Solana and Hyperliquid dominate 2025 chain revenue!

ambcryptoPublished on 2025-12-26Last updated on 2025-12-26

Abstract

Solana and Hyperliquid are the top blockchain revenue generators in 2025, with Solana leading at $1.3 billion and Hyperliquid following with $816 million—both surpassing Ethereum. Solana maintains high transaction volumes and revenue despite stable TVL ($7B–$12B), driven by usage in DeFi, memecoin trading, and DePIN. Hyperliquid, a specialized derivatives platform, saw TVL grow from $2B to a $6B peak before stabilizing around $4.1B, with revenue remaining strong due to sustained trading activity. Both networks demonstrate that execution efficiency and high-throughput usage, rather than TVL size or social sentiment, are key to value capture in 2025.

Two very different blockchain networks are emerging as the biggest revenue generators of 2025: Solana and Hyperliquid.

According to CryptoRank data, Solana has generated $1.3 billion in revenue this year, placing it firmly at the top of all blockchains. Hyperliquid ranks second with $816 million.

The figures put both networks ahead of far more capital-heavy chains, including Ethereum, which posted roughly $524 million over the same period.

The rankings highlight a broader shift in 2025: on-chain value is increasingly being captured by networks optimised for execution and throughput rather than sheer liquidity depth.

Solana leads revenue with stable capital base

Throughout 2025, Solana’s Total Value Locked has remained broadly range-bound. It fluctuates between roughly $7 billion and $12 billion, according to DeFi data.

Despite the lack of sustained TVL expansion, transaction volumes have remained consistently high, with several mid-year spikes.

That combination suggests Solana is extracting more revenue per unit of capital, rather than relying on liquidity growth to drive fees.

High-frequency usage across decentralized exchanges, consumer applications, memecoin trading, and DePIN-related activity has directly translated into fee generation.

Social sentiment data adds another layer to the picture. Weighted sentiment around SOL has been highly volatile this year, frequently swinging between positive and negative territory and spending long stretches near neutral.

Yet those sentiment shifts have had little visible impact on usage or revenue.

The divergence points to demand that is usage-driven rather than narrative-driven. This reinforces Solana’s position as a high-throughput execution layer rather than a chain dependent on speculative enthusiasm.

Hyperliquid validates specialised execution model

Built as a specialised derivatives trading platform rather than a general-purpose blockchain, Hyperliquid has generated more revenue in 2025 than most major Layer-1 and Layer-2 networks.

TVL data shows Hyperliquid’s locked capital climbing from around $2 billion early in the year to a peak above $6 billion before settling near $4.1 billion.

Even after that pullback, TVL remains roughly double its level at the start of the year, suggesting capital has remained sticky despite changing market conditions.

Revenue, meanwhile, has stayed elevated relative to its capital base. This indicates that Hyperliquid’s fee generation is supported by sustained trading activity rather than one-off volume spikes.

Sentiment trends tell a similar story. While social sentiment around HYPE cooled in the second half of the year, moving closer to neutral or slightly negative levels, there was no corresponding collapse in TVL or revenue.

That resilience suggests traders are continuing to rely on the platform regardless of broader market mood.

A broader shift in on-chain value capture

Taken together, the data show that in 2025, chains that prioritise execution quality and throughput are outperforming those that rely on large but passive liquidity pools.

Solana represents the general-purpose end of that spectrum, offering broad application coverage with high transaction capacity. Hyperliquid sits at the specialised end, focusing almost exclusively on high-intensity derivatives trading.

Despite their differences, both networks are converting activity into revenue more efficiently than many of their peers.


Final Thoughts

  • Solana and Hyperliquid’s revenue dominance in 2025 shows that execution quality and sustained usage are now driving on-chain value more than TVL growth or social sentiment.
  • As capital efficiency becomes a clearer differentiator, networks that consistently convert activity into fees may continue to outperform larger but less productive chains.

Trending Cryptos

Related Questions

QAccording to the article, which two blockchain networks are the biggest revenue generators of 2025 and what are their respective revenues?

AAccording to the article, Solana and Hyperliquid are the biggest revenue generators of 2025. Solana generated $1.3 billion in revenue, while Hyperliquid generated $816 million.

QWhat does the article suggest is the key reason behind Solana's high revenue despite its range-bound Total Value Locked (TVL)?

AThe article suggests that Solana's high revenue is due to it extracting more revenue per unit of capital, driven by high-frequency usage across decentralized exchanges, consumer applications, memecoin trading, and DePIN-related activity, rather than relying on liquidity growth.

QHow does Hyperliquid's revenue model differ from that of a general-purpose blockchain, and what does its sustained revenue indicate?

AHyperliquid is built as a specialized derivatives trading platform, not a general-purpose blockchain. Its sustained revenue indicates that its fee generation is supported by consistent trading activity rather than one-off volume spikes, and capital has remained sticky on the platform.

QWhat broader shift in on-chain value capture does the data from 2025 highlight?

AThe data highlights a shift where chains that prioritize execution quality and throughput, like Solana and Hyperliquid, are outperforming those that rely on large but passive liquidity pools. Value is increasingly captured by networks optimized for execution and sustained usage.

QWhat conclusion does the article draw about the relationship between social sentiment and on-chain activity for both Solana and Hyperliquid?

AThe article concludes that social sentiment shifts had little visible impact on usage or revenue for both networks. This points to demand that is usage-driven rather than narrative-driven, showing resilience regardless of broader market mood.

Related Reads

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit40m ago

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit40m ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit44m ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit44m ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit44m ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit44m ago

Trading

Spot

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of SOL (SOL) are presented below.

活动图片