14 posts tagged with "Finance"

Vlad Tenev has emerged as one of traditional finance's most bullish voices on cryptocurrency, declaring that tokenization is an unstoppable "freight train" that will eventually consume the entire financial system. Throughout 2024-2025, the Robinhood CEO delivered increasingly bold predictions about crypto's inevitable convergence with traditional finance, backed by aggressive product launches including a $200 million acquisition of Bitstamp, tokenized stock trading in Europe, and a proprietary Layer 2 blockchain. His vision centers on blockchain technology offering an "order of magnitude" cost advantage that will eliminate the distinction between crypto and traditional finance within 5-10 years, though he candidly admits the U.S. will lag behind Europe due to "sticking power" of existing infrastructure. This transformation accelerated dramatically after the 2024 election, with Robinhood's crypto business quintupling post-election as regulatory hostility shifted to enthusiasm under the Trump administration.

The freight train thesis: Tokenization will consume everything​

At Singapore's Token2049 conference in October 2025, Tenev delivered his most memorable statement on crypto's future: "Tokenization is like a freight train. It can't be stopped, and eventually it's going to eat the entire financial system." This wasn't hyperbole but a detailed thesis he's been building throughout 2024-2025. He predicts most major markets will establish tokenization frameworks within five years, with full global adoption taking a decade or more. The transformation will expand addressable financial markets from single-digit trillions to tens of trillions of dollars.

His conviction rests on structural advantages of blockchain technology. "The cost of running a crypto business is an order of magnitude lower. There's just an obvious technology advantage," he told Fortune's Brainstorm Tech conference in July 2024. By leveraging open-source blockchain infrastructure, companies can eliminate expensive intermediaries for trade settlement, custody, and clearing. Robinhood is already using stablecoins internally to power weekend settlements, experiencing firsthand the efficiency gains from 24/7 instant settlement versus traditional rails.

The convergence between crypto and traditional finance forms the core of his vision. "I actually think cryptocurrency and traditional finance have been living in two separate worlds for a while, but they're going to fully merge," he stated at Token2049. "Crypto technology has so many advantages over the traditional way we're doing things that in the future there's going to be no distinction." He frames this not as crypto replacing finance, but as blockchain becoming the invisible infrastructure layer—like moving from filing cabinets to mainframes—that makes the financial system dramatically more efficient.

Stablecoins represent the first wave of this transformation. Tenev describes dollar-pegged stablecoins as the most basic form of tokenized assets, with billions already in circulation reinforcing U.S. dollar dominance abroad. "In the same way that stablecoins have become the default way to get digital access to dollars, tokenized stocks will become the default way for people outside the U.S. to get exposure to American equities," he predicted. The pattern will extend to private companies, real estate, and eventually all asset classes.

Building the tokenized future with stock tokens and blockchain infrastructure​

Robinhood backed Tenev's rhetoric with concrete product launches throughout 2024-2025. In June 2025, the company hosted a dramatic event in Cannes, France titled "To Catch a Token," where Tenev presented a metal cylinder containing "keys to the first-ever stock tokens for OpenAI" while standing by a reflecting pool overlooking the Mediterranean. The company launched over 200 tokenized U.S. stocks and ETFs in the European Union, offering 24/5 trading with zero commissions or spreads, initially on the Arbitrum blockchain.

The launch wasn't without controversy. OpenAI immediately distanced itself, posting "We did not partner with Robinhood, were not involved in this, and do not endorse it." Tenev defended the product, acknowledging the tokens aren't "technically" equity but maintain they give retail investors exposure to private assets that would otherwise be inaccessible. He dismissed the controversy as part of broader U.S. regulatory delays, noting "the obstacles are legal rather than technical."

More significantly, Robinhood announced development of a proprietary Layer 2 blockchain optimized for tokenized real-world assets. Built on Arbitrum's technology stack, this blockchain infrastructure aims to support 24/7 trading, seamless bridging between chains, and self-custody capabilities. Tokenized stocks will eventually migrate to this platform. Johann Kerbrat, Robinhood's crypto general manager, explained the strategy: "Crypto was built by engineers for engineers, and has not been accessible to most people. We're onboarding the world to crypto by making it as easy to use as possible."

Tenev's timeline projections reveal measured optimism despite his bold vision. He expects the U.S. to be "among the last economies to actually fully tokenize" due to infrastructure inertia. Drawing an analogy to transportation, he noted: "The biggest challenge in the U.S. is that the financial system basically works. It's why we don't have bullet trains—medium-speed trains get you there well enough." This candid assessment acknowledges that working systems have greater sticking power than in regions where blockchain offers more dramatic improvement over dysfunctional alternatives.

Bitstamp acquisition unlocks institutional crypto and global expansion​

Robinhood completed its $200 million acquisition of Bitstamp in June 2025, marking a strategic inflection point from pure retail crypto trading to institutional capabilities and international scale. Bitstamp brought 50+ active crypto licenses across Europe, the UK, U.S., and Asia, plus 5,000 institutional clients and $8 billion in cryptocurrency assets under custody. This acquisition addresses two priorities Tenev repeatedly emphasized: international expansion and institutional business development.

"There's two interesting things about the Bitstamp acquisition you should know. One is international. The second is institutional," Tenev explained on the Q2 2024 earnings call. The global licenses dramatically accelerate Robinhood's ability to enter new markets without building regulatory infrastructure from scratch. Bitstamp operates in over 50 countries, providing instant global footprint that would take years to replicate organically. "The goal is for Robinhood to be everywhere, anywhere where customers have smartphones, you should be able to open up a Robinhood account," he stated.

The institutional dimension proves equally strategic. Bitstamp's established relationships with institutional clients, lending infrastructure, staking services, and white-label crypto-as-a-service offerings transform Robinhood from retail-only to a full-stack crypto platform. "Institutions also want low-cost market access to crypto," Tenev noted. "We're really excited about bringing the same sort of Robinhood effect that we've brought to retail to the institutional space with crypto."

Integration proceeded rapidly through 2025. By Q2 2025 earnings, Robinhood reported Bitstamp exchange crypto notional trading volumes of $7 billion, complementing the Robinhood app's $28 billion in crypto volumes. The company also announced plans to hold its first crypto-focused customer event in France around midyear, signaling international expansion priorities. Tenev emphasized that unlike the U.S. where they started with stocks then added crypto, international markets might lead with crypto depending on regulatory environments and market demand.

Crypto revenue explodes from $135 million to over $600 million annually​

Financial metrics underscore the dramatic shift in crypto's importance to Robinhood's business model. Annual crypto revenue surged from $135 million in 2023 to $626 million in 2024—a 363% increase. This acceleration continued into 2025, with Q1 alone generating $252 million in crypto revenue, representing over one-third of total transaction-based revenues. Q4 2024 proved particularly explosive, with $358 million in crypto revenue, up over 700% year-over-year, driven by the post-election "Trump pump" and expanding product capabilities.

These numbers reflect both volume growth and strategic pricing changes. Robinhood's crypto take rate expanded from 35 basis points at the start of 2024 to 48 basis points by October 2024, as CFO Jason Warnick explained: "We always want to have great prices for customers, but also balance the return that we generate for shareholders on that activity." Crypto notional trading volumes reached approximately $28 billion monthly by late 2024, with assets under custody totaling $38 billion as of November 2024.

Tenev described the post-election environment on CNBC as producing "basically what people are calling the 'Trump Pump,'" noting "widespread optimism that the Trump administration, which has stated that they wish to embrace cryptocurrencies and make America the center of cryptocurrency innovation worldwide, is going to have a much more forward-looking policy." On the Unchained podcast in December 2024, he revealed Robinhood's crypto business "quintupled post-election."

The Bitstamp acquisition adds significant scale. Beyond the $8 billion in crypto assets and institutional client base, Bitstamp's 85+ tradable crypto assets and staking infrastructure expand Robinhood's product capabilities. Cantor Fitzgerald analysis noted Robinhood's crypto volume spiked 36% in May 2025 while Coinbase's fell, suggesting market share gains. With crypto representing 38% of projected 2025 revenues, the business has evolved from speculative experiment to core revenue driver.

From regulatory "carpet bombing" to playing offense under Trump​

Tenev's commentary on crypto regulation represents one of the starkest before-and-after narratives in his 2024-2025 statements. Speaking at the Bitcoin 2025 conference in Las Vegas, he characterized the previous regulatory environment bluntly: "Under the previous administration, we have been subject to…it was basically a carpet bombing of the entire industry." He expanded on a podcast: "In the previous administration with Gary Gensler at the SEC, we were very much in a defensive posture. There was crypto, which was, as you guys know, basically they were trying to delete crypto from the U.S."

This wasn't abstract criticism. Robinhood Crypto received an SEC Wells Notice in May 2024 signaling potential enforcement action. Tenev responded forcefully: "This is a disappointing development. We firmly believe U.S. consumers should have access to this asset class. They deserve to be on equal footing with people all over the world." The investigation eventually closed in February 2025 with no action, prompting Chief Legal Officer Dan Gallagher to state: "This investigation never should have been opened. Robinhood Crypto always has and will always respect federal securities laws and never allowed transactions in securities."

The Trump administration's arrival transformed the landscape. "Now suddenly, you're allowed to play some offense," Tenev told CBS News at the Bitcoin 2025 conference. "And we have an administration that's open to the technology." His optimism extended to specific personnel, particularly Paul Atkins' nomination to lead the SEC: "This administration has been hostile to crypto. Having people that understand and embrace it is very important for the industry."

Perhaps most significantly, Tenev revealed direct engagement with regulators on tokenization: "We've actually been engaging with the SEC crypto task force as well as the administration. And it's our belief, actually, that we don't even need congressional action to make tokenization real. The SEC can just do it." This represents a dramatic shift from regulation-by-enforcement to collaborative framework development. He told Bloomberg Businessweek: "Their intent appears to be to ensure that the US is the best place to do business and the leader in both of the emergent technology industries coming to the fore: crypto and AI."

Tenev also published a Washington Post op-ed in January 2025 advocating for specific policy reforms, including creating security token registration regimes, updating accredited investor rules from wealth-based to knowledge-based certification, and establishing clear guidelines for exchanges listing security tokens. "The world is tokenizing, and the United States should not get left behind," he wrote, noting the EU, Singapore, Hong Kong, and Abu Dhabi have advanced comprehensive frameworks while the U.S. lags.

Bitcoin, Dogecoin, and stablecoins: Selective crypto asset views​

Tenev's statements reveal differentiated views across crypto assets rather than blanket enthusiasm. On Bitcoin, he acknowledged the asset's evolution: "Bitcoin's gone from largely being ridiculed to being taken very seriously," citing Federal Reserve Chair Powell's comparison of Bitcoin to gold as institutional validation. However, when asked about following MicroStrategy's strategy of holding Bitcoin as a treasury asset, Tenev declined. In an interview with Anthony Pompliano, he explained: "We have to do the work of accounting for it, and it's essentially on the balance sheet anyway. So there's a real reason for it [but] it could complicate things for public market investors"—potentially casting Robinhood as a "quasi Bitcoin-holding play" rather than a trading platform.

Notably, he observed that "Robinhood stock is already highly correlated to Bitcoin" even without holding it—HOOD stock rose 202% in 2024 versus Bitcoin's 110% gain. "So I would say we wouldn't rule it out. We haven't done it thus far but those are the kind of considerations we have." This reveals pragmatic rather than ideological thinking about crypto assets.

Dogecoin holds special significance in Robinhood's history. On the Unchained podcast, Tenev discussed "how Dogecoin became one of Robinhood's biggest assets for user onboarding," acknowledging that millions of users came to the platform through meme coin interest. Johann Kerbrat stated: "We don't see Dogecoin as a negative asset for us." Despite efforts to distance from 2021's meme stock frenzy, Robinhood continues offering Dogecoin, viewing it as a legitimate entry point for crypto-curious retail investors. Tenev even tweeted in 2022 asking whether "Doge can truly be the future currency of the Internet," showing genuine curiosity about the asset's properties as an "inflationary coin."

Stablecoins receive Tenev's most consistent enthusiasm as practical infrastructure. Robinhood invested in the Global Dollar Network's USDG stablecoin, which he described on the Q4 2024 earnings call: "We have USDG that we partner with a few other great companies on...a stablecoin that passes back yield to holders, which we think is the future. I think many of the leading stablecoins don't have a great way to pass yield to holders." More significantly, Robinhood uses stablecoins internally: "We see the power of that ourselves as a company...there's benefits to the technology and the 24-hour instant settlements for us as a business. In particular, we're using stablecoin to power a lot of our weekend settlements now." He predicted this internal adoption will drive broader institutional stablecoin adoption industrywide.

For Ethereum and Solana, Robinhood launched staking services in both Europe (enabled by MiCA regulations) and the U.S. Tenev noted "increasing interest in crypto staking" without it cannibalizing traditional cash-yield products. The company expanded its European crypto offerings to include SOL, MATIC, and ADA after these faced SEC scrutiny in the U.S., illustrating geographic arbitrage in regulatory approaches.

Prediction markets emerge as hybrid disruption opportunity​

Prediction markets represent Tenev's most surprising crypto-adjacent bet, launching event contracts in late 2024 and rapidly scaling to over 4 billion contracts traded by October 2025, with 2 billion contracts in Q3 2025 alone. The 2024 presidential election proved the concept, with Tenev revealing "over 500 million contracts traded in right around a week leading up to the election." But he emphasized this isn't cyclical: "A lot of people had skepticism about whether this would only be an election thing...It's really much bigger than that."

At Token2049, Tenev articulated prediction markets' unique positioning: "Prediction markets has some similarities with traditional sports betting and gambling, there's also similarities with active trading in that there are exchange-traded products. It also has some similarities to traditional media news products because there's a lot of people that use prediction markets not to trade or speculate, but because they want to know." This hybrid nature creates disruption potential across multiple industries. "Robinhood will be front and center in terms of giving access to retail," he declared.

The product expanded beyond politics to sports (college football proving particularly popular), culture, and AI topics. "Prediction markets communicate information more quickly than newspapers or broadcast media," Tenev argued, positioning them as both trading instruments and information discovery mechanisms. On the Q4 2024 earnings call, he promised: "What you should expect from us is a comprehensive events platform that will give access to prediction markets across a wide variety of contracts later this year."

International expansion presents challenges due to varying regulatory classifications—futures contracts in some jurisdictions, gambling in others. Robinhood initiated talks with the UK's Financial Conduct Authority and other regulators about prediction market frameworks. Tenev acknowledged: "As with any new innovative asset class, we're pushing the boundaries here. And there's not regulatory clarity across all of it yet in particular sports which you mentioned. But we believe in it and we're going to be a leader."

AI-powered tokenized one-person companies represent convergence vision​

At the Bitcoin 2025 conference, Tenev unveiled his most futuristic thesis connecting AI, blockchain, and entrepreneurship: "We're going to see more one-person companies. They're going to be tokenized and traded on the blockchain, just like any other asset. So it's going to be possible to invest economically in a person or a project that that person is running." He explicitly cited Satoshi Nakamoto as the prototype: "This is essentially like Bitcoin itself. Satoshi Nakamoto's personal brand is powered by technology."

The logic chains together several trends. "One of the things that AI makes possible is that it produces more and more value with fewer and fewer resources," Tenev explained. If AI dramatically reduces the resources required to build valuable companies, and blockchain provides instant global investment infrastructure through tokenization, entrepreneurs can create and monetize ventures without traditional corporate structures, employees, or venture capital. Personal brands become tradable assets.

This vision connects to Tenev's role as executive chairman of Harmonic, an AI startup focused on reducing hallucinations through Lean code generation. His mathematical background (Stanford BS, UCLA MS in Mathematics) informs optimism about AI solving complex problems. In an interview, he described the aspiration of "solving the Riemann hypothesis on a mobile app"—referencing one of mathematics' greatest unsolved problems.

The tokenized one-person company thesis also addresses wealth concentration concerns. Tenev's Washington Post op-ed criticized current accredited investor laws restricting private market access to high-net-worth individuals, arguing this concentrates wealth among the top 20%. If early-stage ventures can tokenize equity and distribute it globally via blockchain with appropriate regulatory frameworks, wealth creation from high-growth companies becomes more democratically accessible. "It's time to update our conversation about crypto from bitcoin and meme coins to what blockchain is really making possible: A new era of ultra-inclusive and customizable investing fit for this century," he wrote.

Robinhood positions at the intersection of crypto and traditional finance​

Tenev consistently describes Robinhood's unique competitive positioning: "I think Robinhood is uniquely positioned at the intersection of traditional finance and DeFi. We're one of the few players that has scale, both in traditional financial assets and cryptocurrencies." This dual capability creates network effects competitors struggle to replicate. "What customers really love about trading crypto on Robinhood is that they not only have access to crypto, but they can trade equities, options, now futures, soon a comprehensive suite of event contracts all in one place," he told analysts.

The strategy involves building comprehensive infrastructure across the crypto stack. Robinhood now offers: crypto trading with 85+ assets via Bitstamp, staking for ETH and SOL, non-custodial Robinhood Wallet for accessing thousands of additional tokens and DeFi protocols, tokenized stocks and private companies, crypto perpetual futures in Europe with 3x leverage, proprietary Layer 2 blockchain under development, USDG stablecoin investment, and smart exchange routing allowing active traders to route directly to exchange order books.

This vertical integration contrasts with specialized crypto exchanges lacking traditional finance integration or traditional brokerages dabbling in crypto. "Tokenization once permissible in the U.S., I think, is going to be a huge opportunity that Robinhood is going to be front and center in," Tenev stated on the Q4 2024 earnings call. The company launched 10+ product lines each on track for $100 million+ annual revenue, with crypto representing a substantial pillar alongside options, stocks, futures, credit cards, and retirement accounts.

Asset listing strategy reflects balancing innovation with risk management. Robinhood lists fewer cryptocurrencies than competitors—20 in the U.S., 40 in Europe—maintaining what Tenev calls a "conservative approach." After receiving the SEC Wells Notice, he emphasized: "We've operated our crypto business in good faith. We've been very conservative in our approach in terms of coins listed and services offered." However, regulatory clarity is changing this calculus: "In fact, we've added seven new assets since the election. And as we continue to get more and more regulatory clarity, you should expect to see that continue and accelerate."

The competitive landscape includes Coinbase as the dominant U.S. crypto exchange, plus traditional brokerages like Schwab and Fidelity adding crypto. CFO Jason Warnick addressed competition on earnings calls: "While there may be more competition over time, I do expect that there will be greater demand for crypto as well. I think we're beginning to see that crypto is becoming more mainstream." Robinhood's crypto volume spike of 36% in May 2025 while Coinbase's declined suggests the integrated platform approach is winning share.

Timeline and predictions: Five years to frameworks, decades to completion​

Tenev provides specific timeline predictions rare among crypto optimists. At Token2049, he stated: "I think most major markets will have some framework in the next five years," targeting roughly 2030 for regulatory clarity across major financial centers. However, reaching "100% adoption could take more than a decade," acknowledging the difference between frameworks existing and complete migration to tokenized systems.

