On-chain Giants Clash: The Merger and Expansion Battle between Jupiter and Hyperliquid

Written by: Saurabh Deshpande, Decentralised.co

Compiled by: AididiaoJP, Foresight News

Original Title: The On-Chain Behemoth War: Who Will Control the Flow of Value and Become the New Oligarchs of Web3?

In November 2023, Blackstone Group acquired a pet care app called Rover. Rover was originally just for finding people to walk dogs or take care of cats. The pet care industry typically consists of tens of thousands of small, mostly localized, and offline service providers. Rover integrated these suppliers into a searchable marketplace, adding review and payment functions, making it the default platform for pet care services. By the time Blackstone privatizes it in 2024, Rover had become a hub for demand in the field. Pet owners think of Rover first, and service providers have no choice but to list on this platform.

ZipRecruiter has done something similar in the recruitment field. It collects job information from employers, job boards, and applicant tracking systems and distributes it across multiple channels. ZipRecruiter posts job listings on social networks like Facebook. For employers, ZipRecruiter becomes a one-stop distribution channel; for job seekers, it is a unified gateway to the market. ZipRecruiter does not own companies or positions, but rather owns the relationship with both parties. Once this relationship is solidified, it can charge for visibility and job matching, which is an introductory course in aggregation economics.

Aswath Damodaran refers to this model as "owning the shelf": consolidating chaotic and fragmented supply, controlling its display, and charging for access. Ben Thompson calls it the "aggregation theory": establishing a direct relationship with end users, allowing suppliers to compete to serve them, and extracting value from each transaction. The core characteristics across different fields are consistent: Google with web pages, Airbnb with listings, Amazon with products.

The Amazon flywheel is a classic interpretation of this concept. During the downturn after the internet bubble burst, Jeff Bezos and his team drew on Jim Collins' "flywheel" concept to sketch a cycle that every MBA can now recite: more choices lead to better customer experiences, attracting more traffic, which in turn attracts more sellers, lowers unit cost structures, thereby offering lower prices, ultimately resulting in more choices. The effect of turning the flywheel once is limited, but after turning it a thousand times, the machine starts to roar. Bezos' motto during this period was: "Your profit is my opportunity." The core of it lies in self-reinforcement: more users, more suppliers, lower costs, ultimately achieving higher profits.

Once this model is successful, it can be considered perfect. The rate of cost increase is far lower than that of revenue, and the product will optimize as the number of users grows. However, this only holds under two conditions: the aggregated content must have value, and the suppliers must find it difficult to exit easily; both are essential; otherwise, the moat will become shallow. Taking eBay as an example, it aggregated millions of unique niche sellers and buyers in the early 21st century. This aggregation was once highly valuable, but when sellers realized they could set up their shops on Shopify or turn to Amazon, they began to leave in droves. The flywheel does not stop overnight, but if the suppliers are no longer controlled, it begins to wobble and ultimately becomes ordinary.

Damodaran explains the power of platforms and aggregators in a concrete way. He mentions "controlling the shelf," which is not literally about supermarket shelves, but rather the space that customers first encounter when demand arises. Controlling this space means deciding what content is displayed, how it is displayed, and the cost of entry. You don't need to own the products themselves; you just need to have a relationship with the buyers, and others must go through you to reach the buyers. In analyzing Instacart, Uber, Airbnb, or Zomato, Damodaran repeatedly emphasizes that the task of aggregators is to consolidate the chaotic and fragmented market into a single display window and make that window the only one worth paying attention to. Once that is achieved, you can charge for the "viewing rights."

Ben Thompson believes that aggregators are businesses that establish direct relationships with end users at internet scale, provide standardized and reliable experiences, and allow suppliers to compete to serve them. At internet scale, you are not the biggest store in town, but a store that covers all towns simultaneously.

The marginal cost of serving the next customer is nearly zero, but the marginal value of having them is huge. Each customer reinforces your brand, data, and network effects. As aggregators control demand, suppliers become interchangeable. This does not mean that there is no difference in quality, but rather that suppliers cannot take customer relationships with them when they leave. Hotels on Expedia, drivers on Uber, sellers on Amazon, they all need each other more than the aggregator.

Damodaran's research reminds us that flywheels do not operate the same way in all markets. For example, Uber aggregates local driver liquidity, but drivers can open three apps at the same time and choose the first order they receive. This creates vulnerabilities in the moat. In contrast, Airbnb hosts offer unique properties with limited alternative channels, making their commission more enduring.

In low-margin areas, shelves may have value, but the commission space is limited, and suppliers can easily push back. This is why Instacart must venture into advertising and white-label logistics for growth.

