resemble limit orders, they’re not true limit orders. They continue providing liquidity in both directions, can be partially filled multiple times, and don’t guarantee execution at a specific

The Spot Success and Perpetuals Problem

So while AMMs revolutionized spot trading (Uniswap alone has facilitated over $2 trillion in cumulative volume), their success couldn't translate to perpetual futures markets. This divergence reveals a fundamental truth about market structure: different instruments demand different infrastructure.

What does that mean? Spot markets are forgiving. A trader swapping ETH for USDC accepts slippage as a cost of immediate execution. The transaction is atomic, settled instantly, with no ongoing obligations. AMMs excel here because their simplicity aligns with spot trading's straightforward nature.

Perpetual futures on the other hand are unforgiving. They require precise entry and exit prices, continuous funding rate calculations, real-time liquidation engines, and leverage management. @GMX_IO and other AMM-based perpetual platforms struggled with these demands. Their oracle-dependent pricing created toxic flow opportunities, traders could exploit price discrepancies between the oracle feed and actual market conditions. The lack of genuine price discovery meant positions were often mispriced, leading to asymmetric risk for liquidity providers. AMM-based perpetual platforms implemented stop losses and limit orders through auxiliary keeper systems, but these lacked the granularity, reliability, and price discovery benefits of true order book markets The result was predictable: professional traders stayed on centralized exchanges. While Uniswap captured significant spot market share from Coinbase, GMX and its peers barely dented Binance's perpetual dominance. The total addressable market for perpetual futures, which generates 3-5x the volume of spot markets, remained firmly in CeFi's hands.

This wasn't a failure of execution but a mismatch of architecture. Perpetual futures evolved from traditional futures markets, which have always relied on order books for price discovery and risk management. Attempting to force them into an AMM model was like asking a Formula 1 car to run on square wheels, technically possible, but fundamentally inefficient.

The market was primed for a solution, which Hyperliquid and the new generation of onchain CLOBs are now providing, having recognized a simple truth: to capture institutional perpetual flow, you need institutional-grade infrastructure. Not an approximation, not a workaround, but the real thing, an order book, on-chain, with the performance to match centralized venues. More on this in the next section.

Part 2: The Central Limit Order Book (CLOB) - Precision and Efficiency

The CLOB is the bedrock of traditional finance, powering everything from the New York Stock Exchange to Coinbase. It's a transparent and efficient system for matching buyers and sellers.

Core Mechanics

A CLOB is essentially two lists of orders for a specific asset pair:

• The Bid Side: A list of buy orders (bids), sorted from the highest price to the lowest.

• The Ask Side: A list of sell orders (asks), sorted from the lowest price to the highest.

The difference between the highest bid (the most someone is willing to pay) and the lowest ask (the least someone is willing to accept) is called the bid-ask spread. Beyond the spread, the depth at each price level also affects execution quality. For example, a CLOB with 100 ETH available at $4,500 provides better execution for large trades than one with only 10 ETH at that level, since deeper liquidity reduces slippage.

Order Types and Matching Engine

Users can interact with a CLOB using many order types:

1. Limit Order: An order to buy or sell at a specific price or better. A buy limit order for ETH at $4,495 will only execute if the ask price is $4,495 or lower. If it can't be filled immediately, it rests on the order book, adding to the market's depth. This is how market makers provide liquidity.

2. Market Order: An order to buy or sell immediately at the best available market price. A market buy order will "walk the book," consuming the lowest asks one by one until the entire order is filled. This provides certainty of execution but not of price.

3. Stop Order: Becomes active only when a specified trigger price is reached. For example, a stop-loss sell order at $4,400 will execute once ETH drops to that level, helping traders manage downside risk.

The matching engine is the core algorithm that enforces these rules, typically following a price-time priority. Orders at a better price level are matched first. If multiple orders exist at the same price, the one that was placed first gets matched first. This FIFO (First In, First Out) approach at each price level ensures fairness and prevents queue-jumping, unlike AMMs where larger trades can extract more value.

The Engine of the Order Book: Professional Market Makers

An order book is just a list of intentions until liquidity is present. Unlike AMMs, where liquidity is passively provided by a diverse pool of LPs, a CLOB relies on a specialized class of participants to function effectively: market makers. These are sophisticated entities, often professional trading firms or dedicated liquidity funds, whose primary business is to provide liquidity.

What Do Market Makers Actually Do?

The core function of a market maker (MM) is to stand ready to both buy and sell an asset at any given time. They do this by simultaneously placing a buy order (a bid) and a sell order (an ask) on the order book. This action achieves two critical goals:

1. Ensuring Liquidity: Market makers guarantee that there is always an order available for retail traders (takers) to trade against. A trader wanting to sell an asset can immediately hit the MM's bid, and a trader wanting to buy can immediately hit their ask.

2. Tightening the Spread: The competition between multiple market makers forces the difference between the highest bid and the lowest ask (the bid-ask spread) to be as narrow as possible. A tight spread is a sign of a healthy, liquid market and results in better prices for traders.

A market maker's primary profit comes from capturing the bid-ask spread. For example, if they have a bid for ETH at $1,999.50 and an ask at $2,000.00, they aim to buy from sellers at the lower price and sell to buyers at the higher price, pocketing the $0.50 difference on each round-trip trade. Their total profit is essentially (Spread) x (Trading Volume).

This is not a risk-free activity. Market makers face significant inventory risk.

• If the overall market price of ETH falls suddenly, the MM will be filled on their bids, accumulating an inventory of ETH that is now worth less than what they paid for it. If a market maker accumulates 100 ETH at $4,500 and the price drops to $4,400, they face an immediate $10,000 unrealized loss on inventory (100 ETH X $100 price drop)

• Conversely, if the price of ETH rises sharply, they will be filled on their asks, selling off their inventory for less than the new, higher market price.