His predictions break down by geography and asset class. Europe leads on regulatory frameworks through MiCA regulations and will likely see tokenized stock trading go mainstream first. The U.S. will be "among the last economies to actually fully tokenize" due to infrastructure sticking power, but the Trump administration's crypto-friendly posture accelerates timelines versus previous expectations. Asia, particularly Singapore, Hong Kong, and Abu Dhabi, advances rapidly due to both regulatory clarity and less legacy infrastructure to overcome.

Asset class predictions show staggered adoption. Stablecoins already achieved product-market fit as the "most basic form of tokenized assets." Stocks and ETFs enter tokenization phase now in Europe, with U.S. timelines depending on regulatory developments. Private company equity represents near-term opportunity, with Robinhood already offering tokenized OpenAI and SpaceX shares despite controversy. Real estate comes next—Tenev noted tokenizing real estate is "mechanically no different from tokenizing a private company"—assets placed into corporate structures, then tokens issued against them.

His boldest claim suggests crypto entirely absorbs traditional finance architecture: "In the future, everything will be on-chain in some form" and "the distinction between crypto and TradFi will disappear." The transformation occurs not through crypto replacing finance but blockchain becoming the invisible settlement and custody layer. "You don't have to squint too hard to imagine a world where stocks are on blockchains," he told Fortune. Just as users don't think about TCP/IP when browsing the web, future investors won't distinguish between "crypto" and "regular" assets—blockchain infrastructure simply powers all trading, custody, and settlement invisibly.

Conclusion: Technology determinism meets regulatory pragmatism​

Vlad Tenev's cryptocurrency vision reveals a technology determinist who believes blockchain's cost and efficiency advantages make adoption inevitable, combined with a regulatory pragmatist who acknowledges legacy infrastructure creates decade-long timelines. His "freight train" metaphor captures this duality—tokenization moves with unstoppable momentum but at measured speed requiring regulatory tracks to be built ahead of it.

Several insights distinguish his perspective from typical crypto boosterism. First, he candidly admits the U.S. financial system "basically works," acknowledging working systems resist replacement regardless of theoretical advantages. Second, he doesn't evangelize blockchain ideologically but frames it pragmatically as infrastructure evolution comparable to filing cabinets giving way to computers. Third, his revenue metrics and product launches back rhetoric with execution—crypto grew from $135 million to over $600 million annually, with concrete products like tokenized stocks and a proprietary blockchain under development.

The dramatic regulatory shift from "carpet bombing" under the Biden administration to "playing offense" under Trump provides the catalyst Tenev believes enables U.S. competitiveness. His direct SEC engagement on tokenization frameworks and public advocacy through op-eds position Robinhood as a partner in writing rules rather than evading them. Whether his prediction of convergence between crypto and traditional finance within 5-10 years proves accurate depends heavily on regulators following through with clarity.

Most intriguingly, Tenev's vision extends beyond speculation and trading to structural transformation of capital formation itself. His AI-powered tokenized one-person companies and advocacy for reformed accredited investor laws suggest belief that blockchain plus AI democratizes wealth creation and entrepreneurship fundamentally. This connects his mathematical background, immigrant experience, and stated mission of "democratizing finance for all" into a coherent worldview where technology breaks down barriers between ordinary people and wealth-building opportunities.

Whether this vision materializes or falls victim to regulatory capture, entrenched interests, or technical limitations remains uncertain. But Tenev has committed Robinhood's resources and reputation to the bet that tokenization represents not just a product line but the future architecture of the global financial system. The freight train is moving—the question is whether it reaches the destination on his timeline.

The question of whether traditional finance is eating DeFi or DeFi is disrupting TradFi has been definitively answered in 2024-2025: neither is consuming the other. Instead, a sophisticated convergence is underway where TradFi institutions are deploying $21.6 billion per quarter into crypto infrastructure while simultaneously DeFi protocols are building institutional-grade compliance layers to accommodate regulated capital. JPMorgan has processed over $1.5 trillion in blockchain transactions, BlackRock's tokenized fund controls $2.1 billion across six public blockchains, and 86% of surveyed institutional investors now have or plan crypto exposure. Yet paradoxically, most of this capital flows through regulated wrappers rather than directly into DeFi protocols, revealing a hybrid "OneFi" model emerging where public blockchains serve as infrastructure with compliance features layered on top.

The five industry leaders examined—Thomas Uhm of Jito, TN of Pendle, Nick van Eck of Agora, Kaledora Kiernan-Linn of Ostium, and David Lu of Drift—present remarkably aligned perspectives despite operating in different segments. They universally reject the binary framing, instead positioning their protocols as bridges enabling bidirectional capital flow. Their insights reveal a nuanced convergence timeline: stablecoins and tokenized treasuries gaining immediate adoption, perpetual markets bridging before tokenization can achieve liquidity, and full institutional DeFi engagement projected for 2027-2030 once legal enforceability concerns are resolved. The infrastructure exists today, the regulatory frameworks are materializing (MiCA implemented December 2024, GENIUS Act signed July 2025), and the capital is mobilizing at unprecedented scale. The financial system isn't experiencing disruption—it's experiencing integration.

Traditional finance has moved beyond pilots to production-scale blockchain deployment​

The most decisive evidence of convergence comes from what major banks accomplished in 2024-2025, moving from experimental pilots to operational infrastructure processing trillions in transactions. JPMorgan's transformation is emblematic: the bank rebranded its Onyx blockchain platform to Kinexys in November 2024, having already processed over $1.5 trillion in transactions since inception with daily volumes averaging $2 billion. More significantly, in June 2025, JPMorgan launched JPMD, a deposit token on Coinbase's Base blockchain—marking the first time a commercial bank placed deposit-backed products on a public blockchain network. This isn't experimental—it's a strategic pivot to make "commercial banking come on-chain" with 24/7 settlement capabilities that directly compete with stablecoins while offering deposit insurance and interest-bearing capabilities.

BlackRock's BUIDL fund represents the asset management analog to JPMorgan's infrastructure play. Launched in March 2024, the BlackRock USD Institutional Digital Liquidity Fund surpassed $1 billion in assets under management within 40 days and now controls over $2.1 billion deployed across Ethereum, Aptos, Arbitrum, Avalanche, Optimism, and Polygon. CEO Larry Fink's vision that "every stock, every bond will be on one general ledger" is being operationalized through concrete products, with BlackRock planning to tokenize ETFs representing $2 trillion in potential assets. The fund's structure demonstrates sophisticated integration: backed by cash and U.S. Treasury bills, it distributes yield daily via blockchain, enables 24/7 peer-to-peer transfers, and already serves as collateral on crypto exchanges like Crypto.com and Deribit. BNY Mellon, custodian for the BUIDL fund and the world's largest with $55.8 trillion in assets under custody, began piloting tokenized deposits in October 2025 to transform its $2.5 trillion daily payment volume onto blockchain infrastructure.

Franklin Templeton's BENJI fund showcases multi-chain strategy as competitive advantage. The Franklin OnChain U.S. Government Money Fund launched in 2021 as the first U.S.-registered mutual fund on blockchain and has since expanded to eight different networks: Stellar, Polygon, Avalanche, Aptos, Arbitrum, Base, Ethereum, and BNB Chain. With $420-750 million in assets, BENJI enables daily yield accrual via token airdrops, peer-to-peer transfers, and potential DeFi collateral use—essentially transforming a traditional money market fund into a composable DeFi primitive while maintaining SEC registration and compliance.

The custody layer reveals banks' strategic positioning. Goldman Sachs holds $2.05 billion in Bitcoin and Ethereum ETFs as of late 2024, representing a 50% quarterly increase, while simultaneously investing $135 million with Citadel into Digital Asset's Canton Network for institutional blockchain infrastructure. Fidelity, which began mining Bitcoin in 2014 and launched Fidelity Digital Assets in 2018, now provides institutional custody as a limited purpose trust company licensed by New York State. These aren't diversionary experiments—they represent core infrastructure buildout by institutions collectively managing over $10 trillion in assets.

Five DeFi leaders converge on "hybrid rails" as the path forward​

Thomas Uhm's journey from Jane Street Capital to Jito Foundation crystallizes the institutional bridge thesis. After 22 years at Jane Street, including as Head of Institutional Crypto, Uhm observed "how crypto has shifted from the fringes to a core pillar of the global financial system" before joining Jito as Chief Commercial Officer in April 2025. His signature achievement—the VanEck JitoSOL ETF filing in August 2025—represents a landmark moment: the first spot Solana ETF 100% backed by a liquid staking token. Uhm worked directly with ETF issuers, custodians, and the SEC through months of "collaborative policy outreach" beginning in February 2025, culminating in regulatory clarity that liquid staking tokens structured without centralized control are not securities.

Uhm's perspective rejects absorption narratives in favor of convergence through superior infrastructure. He positions Jito's Block Assembly Marketplace (BAM), launched July 2025, as creating "auditable markets with execution assurances that rival traditional finance" through TEE-based transaction sequencing, cryptographic attestations for audit trails, and deterministic execution guarantees institutions demand. His critical insight: "A healthy market has makers economically incentivized by genuine liquidity demand"—noting that crypto market making often relies on unsustainable token unlocks rather than bid-ask spreads, meaning DeFi must adopt TradFi's sustainable economic models. Yet he also identifies areas where crypto improves on traditional finance: expanded trading hours, more efficient intraday collateral movements, and composability that enables novel financial products. His vision is bidirectional learning where TradFi brings regulatory frameworks and risk management sophistication while DeFi contributes efficiency innovations and transparent market structure.

TN, CEO and founder of Pendle Finance, articulates the most comprehensive "hybrid rails" strategy among the five leaders. His "Citadels" initiative launched in 2025 explicitly targets three institutional bridges: PT for TradFi (KYC-compliant products packaging DeFi yields for regulated institutions through isolated SPVs managed by regulated investment managers), PT for Islamic Funds (Shariah-compliant products targeting the $3.9 trillion Islamic finance sector growing at 10% annually), and non-EVM expansion to Solana and TON networks. TN's Pendle 2025: Zenith roadmap positions the protocol as "the doorway to your yield experience" serving everyone "from a degenerate DeFi ape to a Middle Eastern sovereign fund."

His key insight centers on market size asymmetry: "Limiting ourselves only to DeFi-native yields would be missing the bigger picture" given that the interest rate derivatives market is $558 trillion—roughly 30,000 times larger than Pendle's current market. The Boros platform launched in August 2025 operationalizes this vision, designed to support "any form of yield, from DeFi protocols to CeFi products, and even traditional benchmarks like LIBOR or mortgage rates." TN's 10-year vision sees "DeFi becoming a fully integrated part of the global financial system" where "capital will flow freely between DeFi and TradFi, creating a dynamic landscape where innovation and regulation coexist." His partnership with Converge blockchain (launching Q2 2025 with Ethena Labs and Securitize) creates a settlement layer blending permissionless DeFi with KYC-compliant tokenized RWAs including BlackRock's BUIDL fund.

Nick van Eck of Agora provides the crucial stablecoin perspective, tempering crypto industry optimism with realism informed by his traditional finance background (his grandfather founded VanEck, the $130+ billion asset management firm). After 22 years at Jane Street, van Eck projects that institutional stablecoin adoption will take 3-4 years, not 1-2 years, because "we live in our own bubble in crypto" and most CFOs and CEOs of large U.S. corporations "aren't necessarily aware of the developments in crypto, even when it comes to stablecoins." Having conversations with "some of the largest hedge funds in the US," he finds "there's still a lack of understanding when it comes to the role that stablecoins play." The real curve is educational, not technological.

Yet van Eck's long-term conviction is absolute. He recently tweeted about discussions to move "$500M-$1B in monthly cross-border flows to stables," describing stablecoins as positioned to "vampire liquidity from the correspondent banking system" with "100x improvement" in efficiency. His strategic positioning of Agora emphasizes "credible neutrality"—unlike USDC (which shares revenue with Coinbase) or Tether (opaque) or PYUSD (PayPal subsidiary competing with customers), Agora operates as infrastructure sharing reserve yield with partners building on the platform. With institutional partnerships including State Street (custodian with $49 trillion in assets), VanEck (asset manager), PwC (auditor), and banking partners Cross River Bank and Customers Bank, van Eck is constructing TradFi-grade infrastructure for stablecoin issuance while deliberately avoiding yield-bearing structures to maintain broader regulatory compliance and market access.

Perpetual markets may frontrun tokenization in bringing traditional assets on-chain​

Kaledora Kiernan-Linn of Ostium Labs presents perhaps the most contrarian thesis among the five leaders: "perpification" will precede tokenization as the primary mechanism for bringing traditional financial markets on-chain. Her argument is rooted in liquidity economics and operational efficiency. Comparing tokenized solutions to Ostium's synthetic perpetuals, she notes users "pay roughly 97x more to trade tokenized TSLA" on Jupiter than through Ostium's synthetic stock perpetuals—a liquidity differential that renders tokenization commercially unviable for most traders despite being technically functional.

Kiernan-Linn's insight identifies the core challenge with tokenization: it requires coordination of asset origination, custody infrastructure, regulatory approval, composable KYC-enforced token standards, and redemption mechanisms—massive operational overhead before a single trade occurs. Perpetuals, by contrast, "only require sufficient liquidity and robust data feeds—no need for underlying asset to exist on-chain." They avoid security token frameworks, eliminate counterparty custody risk, and provide superior capital efficiency through cross-margining capabilities. Her platform has achieved remarkable validation: Ostium ranks #3 in weekly revenues on Arbitrum behind only Uniswap and GMX, with over $14 billion in volume and nearly $7 million in revenue, having 70x'd revenues in six months from February to July 2025.

The macroeconomic validation is striking. During weeks of macroeconomic instability in 2024, RWA perpetual volumes on Ostium outpaced crypto volumes by 4x, and 8x on days with heightened instability. When China announced QE measures in late September 2024, FX and commodities perpetuals volumes surged 550% in a single week. This demonstrates that when traditional market participants need to hedge or trade macro events, they're choosing DeFi perpetuals over both tokenized alternatives and sometimes even traditional venues—validating the thesis that derivatives can bridge markets faster than spot tokenization.

Her strategic vision targets the 80 million monthly active forex traders in the $50 trillion traditional retail FX/CFD market, positioning perpetuals as "fundamentally better instruments" than the cash-settled synthetic products offered by FX brokers for years, thanks to funding rates that incentivize market balance and self-custodial trading that eliminates adversarial platform-user dynamics. Co-founder Marco Antonio predicts "the retail FX trading market will be disrupted in the next 5 years and it will be done by perps." This represents DeFi not absorbing TradFi infrastructure but instead out-competing it by offering superior products to the same customer base.

David Lu of Drift Protocol articulates the "permissionless institutions" framework that synthesizes elements from the other four leaders' approaches. His core thesis: "RWA as the fuel for a DeFi super-protocol" that unites five financial primitives (borrow/lend, derivatives, prediction markets, AMM, wealth management) into capital-efficient infrastructure. At Token2049 Singapore in October 2024, Lu emphasized that "the key is infrastructure, not speculation" and warned that "Wall Street's move has started. Do not chase hype. Put your assets on-chain."

Drift's May 2025 launch of "Drift Institutional" operationalizes this vision through white-glove service guiding institutions in bringing real-world assets into Solana's DeFi ecosystem. The flagship partnership with Securitize to design institutional pools for Apollo's $1 billion Diversified Credit Fund (ACRED) represents the first institutional DeFi product on Solana, with pilot users including Wormhole Foundation, Solana Foundation, and Drift Foundation testing "onchain structures for their private credit and treasury management strategies." Lu's innovation eliminates the traditional $100 million+ minimums that confined credit facility-based lending to the largest institutions, instead enabling comparable structures on-chain with dramatically lower minimums and 24/7 accessibility.

The Ondo Finance partnership in June 2024 demonstrated Drift's capital efficiency thesis: integrating tokenized treasury bills (USDY, backed by short-term U.S. treasuries generating 5.30% APY) as trading collateral meant users "no longer have to choose between generating yield on stablecoins or using them as collateral for trading"—they can earn yield and trade simultaneously. This composability, impossible in traditional finance where treasuries in custody accounts can't simultaneously serve as perpetuals margin, exemplifies how DeFi infrastructure enables superior capital efficiency even for traditional financial instruments. Lu's vision of "permissionless institutions" suggests the future isn't TradFi adopting DeFi technology or DeFi professionalizing toward TradFi standards, but rather creating entirely new institutional forms that combine decentralization with professional-grade capabilities.

Regulatory clarity is accelerating convergence while revealing implementation gaps​

The regulatory landscape transformed dramatically in 2024-2025, shifting from uncertainty to actionable frameworks in both Europe and the United States. MiCA (Markets in Crypto-Assets) achieved full implementation in the EU on December 30, 2024, with remarkable compliance velocity: 65%+ of EU crypto businesses achieved compliance by Q1 2025, 70%+ of EU crypto transactions now occur on MiCA-compliant exchanges (up from 48% in 2024), and regulators issued €540 million in penalties to non-compliant firms. The regulation drove a 28% increase in stablecoin transactions within the EU and catalyzed EURC's explosive growth from $47 million to $7.5 billion monthly volume—a 15,857% increase—between June 2024 and June 2025.

In the United States, the GENIUS Act signed in July 2025 established the first federal stablecoin legislation, creating state-based licensing with federal oversight for issuers exceeding $10 billion in circulation, mandating 1:1 reserve backing, and requiring supervision by the Federal Reserve, OCC, or NCUA. This legislative breakthrough directly enabled JPMorgan's JPMD deposit token launch and is expected to catalyze similar initiatives from other major banks. Simultaneously, the SEC and CFTC launched joint harmonization efforts through "Project Crypto" and "Crypto Sprint" in July-August 2025, holding a joint roundtable on September 29, 2025, focused on "innovation exemptions" for peer-to-peer DeFi trading and publishing joint staff guidance on spot crypto products.

Thomas Uhm's experience navigating this regulatory evolution is instructive. His move from Jane Street to Jito was directly tied to regulatory developments—Jane Street reduced crypto operations in 2023 due to "regulatory challenges," and Uhm's appointment at Jito came as this landscape cleared. The VanEck JitoSOL ETF achievement required months of "collaborative policy outreach" beginning in February 2025, culminating in SEC guidance in May and August 2025 clarifying that liquid staking tokens structured without centralized control are not securities. Uhm's role explicitly involves "positioning the Jito Foundation for a future shaped by regulatory clarity"—indicating he sees this as the key enabler of convergence, not just an accessory.