The economic structure of supply is just as important as the number of users on the platform. If the goods are readily available within the platform, you are merely a convenience store with better visibility; however, if the content is scarce, differentiated, and difficult to substitute, people will continue to visit, even if you charge higher fees. Think of the high-end listings on Airbnb.

Why did the aggregator fail

When conditions are missing, the aggregator is no longer a flywheel, but just a costly carousel.

Quibi is a typical case of failing to control the shelf. The platform has expensive Hollywood content and a beautifully designed app, yet lacks direct channels to users. Potential users have long gathered on YouTube, Instagram, and TikTok. These platforms command attention, while Quibi locks content in a standalone app, away from users, leading it to attract users only through advertising and promotions.

Excellent aggregators start with a user reach model of zero marginal cost, such as built-in distribution, installation volume, or daily habits. Quibi had nothing and ultimately wasted time and money before building these.

Facebook's Instant Articles also faces similar issues. Its concept is to aggregate content from publishers, accelerate loading within Facebook, and monetize traffic. However, publishers can easily distribute their content to their open networks, apps, or other social platforms. Instant Articles has never become the default reading platform, just an option within the information stream.

Both cases violate the same rule: the company fails to establish a user relationship in a way that creates a default behavior, and the supplier will not suffer significant harm after exiting.

The list of excellent aggregators is simple:

• Directly connect and own user relationships;
• The supplier must be either unique or replaceable so as not to be held hostage by a single supplier;
• The marginal cost of increasing supply is close to zero or low enough to optimize the business model with scale.

If these conditions are not met, you are just another easily replaceable intermediary.

How liquidity becomes a moat

In the crypto industry, projects can build moats in different ways. Some establish trust through licenses and regulations (like USDC), some rely on technology (such as Starkware's proof system or Solana's parallel execution), and others depend on community and network effects (like Farcaster's user graph). However, the hardest to shake is liquidity.

"Proper execution" is crucial. However, if the incentives are strong enough, liquidity can shift rapidly. In 2020, Sushiswap siphoned off over $1 billion from Uniswap within days through liquidity mining rewards. The lesson is simple: liquidity only solidifies when leaving is more painful than staying.

Hyperliquid is well-versed in this area. It not only builds the deepest order book for perpetual contract exchanges but also allows other applications and wallets to directly access its liquidity. For example, Phantom can access Hyperliquid's order flow, providing users with narrow spreads without having to build their own market. In this model, aggregators need suppliers even more. When traders and applications default to your routing, you are no longer an ordinary aggregator but an indispensable core channel for them.

In addition to its own platform, Hyperliquid processed over $13 billion in transaction volume through other builders last month. Phantom processed $3 billion in transaction volume through its routing, earning over $1.5 million. This demonstrates Hyperliquid's strong current network effects.

Liquidity allows you to convert assets without affecting prices. In the finance and DeFi sectors, deep liquidity makes trading cheaper, borrowing safer, and derivatives possible. A lack of liquidity can turn even the most perfect protocol into a ghost town. Once successfully established, liquidity often persists. Traders and applications will flow to deep pools, further increasing liquidity, narrowing spreads, and attracting more trades.

This is the reason why protocols like Aave continue to thrive. Aave has large-scale lending pools with multiple assets, making it the preferred choice for borrowers and lenders pursuing scale and security. As of August 6, the total locked value of Aave across chains exceeds $24 billion. In the past 12 months, borrowers have paid $640 million in fees, and the platform's revenue is approximately $110 million.

The aggregator Jupiter, also based on Solana, has evolved from a routing tool to the default entry point for transactions on that network. On Ethereum, Uniswap has concentrated most of the spot liquidity, so aggregators like 1inch can only offer marginal improvements. However, on Solana, liquidity is dispersed across platforms like Orca, Raydium, and Serum. Jupiter integrates them into a single routing layer, always providing the best prices. Its trading volume once accounted for nearly half of the total computational usage of Solana, and any delays or interruptions would immediately affect the execution quality across the entire network.

By viewing liquidity as an aggregated object, Jupiter's product decisions are easier to understand. Acquisitions, mobile applications, and expansion into new trading and lending products are all aimed at capturing more order flow, maintaining liquidity through the Jupiter routing, and consolidating its position.

Jupiter is worth paying attention to because it is a clear example of evolving from a niche tool to a liquidity platform in DeFi. It started with the search for the best spot prices and gradually became the default routing for Solana liquidity, subsequently expanding to attract new liquidity products. Observe how it goes through these stages and reinforces each other, providing a vivid case for aggregating dynamics.