To manage this, MMs use complex algorithms to constantly adjust their quotes based on market volatility, trading volume, and their current inventory levels. Professional MMs often hedge inventory risk using perpetual futures or options on centralized exchanges, maintaining delta-neutral positions. It is an intensely active and data-driven process, standing in stark contrast to the "deposit and forget" nature of passive LPing in a standard AMM.

The On-Chain Liquidity Fund Landscape

The transition to on-chain CLOBs has attracted professional liquidity funds and trading firms that honed their skills in traditional finance and centralized crypto markets. Firms like @wintermute_t, @jump_, and @GSR_io (a contributor for an upcoming co-authored research piece) are now major players in DeFi, providing deep liquidity to on-chain order books.

These firms don't manually place trades. They connect to DEX protocols via APIs and run high-frequency, automated strategies. To attract these crucial players, on-chain CLOBs have developed powerful incentive structures:

• Maker Rebates: Many order books offer "maker-taker" fee models. A "taker" (who executes a market order) pays a fee, while a "maker" (whose limit order is filled) receives a small rebate. For high-volume MMs, these rebates can become a significant revenue stream.

• Liquidity Mining Programs: Protocols often reward MMs directly with their native governance tokens. These programs typically require MMs to meet specific Key Performance Indicators (KPIs), such as maintaining a certain order depth, a maximum spread, and uptime of over 90% on designated trading pairs. This is a highly effective strategy for a protocol to bootstrap liquidity for a new market.

Operating on a blockchain presents unique challenges not found in traditional finance:

• Gas Costs: Every single order placement, cancellation, and update is an on-chain transaction that costs gas. This creates a constant operational cost that MMs must factor into their profitability models. The low fees of L2s and high-throughput L1s are essential for making this viable.

• Latency & MEV: Blockchain block times introduce latency. With Ethereum’s ~12-second blocks, an MM’s order can be “in flight” and unmodifiable for up to 12 seconds compared to the microsecond updates possible in TradFi. During this window, the market may move against them. Compounding the risk, orders are visible in the public mempool before confirmation, exposing them to Maximal Extractable Value (MEV) strategies like front-running. To mitigate this, MMs employ techniques such as splitting orders, routing through private mempools, or leveraging off-chain execution.

Why CLOBs Are Back: The Technical Enablers

The initial hurdles for on-chain CLOBs were computational. So what changed?

1. High-Throughput L1s: Chains like @solana, @SeiNetwork, @monad, @Aptos, and @SuiNetwork were built for high throughput and low latency, making on-chain order books feasible. These are general-purpose L1s, designed to host many apps. In contrast, specialized L1s like Hyperliquid’s HyperCore are purpose-built for trading, with a matching engine optimized for speed and performance.

2. Rollups: High-performance rollups like @megaeth_labs, @fuel_network, and @rise_chain aim to deliver real-time, low-latency trading on Ethereum by leveraging parallel transaction processing. Beyond general-purpose rollups, we’re also seeing specialized L2 appchains. For example, @hibachi_xyz on @celestia or Solana network extensions like @bulletxyz_ are purpose-built to host on-chain matching engines.
   Crucially, these designs depend on scalable data availability layers like @eigen_da and Celestia, which enable the throughput needed for orderbook-style trading. At the same time, advances in ZK infrastructure make it possible to run verifiable off-chain CLOBs (Central Limit Order Books), combining performance with Ethereum-grade security.

Projects like Hyperliquid, Bullet, @dYdX are great examples of CLOB based model DEX.

Part 3: Head-to-Head Comparison: AMM vs. CLOB

What This Means for Users

The shift from AMMs to CLOBs isn’t just a technical upgrade it directly reshapes the user experience:

• Retail traders: Get better prices and lower slippage, with trading interfaces that feel familiar to anyone used to centralized exchanges.

• Institutions: Access professional-grade tools, advanced order types, risk management, and deep liquidity now on transparent, decentralized rails.

• DeFi protocols: Unlock more composable liquidity, where capital is allocated efficiently and integrates seamlessly across the ecosystem.

As blockchains approach TradFi-level performance, the gap between centralized and decentralized trading experiences will narrow, making on-chain markets not just an alternative, but a competitive venue for global finance.

Conclusion: The Maturation of DeFi Trading

The AMM was DeFi’s zero-to-one innovation, solving the cold start problem that made on-chain trading possible when blockchains were slow and expensive. It democratized market making and provided a simple, unstoppable way to trade on-chain.

However, as DeFi matures from a niche for early adopters to a parallel financial system seeking to attract institutional capital and professional traders, its infrastructure must also mature. The Central Limit Order Book represents this maturation. It offers unparalleled capital efficiency, precise price control for market makers, and the granular control required for sophisticated trading strategies.

While AMMs will always have a place for long-tail assets and simple swaps, the future of high-volume, professional-grade decentralized trading belongs to the CLOB. The technical hurdles have been overcome, and the demand for a familiar, efficient, and precise trading experience is undeniable. The campaign for the CLOB is not about replacing the AMM, but about building the next layer of sophisticated financial infrastructure on-chain.

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The content provided is for educational and informational purposes only and should not be construed as financial, investment, or trading advice. Digital assets are highly volatile and involve substantial risk. Past performance is not indicative of future results. Always conduct your own research and consult with qualified financial advisors before making any investment decisions. A1 Research is not responsible for any losses incurred based on the information provided in this article. This campaign contains sponsored content. A1 Research and its affiliates may hold positions in the projects and protocols mentioned in this article.