Nick van Eck designed Agora's architecture around anticipated regulation, deliberately avoiding yield-bearing stablecoins despite competitive pressure because he expected "the US government and the SEC would not allow interest-bearing stablecoins." This regulatory-first design philosophy positions Agora to serve U.S. entities once legislation is fully enacted while maintaining international focus. His prediction that institutional adoption requires 3-4 years rather than 1-2 years stems from recognizing that regulatory clarity, while necessary, is insufficient—education and internal operational changes at institutions require additional time.

Yet critical gaps persist. DeFi protocols themselves remain largely unaddressed by current frameworks—MiCA explicitly excludes "fully decentralized protocols" from its scope, with EU policymakers planning DeFi-specific regulations for 2026. The FIT21 bill, which would establish clear CFTC jurisdiction over "digital commodities" versus SEC oversight of securities-classified tokens, passed the House 279-136 in May 2024 but remains stalled in the Senate as of March 2025. The EY institutional survey reveals that 52-57% of institutions cite "uncertain regulatory environment" and "unclear legal enforceability of smart contracts" as top barriers—suggesting that while frameworks are materializing, they haven't yet provided sufficient certainty for the largest capital pools (pensions, endowments, sovereign wealth funds) to fully engage.

Institutional capital is mobilizing at unprecedented scale but flowing through regulated wrappers​

The magnitude of institutional capital entering crypto infrastructure in 2024-2025 is staggering. $21.6 billion in institutional investments flowed into crypto in Q1 2025 alone, with venture capital deployment reaching $11.5 billion across 2,153 transactions in 2024 and analysts projecting $18-25 billion total for 2025. BlackRock's IBIT Bitcoin ETF accumulated $400 billion+ in assets under management within approximately 200 days of launch—the fastest ETF growth in history. In May 2025 alone, BlackRock and Fidelity collectively purchased $590 million+ in Bitcoin and Ethereum, with Goldman Sachs revealing $2.05 billion in combined Bitcoin and Ethereum ETF holdings by late 2024, representing a 50% quarter-over-quarter increase.

The EY-Coinbase institutional survey of 352 institutional investors in January 2025 quantifies this momentum: 86% of institutions have exposure to digital assets or plan to invest in 2025, 85% increased allocations in 2024, and 77% plan to increase in 2025. Most significantly, 59% plan to allocate more than 5% of AUM to crypto in 2025, with U.S. respondents particularly aggressive at 64% versus 48% for European and other regions. The allocation preferences reveal sophistication: 73% hold at least one altcoin beyond Bitcoin and Ethereum, 60% prefer registered vehicles (ETPs) over direct holdings, and 68% express interest in both diversified crypto index ETPs and single-asset altcoin ETPs for Solana and XRP.

Yet a critical disconnect emerges when examining DeFi engagement specifically. Only 24% of surveyed institutions currently engage with DeFi protocols, though 75% expect to engage by 2027—suggesting a potential tripling of institutional DeFi participation within two years. Among those engaged or planning engagement, use cases center on derivatives (40%), staking (38%), lending (34%), and access to altcoins (32%). Stablecoin adoption is higher at 84% using or expressing interest, with 45% currently using or holding stablecoins and hedge funds leading at 70% adoption. For tokenized assets, 57% express interest and 72% plan to invest by 2026, focusing on alternative funds (47%), commodities (44%), and equities (42%).

The infrastructure to serve this capital exists and functions well. Fireblocks processed $60 billion in institutional digital asset transactions in 2024, custody providers like BNY Mellon and State Street hold $2.1 billion+ in digital assets with full regulatory compliance, and institutional-grade solutions from Fidelity Digital Assets, Anchorage Digital, BitGo, and Coinbase Custody provide enterprise security and operational controls. Yet the infrastructure's existence hasn't translated to massive capital flows directly into DeFi protocols. The tokenized private credit market reached $17.5 billion (32% growth in 2024), but this capital primarily comes from crypto-native sources rather than traditional institutional allocators. As one analysis noted, "Large institutional capital is NOT flowing to DeFi protocols" despite infrastructure maturity, with the primary barrier being "legal enforceability concerns that prevent pension and endowment participation."

This reveals the paradox of current convergence: banks like JPMorgan and asset managers like BlackRock are building on public blockchains and creating composable financial products, but they're doing so within regulated wrappers (ETFs, tokenized funds, deposit tokens) rather than directly utilizing permissionless DeFi protocols. The capital isn't flowing through Aave, Compound, or Uniswap interfaces in meaningful institutional scale—it's flowing into BlackRock's BUIDL fund, which uses blockchain infrastructure while maintaining traditional legal structures. This suggests convergence is occurring at the infrastructure layer (blockchains, settlement rails, tokenization standards) while the application layer diverges into regulated institutional products versus permissionless DeFi protocols.

The verdict: convergence through layered systems, not absorption​

Synthesizing perspectives across all five industry leaders and market evidence reveals a consistent conclusion: neither TradFi nor DeFi is "eating" the other. Instead, a layered convergence model is emerging where public blockchains serve as neutral settlement infrastructure, compliance and identity systems layer on top, and both regulated institutional products and permissionless DeFi protocols operate within this shared foundation. Thomas Uhm's framework of "crypto as core pillar of the global financial system" rather than peripheral experiment captures this transition, as does TN's vision of "hybrid rails" and Nick van Eck's emphasis on "credible neutrality" in infrastructure design.

The timeline reveals phased convergence with clear sequencing. Stablecoins achieved critical mass first, with $210 billion market capitalization and institutional use cases spanning yield generation (73%), transactional convenience (71%), foreign exchange (69%), and internal cash management (68%). JPMorgan's JPMD deposit token and similar initiatives from other banks represent traditional finance's response—offering stablecoin-like capabilities with deposit insurance and interest-bearing features that may prove more attractive to regulated institutions than uninsured alternatives like USDT or USDC.

Tokenized treasuries and money market funds achieved product-market fit second, with BlackRock's BUIDL reaching $2.1 billion and Franklin Templeton's BENJI exceeding $400 million. These products demonstrate that traditional assets can successfully operate on public blockchains with traditional legal structures intact. The $10-16 trillion tokenized asset market projected by 2030 by Boston Consulting Group suggests this category will dramatically expand, potentially becoming the primary bridge between traditional finance and blockchain infrastructure. Yet as Nick van Eck cautions, institutional adoption requires 3-4 years for education and operational integration, tempering expectations for immediate transformation despite infrastructure readiness.

Perpetual markets are bridging traditional asset trading before spot tokenization achieves scale, as Kaledora Kiernan-Linn's thesis demonstrates. With 97x better pricing than tokenized alternatives and revenue growth that placed Ostium among top-3 Arbitrum protocols, synthetic perpetuals prove that derivatives markets can achieve liquidity and institutional relevance faster than spot tokenization overcomes regulatory and operational hurdles. This suggests that for many asset classes, DeFi-native derivatives may establish price discovery and risk transfer mechanisms while tokenization infrastructure develops, rather than waiting for tokenization to enable these functions.

Direct institutional engagement with DeFi protocols represents the final phase, currently at 24% adoption but projected to reach 75% by 2027. David Lu's "permissionless institutions" framework and Drift's institutional service offering exemplify how DeFi protocols are building white-glove onboarding and compliance features to serve this market. Yet the timeline may extend longer than protocols hope—legal enforceability concerns, operational complexity, and internal expertise gaps mean that even with infrastructure readiness and regulatory clarity, large-scale pension and endowment capital may flow through regulated wrappers for years before directly engaging permissionless protocols.

The competitive dynamics suggest TradFi holds advantages in trust, regulatory compliance, and established customer relationships, while DeFi excels in capital efficiency, composability, transparency, and operational cost structure. JPMorgan's ability to launch JPMD with deposit insurance and integration into traditional banking systems demonstrates TradFi's regulatory moat. Yet Drift's ability to enable users to simultaneously earn yield on treasury bills while using them as trading collateral—impossible in traditional custody arrangements—showcases DeFi's structural advantages. The convergence model emerging suggests specialized functions: settlement and custody gravitating toward regulated entities with insurance and compliance, while trading, lending, and complex financial engineering gravitating toward composable DeFi protocols offering superior capital efficiency and innovation velocity.

Geographic fragmentation will persist, with Europe's MiCA creating different competitive dynamics than U.S. frameworks, and Asian markets potentially leapfrogging Western adoption in certain categories. Nick van Eck's observation that "financial institutions outside of the U.S. will be quicker to move" is validated by Circle's EURC growth, Asia-focused stablecoin adoption, and the Middle Eastern sovereign wealth fund interest that TN highlighted in his Pendle strategy. This suggests convergence will manifest differently across regions, with some jurisdictions seeing deeper institutional DeFi engagement while others maintain stricter separation through regulated products.

What this means for the next five years​

The 2025-2030 period will likely see convergence acceleration across multiple dimensions simultaneously. Stablecoins reaching 10% of world money supply (Circle CEO's prediction for 2034) appears achievable given current growth trajectories, with bank-issued deposit tokens like JPMD competing with and potentially displacing private stablecoins for institutional use cases while private stablecoins maintain dominance in emerging markets and cross-border transactions. The regulatory frameworks now materializing (MiCA, GENIUS Act, anticipated DeFi regulations in 2026) provide sufficient clarity for institutional capital deployment, though operational integration and education require the 3-4 year timeline Nick van Eck projects.

Tokenization will scale dramatically, potentially reaching BCG's $16 trillion projection by 2030 if current growth rates (32% annually for tokenized private credit) extend across asset classes. Yet tokenization serves as infrastructure rather than end-state—the interesting innovation occurs in how tokenized assets enable new financial products and strategies impossible in traditional systems. TN's vision of "every type of yield tradable through Pendle"—from DeFi staking to TradFi mortgage rates to tokenized corporate bonds—exemplifies how convergence enables previously impossible combinations. David Lu's thesis of "RWAs as fuel for DeFi super-protocols" suggests tokenized traditional assets will unlock order-of-magnitude increases in DeFi sophistication and scale.

The competitive landscape will feature both collaboration and displacement. Banks will lose cross-border payment revenue to blockchain rails offering 100x efficiency improvements, as Nick van Eck projects stablecoins will "vampire liquidity from the correspondent banking system." Retail FX brokers face disruption from DeFi perpetuals offering better economics and self-custody, as Kaledora Kiernan-Linn's Ostium demonstrates. Yet banks gain new revenue streams from custody services, tokenization platforms, and deposit tokens that offer superior economics to traditional checking accounts. Asset managers like BlackRock gain efficiency in fund administration, 24/7 liquidity provision, and programmable compliance while reducing operational overhead.

For DeFi protocols, survival and success require navigating the tension between permissionlessness and institutional compliance. Thomas Uhm's emphasis on "credible neutrality" and infrastructure that enables rather than extracts value represents the winning model. Protocols that layer compliance features (KYC, clawback capabilities, geographic restrictions) as opt-in modules while maintaining permissionless core functionality can serve both institutional and retail users. TN's Citadels initiative—creating parallel KYC-compliant institutional access alongside permissionless retail access—exemplifies this architecture. Protocols unable to accommodate institutional compliance requirements may find themselves limited to crypto-native capital, while those that compromise core permissionlessness for institutional features risk losing their DeFi-native advantages.

The ultimate trajectory points toward a financial system where blockchain infrastructure is ubiquitous but invisible, similar to how TCP/IP became the universal internet protocol while users remain unaware of underlying technology. Traditional financial products will operate on-chain with traditional legal structures and regulatory compliance, permissionless DeFi protocols will continue enabling novel financial engineering impossible in regulated contexts, and most users will interact with both without necessarily distinguishing which infrastructure layer powers each service. The question shifts from "TradFi eating DeFi or DeFi eating TradFi" to "which financial functions benefit from decentralization versus regulatory oversight"—with different answers for different use cases producing a diverse, polyglot financial ecosystem rather than winner-take-all dominance by either paradigm.

Decentralized exchanges (DEXs) have matured into core pillars of crypto trading, now capturing roughly 20% of total market volumes. Within this space, Hyperliquid has emerged as the undisputed leader in on-chain derivatives. Launched in 2022 with the ambitious goal of matching centralized exchange (CEX) performance on-chain, Hyperliquid today processes billions in daily trading and controls about 70–75% of the DEX perpetual futures market. It achieves this by combining CEX-grade speed and deep liquidity with DeFi’s transparency and self-custody. The result is a vertically integrated Layer-1 blockchain and exchange that many now call “the blockchain to house all finance.” This report delves into Hyperliquid’s technical architecture, tokenomics, 2025 growth metrics, comparisons with other DEX leaders, ecosystem developments, and its vision for the future of on-chain finance.

Technical Architecture: A Vertically Integrated, High-Performance Chain

Hyperliquid is not just a DEX application – it is an entire Layer-1 blockchain built for trading performance. Its architecture consists of three tightly coupled components operating in a unified state:

• HyperBFT (Consensus): A custom Byzantine Fault Tolerant consensus mechanism optimized for speed and throughput. Inspired by modern protocols like HotStuff, HyperBFT provides sub-second finality and high consistency to ensure all nodes agree on the order of transactions. This Proof-of-Stake consensus is designed to handle the intense load of a trading platform, supporting on the order of 100,000–200,000 operations per second in practice. By early 2025, Hyperliquid had around 27 independent validators securing the network, a number that is steadily growing to decentralize consensus.
• HyperCore (Execution Engine): A specialized on-chain engine for financial applications. Rather than using generic smart contracts for critical exchange logic, HyperCore implements built-in central limit order books (CLOBs) for perpetual futures and spot markets, as well as other modules for lending, auctions, oracles, and more. Every order placement, cancellation, trade match, and liquidation is processed on-chain with one-block finality, yielding execution speeds comparable to traditional exchanges. By eschewing AMMs and handling order matching within the protocol, Hyperliquid achieves deep liquidity and low latency – it has demonstrated <1s trade finality and throughput that rivals centralized venues. This custom execution layer (written in Rust) can reportedly handle up to 200k orders per second after recent optimizations, eliminating the bottlenecks that previously made on-chain order books infeasible.
• HyperEVM (Smart Contracts): A general-purpose Ethereum-compatible execution layer introduced in Feb 2025. HyperEVM allows developers to deploy Solidity smart contracts and dApps on Hyperliquid with full EVM compatibility, similar to building on Ethereum. Crucially, HyperEVM is not a separate shard or rollup – it shares the same unified state with HyperCore. This means that dApps on HyperEVM can natively interoperate with the exchange’s order books and liquidity. For example, a lending protocol on HyperEVM can read live prices from HyperCore’s order book or even post liquidation orders directly into the order book via system calls. This composability between smart contracts and the high-speed exchange layer is a unique design: no bridges or off-chain oracles are needed for dApps to leverage Hyperliquid’s trading infrastructure.

Figure: Hyperliquid's vertically integrated architecture showing the unified state between consensus (HyperBFT), exchange engine (HyperCore), smart contracts (HyperEVM), and asset bridging (HyperUnit).

Integration with On-Chain Infrastructure: By building its own chain, Hyperliquid tightly integrates normally siloed functions into one platform. HyperUnit, for instance, is Hyperliquid’s decentralized bridging and asset tokenization module enabling direct deposits of external assets like BTC, ETH, and SOL without custodial wrappers. Users can lock native BTC or ETH and receive equivalent tokens (e.g. uBTC, uETH) on Hyperliquid for use as trading collateral, without relying on centralized custodians. This design provides “true collateral mobility” and a more regulatory-aware framework for bringing real-world assets on-chain. Thanks to HyperUnit (and Circle’s USDC integration discussed later), traders on Hyperliquid can seamlessly move liquidity from other networks into Hyperliquid’s fast exchange environment.

Performance and Latency: All parts of the stack are optimized for minimal latency and maximal throughput. HyperBFT finalizes blocks within a second, and HyperCore processes trades in real time, so users experience near-instant order execution. There are effectively no gas fees for trading actions – HyperCore transactions are feeless, enabling high-frequency order placement and cancellation without cost to users. (Normal EVM contract calls on HyperEVM do incur a low gas fee, but the exchange’s operations run gas-free on the native engine.) This zero-gas, low-latency design makes advanced trading features viable on-chain. Indeed, Hyperliquid supports the same advanced order types and risk controls as top CEXs, such as limit and stop orders, cross-margining, and up to 50× leverage on major markets. In sum, Hyperliquid’s custom L1 chain eliminates the traditional trade-off between speed and decentralization. Every operation is on-chain and transparent, yet the user experience – in terms of execution speed and interface – parallels that of a professional centralized exchange.

Evolution and Scalability: Hyperliquid’s architecture was born from first principles engineering. The project launched quietly in 2022 as a closed-alpha perpetuals DEX on a custom Tendermint-based chain, proving the CLOB concept with ~20 assets and 50× leverage. By 2023 it transitioned into a fully sovereign L1 with the new HyperBFT consensus, achieving 100K+ orders per second and introducing zero-gas trading and community liquidity pools. The addition of HyperEVM in early 2025 opened the floodgates for developers, marking Hyperliquid’s evolution from a single-purpose exchange into a full DeFi platform**. Notably, all these enhancements have kept the system stable – Hyperliquid reports** 99.99% uptime historically[25]_. This track record and vertical integration_ give Hyperliquid a significant technical moat: it controls the entire stack (consensus, execution, application), allowing continuous optimization. As demand grows, the team continues to refine the node software for even higher throughput, ensuring scalability for the next wave of users and more complex on-chain markets.

Tokenomics of $HYPE: Governance, Staking, and Value Accrual

Hyperliquid’s economic design centers on its native token $HYPE, introduced in late 2024 to decentralize ownership and governance of the platform. The token’s launch and distribution were notably community-centric: in November 2024, Hyperliquid conducted an airdrop Token Generation Event (TGE), allocating 31% of the 1 billion fixed supply to early users as a reward for their participation. An even larger portion (≈38.8%) was set aside for future community incentives like liquidity mining or ecosystem development. Importantly, $HYPE had zero allocations to VCs or private investors, reflecting a philosophy of prioritizing community ownership. This transparent distribution aimed to avoid the heavy insider ownership seen in many projects and instead empower the actual traders and builders on Hyperliquid.