Aggregated Layer

The three questions are a quick checklist for identifying potential aggregators:

• What are the key differentiating factors for existing enterprises? Can they be digitized? In DeFi, the differentiating factor is liquidity. Deep liquidity pools can provide tighter spreads and safer loans. Liquidity has already been digitized, making it easy to read and compare.
• If differentiated factors are digitized, will competition shift to user experience? When liquidity can be accessed arbitrarily, competition revolves around execution quality: faster settlements, better routing, and fewer failed transactions. Products like BasedApp and Lootbase have emerged as a result. The former encapsulates DeFi primitives into a smooth mobile experience, while the latter brings the deep perpetual liquidity of Hyperliquid to mobile.
• If winning the user experience, can a virtuous cycle be built? Traders come for better prices, attracting more liquidity, which in turn provides better prices. When liquidity becomes a habit and is integrated, it becomes sticky.

Become the default entry point for the market. If the supply side cannot bear your absence, you can collect display fees or decide the order flow in DeFi.

Note: The boundaries between different levels are often blurred. The classification is not precise, but rather provides a cognitive model of aggregation levels.

Level One: Price Discovery

This is the most basic job: telling people where the best trades are. Kayak is for flights, Trivago is for hotels. In the crypto space, early DEX aggregators like 1inch or Matcha fall into this category. They check available pools, display the best exchange rates, and provide jump-in entries. Price discovery is useful but fragile, and so is DeFiLlama's exchange feature.

If the underlying market is already concentrated (such as Ethereum spot trading on Uniswap), the improvement of routing has little effect, and users can go directly to the trading venue; the assistance you provide is not essential.

Level 2: Execute

At this point, you no longer direct users elsewhere, but operate on their behalf. Amazon's "one-click purchase" belongs to this level. In DeFi, Aave's lending functionality is at this level. Liquidity is already present in its contract when borrowing. The execution increases stickiness because the results are directly related to you: fast settlement and a good experience of no failed transactions.

Level 3: Distribution Control

You become the entry point. Just as Google Search is to web pages, the app store is to mobile applications, both fall into this category. In the cryptocurrency field, the built-in exchange label in wallets can serve as the starting and ending point for ordinary users.

On Solana, Jupiter has reached this level. It started as a price discovery tool, transitioned into an execution layer through smart order routing, and then embedded in front ends like Phantom and Drift. A large number of Solana trades are actually Jupiter trades, even if users have never entered "jup.ag". This is distribution control, where suppliers cannot bypass you to reach users.

climbing the ranks in DeFi

The challenge of DeFi lies in the potential for liquidity to shift rapidly. Incentives can drain liquidity pools overnight. Therefore, moving from the first layer to the third layer is not only about becoming a top aggregator, but also about creating sufficient reasons for liquidity and order flow to continuously pass through your routing.

On Ethereum, 1inch primarily stays at the second layer, as Uniswap has completed the aggregation work through concentrated liquidity. Routing for edge cases still has value, but improvements are limited, and many traders choose to skip it. Additionally, aggregators like CowSwap and KyberSwap also occupy a considerable share. Aave belongs to the second layer, as it controls execution in niche areas, but it is infrastructure, not a starting point.

Jupiter's advantage on Solana lies in its ability to climb three tiers in succession. The liquidity is decentralized, with significant value at the first tier; the routing engine is superior to manual exchanges, naturally transitioning to the second tier; by directly integrating wallets and dApps, it reaches the third tier, fully controlling the distribution of liquidity on Solana. At one point, nearly half of Solana's computing power was utilized by Jupiter transactions, as both the demand side from traders and the supply side from liquidity pools rely on Jupiter.

After reaching the third level, the question becomes "What else can be run through this distribution?" Amazon started with books and ended up with everything; Google began with search and ultimately gained control of maps, email, and cloud computing. For Jupiter, distribution is the order flow. The obvious next step is to add products like perpetual contracts, lending, and portfolio tracking, leveraging the same liquidity relationships.

The bigger move is Jupnet. Solana has yet to match the throughput and execution characteristics of venues like Hyperliquid, which are designed for financial-grade latency and determinism. These characteristics are crucial for scaling the full financial stack to real-world levels. A simpler option would be to launch products on chains that already have these features, but Jupiter has chosen the more challenging path of building Jupnet as an application-controlled low-latency execution layer running in parallel with Solana.