The $HYPE token serves multiple roles in the Hyperliquid ecosystem:

• Governance: $HYPE is a governance token enabling holders to vote on Hyperliquid Improvement Proposals (HIPs) and shape the protocol’s evolution. Already, critical upgrades like HIP-1, HIP-2, and HIP-3 have been passed, which established permissionless listing standards for spot tokens and perpetual markets. For example, HIP-3 opened up the ability for community members to permissionlessly deploy new perp markets, much like Uniswap did for spot trading, unlocking long-tail assets (including traditional market perps) on Hyperliquid. Governance will increasingly decide listings, parameter tweaks, and the use of community incentive funds.
• Staking & Network Security: Hyperliquid is a Proof-of-Stake chain, so staking $HYPE to validators secures the HyperBFT network. Stakers delegate to validators and earn a portion of block rewards and fees. Shortly after launch, Hyperliquid enabled staking with an annual yield ~2–2.5% to incentivize participation in consensus. As more users stake, the chain’s security and decentralization improve. Staked $HYPE (or derivative forms like upcoming beHYPE liquid staking) may also be used in governance voting, aligning security participants with decision-making.
• Exchange Utility (Fee Discounts): Holding or staking $HYPE confers trading fee discounts on Hyperliquid’s exchange. Similar to how Binance’s BNB or dYdX’s DYDX token offer reduced fees, active traders are incentivized to hold $HYPE to minimize their costs. This creates a natural demand for the token among the exchange’s user base, especially high-volume traders.
• Value Accrual via Buybacks: The most striking aspect of Hyperliquid's tokenomics is its aggressive fee-to-value mechanism. Hyperliquid uses the vast majority of its trading fee revenue to buy back and burn $HYPE on the open market, directly returning value to token holders. In fact, 97% of all protocol trading fees are allocated to buying back $HYPE (and the remainder to an insurance fund and liquidity providers). This is one of the highest fee return rates in the industry. By mid-2025, Hyperliquid was generating over $65 million in protocol revenue per month from trading fees – and virtually all of that went toward $HYPE repurchases, creating constant buy pressure. This deflationary token model, combined with a fixed 1B supply, means $HYPE's tokenomics are geared for long-term value accrual for loyal stakeholders. It also signals that Hyperliquid's team forgoes short-term profit (no fee revenue is taken as profit or distributed to insiders; even the core team presumably only benefits as token holders), instead funneling revenue to the community treasury and token value.

• Liquidity Provider Rewards: A small portion of fees (≈3–8%) is used to reward liquidity providers in Hyperliquid’s unique HyperLiquidity Pool (HLP). HLP is an on-chain USDC liquidity pool that facilitates market-making and auto-settlement for the order books, analogous to an “LP vault.” Users who provide USDC to HLP earn a share of trading fees in return. By early 2025, HLP was offering depositors an ~11% annualized yield from accrued trading fees. This mechanism lets community members share in the exchange’s success by contributing capital to backstop liquidity (similar in spirit to GMX’s GLP pool, but for an orderbook system). Notably, Hyperliquid’s insurance Assistance Fund (denominated in $HYPE) also uses a portion of revenue to cover any HLP losses or unusual events – for instance, a “Jelly” exploit in Q1 2025 incurred a $12M shortfall in HLP, which was fully reimbursed to pool users. The fee buyback model was so robust that despite that hit, $HYPE buybacks continued unabated and HLP remained profitable, demonstrating strong alignment between the protocol and its community liquidity providers.

In summary, Hyperliquid’s tokenomics emphasize community ownership, security, and long-term sustainability. The absence of VC allocations and the high buyback rate were decisions that signaled confidence in organic growth. The early results have been positive – since its TGE, $HYPE’s price climbed 4× (as of mid-2025) on the back of real adoption and revenue. More importantly, users remained engaged after the airdrop; trading activity actually accelerated post-token launch, rather than suffering the typical post-incentive drop-off. This suggests the token model is successfully aligning user incentives with the platform’s growth, creating a virtuous cycle for Hyperliquid’s ecosystem.

Trading Volume, Adoption, and Liquidity in 2025

Hyperliquid by the Numbers: In 2025, Hyperliquid stands out not just for its technology but for the sheer scale of its on-chain activity. It has rapidly become the largest decentralized derivatives exchange by a wide margin, setting new benchmarks for DeFi. Key metrics illustrating Hyperliquid’s traction include:

• Market Dominance: Hyperliquid handles roughly 70–77% of all DEX perpetual futures volume in 2025 – an 8× larger share than the next competitor. In other words, Hyperliquid by itself accounts for well over three-quarters of decentralized perp trading worldwide, making it the clear leader in its category. (For context, as of Q1 2025 this equated to about 56–73% of decentralized perp volume, up from ~4.5% at the start of 2024 – a stunning rise in one year.)
• Trading Volumes: Cumulative trading volume on Hyperliquid blew past $1.5 trillion in mid-2025, highlighting how much liquidity has flowed through its markets. By late 2024 the exchange was already seeing daily volumes around $10–14 billion, and volume continued to climb with new user influxes in 2025. In fact, during peak market activity (e.g. a memecoin frenzy in May 2025), Hyperliquid’s weekly trading volume reached as high as $780 billion in one week – averaging well over $100B per day – rivaling or exceeding many mid-sized centralized exchanges. Even in steady conditions, Hyperliquid was averaging roughly $470B in weekly volume in the first half of 2025. This scale is unprecedented for a DeFi platform; by mid-2025 Hyperliquid was executing about 6% of *all* crypto trading volume globally (including CEXs), narrowing the gap between DeFi and CeFi.
• Open Interest and Liquidity: The depth of Hyperliquid’s markets is also evident in its open interest (OI) – the total value of active positions. OI grew from ~$3.3B at 2024’s end to around $15 billion by mid-2025. For perspective, this OI is about 60–120% of the levels on major CEXs like Bybit, OKX, or Bitget, indicating that professional traders are as comfortable deploying large positions on Hyperliquid as on established centralized venues. Order book depth on Hyperliquid for major pairs like BTC or ETH is reported to be comparable to top CEXs, with tight bid-ask spreads. During certain token launches (e.g. the popular PUMP meme coin), Hyperliquid even achieved the deepest liquidity and highest volume of any venue, beating out CEXs for that asset. This showcases how an on-chain order book, when well-designed, can match CEX liquidity – a milestone in DEX evolution.
• Users and Adoption: The platform’s user base has expanded dramatically through 2024–2025. Hyperliquid surpassed 500,000 unique user addresses in mid-2025. In the first half of 2025 alone, the count of active addresses nearly doubled (from ~291k to 518k). This 78% growth in six months was fueled by word-of-mouth, a successful referral & points program, and the buzz around the $HYPE airdrop (which interestingly retained users rather than just attracting mercenaries – there was no drop-off in usage after the airdrop, and activity kept climbing). Such growth indicates not just one-time curiosity but genuine adoption by traders. A significant portion of these users are believed to be “whales” and professional traders who migrated from CEXs, drawn by Hyperliquid’s liquidity and lower fees. Indeed, institutions and high-volume trading firms have begun treating Hyperliquid as a primary venue for perpetuals trading, validating DeFi’s appeal when performance issues are solved.
• Revenue and Fees: Hyperliquid’s robust volumes translate into substantial protocol revenue (which, as noted, largely accrues to $HYPE buybacks). In the last 30 days (as of mid-2025), Hyperliquid generated about $65.45 million in protocol fees. On a daily basis that’s roughly $2.0–2.5 million in fees earned from trading activity. Annualized, the platform is on track for $800M+ in revenue – an astonishing figure that approaches revenues of some major centralized exchanges, and far above typical DeFi protocols. It underscores how Hyperliquid’s high volume and fee structure (small per-trade fees that add up at scale) produce a thriving revenue model to support its token economy.
• Total Value Locked (TVL) and Assets: Hyperliquid’s ecosystem TVL – representing assets bridged into its chain and liquidity in its DeFi protocols – has climbed rapidly alongside trading activity. At the start of Q4 2024 (pre-token) Hyperliquid’s chain TVL was around $0.5B, but after the token launch and HyperEVM expansion, TVL soared to $2+ billion by early 2025. By mid-2025, it reached approximately $3.5 billion (June 30, 2025) and continued upward. The introduction of native USDC (via Circle) and other assets boosted on-chain capital to an estimated $5.5 billion AUM by July 2025. This includes assets in the HLP pool, DeFi lending pools, AMMs, and users’ collateral balances. Hyperliquid’s HyperLiquidity Pool (HLP) itself held a TVL around $370–$500 million in H1 2025, providing a deep USDC liquidity reserve for the exchange. Additionally, the HyperEVM DeFi TVL (excluding the core exchange) surpassed $1 billion within a few months of launch, reflecting rapid growth of new dApps on the chain. These figures firmly place Hyperliquid among the largest blockchain ecosystems by TVL, despite being a specialized chain.

In summary, 2025 has seen Hyperliquid scale to CEX-like volumes and liquidity. It consistently ranks as the top DEX by volume, and even measures as a significant fraction of overall crypto trading. The ability to sustain half a trillion dollars in weekly volume on-chain, with half a million users, illustrates that the long-held promise of high-performance DeFi is being realized. Hyperliquid’s success is expanding the boundaries of what on-chain markets can do: for instance, it became the go-to venue for fast listing of new coins (it often is first to list perps for trending assets, attracting huge activity) and has proven that on-chain order books can handle blue-chip trading at scale (its BTC and ETH markets have liquidity comparable to leading CEXs). These achievements underpin Hyperliquid’s claim as a potential foundation for all on-chain finance going forward.

Comparison with Other Leading DEXs (dYdX, GMX, UniswapX, etc.)

The rise of Hyperliquid invites comparisons to other prominent decentralized exchanges. Each of the major DEX models – from order-book-based derivatives like dYdX, to liquidity pool-based perps like GMX, to spot DEX aggregators like UniswapX – takes a different approach to balancing performance, decentralization, and user experience. Below, we analyze how Hyperliquid stacks up against these platforms:

• Hyperliquid vs. dYdX: dYdX was the early leader in decentralized perps, but its initial design (v3) relied on a hybrid approach: an off-chain order book and matching engine, combined with an L2 settlement on StarkWare. This gave dYdX decent performance but came at the cost of decentralization and composability – the order book was run by a central server, and the system was not open to general smart contracts. In late 2023, dYdX launched v4 as a Cosmos app-chain, aiming to fully decentralize the order book within a dedicated PoS chain. This is philosophically similar to Hyperliquid’s approach (both built custom chains for on-chain order matching). Hyperliquid’s key edge has been its unified architecture and head start in performance tuning. By designing HyperCore and HyperEVM together, Hyperliquid achieved CEX-level speed entirely on-chain before dYdX’s Cosmos chain gained traction. In fact, Hyperliquid’s performance surpassed dYdX – it can handle far more throughput (hundreds of thousands of tx/sec) and offers cross-contract composability that dYdX (an app-specific chain without an EVM environment) currently lacks. Artemis Research notes: earlier protocols either compromised on performance (like GMX) or on decentralization (like dYdX), but Hyperliquid delivered both, solving the deeper challenge. This is reflected in market share: by 2025 Hyperliquid commands ~75% of the perp DEX market, whereas dYdX’s share has dwindled to single digits. In practical terms, traders find Hyperliquid’s UI and speed comparable to dYdX (both offer pro exchange interfaces, advanced orders, etc.), but Hyperliquid offers greater asset variety and on-chain integration. Another difference is fee and token models: dYdX’s token is mainly a governance token with indirect fee discounts, while Hyperliquid’s $HYPE directly accrues exchange value (via buybacks) and offers staking rights. Lastly, on decentralization, both are PoS chains – dYdX had ~20 validators at launch vs Hyperliquid’s ~27 by early 2025 – but Hyperliquid’s open builder ecosystem (HyperEVM) arguably makes it more decentralized in terms of development and usage. Overall, Hyperliquid can be seen as the spiritual successor to dYdX: it took the order book DEX concept and fully on-chain-ified it with greater performance, which is evidenced by Hyperliquid pulling significant volume even from centralized exchanges (something dYdX v3 struggled to do).
• Hyperliquid vs. GMX: GMX represents the AMM/pool-based model for perpetuals. It became popular on Arbitrum in 2022 by allowing users to trade perps against a pooled liquidity (GLP) with oracle-based pricing. GMX’s approach prioritized simplicity and zero price impact for small trades, but it sacrifices some performance and capital efficiency. Because GMX relies on price oracles and a single liquidity pool, large or frequent trades can be challenging – the pool can incur losses if traders win (GLP holders take the opposite side of trades), and oracle price latency can be exploited. Hyperliquid’s order book model avoids these issues by matching traders peer-to-peer at market-driven prices, with professional market makers providing deep liquidity. This yields far tighter spreads and better execution for big trades compared to GMX’s model. In essence, GMX’s design compromises on high-frequency performance (trades only update when oracles push prices, and there’s no rapid order placement/cancellation) whereas Hyperliquid’s design excels at it. The numbers reflect this: GMX’s volumes and OI are an order of magnitude smaller, and its market share has been dwarfed by Hyperliquid’s rise. For example, GMX typically supported under 20 markets (mostly large caps), whereas Hyperliquid offers 100+ markets including many long-tail assets – the latter is possible because maintaining many order books is feasible on Hyperliquid’s chain, whereas in GMX adding new asset pools is slower and riskier. From a user experience standpoint, GMX offers a simple swap-style interface (good for DeFi novices), while Hyperliquid provides a full exchange dashboard with charts and order books catering to advanced traders. Fees: GMX charges a ~0.1% fee on trades (which goes to GLP and GMX stakers) and has no token buyback; Hyperliquid charges very low maker/taker fees (on the order of 0.01–0.02%) and uses fees to buy back $HYPE for holders. Decentralization: GMX runs on Ethereum L2s (Arbitrum, Avalanche), inheriting strong base security, but its dependency on a centralized price oracle (Chainlink) and single liquidity pool introduces different centralized risks. Hyperliquid runs its own chain, which is newer/less battle-tested than Ethereum, but its mechanisms (order book + many makers) avoid centralized oracle dependence. In summary, Hyperliquid offers superior performance and institutional-grade liquidity relative to GMX, at the cost of more complex infrastructure. GMX proved there is demand for on-chain perps, but Hyperliquid’s order books have proven far more scalable for high-volume trading.
• Hyperliquid vs. UniswapX (and Spot DEXs): UniswapX is a recently introduced trade aggregator for spot swaps (built by Uniswap Labs) that finds the best price across AMMs and other liquidity sources. While not a direct competitor on perpetuals, UniswapX represents the cutting-edge of spot DEX user experience. It enables gas-free, aggregation-optimized token swaps by letting off-chain “fillers” execute trades for users. By contrast, Hyperliquid’s spot trading uses its own on-chain order books (and also has a native AMM called HyperSwap in its ecosystem). For a user looking to trade tokens spot, how do they compare? Performance: Hyperliquid’s spot order books offer immediate execution with low latency, similar to a centralized exchange, and thanks to no gas fees on HyperCore, taking an order is cheap and fast. UniswapX aims to save users gas on Ethereum by abstracting execution, but ultimately the trade settlement still happens on Ethereum (or other underlying chains) and may incur latency (waiting for fillers and block confirmations). Liquidity: UniswapX sources liquidity from many AMMs and market makers across multiple DEXs, which is great for long-tail tokens on Ethereum; however, for major pairs, Hyperliquid’s single order book often has deeper liquidity and less slippage because all traders congregate in one venue. Indeed, after launching spot markets in March 2024, Hyperliquid quickly saw spot volumes surge to record levels, with large traders bridging assets like BTC, ETH, and SOL into Hyperliquid for spot trading due to the superior execution, then bridging back out. UniswapX excels at breadth of token access, whereas Hyperliquid focuses on depth and efficiency for a more curated set of assets (those listed via its governance/auction process). Decentralization and UX: Uniswap (and X) leverage Ethereum’s very decentralized base and are non-custodial, but aggregators like UniswapX do introduce off-chain actors (fillers relaying orders) – albeit in a permissionless way. Hyperliquid’s approach keeps all trading actions on-chain with full transparency, and any asset listed on Hyperliquid gets the benefits of native order book trading plus composability with its DeFi apps. The user experience on Hyperliquid is closer to a centralized trading app (which advanced users prefer), while UniswapX is more like a “meta-DEX” for one-click swaps (convenient for casual trades). Fees: UniswapX’s fees depend on the DEX liquidity used (typically 0.05–0.3% on AMMs) plus possibly a filler incentive; Hyperliquid’s spot fees are minimal and often offset by $HYPE discounts. In short, Hyperliquid competes with Uniswap and other spot DEXs by offering a new model: an order-book-based spot exchange on a custom chain. It has carved out a niche where high-volume spot traders (especially for large-cap assets) prefer Hyperliquid for its deeper liquidity and CEX-like experience, whereas retail users swapping obscure ERC-20s may still prefer Uniswap’s ecosystem. Notably, Hyperliquid’s ecosystem even introduced Hyperswap (an AMM on HyperEVM with ~$70M TVL) to capture long-tail tokens via AMM pools – acknowledging that AMMs and order books can coexist, serving different market segments.

Summary of Key Differences: The table below outlines a high-level comparison:

DEX Platform	Design & Chain	Trading Model	Performance	Decentralization	Fee Mechanism
Hyperliquid	Custom L1 (HyperBFT PoS, ~27 validators)	On-chain CLOB for perps/spot; also EVM apps	~0.5s finality, 100k+ tx/sec, CEX-like UI	PoS chain (community-run, unified state for dApps)	Tiny trading fees, ~97% of fees buy back $HYPE (indirectly rewarding holders)
dYdX v4	Cosmos SDK app-chain (PoS, ~20 validators)	On-chain CLOB for perps only (no general smart contracts)	~1-2s finality, high throughput (order matching by validators)	PoS chain (decentralized matching, but not EVM-composable)	Trading fees paid in USDC; DYDX token for governance & discounts (no fee buyback)
GMX	Arbitrum & Avalanche (Ethereum L2/L1)	AMM pooled liquidity (GLP) with oracle pricing for perps	Dependent on oracle update (~30s); good for casual trades, not HFT	Secured by Ethereum/Avax L1; fully on-chain but relies on centralized oracles	~0.1% trading fee; 70% to liquidity providers (GLP), 30% to GMX stakers (revenue sharing)
UniswapX	Ethereum mainnet (and cross-chain)	Aggregator for spot swaps (routes across AMMs or RFQ market makers)	~12s Ethereum block time (fills abstracted off-chain); gas fees abstracted	Runs on Ethereum (high base security); uses off-chain filler nodes for execution	Uses underlying AMM fees (0.05–0.3%) + potential filler incentive; UNI token not required for use

In essence, Hyperliquid has set a new benchmark by combining the strengths of these approaches without the usual weaknesses: it offers the sophisticated order types, speed, and liquidity of a CEX (surpassing dYdX’s earlier attempt), without sacrificing the transparency and permissionless nature of DeFi (improving on GMX’s performance and Uniswap’s composability). As a result, rather than simply stealing market share from dYdX or GMX, Hyperliquid actually expanded the on-chain trading market by attracting traders who previously stayed on CEXs. Its success has spurred others to evolve – for example, even Coinbase and Robinhood have eyed entering the on-chain perps market, though with much lower leverage and liquidity so far. If this trend continues, we can expect a competitive push where both CEXs and DEXs race to combine performance with trustlessness – a race where Hyperliquid currently enjoys a strong lead.