Jupnet aims to become a shared infrastructure within the Solana ecosystem, supporting latency-sensitive transactions such as perpetual contracts, quote request systems, and batch auctions, with the final native settlement on Solana. If successful, it will provide the speed and certainty expected from vertically integrated venues while maintaining user and asset retention. This is an attempt to bridge the gap between general blockchain throughput and the global financial micro-latency demand, without the need to split liquidity across chains.

However, it is important to note that despite Jupiter's dominance within Solana, the industry still faces fierce competition. In the cross-chain space, 1inch, CoWSwap, and OKX Swap maintain significant positions. By 2025, Jupiter is expected to average about 55% of the top five DEX aggregators, but this percentage fluctuates with on-chain activity and integrations. The chart below shows the level of decentralization of aggregation layers outside of Solana.

Clearly, Jupiter has become an aggregator in the Solana ecosystem. The flywheel has been set in motion: more traders bring more liquidity, more liquidity optimizes execution, and better execution attracts more traders. At this point, you are not only a liquidity aggregator but also a shelf, a habit, and an entry point to the market. So, how to continue growing when liquidity is no longer sufficient? Jupiter's answer is to acquire projects that have taken control of new user flows.

Mergers and Acquisitions as a Growth Engine

Previously, I wrote about two major themes of enterprise scaling: the essence of compound innovation and how companies can accelerate this process through mergers and acquisitions. The former concerns building new products, features, or capabilities based on existing advantages, while the latter is about identifying when "buying" is a faster way to establish an advantage than "building."

The evolution of Jupiter combines both aspects. Its acquisition strategy is rooted in seeking founder teams with real appeal and integrating them into a distribution network that amplifies influence. The company looks for expert teams in vertical fields to expand coverage without dragging down the core roadmap.

This is not just about purchasing functional additions, but rather acquiring teams that have dominated the target market segments of Jupiter. Once these teams integrate with Jupiter's distribution wallet interface, API, and routing, their product growth accelerates, and the generated traffic feeds back into Jupiter's core.

Moonshot brings a token launchpad that transforms new token creation into direct exchange and trading activities within the Jupiter ecosystem; DRiP adds a community-driven NFT minting and distribution platform, attracting audiences away from trading interfaces and converting them into on-chain behavior; Portfolio acquisition provides position management tools for active traders. Jupiter could have built these features internally at a lower cost, but its goal is to acquire the founders, not just the functionality.

However, the growth of some indicators has yet to be seen. Taking the launchpad sector as an example, market leaders Pumpdotfun and LetsBonk control over 80% of daily token issuance, while Jup Studio and Moonshot combined account for less than 10%. The chart below shows the dominance of incumbents. In this case, the default pattern may have solidified, and Jupiter may need a radically different approach to break through.

Power Multiplier: Founder-led Acquisition

To expand the shelf, it is necessary to introduce operators who have mastered the target market segment. Jupiter's selection criteria are: does the team bring new liquidity or users that enhance the flywheel? This logic echoes Amazon's early flywheel: each additional category or supplier expands "selection", optimizes customer experience, drives more traffic, and in turn attracts more suppliers.

For Jupiter, each acquisition is like adding new shelves to a store, expanding choices and deepening the relationship between traders and liquidity providers.

Acquiring creative founders allows Jupiter to penetrate unfamiliar areas (such as the NFT culture of DRiP or mass retail token issuance) without diluting its core competencies. These founders understand the niche markets, have communities that trust them, and can act quickly. Accessing Jupiter's distribution channels amplifies its reach overnight, while Jupiter gains new user flows and liquidity.

Acquisition cases reflect this point: Moonshot is a minting and trading platform oriented towards mainstream behavior, and the tokens it issues can be seamlessly transferred into the exchange, money market, and perpetual contracts within the Jupiter ecosystem; DRiP is a creator-first distribution channel for collectibles, attracting communities that would otherwise not engage with trading interfaces.

Moonshot attracted over 250,000 new users within three days of the launch of the TRUMP token, processing over $1.5 billion in transaction volume; DRiP attracted over 2 million collectors, minting over 200 million collectibles, with over 6 million secondary sales.

Integration follows a clear pattern: the founder retains control over the product direction; the product goes live and immediately connects to the Jupiter interface and backend, benefiting from its user base while Jupiter gains new traffic; each acquisition adds unique liquidity primitives (such as issuance, culture, leverage) rather than duplicating existing features. The core competitiveness remains unchanged, and all paths still return to Jupiter.

In DeFi, code can fork overnight, but market share is hard to replicate. Founder-led mergers and acquisitions allow Jupiter to gain market share without losing its core path, making its flywheel harder to replicate. As application-controlled execution and low-latency infrastructure mature, Jupiter may target teams such as risk engines, matching layers, and specialized venues, integrating them into Jupnet.

aggregator vs supplier

Looking at the big picture, two dominant models are emerging in DeFi: Jupiter and Hyperliquid. Both are powerful, but their strategies are completely different.