Ecosystem Growth, Partnerships, and Community Initiatives

One of Hyperliquid’s greatest achievements in 2025 is growing from a single-product exchange into a thriving blockchain ecosystem. The launch of HyperEVM unlocked a Cambrian explosion of projects and partnerships building around Hyperliquid’s core, making it not just a trading venue but a full DeFi and Web3 environment. Here we explore the ecosystem’s expansion and key strategic alliances:

Ecosystem Projects and Developer Traction: Since early 2025, dozens of dApps have deployed on Hyperliquid, attracted by its built-in liquidity and user base. These span the gamut of DeFi primitives and even extend to NFTs and gaming:

• Decentralized Exchanges (DEXs): Besides Hyperliquid’s native order books, community-built DEXs have appeared to serve other needs. Notably, Hyperswap launched as an AMM on HyperEVM, quickly becoming the leading liquidity hub for long-tail tokens (it amassed >$70M TVL and $2B volume within 4 months). Hyperswap’s automated pools complement Hyperliquid’s CLOB by allowing permissionless listing of new tokens and providing an easy venue for projects to bootstrap liquidity. Another project, KittenSwap (a Velodrome fork with ve(3,3) tokenomics), also went live to offer incentivized AMM trading for smaller assets. These DEX additions ensure that even meme coins and experimental tokens can thrive on Hyperliquid via AMMs, while the major assets trade on order books – a synergy that drives overall volume.
• Lending and Yield Protocols: The Hyperliquid ecosystem now features money markets and yield optimizers that interlink with the exchange. HyperBeat is a flagship lending/borrowing protocol on HyperEVM (with ~$145M TVL as of mid-2025). It allows users to deposit assets like $HYPE, stablecoins, or even LP tokens to earn interest, and to borrow against collateral to trade on Hyperliquid with extra leverage. Because HyperBeat can read Hyperliquid’s order book prices directly and even trigger on-chain liquidations via HyperCore, it operates more efficiently and safely than cross-chain lending protocols. Yield aggregators are emerging too – HyperBeat’s “Hearts” rewards program and others incentivize providing liquidity or vault deposits. Another notable entrant is Kinetiq, a liquid staking project for $HYPE that drew over $400M in deposits on day one, indicating huge community appetite for earning yield on HYPE. Even external Ethereum-based protocols are integrating: EtherFi, a major liquid staking provider (with ~$9B in ETH staked) announced a collaboration to bring staked ETH and new yield strategies into Hyperliquid via HyperBeat. This partnership will introduce beHYPE, a liquid staking token for HYPE, and potentially bring EtherFi’s staked ETH as collateral to Hyperliquid’s markets. Such moves show confidence from established DeFi players in the Hyperliquid ecosystem’s potential.
• Stablecoins and Crypto Banking: Recognizing the need for stable on-chain currency, Hyperliquid has attracted both external and native stablecoin support. Most significantly, Circle (issuer of USDC) formed a strategic partnership to launch native USDC on Hyperliquid in 2025. Using Circle’s Cross-Chain Transfer Protocol (CCTP), users will be able to burn USDC on Ethereum and mint 1:1 USDC on Hyperliquid, eliminating wrappers and enabling direct stablecoin liquidity on the chain. This integration is expected to streamline large transfers of capital into Hyperliquid and reduce reliance on only bridged USDT/USDC. In fact, by the time of announcement, Hyperliquid’s assets under management surged to $5.5B, partly on anticipation of native USDC support. On the native side, projects like Hyperstable have launched an over-collateralized stablecoin (USH) on HyperEVM with yield-bearing governance token PEG – adding diversity to the stablecoin options available for traders and DeFi users.
• Innovative DeFi Infrastructure: Hyperliquid’s unique capabilities have spurred innovation in DEX design and derivatives. Valantis, for example, is a modular DEX protocol on HyperEVM that lets developers create custom AMMs and “sovereign pools” with specialized logic. It supports advanced features like rebase tokens and dynamic fees, and has $44M TVL, showcasing that teams see Hyperliquid as fertile ground for pushing DeFi design forward. For perpetuals specifically, the community passed HIP-3 which opened Hyperliquid’s Core engine to anyone who wants to launch a new perpetual market. This is a game-changer: it means if a user wants a perp market for, say, a stock index or a commodity, they can deploy it (subject to governance parameters) without needing Hyperliquid’s team – a truly permissionless derivative framework much like Uniswap did for ERC20 swaps. Already, community-launched markets for novel assets are appearing, demonstrating the power of this openness.
• Analytics, Bots, and Tooling: A vibrant array of tools has emerged to support traders on Hyperliquid. For instance, PvP.trade is a Telegram-based trading bot that integrates with Hyperliquid’s API, enabling users to execute perp trades via chat and even follow friends’ positions for a social trading experience. It ran a points program and token airdrop that proved quite popular. On the analytics side, AI-driven platforms like Insilico Terminal and Katoshi AI have added support for Hyperliquid, providing traders with advanced market signals, automated strategy bots, and predictive analytics tailored to Hyperliquid’s markets. The presence of these third-party tools indicates that developers view Hyperliquid as a significant market – worth building bots and terminals for – similar to how many tools exist for Binance or Uniswap. Additionally, infrastructure providers have embraced Hyperliquid: QuickNode and others offer RPC endpoints for the Hyperliquid chain, Nansen has integrated Hyperliquid data into its portfolio tracker, and blockchain explorers and aggregators are supporting the network. This infrastructure adoption is crucial for user experience and signifies that Hyperliquid is recognized as a major network in the multi-chain landscape.
• NFTs and Gaming: Beyond pure finance, Hyperliquid’s ecosystem also dabbles in NFTs and crypto gaming, adding community flavor. HypurrFun is a meme coin launchpad that gained attention by using a Telegram bot auction system to list jokey tokens (like $PIP and $JEFF) on Hyperliquid’s spot market. It provided a fun, Pump.win-style experience for the community and was instrumental in testing Hyperliquid’s token auction mechanisms pre-HyperEVM. NFT projects like Hypio (an NFT collection integrating DeFi utility) have launched on Hyperliquid, and even an AI-powered game (TheFarm.fun) is leveraging the chain for minting creative NFTs and planning a token airdrop. These may be niche, but they indicate an organic community forming – traders who also engage in memes, NFTs, and social games on the same chain, increasing user stickiness.

Strategic Partnerships: Alongside grassroots projects, Hyperliquid’s team (via the Hyper Foundation) has actively pursued partnerships to extend its reach:

• Phantom Wallet (Solana Ecosystem): In July 2025, Hyperliquid announced a major partnership with Phantom, the popular Solana wallet, to bring in-wallet perpetuals trading to Phantom’s users. This integration allows Phantom’s mobile app (with millions of users) to trade Hyperliquid perps natively, without leaving the wallet interface. Over 100+ markets with up to 50× leverage became available in Phantom, covering BTC, ETH, SOL and more, with built-in risk controls like stop-loss orders. The significance is twofold: it gives Solana community users easy access to Hyperliquid’s markets (bridging ecosystems), and it showcases Hyperliquid’s API and backend strength – Phantom wouldn’t integrate a DEX that couldn’t handle large user flow. Phantom’s team highlighted that Hyperliquid’s liquidity and quick settlement were key to delivering a smooth mobile trading UX. This partnership essentially embeds Hyperliquid as the “perps engine” inside a leading crypto wallet, dramatically lowering friction for new users to start trading on Hyperliquid. It’s a strategic win for user acquisition and demonstrates Hyperliquid’s intent to collaborate rather than compete with other ecosystems (Solana in this case).
• Circle (USDC): As mentioned, Circle’s partnership to deploy native USDC via CCTP on Hyperliquid is a cornerstone integration. It not only legitimizes Hyperliquid as a first-class chain in the eyes of a major stablecoin issuer, but it also solves a critical piece of infrastructure: fiat liquidity. When Circle turns on native USDC for Hyperliquid, traders will be able to transfer dollars in/out of Hyperliquid’s network with the same ease (and trust) as moving USDC on Ethereum or Solana. This streamlines arbitrage and cross-exchange flows. Additionally, Circle’s Cross-Chain Transfer Protocol v2 will allow USDC to move between Hyperliquid and other chains without intermediaries, further integrating Hyperliquid into the multi-chain liquidity network. By July 2025, anticipation of USDC and other assets coming on board had already driven Hyperliquid’s total asset pools to $5.5B. We can expect this number to grow once the Circle integration is fully live. In essence, this partnership addresses one of the last barriers for traders: easy fiat on/off ramps into Hyperliquid’s high-speed environment.
• Market Makers and Liquidity Partners: While not always publicized, Hyperliquid has likely cultivated relationships with professional market-making firms to bootstrap its order book liquidity. The depth observed (often rivaling Binance on some pairs) suggests that major crypto liquidity providers (possibly firms like Wintermute, Jump, etc.) are actively making markets on Hyperliquid. One indirect indicator: Auros Global, a trading firm, published a “Hyperliquid listing 101” guide in early 2025 noting Hyperliquid averaged $6.1B daily perps volume in Q1 2025, which implies market makers are paying attention. Additionally, Hyperliquid’s design (with incentives like maker rebates or HLP yields) and the no-gas benefit are very attractive to HFT firms. Although specific MM partnerships aren’t named, the ecosystem clearly benefits from their participation.
• Others: The Hyper Foundation, which stewards protocol development, has begun initiatives like a Delegation Program to incentivize reliable validators and global community programs (a Hackathon with $250k prizes was held in 2025). These help strengthen the network’s decentralization and bring in new talent. There’s also collaboration with oracle providers (Chainlink or Pyth) for external data when needed – e.g. if any synthetic real-world asset markets launch, those partnerships will be important. Given that Hyperliquid is EVM-compatible, tooling from Ethereum (like Hardhat, The Graph, etc.) can be relatively easily extended to Hyperliquid as developers demand.

Community and Governance: Community engagement in Hyperliquid has been high due to the early airdrop and ongoing governance votes. The Hyperliquid Improvement Proposal (HIP) framework has seen important proposals (HIP-1 to HIP-3) passed in its first year, signaling an active governance process. The community has played a role in token listings via Hyperliquid’s auction model – new tokens launch through an on-chain auction (often facilitated by HypurrFun or similar), and successful auctions get listed on the order book. This process, while permissioned by a fee and vetting, has allowed community-driven tokens (like meme coins) to gain traction on Hyperliquid without centralized gatekeeping. It also helped Hyperliquid avoid spam tokens since there’s a cost to list, ensuring only serious projects or enthusiastic communities pursue it. The result is an ecosystem that balances permissionless innovation with a degree of quality control – a novel approach in DeFi.

Moreover, the Hyper Foundation (a non-profit entity) was set up to support ecosystem growth. It has been responsible for initiatives like the $HYPE token launch and managing the incentive funds. The Foundation’s decision to not issue incentives recklessly (as noted in The Defiant, they provided no extra liquidity mining after the airdrop) may have initially tempered some yield-farmers, but it underscores a focus on organic usage over short-term TVL boosts. This strategy appears to have paid off with steady growth. Now, moves like EtherFi’s involvement and others show that even without massive liquidity mining, real DeFi activity is taking root on Hyperliquid due to its unique opportunities (like high yields from actual fee revenue and access to an active trading base).

To summarize, Hyperliquid in 2025 is surrounded by a flourishing ecosystem and strong alliances. Its chain is home to a comprehensive DeFi stack – from perps and spot trading, to AMMs, lending, stablecoins, liquid staking, NFTs, and beyond – much of which sprung up just in the past year. Strategic partnerships with the likes of Phantom and Circle are expanding its user reach and liquidity access across the crypto universe. The community-driven aspects (auctions, governance, hackathons) show an engaged user base that is increasingly invested in Hyperliquid's success. All these factors reinforce Hyperliquid's position as more than an exchange; it's becoming a holistic financial layer.

Future Outlook: Hyperliquid’s Vision for Onchain Finance (Derivatives, RWAs, and Beyond)

Hyperliquid’s rapid ascent begs the question: What’s next? The project’s vision has always been ambitious – to become the foundational infrastructure for all of onchain finance. Having achieved dominance in on-chain perps, Hyperliquid is poised to expand into new products and markets, potentially reshaping how traditional financial assets interact with crypto. Here are some key elements of its forward-looking vision:

• Expanding the Derivatives Suite: Perpetual futures were the initial beachhead, but Hyperliquid can extend to other derivatives. The architecture (HyperCore + HyperEVM) could support additional instruments like options, interest rate swaps, or structured products. A logical next step might be an on-chain options exchange or an options AMM launching on HyperEVM, leveraging the chain’s liquidity and fast execution. With unified state, an options protocol on Hyperliquid could directly hedge via the perps order book, creating efficient risk management. We haven’t seen a major on-chain options platform emerge on Hyperliquid yet, but given the ecosystem’s growth, it’s plausible for 2025-26. Additionally, traditional futures and tokenized derivatives (e.g. futures on stock indices, commodities, or FX rates) could be introduced via HIP proposals – essentially bringing traditional finance markets on-chain. Hyperliquid’s HIP-3 already paved the way for listing “any asset, crypto or traditional” as a perp market so long as there’s an oracle or price feed. This opens the door for community members to launch markets on equities, gold, or other assets in a permissionless way. If liquidity and legal considerations allow, Hyperliquid could become a hub for 24/7 tokenized trading of real-world markets, something even many CEXs don’t offer at scale. Such a development would truly realize the vision of a unified global trading platform on-chain.
• Real-World Assets (RWAs) and Regulated Markets: Bridging real-world assets into DeFi is a major trend, and Hyperliquid is well-positioned to facilitate it. Through HyperUnit and partnerships like Circle, the chain is integrating with real assets (fiat via USDC, BTC/SOL via wrapped tokens). The next step might be tokenized securities or bonds trading on Hyperliquid. For example, one could imagine a future where government bonds or stocks are tokenized (perhaps under regulatory sandbox) and traded on Hyperliquid’s order books 24/7. Already, Hyperliquid’s design is “regulatory-aware” – the use of native assets instead of synthetic IOUs can simplify compliance. The Hyper Foundation could explore working with jurisdictions to allow certain RWAs on the platform, especially as on-chain KYC/whitelisting tech improves (HyperEVM could support permissioned pools if needed for regulated assets). Even without formal RWA tokens, Hyperliquid’s permissionless perps could list derivatives that track RWAs (for instance, a perpetual swap on the S&P 500 index). That would bring RWA exposure to DeFi users in a roundabout but effective way. In summary, Hyperliquid aims to blur the line between crypto markets and traditional markets – to house all finance, you eventually need to accommodate assets and participants from the traditional side. The groundwork (in tech and liquidity) is being laid for that convergence.
• Scaling and Interoperability: Hyperliquid will continue to scale vertically (more throughput, more validators) and likely horizontally via interoperability. With Cosmos IBC or other cross-chain protocols, Hyperliquid might connect to wider networks, allowing assets and messages to flow trustlessly. It already uses Circle’s CCTP for USDC; integration with something like Chainlink’s CCIP or Cosmos’s IBC could extend cross-chain trading possibilities. Hyperliquid could become a liquidity hub that other chains tap into (imagine dApps on Ethereum or Solana executing trades on Hyperliquid via trustless bridges – getting Hyperliquid’s liquidity without leaving their native chain). The mention of Hyperliquid as a “liquidity hub” and its growing open interest share (already ~18% of the entire crypto futures OI by mid-2025) indicates it might anchor a larger network of DeFi protocols. The Hyper Foundation’s collaborative approach (e.g. partnering with wallets, other L1s) suggests they see Hyperliquid as part of a multi-chain future rather than an isolated island.
• Advanced DeFi Infrastructure: By combining a high-performance exchange with general programmability, Hyperliquid could enable sophisticated financial products that were not previously feasible on-chain. For example, on-chain hedge funds or vault strategies can be built on HyperEVM that execute complex strategies directly through HyperCore (arbitrage, automated market making on order books, etc.) all on one chain. This vertical integration eliminates inefficiencies like moving funds across layers or being front-run by MEV bots during cross-chain arbitrage – everything can happen under HyperBFT consensus with full atomicity. We may see growth in automated strategy vaults that use Hyperliquid’s primitives to generate yield (some early vaults likely exist already, possibly run by HyperBeat or others). Hyperliquid’s founder summarized the strategy as “polish a native application and then grow into general-purpose infrastructure”. Now that the native trading app is polished and a broad user base is present, the door is open for Hyperliquid to become a general DeFi infrastructure layer. This could put it in competition not just with DEXs but with Layer-1s like Ethereum or Solana for hosting financial dApps – albeit Hyperliquid’s specialty will remain anything requiring deep liquidity or low latency.
• Institutional Adoption and Compliance: Hyperliquid’s future likely involves courting institutional players – hedge funds, market makers, even fintech firms – to use the platform. Already, institutional interest is rising given the volumes and the fact that firms like Coinbase, Robinhood, and others are eyeing perps. Hyperliquid might position itself as the infrastructure provider for institutions to go on-chain. It could offer features like sub-accounts, compliance reporting tools, or whitelisted pools (if needed for certain regulated users) – all while preserving the public, on-chain nature for retail. The regulatory climate will influence this: if jurisdictions clarify the status of DeFi derivatives, Hyperliquid could either become a licensed venue in some form or remain a purely decentralized network that institutions plug into indirectly. The mention of “regulatory-aware design” suggests the team is mindful of striking a balance that allows real-world integration without falling afoul of laws.
• Continuous Community Empowerment: As the platform grows, more decision-making may shift to token holders. We can expect future HIPs to cover things like adjusting fee parameters, allocating the incentive fund (the ~39% of supply set aside), introducing new products (e.g. if an options module were proposed), and expanding validator sets. The community will play a big role in guiding Hyperliquid’s trajectory, effectively acting as the shareholders of this decentralized exchange. The community treasury (funded by any tokens not yet distributed and possibly by any revenue not used in buybacks) could be directed to fund new projects on Hyperliquid or provide grants, further bolstering ecosystem development.

Conclusion: Hyperliquid in 2025 has achieved what many thought impossible: a fully on-chain exchange that rivals centralized platforms in performance and liquidity. Its technical architecture – HyperBFT, HyperCore, HyperEVM – has proven to be a blueprint for the next generation of financial networks. The $HYPE token model aligns the community tightly with the platform’s success, creating one of the most lucrative and deflationary token economies in DeFi. With massive trading volumes, a ballooning user base, and a fast-growing DeFi ecosystem around it, Hyperliquid has positioned itself as a premier layer-1 for financial applications. Looking ahead, its vision of becoming “the blockchain to house all finance” does not seem far-fetched. By bringing more asset classes on-chain (potentially including real-world assets) and continuing to integrate with other networks and partners, Hyperliquid could serve as the backbone for a truly global, 24/7, decentralized financial system. In such a future, the lines between crypto and traditional markets blur – and Hyperliquid’s blend of high performance and trustless architecture may well be the model that bridges them, building the future of onchain finance one block at a time.

Sources:

1. QuickNode Blog – “Hyperliquid in 2025: A High-Performance DEX...” (Architecture, metrics, tokenomics, vision)
2. Artemis Research – “Hyperliquid: A Valuation Model and Bull Case” (Market share, token model, comparisons)
3. The Defiant – “EtherFi Expands to HyperLiquid…HyperBeat” (Ecosystem TVL, institutional interest)
4. BlockBeats – “Inside Hyperliquid’s Growth – Semiannual Report 2025” (On-chain metrics, volume, OI, user stats)
5. Coingape – “Hyperliquid Expands to Solana via Phantom Partnership” (Phantom wallet integration, mobile perps)
6. Mitrade/Cryptopolitan – “Circle integrates USDC with Hyperliquid” (Native USDC launch, $5.5B AUM)
7. Nansen – “What is Hyperliquid? – Blockchain DEX & Trading Explained” (Technical overview, sub-second finality, token uses)
8. DeFi Prime – “Exploring the Hyperliquid Chain Ecosystem: Deep Dive” (Ecosystem projects: DEXs, lending, NFTs, etc.)
9. Hyperliquid Wiki/Docs – Hyperliquid GitBook & Stats (Asset listings via HIPs, stats dashboard)
10. CoinMarketCap – Hyperliquid (HYPE) Listing (Basic info on Hyperliquid L1 and on-chain order book design)

1. Overall Overview​

Quantitative Trading: How to Build Your Own Algorithmic Trading Business is a practical guide written by quantitative trading expert Dr. Ernest P. Chan (often called Ernie Chan), designed to help independent traders build and operate their own algorithmic trading businesses. The first edition was published by Wiley in 2009 as part of its Wiley Trading series, spanning approximately 200 pages. More than a decade after the first edition, the author released a second edition in 2021 (ISBN: 9781119800064, 256 pages), updating and expanding its content.

• Target Audience: The book is aimed at individual investors and small trading teams who wish to use quantitative methods for trading, as well as readers aspiring to work in quantitative trading at financial institutions. The author assumes readers have a basic knowledge of mathematics, statistics, an d programming but does not require an advanced degree. He emphasizes that even a high school-level background in math, statistics, programming, or economics is sufficient to get started with basic quantitative strategies. As the book states: "If you have taken a few high school-level courses in mathematics, statistics, computer programming, or economics, you are probably as qualified as anyone to try your hand at some basic statistical arbitrage strategies." This accessible positioning significantly lowers the barrier to entry for quantitative trading, reflecting the book's mission of "democratizing quantitative trading."

• Main Content: The book is structured around the complete process of developing, testing, and executing quantitative trading strategies, from idea conception to business setup. The author begins by explaining what quantitative trading is and why individual traders can compete with institutions in this field. He then delves into topics such as finding ideas for trading strategies, conducting historical backtests to validate strategy effectiveness, building trading infrastructure and execution systems, and implementing proper money and risk management. The book discusses not only technical details (like data processing, model selection, and backtesting pitfalls) but also business-level considerations (such as the organizational structure of a trading business, broker selection, and hardware/software configuration). Furthermore, the author uses examples and case studies to demonstrate the implementation of specific strategies like mean-reversion, momentum, factor models, and seasonal effects, providing corresponding code or pseudocode to aid reader comprehension.

• Impact and Influence: As one of the classic introductory texts in the quantitative trading field, the book has been widely acclaimed since its publication and is regarded as one of the "bibles for independent quantitative traders." Many readers believe that among the numerous books and articles on quantitative trading, Dr. Chan's work stands out for its practical value. As one industry insider commented: "Many books on quantitative trading are written by authors with no practical experience, or they hold back from revealing their trading secrets. Ernie adheres to a different philosophy: sharing meaningful information and engaging deeply with the quantitative community. He has successfully distilled a vast amount of detailed and complex subject matter into a clear and comprehensive resource from which both novices and professionals can benefit." Following the publication of the first edition, Dr. Chan remained active in the quantitative trading space for over a decade, authoring books like Algorithmic Trading (2013) and Machine Trading (2017) to expand on related topics. In the second edition released in 2021, the author updated the technology and case studies, adding new machine learning techniques for parameter optimization, Python and R code examples, and the latest strategy backtest results, keeping the content current with contemporary developments in quantitative trading. Although tools and market environments have evolved, as emphasized in the preface to the second edition, the fundamental principles of quantitative trading taught in the book have stood the test of time, and its core concepts remain applicable more than a decade later.

In summary, Quantitative Trading is a practice-oriented guide that provides readers with a roadmap to build quantitative trading strategies and businesses from scratch. It helps independent traders challenge Wall Street professionals and offers a valuable knowledge framework and practical tools for investors seeking a systematic and objective approach to trading.

2. Core Ideas Distilled​

The book embodies the author's key viewpoints and philosophy on quantitative trading. The core ideas are distilled below:

• The Essence of Quantitative Trading: Data-Driven, Transcending Subjective Judgment. Quantitative trading (or algorithmic trading) refers to a trading method where buy and sell decisions are made entirely by computer algorithms. This is not merely an upgrade of traditional technical analysis but a process that transforms any quantifiable information (prices, fundamental indicators, news sentiment, etc.) into algorithmic inputs, executed by an automated system to eliminate the influence of human emotions and subjective biases on trading decisions. In simple terms, quantitative trading aims to achieve excess returns in a systematic and disciplined manner, using computers to strictly follow tested strategies and adhere to predefined rules regardless of market conditions or personal feelings.

• The Democratization of Quantitative Trading: An Arena Open to Individuals. Chan emphasizes that quantitative trading is no longer the exclusive domain of large Wall Street institutions. With modern computing resources and public data, individual investors can also make their mark in this field. The author points out that possessing basic mathematical and statistical concepts and some programming/Excel skills is sufficient to develop and test simple statistical arbitrage strategies. This proliferation of technology and knowledge gives independent traders the opportunity to challenge institutional traders in certain niche areas, thus redefining the competitive landscape. The author encourages readers to leverage open-source tools and inexpensive data sources, approaching quantitative trading with a spirit of small-scale experimentation, rather than being intimidated by the high barriers of financial engineering.

• Rigorous Backtesting and Avoiding Pitfalls. Throughout the book, Chan repeatedly stresses that backtesting (testing on historical data) is the core of quantitative strategy development and a crucial basis for independent traders to build confidence and persuade potential investors (if any). However, he warns readers to be cautious with backtest results and to guard against common biases and pitfalls. For instance, he discusses in detail issues like look-ahead bias, data-snooping bias, and survivorship bias, as well as the risks of insufficient sample size and overfitting, which can create "illusory profits." The author recommends using out-of-sample testing by dividing data into training and testing sets, performing sensitivity analysis on strategy parameters, and considering real-world transaction costs and slippage to ensure that strategy returns are robust and not merely a product of curve-fitting.

• The Importance of Business Architecture and Automated Execution. Chan treats quantitative trading as a serious business, not a hobby, reminding readers to focus on the organizational and execution architecture of their trading business in addition to the technology. He discusses the differences between being an independent retail trader and joining a professional trading firm, weighing the pros and cons of aspects like account permissions, leverage limits, and regulatory requirements. Regardless of the model, the author emphasizes that building reliable trading infrastructure and an automated trading system is crucial. On one hand, a semi-automated or fully automated system can significantly reduce the intensity of manual operations and the probability of errors, ensuring consistent strategy execution. On the other hand, good infrastructure (including high-speed, stable internet, low-latency order execution APIs, and rigorous monitoring and alert systems) can help independent traders narrow the execution efficiency gap with large institutions. The author notes that automated trading also helps reduce transaction costs (e.g., through algorithmic order optimization and avoiding high-fee periods) and control the deviation between actual and expected performance, as live results often differ from backtested returns, a problem that can be identified early through simulated trading.

• Money Management and Risk Control: Survive First, Then Thrive. Risk management is placed on an equal, if not higher, level of importance as strategy development. Chan delves into how to determine optimal capital allocation and leverage ratios to enhance returns while controlling risk. The book introduces methods like the Kelly Criterion to calculate the optimal bet size given a certain win rate and payoff ratio, complete with mathematical derivations for the reader's reference. The author also elaborates on a range of risk categories, such as model risk (the risk of the strategy model itself failing), software risk (losses due to programming bugs or system failures), and extreme event risk (abnormal losses from natural disasters or black swan events). These risks are often overlooked by novices, but Chan reminds readers that they must have contingency plans. Furthermore, he emphasizes the importance of psychological preparedness: traders need the mental fortitude and discipline to withstand consecutive losses and continue executing the strategy as long as its statistical edge remains, without deviating from the plan due to short-term setbacks. Overall, his philosophy on money and risk management is to first ensure that devastating losses are avoided while pursuing profit maximization. Only by surviving can one hope to profit in the long run.

• Mean Reversion vs. Momentum Trading: A Trade-off of Different Philosophies. In discussing special topics, Chan provides a comparative analysis of mean-reversion and trend-following (momentum) strategies. He points out that all trading strategies profit on the premise that prices either exhibit mean-reverting characteristics or trend-continuing characteristics; otherwise, if prices follow a random walk, there is no profit to be made. Mean-reversion strategies are based on the idea that prices will eventually return to their long-term equilibrium after deviating, so these strategies often take counter-trend positions, profiting from the correction of excessive volatility. Momentum strategies, conversely, assume that once a trend (up or down) is established, it will persist for some time, so they follow the trend, profiting by riding its continuation. The author particularly emphasizes the different roles of stop-loss orders in these two types of trading. In momentum strategies, if the price moves against the position, it likely signals a trend reversal, and a timely stop-loss can prevent larger losses. In mean-reversion strategies, however, an adverse price movement might just be a normal deviation, and a premature stop-loss could cause one to miss the subsequent profit opportunity as the price reverts to the mean. However, identifying whether the market is currently in a trending or mean-reverting state is not easy—news or fundamental-driven moves are often trending, and one should not "try to stand in front of a freight train" by shorting against the trend. Conversely, non-news-driven fluctuations are more likely to be mean-reverting. He also explores the mechanisms that generate momentum (such as post-earnings announcement drift caused by information diffusion lags, and investor herding behavior) and notes that increased competition shortens the duration of momentum. As information spreads faster and more traders participate, the window for trend continuation often becomes shorter. Consequently, momentum models need constant adjustment to adapt to a faster pace. For mean-reversion strategies, the author introduces statistical methods to estimate the half-life of mean reversion to select holding periods, which is less reliant on subjective judgment than momentum strategies. In summary, Chan advises traders to adopt different risk control and parameter optimization methods based on the strategy's characteristics, fully understanding the performance differences between "mean-reversion" and "momentum" strategies under different market states. The table below summarizes some of the book's comparisons of these two strategy types:

Feature	Mean-Reversion Strategy	Momentum Strategy
Core Logic	Prices revert to a historical mean.	Price trends will continue.
Entry Signal	Buy when price is low, sell when high (relative to mean).	Buy when price is rising, sell when falling.
Positioning	Counter-trend (contrarian).	Trend-following.
Role of Stop-Loss	Risky; can exit prematurely before reversion.	Crucial; signals a potential trend reversal.
Profit Source	Correction of over-reactions and volatility.	Riding the continuation of a price move.
Market Condition	Best in ranging or non-trending markets.	Best in trending markets (driven by news, fundamentals).
Typical Challenge	Identifying a true, stable mean.	Identifying the start and end of a trend.

• The Niche Advantage of Independent Traders: Fly Under the Radar, Focus on Niche Strategies. The author believes that for independent traders to succeed, they should choose strategy areas that are not on the radar of large institutions or are difficult for them to engage in, thereby leveraging the advantage of being "small and nimble." He proposes that when evaluating a strategy, one should ask: "Is this strategy outside the 'radar' coverage of institutional funds?" That is, try to discover obscure strategies or assets, because if a strategy is too obvious and has high capacity, the major players on Wall Street are likely already involved, leaving little room and alpha for smaller players. Conversely, in some niche markets or with specific strategies (such as very short-term statistical arbitrage or strategies driven by very new alternative data), individual traders may be able to avoid direct competition with giants and earn relatively stable excess returns. Chan encourages independent traders to cultivate a keen sense for subtle market inefficiencies. Even if a strategy seems simple and has a low profit margin, if it can consistently make money and does not compete head-on with large funds, it is a good strategy worth considering. This philosophy of "surviving in the cracks" permeates the book and is reflected in the expectations he sets for the reader: rather than fantasizing about finding a magic formula to disrupt the market, it is better to build a few small but effective trading strategies and accumulate returns over time.

These core ideas form the foundation of the author's quantitative trading philosophy: treat trading rationally using scientific methodologies and tools, simplify complex problems, focus on one's own advantages and market inefficiencies, and adhere to discipline for long-term, stable returns.

3. Detailed Chapter Summaries​

The book is divided into 8 chapters by theme, along with several appendices. The following is an overview of the main content and key concepts of each chapter:

• Chapter 1. The Whats, Whos, and Whys of Quantitative Trading This opening chapter answers three fundamental questions: "What is quantitative trading, who can do it, and why should they?" The author first defines quantitative trading: a trading method that uses computer algorithms to make decisions automatically based on quantitative indicators, distinguishing it from traditional technical analysis and discretionary trading. Next, the author addresses the question of who can become a quantitative trader, emphasizing that independent traders can be perfectly competent with basic math, programming, and statistical intuition, without needing a prestigious degree or a Wall Street background. He lists several major advantages of independent quantitative trading, which constitute its business value: first, Scalability (an effective algorithmic strategy can proportionally increase profits as capital grows); second, Time Efficiency (algorithms can run automatically, reducing the need for manual monitoring, allowing a trader to manage multiple strategies and have more free time); third, since decisions are entirely data-driven, little to no marketing is needed to validate a strategy's effectiveness (unlike manual trading, which requires telling a story to attract capital)—the performance itself is the best "marketing." These factors together form the business motivation for individuals to engage in quantitative trading. The chapter concludes by outlining the development trajectory of quantitative trading and the reader's path forward, encouraging beginners to start with small capital and simple strategies, gradually accumulating experience and capital (a pyramid-style growth), and setting the stage for subsequent chapters.

• Chapter 2. Fishing for Ideas This chapter focuses on how to capture and evaluate ideas for quantitative trading strategies. The author first answers "where to find good strategy ideas," pointing out that inspiration can come from various sources: academic papers, financial blogs, trading forums, business news, and even everyday experiences. But more importantly, he discusses how to assess whether a strategy is suitable for you. Chan provides a series of self-assessment dimensions to help readers filter strategies that match their personal circumstances:

  • Available Work Time: Some strategies require high-frequency monitoring and position adjustments, suitable for full-time traders. For those who can only trade part-time, they should choose low-frequency or end-of-day execution strategies.
  • Programming Ability: If a reader's programming skills are not strong, they can start with simple strategies in Excel or chart-based trading. Conversely, those proficient in programming can directly implement complex models using MATLAB, Python, etc.
  • Trading Capital Size: The amount of capital affects strategy choice. Small capital is suitable for low-capacity strategies like short-term trading in small-cap stocks or high-frequency arbitrage. Large capital needs to consider strategy scalability and market capacity to avoid impacting the market itself. (Chan provides a table comparing choices at different capital levels, e.g., low-capital traders might lean towards joining a prop trading firm for leverage, while high-capital traders could consider an independent account).
  • Return Objectives: Different strategies have different risk-return profiles and should align with personal financial goals. Some seek stable, modest returns, while others aim for high returns and are willing to bear high volatility; strategies should be matched accordingly. After this self-assessment, the latter half of the chapter provides key points for a "preliminary strategy feasibility screen"—checking critical questions before committing to a full backtest:
  • Benchmark Comparison & Return Robustness: Does the strategy's historical performance significantly outperform a simple benchmark (like an index), and is the source of returns reasonable? Is the equity curve smooth, or is it highly dependent on a few large trades?
  • Maximum Drawdown & Duration: What is the strategy's historical maximum drawdown and its duration? Is the drawdown so deep and long that an investor couldn't tolerate it? This is an intuitive indicator of the strategy's risk level.
  • Impact of Transaction Costs: If actual commissions and slippage are considered, is the strategy's profit wiped out? High-frequency strategies, in particular, are extremely sensitive to costs.
  • Survivorship Bias in Data: Does the historical data used suffer from survivorship bias (only including surviving securities while ignoring those that were delisted)? Incomplete data leads to overly optimistic backtest results. Chan warns that free data (like from Yahoo Finance) often has this bias, while bias-free data is expensive and hard to obtain.
  • Long-Term Validity: Has the strategy's performance changed over the decades? That is, was it only effective in a specific historical period, or has it maintained its edge through changing market conditions? If a strategy has failed recently, be wary that it may have been arbitraged away.
  • Data-Snooping Bias (Data-Dredging Pitfall): Could this strategy be a product of overfitting? Chan stresses suspicion of "coincidental good performance"—if parameters were chosen after the fact to match historical data, the returns might be spurious noise. This must be validated with rigorous out-of-sample testing.
  • Institutional Attention: The aforementioned question of "flying below the institutional radar." If a strategy is already used by many large hedge funds, it will be difficult for an individual to compete. Niche strategies have a higher chance of success. Through this series of questions, the author helps readers conduct a preliminary feasibility assessment of strategy ideas before investing valuable time and effort in full development.

• Chapter 3. Backtesting This is one of the more technical chapters, systematically explaining how to correctly conduct historical backtesting, including the tools to use, data processing, and avoiding common mistakes.

  • Tools: Chan introduces several common backtesting platforms and tools: Spreadsheets (Excel) for beginners, MATLAB for powerful scientific computing (an appendix provides a quick intro), Python/R (added in the second edition as they have become mainstream), and integrated platforms like TradeStation.
  • Data: He discusses acquiring and processing historical data, emphasizing the importance of adjusted prices (for splits and dividends) and the critical issue of survivorship bias. He notes that "a survivorship-bias-free database is usually not cheap."
  • Performance Metrics: Beyond standard metrics like Sharpe ratio, Chan emphasizes focusing on Maximum Drawdown and its recovery period, as these directly relate to a strategy's real-world tolerability.
  • Backtesting Pitfalls: This is a crucial section covering:
    • Look-Ahead Bias: Using future information in a backtest.
    • Data-Snooping Bias: Reporting only the best results from many tested strategies. Chan recommends strict out-of-sample validation to combat this.
    • Insufficient Sample Size: A small number of trades makes results statistically unreliable.
    • Overfitting: Creating a strategy with too many parameters that is "deceptively optimized" for the past. He suggests cross-validation or rolling-sample backtests to check for robustness.
    • Neglecting Transaction Costs: Ignoring commissions and slippage. Chan advises being conservative and even overestimating costs. The chapter concludes that the purpose of backtesting is not just to find "optimal" historical parameters but to validate the strategy's logic and understand its risks.

• Chapter 4. Setting up Your Business This chapter shifts from the technical to the practical, discussing how to start and structure quantitative trading as a business.