Hyperliquid aims to control liquidity rather than directly owning end-user relationships. It offers liquidity as a service. If a better user experience can be built, feel free to use Hyperliquid's order book and execution engine. Builder Codes are based on this concept, allowing others to own the front-end experience while Hyperliquid quietly supports the back-end, which is a vendor-first model.

Jupiter focuses on distribution, hoping to have an interface, shelves, and market entry points, aggregating decentralized liquidity by becoming the default interface and directing it where needed. This means controlling user relationships, not just executing the track. From perpetual contracts to portfolios, Jupiter tries to make all financial interfaces begin and end within its track.

However, perpetual contracts may expose the current limitations of this strategy the most. Jupiter has made progress on Solana, but Hyperliquid still dominates the market with about 75% of the perpetual DEX market share. The chart below shows the leading margin of Hyperliquid in terms of original trading volume:

Both modes bet on the scale, but start from opposite ends. Jupiter believes that liquidity follows the user interface; Hyperliquid believes that liquidity is the interface. Jupiter builds the entrance, while Hyperliquid builds the endpoint.

In practice, we witness a differentiation: if a broad interface and user aggregation are needed, choose Jupiter; if depth, certainty, and composability are needed, choose Hyperliquid. One side transforms liquidity into a reliance network, while the other becomes the underlying structure built by the masses.

The winner is not only the one who arrives first, but also the one who others cannot abandon.

This is exactly what makes DeFi so exciting right now. We are witnessing a philosophical showdown for the first time: one side believes distribution is the moat, while the other firmly believes it is liquidity.

Application is a new platform

When Ethereum Layer 2 first emerged, people hoped it would become a new platform: a neutral ground where applications could be composed, compete, and scale. However, it turned out that L2 has not become the platform as imagined, but remains more at the infrastructure level: providing the technological foundation for speed, security, and scalability, yet not controlling user relationships.

The platform is the interface where the user's journey begins, where demands are aggregated, habits are formed, and distribution survives. Few L2s cross this line; most are pipelines rather than shelves, rarely building meaningful distribution, and even more rarely becoming the default entry point for users.

In contrast, applications like Jupiter and Hyperliquid are gradually revealing platform characteristics. They have user relationships, embedded in daily habits, and strengthen their position through acquisitions or integrations with other applications. In fact, they are starting to resemble Web2.

Google has gone beyond search engines by acquiring YouTube, transforming its search advantage into a video dominance; Facebook has expanded its control over attention by acquiring Instagram and WhatsApp. They target adjacent fields where they are absent but where users have already congregated, and the key is to acquire the core players in these fields. Once acquired, these applications can immediately tap into the existing distribution flywheel of Google and Facebook, resulting in the capture of multi-channel user attention.

Jupiter is running similar strategies. Launchpad, NFT minting tools, portfolio managers, and now Jupnet all serve the same purpose: to expand reach, capture more user behaviors, and route more liquidity to itself. Its strategy is to become the shelf, the default choice, and the starting point for financial interactions.

But aggregation is not a guaranteed strategy for success. History is full of failed platform acquisitions and aggregation attempts, either due to a lack of user relationships or a misunderstanding of the way habits are formed.

Taking Microsoft's acquisition of Nokia as an example. This is a bet on controlling mobile distribution, but users have shifted to the iOS and Android ecosystems. Microsoft has hardware and software, but its mobile devices and operating systems are either too similar to existing products or not compelling enough to drive user switch. It did not control the application layer, did not win developer loyalty, and did not provide reasons for behavioral change. A lack of control over supply or clear differentiation has left the shelves unexamined.

These cases reflect a core truth: the acquisition itself does not create a flywheel. Without a starting point, habit, or interface, no matter how many features are bundled, users will not follow.

This makes DeFi particularly interesting at the current moment. Jupiter acquires front-end, distribution channels, and liquidity primitives, attempting to become the default entry point for the Solana financial stack; Hyperliquid takes the opposite approach: building depth rather than breadth, allowing others to build around its portfolio.

In a sense, we are witnessing a real platform war unfolding between applications, rather than between public chains as many expected. This raises a bigger question: if L2 does not control distribution, where will the value flow when the applications on it take control? What will happen to fat protocols?

We conclude with unresolved issues, as there are still no definitive conclusions. In the future, we will bring sharper insights, new data points, and more stories and analogies to clarify the direction of all this.