  • Business Structure: Chan weighs the pros and cons of two paths: trading as an independent retail trader (full autonomy but limited leverage and higher costs) versus joining/forming a proprietary trading firm (higher leverage, lower costs, but profit sharing and less autonomy).
  • Broker Selection: He lists key criteria for choosing a brokerage: commission rates, available leverage (e.g., portfolio margin), market access, API quality, and reputation. Interactive Brokers is mentioned as a suitable choice for quants.
  • Infrastructure: He covers the physical setup for an independent trader: hardware (powerful computers), network connectivity (high-speed internet), data feeds, and backup/disaster recovery plans (UPS, backup internet). He also introduces the concept of co-location for latency-sensitive strategies, though he notes it's unnecessary for most independent traders. The core message is to treat quantitative trading as a serious entrepreneurial venture, planning the business architecture and infrastructure carefully.

• Chapter 5. Execution Systems This chapter delves into the process of trade execution and building an automated system.

  • Automation Levels: Chan recommends beginners start with a semi-automated system (e.g., a program generates signals, trader executes manually) before moving to a fully automated system that connects to a broker's API to handle everything from signal generation to order placement.
  • System Design: He emphasizes building robust and fault-tolerant systems that can handle exceptions like network outages or rejected orders.
  • Minimizing Transaction Costs: An automated system can intelligently reduce costs through algorithmic order splitting or choosing between market and limit orders.
  • Paper Trading: The author strongly recommends testing the system in a live market simulation (paper trading) before risking real money. This helps identify bugs and logistical issues.
  • Performance Slippage: Chan acknowledges that live performance often falls short of backtested results due to factors like slippage, latency, and market impact. He advises traders to monitor these discrepancies and continuously refine the execution model. The key takeaway is that efficient and reliable execution is the "last mile" problem in converting a good strategy into actual profits.

• Chapter 6. Money and Risk Management This chapter focuses on managing capital and controlling risk, which is crucial for survival and long-term profitability.

  • Optimal Capital Allocation: Chan introduces the Kelly Criterion as a theoretical guide for determining the optimal position size to maximize long-term wealth growth. However, he warns that using the full Kelly stake can be too volatile and suggests using a "half-Kelly" or "fractional Kelly" approach in practice.
  • Types of Risk: The chapter covers a comprehensive view of risk:
    • Portfolio-Level Risk: Setting risk budgets for strategies and monitoring correlations between them.
    • Leverage Risk: Using leverage cautiously and monitoring margin requirements.
    • Model Risk: The risk that the strategy's underlying assumptions are wrong or become invalid.
    • Technological and Operational Risk: Risks from software bugs, hardware failures, or power outages. He recommends having contingency plans.
    • Psychological Risk: The risk of a trader emotionally interfering with a systematic strategy. The guiding philosophy is "risk-first." Success depends not just on capturing gains but on controlling downside and surviving long enough to profit.

• Chapter 7. Special Topics in Quantitative Trading This chapter covers a collection of advanced topics and specific strategy types.

  • Mean Reversion vs. Momentum: A detailed comparison of the two dominant strategy philosophies, emphasizing the importance of identifying the market "regime" (trending or ranging).
  • Regime Switching and Conditional Parameters: Discusses building models that adapt to changing market conditions. Example 7.1 shows using machine learning to detect market turning points and adjust strategy parameters accordingly.
  • Stationarity and Cointegration: Explains the statistical concept of cointegration for pairs trading. The GLD vs. GDX pairs trade (Example 3.6/7.2) is a classic case study used to demonstrate the entire process from testing for cointegration to backtesting the strategy. A counterexample using KO vs. PEP (Example 7.3) shows that high correlation does not guarantee cointegration.
  • Factor Models: Introduces multifactor models (like Fama-French) for explaining returns and managing risk. He shows how Principal Component Analysis (PCA) can be used to extract underlying factors (Example 7.4).
  • Exit Strategies: Discusses the importance of a well-defined exit plan, covering methods like profit targets, stop-losses, time-based exits, and trailing stops.
  • Seasonal Trading Strategies: Explores calendar effects, using the "January Effect" in small-cap stocks as a concrete, backtested example (Example 7.6).
  • High-Frequency Trading (HFT): Briefly introduces HFT concepts and strategies (market making, latency arbitrage), acknowledging that while true HFT is out of reach for most individuals, the principles can be informative.
  • High Leverage vs. High Beta: A discussion on whether it's better to leverage a low-risk portfolio or invest in a high-risk (high-beta) one without leverage, concluding that a high-Sharpe, low-volatility strategy with modest leverage is generally superior.

• Chapter 8. Conclusion The final chapter summarizes the book's key messages and provides guidance for the reader's next steps. Chan reiterates that independent traders can succeed by following a disciplined, scientific path. He encourages readers to:

  • Continue Learning and Practicing: Read more, follow blogs, and experiment with small amounts of capital.
  • Network and Collaborate: Find partners or mentors to build a team.
  • Consider Career Paths: Use self-developed strategies as a portfolio to seek jobs in the industry.
  • Stay Current: Keep up with new technologies and market changes, such as the use of machine learning. The chapter ends on a realistic yet encouraging note, emphasizing patience and persistence as the keys to long-term success.

• Appendices:

  • Appendix A: A brief tutorial on MATLAB for readers unfamiliar with the software.
  • Appendix B (Implicit): A mathematical derivation of the Kelly Criterion for normally distributed returns.

4. Specific Methodology​

The book outlines a systematic methodology for developing and launching a quantitative trading business. This process can be summarized in the following logical steps:

1. Strategy Ideation & Selection: Start by sourcing ideas from multiple channels (research, observation) and then perform a preliminary feasibility screen based on logic, personal fit (time, skills, capital), and institutional competition.
2. Data Collection & Preparation: Obtain the necessary historical data, prioritizing quality (bias-free if possible). Clean, adjust (for splits/dividends), and format the data for the strategy.
3. Backtest Modeling & Validation: Build a rigorous backtesting engine that avoids look-ahead bias and incorporates realistic costs. Validate the strategy's performance using in-sample optimization and out-of-sample testing to ensure robustness and avoid overfitting.
4. Strategy Optimization & Confirmation: Refine the strategy based on backtest results, but avoid excessive curve-fitting. The goal is a simple, robust model. Confirm the final model and consider building a portfolio of uncorrelated strategies.
5. Business Structure & Account Preparation: Decide on the legal and operational structure (retail vs. prop firm). Set up the necessary brokerage accounts, secure funding, and ensure all API connections are working.
6. Execution System Development: Build or configure an automated or semi-automated trading system to translate signals into live orders. Test this system thoroughly in a simulated environment first.
7. Live Trading & Monitoring: Deploy the strategy with real capital. Continuously monitor its performance against expectations and historical backtests. Maintain strict discipline and adhere to risk management rules.
8. Strategy Iteration & New Development: Use live feedback to make informed adjustments to the existing strategy. Simultaneously, continue the research and development cycle to build new, uncorrelated strategies to grow the business.

Two principles underpin this methodology:

• Combining Quantitative and Qualitative Analysis: While data-driven, Chan advises using common sense and economic intuition to vet ideas and manage risks.
• Prioritizing Simplicity: Following Einstein's maxim, "Make things as simple as possible, but not simpler," he advocates for simple, understandable, and maintainable strategies over complex "black boxes."

5. Practical Application Cases​

The book is rich with practical examples to illustrate its concepts. Key cases include:

Case Study	Chapter(s)	Key Concept Illustrated	Details
GLD vs. GDX Pairs Trade	3, 5, 7	Cointegration, Mean Reversion, Backtesting	A detailed walkthrough of testing for cointegration, optimizing parameters on a training set, validating on a test set, and calculating the mean-reversion half-life.
KO vs. PEP Cointegration Test	7	Cointegration vs. Correlation	Demonstrates that two highly correlated stocks in the same industry are not necessarily cointegrated, warning against making assumptions without statistical proof.
Post-Earnings Drift (PEAD)	7	Momentum Strategy	Cites research on the PEAD phenomenon as a classic example of a momentum strategy driven by the slow diffusion of fundamental information.
January Effect	7	Seasonal Strategy	Provides a backtest (with MATLAB code) of a strategy that buys small-cap stocks in January, showing how a market anomaly can be turned into a rule-based strategy.
Machine Learning for Regimes	7	Regime Switching, Advanced Methods	Introduces the idea of using ML models to predict shifts in market behavior (e.g., from trending to ranging) to adapt strategy parameters dynamically.
Kelly Criterion Application	6	Money Management, Position Sizing	Provides a clear, formula-based method for determining optimal bet size to maximize long-term growth while managing risk, with practical advice to use a fractional approach.
Tool & Data Usage	Various	Practical Skills	Includes code snippets for tasks like scraping historical data from Yahoo Finance with MATLAB, demonstrating how to acquire and process data for analysis.

These concrete examples serve as templates, enabling readers to move from theory to practice and apply the book's methods to their own ideas.

6. Author's Background Information​

Understanding the author, Dr. Ernest P. Chan, is key to appreciating the book's value.

• Education and Wall Street Experience: Dr. Chan holds a Ph.D. in theoretical physics from Cornell University. His strong quantitative background led him to a career on Wall Street, where he worked as a quantitative analyst and developer at institutions like IBM Research, Morgan Stanley, Credit Suisse, and the hedge fund Millennium Partners. This experience gave him hands-on expertise in statistical arbitrage, high-frequency trading, and data mining.

• Entrepreneurship and Consulting: After leaving Wall Street, Chan founded his own quantitative investment management firm, QTS Capital Management, LLC, where he traded systematic strategies for private clients. He later founded PredictNow.ai, a financial machine learning software and consulting company. His entrepreneurial and consulting work has kept him at the cutting edge of practical quantitative finance.

• Author and Educator: Dr. Chan is a prolific author known for his practical and accessible writing style. His other popular books include Algorithmic Trading: Winning Strategies and Their Rationale (2013) and Machine Trading: Deploying Computer Algorithms to Conquer the Markets (2017), and most recently, Generative AI for Trading and Asset Management (2023). His willingness to share code, data, and hard-won lessons has earned him a stellar reputation in the quant community.

• Community Influence: Since 2006, Dr. Chan has maintained a popular blog (epchan.blogspot.com), sharing insights and strategy ideas. He is also an active educator, teaching courses for institutions like QuantInsti and Nanyang Technological University in Singapore.

In summary, Dr. Chan is a respected practitioner-scholar who has successfully bridged the gap between institutional quantitative finance and the independent trading community. His work has been instrumental in demystifying the field and empowering individuals. As one reader, Corey Hoffstein, put it, "Ernie's book is the ideal guide for those aspiring to make the journey from 0 to 1 in quantitative trading." The authority of the book stems not only from its content but from the author's deep and credible experience in both theory and practice.

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References:

• Chan, Ernest P. Quantitative Trading: How to Build Your Own Algorithmic Trading Business. Wiley, 1st Ed. 2009 & 2nd Ed. 2021. (Table of Contents and excerpts).
• Chan, Ernest P. – Preface to the Second Edition and cover copy (2021); Praise for the book.
• SoBrief Book Summary – Quantitative Trading Key Takeaways.
• QuantInsti Faculty Bio – Dr. Ernest P. Chan (education, career, books).
• Akademika Book Detail – Product info and author bio.
• Investarr PDF Excerpts – Example 3.6 (GLD-GDX pair trade); Example 7.1 (Regime switching ML); Example 7.3 (KO-PEP cointegration test); Example 7.6 (January effect code); Momentum vs Mean-reversion discussion; Data and Yahoo Finance references.

Introduction​

Decentralized Physical Infrastructure Networks (DePIN) are blockchain-based projects that incentivize people to deploy real-world hardware in exchange for crypto tokens. By leveraging idle or underutilized resources – from wireless radios to hard drives and GPUs – DePIN projects create crowdsourced networks providing tangible services (connectivity, storage, computing, etc.). This model transforms normally idle infrastructure (like unused bandwidth, disk space, or GPU power) into active, income-generating networks by rewarding contributors with tokens. Major early examples include Helium (crowdsourced wireless networks) and Filecoin (distributed data storage), and newer entrants target GPU computing and 5G coverage sharing (e.g. Render Network, Akash, io.net).

DePIN’s promise lies in distributing the costs of building and operating physical networks via token incentives, thus scaling networks faster than traditional centralized models. In practice, however, these projects must carefully design economic models to ensure that token incentives translate into real service usage and sustainable value. Below, we analyze the economic models of key DePIN networks, evaluate how effectively token rewards have driven actual infrastructure use, and assess how these projects are coupling with the booming demand for AI-related compute.

Economic Models of Leading DePIN Projects​

Helium (Decentralized Wireless IoT & 5G)​

Helium pioneered a decentralized wireless network by incentivizing individuals to deploy radio hotspots. Initially focused on IoT (LoRaWAN) and later expanded to 5G small-cell coverage, Helium’s model centers on its native token HNT. Hotspot operators earn HNT by participating in Proof-of-Coverage (PoC) – essentially proving they are providing wireless coverage in a given location. In Helium’s two-token system, HNT has utility through Data Credits (DC): users must burn HNT to mint non-transferable DC, which are used to pay for actual network usage (device connectivity) at a fixed rate of $0.0001 per 24 bytes. This burn mechanism creates a burn-and-mint equilibrium where increased network usage (DC spending) leads to more HNT being burned, reducing supply over time.

Originally, Helium operated on its own blockchain with an inflationary issuance of HNT that halved every two years (yielding a gradually decreasing supply and an eventual max around ~223 million HNT in circulation). In 2023, Helium migrated to Solana and introduced a “network of networks” framework with sub-DAOs. Now, Helium’s IoT network and 5G mobile network each have their own tokens (IOT and MOBILE respectively) rewarded to hotspot operators, while HNT remains the central token for governance and value. HNT can be redeemed for subDAO tokens (and vice versa) via treasury pools, and HNT is also used for staking in Helium’s veHNT governance model. This structure aims to align incentives in each sub-network: for example, 5G hotspot operators earn MOBILE tokens, which can be converted to HNT, effectively tying rewards to the success of that specific service.

Economic value creation: Helium’s value is created by providing low-cost wireless access. By distributing token rewards, Helium offloaded the capex of network deployment onto individuals who purchased and ran hotspots. In theory, as businesses and IoT devices use the network (by spending DC that require burning HNT), that demand should support HNT’s value and fund ongoing rewards. Helium sustains its economy through a burn-and-spend cycle: network users buy HNT (or use HNT rewards) and burn it for DC to use the network, and the protocol mints HNT (according to a fixed schedule) to pay hotspot providers. In Helium’s design, a portion of HNT emissions was also allocated to founders and a community reserve, but the majority has always been for hotspot operators as an incentive to build coverage. As discussed later, Helium’s challenge has been getting enough paying demand to balance the generous supply-side incentives.

Filecoin (Decentralized Storage Network)​

Filecoin is a decentralized storage marketplace where anyone can contribute disk space and earn tokens for storing data. Its economic model is built around the FIL token. Filecoin’s blockchain rewards storage providers (miners) with FIL block rewards for provisioning storage and correctly storing clients’ data – using cryptographic proofs (Proof-of-Replication and Proof-of-Spacetime) to verify data is stored reliably. Clients, in turn, pay FIL to miners to have their data stored or retrieved, negotiating prices in an open market. This creates an incentive loop: miners invest in hardware and stake FIL collateral (to guarantee service quality), earning FIL rewards for adding storage capacity and fulfilling storage deals, while clients spend FIL for storage services.

Filecoin’s token distribution is heavily weighted toward incentivizing storage supply. FIL has a maximum supply of 2 billion, with 70% reserved for mining rewards. (In fact, ~1.4 billion FIL are allocated to be released over time as block rewards to storage miners over many years.) The remaining 30% was allocated to stakeholders: 15% to Protocol Labs (the founding team), 10% to investors, and 5% to the Filecoin Foundation. Block reward emissions follow a somewhat front-loaded schedule (with a six-year half-life), meaning supply inflation was highest in the early years to quickly bootstrap a large storage network. To balance this, Filecoin requires miners to lock up FIL as collateral for each gigabyte of data they pledge to store – if they fail to prove the data is retained, they can be penalized (slashed) by losing some collateral. This mechanism aligns miner incentives with reliable service.

Economic value creation: Filecoin creates value by offering censorship-resistant, redundant data storage at potentially lower costs than centralized cloud providers. The FIL token’s value is tied to demand for storage and the utility of the network: clients must obtain FIL to pay for storing data, and miners need FIL (both for collateral and often to cover costs or as revenue). Initially, much of Filecoin’s activity was driven by miners racing to earn tokens – even storing zero-value or duplicated data just to increase their storage power and earn block rewards. To encourage useful storage, Filecoin introduced the Filecoin Plus program: clients with verified useful data (e.g. open datasets, archives) can register deals as “verified,” which gives miners 10× the effective power for those deals, translating into proportionally larger FIL rewards. This has incentivized miners to seek out real clients and has dramatically increased useful data stored on the network. By late 2023, Filecoin’s network had grown to about 1,800 PiB of active deals, up 3.8× year-over-year, with storage utilization rising to ~20% of total capacity (from only ~3% at the start of 2023). In other words, token incentives bootstrapped enormous capacity, and now a growing fraction of that capacity is being filled by paying customers – a sign of the model beginning to sustain itself with real demand. Filecoin is also expanding into adjacent services (see AI Compute Trends below), which could create new revenue streams (e.g. decentralized content delivery and compute-over-data services) to bolster the FIL economy beyond simple storage fees.

Render Network (Decentralized GPU Rendering & Compute)​

Render Network is a decentralized marketplace for GPU-based computation, originally focused on rendering 3D graphics and now also supporting AI model training/inference jobs. Its native token RNDR (recently updated to the ticker RENDER on Solana) powers the economy. Creators (users who need GPU work done) pay in RNDR for rendering or compute tasks, and Node Operators (GPU providers) earn RNDR by completing those jobs. This basic model turns idle GPUs (from individual GPU owners or data centers) into a distributed cloud rendering farm. To ensure quality and fairness, Render uses escrow smart contracts: clients submit jobs and burn the equivalent RNDR payment, which is held until node operators submit proof of completing the work, then the RNDR is released as reward. Originally, RNDR functioned as a pure utility/payment token, but the network has recently overhauled its tokenomics to a Burn-and-Mint Equilibrium (BME) model to better balance supply and demand.

Under the BME model, all rendering or compute jobs are priced in stable terms (USD) and paid in RENDER tokens, which are **burned upon job completion. In parallel, the protocol mints new RENDER tokens on a predefined declining emissions schedule to compensate node operators and other participants. In effect, user payments for work destroy tokens while the network inflates tokens at a controlled rate as mining rewards – the net supply can increase or decrease over time depending on usage. The community approved an initial emission of ~9.1 million RENDER in the first year of BME (mid-2023 to mid-2024) as network incentives, and set a long-term max supply of about 644 million RENDER (up from the initial 536.9 million RNDR that were minted at launch). Notably, RENDER’s token distribution heavily favored ecosystem growth: 65% of the initial supply was allocated to a treasury (for future network incentives), 25% to investors, and 10% to team/advisors. With BME, that treasury is being deployed via the controlled emissions to reward GPU providers and other contributors, while the burn mechanism ties those rewards directly to platform usage. RNDR also serves as a governance token (token holders can vote on Render Network proposals). Additionally, node operators on Render can stake RNDR to signal their reliability and potentially receive more work, adding another incentive layer.

Economic value creation: Render Network creates value by supplying on-demand GPU computing at a fraction of the cost of traditional cloud GPU instances. By late 2023, Render’s founder noted that studios had already used the network to render movie-quality graphics with significant cost and speed advantages – “one tenth the cost” and with massive aggregated capacity beyond any single cloud provider. This cost advantage is possible because Render taps into dormant GPUs globally (from hobbyist rigs to pro render farms) that would otherwise be idle. With rising demand for GPU time (for both graphics and AI), Render’s marketplace meets a critical need. Crucially, the BME token model means token value is directly linked to service usage: as more rendering and AI jobs flow through the network, more RENDER is burned (creating buy pressure or reducing supply), while node incentives scale up only as those jobs are completed. This helps avoid “paying for nothing” – if network usage stagnates, the token emissions eventually outpace burns (inflating supply), but if usage grows, the burns can offset or even exceed emissions, potentially making the token deflationary while still rewarding operators. The strong interest in Render’s model was reflected in the market: RNDR’s price rocketed in 2023, rising over 1,000% in value as investors anticipated surging demand for decentralized GPU services amid the AI boom. Backed by OTOY (a leader in cloud rendering software) and used in production by some major studios, Render Network is positioned as a key player at the intersection of Web3 and high-performance computing.

Akash Network (Decentralized Cloud Compute)​

Akash is a decentralized cloud computing marketplace that enables users to rent general compute (VMs, containers, etc.) from providers with spare server capacity. Think of it as a decentralized alternative to AWS or Google Cloud, powered by a blockchain-based reverse auction system. The native token AKT is central to Akash’s economy: clients pay for compute leases in AKT, and providers earn AKT for supplying resources. Akash is built on the Cosmos SDK and uses a delegated Proof-of-Stake blockchain for security and coordination. AKT thus also functions as a staking and governance token – validators stake AKT (and users delegate AKT to validators) to secure the network and earn staking rewards.

Akash’s marketplace operates via a bidding system: a client defines a deployment (CPU, RAM, storage, possibly GPU requirements) and a max price, and multiple providers can bid to host it, driving the price down. Once the client accepts a bid, a lease is formed and the workload runs on the chosen provider’s infrastructure. Payments for leases are handled by the blockchain: the client escrows AKT and it streams to the provider over time for as long as the deployment is active. Uniquely, the Akash network charges a protocol “take rate” fee on each lease to fund the ecosystem and reward AKT stakers: 10% of the lease amount if paid in AKT (or 20% if paid in another currency) is diverted as fees to the network treasury and stakers. This means AKT stakers earn a portion of all usage, aligning the token’s value with actual demand on the platform. To improve usability for mainstream users, Akash has integrated stablecoin and credit card payments (via its console app): a client can pay in USD stablecoin, which under the hood is converted to AKT (with a higher fee rate). This reduces the volatility risk for users while still driving value to the AKT token (since those stablecoin payments ultimately result in AKT being bought/burned or distributed to stakers).

On the supply side, AKT’s tokenomics are designed to incentivize long-term participation. Akash began with 100 million AKT at genesis and has a max supply of 389 million via inflation. The inflation rate is adaptive based on the proportion of AKT staked: it targets 20–25% annual inflation if the staking ratio is low, and around 15% if a high percentage of AKT is staked. This adaptive inflation (a common design in Cosmos-based chains) encourages holders to stake (contributing to network security) by rewarding them more when staking participation is low. Block rewards from inflation pay validators and delegators, as well as funding a reserve for ecosystem growth. AKT’s initial distribution set aside allocations for investors, the core team (Overclock Labs), and a foundation pool for ecosystem incentives (e.g. an early program in 2024 funded GPU providers to join).

Economic value creation: Akash creates value by offering cloud computing at potentially much lower costs than incumbent cloud providers, leveraging underutilized servers around the world. By decentralizing the cloud, it also aims to fill regional gaps and reduce reliance on a few big tech companies. The AKT token accrues value from multiple angles: demand-side fees (more workloads = more AKT fees flowing to stakers), supply-side needs (providers may hold or stake earnings, and need to stake some AKT as collateral for providing services), and general network growth (AKT is needed for governance and as a reserve currency in the ecosystem). Importantly, as more real workloads run on Akash, the proportion of AKT in circulation that is used for staking and fee deposits should increase, reflecting real utility. Initially, Akash saw modest usage for web services and crypto infrastructure hosting, but in late 2023 it expanded support for GPU workloads – making it possible to run AI training, machine learning, and high-performance compute jobs on the network. This has significantly boosted Akash’s usage in 2024. By Q3 2024, the network’s metrics showed explosive growth: the number of active deployments (“leases”) grew 1,729% year-on-year, and the average fee per lease (a proxy for complexity of workloads) rose 688%. In practice, this means users are deploying far more applications on Akash and are willing to run larger, longer workloads (many involving GPUs) – evidence that token incentives have attracted real paying demand. Akash’s team reported that by the end of 2024, the network had over 700 GPUs online with ~78% utilization (i.e. ~78% of GPU capacity rented out at any time). This is a strong signal of efficient token incentive conversion (see next section). The built-in fee-sharing model also means that as this usage grows, AKT stakers receive protocol revenue, effectively tying token rewards to actual service revenue – a healthier long-term economic design.

io.net (Decentralized GPU Cloud for AI)​

io.net is a newer entrant (built on Solana) aiming to become the “world’s largest GPU network” specifically geared toward AI and machine learning workloads. Its economic model draws lessons from earlier projects like Render and Akash. The native token IO has a fixed maximum supply of 800 million. At launch, 500 million IO were pre-minted and allocated to various stakeholders, and the remaining 300 million IO are being emitted as mining rewards over a 20-year period (distributed hourly to GPU providers and stakers). Notably, io.net implements a revenue-based burn mechanism: a portion of network fees/revenue is used to burn IO tokens, directly tying token supply to platform usage. This combination – a capped supply with time-released emissions and a burn driven by usage – is intended to ensure long-term sustainability of the token economy.

To join the network as a GPU node, providers are required to stake a minimum amount of IO as collateral. This serves two purposes: it deters malicious or low-quality nodes (as they have “skin in the game”), and it reduces immediate sell pressure from reward tokens (since nodes must lock up some tokens to participate). Stakers (which can include both providers and other participants) also earn a share of network rewards, aligning incentives across the ecosystem. On the demand side, customers (AI developers, etc.) pay for GPU compute on io.net, presumably in IO tokens or possibly stable equivalents – the project claims to offer cloud GPU power at up to 90% lower cost than traditional providers like AWS. These usage fees drive the burn mechanism: as revenue flows in, a portion of tokens get burned, linking platform success to token scarcity.

Economic value creation: io.net’s value proposition is aggregating GPU power from many sources (data centers, crypto miners repurposing mining rigs, etc.) into a single network that can deliver on-demand compute for AI at massive scale. By aiming to onboard over 1 million GPUs globally, io.net seeks to out-scale any single cloud and meet the surging demand for AI model training and inference. The IO token captures value through a blend of mechanisms: supply is limited (so token value can grow if demand for network services grows), usage burns tokens (directly creating value feedback to the token from service revenue), and token rewards bootstrap supply (gradually distributing tokens to those who contribute GPUs, ensuring the network grows). In essence, io.net’s economic model is a refined DePIN approach where supply-side incentives (hourly IO emissions) are substantial but finite, and they are counter-balanced by token sinks (burns) that scale with actual usage. This is designed to avoid the trap of excessive inflation with no demand. As we will see, the AI compute trend provides a large and growing market for networks like io.net to tap into, which could drive the desired equilibrium where token incentives lead to robust service usage. (io.net is still emerging, so its real-world metrics remain to be proven, but its design clearly targets the AI compute sector’s needs.)

Table 1: Key Economic Model Features of Selected DePIN Projects

Project	Sector	Token (Ticker)	Supply & Distribution	Incentive Mechanism	Token Utility & Value Flow
Helium	Decentralized Wireless (IoT & 5G)	Helium Network Token (HNT); plus sub-tokens IOT & MOBILE	Variable supply, decreasing issuance: HNT emissions halved every ~2 years (as of original blockchain), targeting ~223M HNT in circulation after 50 years. Migrated to Solana with 2 new sub-tokens: IOT and MOBILE rewarded to IoT and 5G hotspot owners.	Proof-of-Coverage mining: Hotspots earn IOT or MOBILE tokens for providing coverage (LoRaWAN or 5G). Those sub-tokens can be converted to HNT via treasury pools. HNT is staked for governance (veHNT) and is the basis for rewards across networks.	Network usage via Data Credits: HNT is burned to create Data Credits (DC) for device connectivity (fixed price $0.0001 per 24 bytes). All network fees (DC purchases) effectively burn HNT (reducing supply). Token value thus ties to demand for IoT/Mobile data transfer. HNT’s value also backs the subDAO tokens (giving them convertibility to a scarce asset).
Filecoin	Decentralized Storage	Filecoin (FIL)	Capped supply 2 billion: 70% allocated to storage mining rewards (released over decades); ~30% to Protocol Labs, investors, and foundation. Block rewards follow a six-year half-life (higher inflation early, tapering later).	Storage mining: Storage providers earn FIL block rewards proportional to proven storage contributed. Clients pay FIL for storing or retrieving data. Miners put up FIL collateral that can be slashed for failure. Filecoin Plus gives 10× power reward for “useful” client data to incentivize real storage.	Payment & collateral: FIL is the currency for storage deals – clients spend FIL to store data, creating organic demand for the token. Miners lock FIL as collateral (temporarily reducing circulating supply) and earn FIL for useful service. As usage grows, more FIL gets tied up in deals and collateral. Network fees (for transactions) are minimal (Filecoin focuses on storage fees which go to miners). Long term, FIL value depends on data storage demand and emerging use cases (e.g. Filecoin Virtual Machine enabling smart contracts for data, potentially generating new fee sinks).
Render Network	Decentralized GPU Compute (Rendering & AI)	Render Token (RNDR / RENDER)	Initial supply ~536.9M RNDR, increased to max ~644M via new emissions. Burn-and-Mint Equilibrium: New RENDER emitted on a fixed schedule (20% inflation pool over ~5 years, then tail emissions). Emissions fund network incentives (node rewards, etc.). Burning: Users’ payments in RENDER are burned for each completed job. Distribution: 65% treasury (network ops and rewards), 25% investors, 10% team/advisors.	Marketplace for GPU work: Node operators do rendering/compute tasks and earn RENDER. Jobs are priced in USD but paid in RENDER; the required tokens are burned when the work is done. In each epoch (e.g. weekly), new RENDER is minted and distributed to node operators based on the work they completed. Node operators can also stake RNDR for higher trust and potential job priority.	Utility & value flow: RENDER is the fee token for GPU services – content creators and AI developers must acquire and spend it to get work done. Because those tokens are burned, usage directly reduces supply. New token issuance compensates workers, but on a declining schedule. If network demand is high (burn > emission), RENDER becomes deflationary; if demand is low, inflation may exceed burns (incentivizing more supply until demand catches up). RENDER also governs the network. The token’s value is thus closely linked to platform usage – in fact, RNDR rallied ~10× in 2023 as AI-driven demand for GPU compute skyrocketed, indicating market confidence that usage (and burns) will be high.
Akash Network	Decentralized Cloud (general compute & GPU)	Akash Token (AKT)	Initial supply 100M; max supply 389M. Inflationary PoS token: Adaptive inflation ~15–25% annually (dropping as staking % rises) to incentivize staking. Ongoing emissions pay validators and delegators. Distribution: 34.5% investors, 27% team, 19.7% foundation, 8% ecosystem, 5% testnet (with lock-ups/vesting).	Reverse-auction marketplace: Providers bid to host deployments; clients pay in AKT for leases. Fee pool: 10% of AKT payments (or 20% of payments in other tokens) goes to the network (stakers) as a protocol fee. Akash uses a Proof-of-Stake chain – validators stake AKT to secure the network and earn block rewards. Clients can pay via AKT or integrated stablecoins (with conversion).	Utility & value flow: AKT is used for all transactions (either directly or via conversion from stable payments). Clients buy AKT to pay for compute leases, creating demand as network usage grows. Providers earn AKT and can sell or stake it. Staking rewards + fee revenue: Holding and staking AKT yields rewards from inflation and a share of all fees, so active network usage benefits stakers directly. This model aligns token value with cloud demand: as more CPU/GPU workloads run on Akash, more fees in AKT flow to holders (and more AKT might be locked as collateral or staked by providers). Governance is also via AKT holdings. Overall, the token’s health improves with higher utilization and has inflation controls to encourage long-term participation.
io.net	Decentralized GPU Cloud (AI-focused)	IO Token (IO)	Fixed cap 800M IO: 500M pre-minted (allocated to team, investors, community, etc.), 300M emitted over ~20 years as mining rewards (hourly distribution). No further inflation after that cap. Built-in burn: Network revenue triggers token burns to reduce supply. Staking: providers must stake a minimum IO to participate (and can stake more for rewards).	GPU sharing network: Hardware providers (data centers, miners) connect GPUs and earn IO rewards continuously (hourly) for contributing capacity. They also earn fees from customers’ usage. Staking requirement: Operators stake IO as collateral to ensure good behavior. Users likely pay in IO (or in stable converted to IO) for AI compute tasks; a portion of every fee is burned by the protocol.	Utility & value flow: IO is the medium of exchange for GPU compute power on the network, and also the security token that operators stake. Token value is driven by a trifecta: (1) Demand for AI compute – clients must acquire IO to pay for jobs, and higher usage means more tokens burned (reducing supply). (2) Mining incentives – new IO distributed to GPU providers motivates network growth, but the fixed cap limits long-term inflation. (3) Staking – IO is locked up by providers (and possibly users or delegates) to earn rewards, reducing liquid supply and aligning participants with network success. In sum, io.net’s token model is designed so that if it successfully attracts AI workloads at scale, token supply becomes increasingly scarce (through burns and staking), benefiting holders. The fixed supply also imposes discipline, preventing endless inflation and aiming for a sustainable “reward-for-revenue” balance.

Sources: Official documentation and research for each project (see inline citations above).

Token Incentives vs. Real-World Service Usage​

A critical question for DePIN projects is how effectively token incentives convert into real service provisioning and actual usage of the network. In the initial stages, many DePIN protocols emphasized bootstrapping supply (hardware deployment) through generous token rewards, even if demand was minimal – a “build it and (hopefully) they will come” strategy. This led to situations where the network’s market cap and token emissions far outpaced the revenue from customers. As of late 2024, the entire DePIN sector (~350 projects) had a combined market cap of ~$50 billion, yet generated only about ~$0.5 billion annualized revenue – an aggregate valuation of ~100× annual revenue. Such a gap underscores the inefficiency in early stages. However, recent trends show improvements as networks shift from purely supply-driven growth to demand-driven adoption, especially propelled by the surge in AI compute needs.

Below we evaluate each example project’s token incentive efficiency, looking at usage metrics versus token outlays:

• Helium: Helium’s IoT network grew explosively in 2021–2022, with nearly 1 million hotspots deployed globally for LoRaWAN coverage. This growth was almost entirely driven by the HNT mining incentives and crypto enthusiasm – not by customer demand for IoT data, which remained low. By mid-2022, it became clear that Helium’s data traffic (devices actually using the network) was minuscule relative to the enormous supply-side investment. One analysis in 2022 noted that less than $1,000 of tokens were burned for data usage per month, even as the network was minting tens of millions of dollars worth of HNT for hotspot rewards – a stark imbalance (essentially, <1% of token emission was being offset by network usage). In late 2022 and 2023, HNT token rewards underwent scheduled halvings (reducing issuance), but usage was still lagging. An example from November 2023: the dollar value of Helium Data Credits burned was only about $156 for that day – whereas the network was still paying out an estimated $55,000 per day in token rewards to hotspot owners (valued in USD). In other words, that day’s token incentive “cost” outweighed actual network usage by a factor of 350:1. This illustrates the poor incentive-to-usage conversion in Helium’s early IoT phase. Helium’s founders recognized this “chicken-and-egg” dilemma: a network needs coverage before it can attract users, but without users the coverage is hard to monetize.

  There are signs of improvement. In late 2023, Helium activated its 5G Mobile network with a consumer-facing cell service (backed by T-Mobile roaming) and began rewarding 5G hotspot operators in MOBILE tokens. The launch of Helium Mobile (5G) quickly brought in paying users (e.g. subscribers to Helium’s $20/month unlimited mobile plan) and new types of network usage. Within weeks, Helium’s network usage jumped – by early 2024, the daily Data Credit burn reached ~$4,300 (up from almost nothing a couple months prior). Moreover, 92% of all Data Credits consumed were from the Mobile network (5G) as of Q1 2024, meaning the 5G service immediately dwarfed the IoT usage. While $4.3k/day is still modest in absolute terms (~$1.6 million annualized), it represents a meaningful step toward real revenue. Helium’s token model is adapting: by isolating the IoT and Mobile networks into separate reward tokens, it ensures that the 5G rewards (MOBILE tokens) will scale down if 5G usage doesn’t materialize, and similarly for IOT tokens – effectively containing the inefficiency. Helium Mobile’s growth also showed the power of coupling token incentives with a service of immediate consumer interest (cheap cellular data). Within 6 months of launch, Helium had ~93,000 MOBILE hotspots deployed in the US (alongside ~1 million IoT hotspots worldwide), and had struck partnerships (e.g. with Telefónica) to expand coverage. The challenge ahead is to substantially grow the user base (both IoT device clients and 5G subscribers) so that burning of HNT for Data Credits approaches the scale of HNT issuance. In summary, Helium started with an extreme supply surplus (and correspondingly overvalued token), but its pivot toward demand (5G, and positioning as an “infrastructure layer” for other networks) is gradually improving the efficiency of its token incentives.

• Filecoin: In Filecoin’s case, the imbalance was between storage capacity vs. actual stored data. Token incentives led to an overabundance of supply: at its peak, the Filecoin network had well over 15 exbibytes (EiB) of raw storage capacity pledged by miners, yet for a long time only a few percent of that was utilized by real data. Much of the space was filled with dummy data (clients could even store random garbage data to satisfy proof requirements) just so miners could earn FIL rewards. This meant a lot of FIL was being minted and awarded for storage that wasn’t actually demanded by users. However, over 2022–2023 the network made big strides in driving demand. Through initiatives like Filecoin Plus and aggressive onboarding of open datasets, the utilization rate climbed from ~3% to over 20% of capacity in 2023. By Q4 2024, Filecoin’s storage utilization had further risen to ~30% – meaning nearly one-third of the enormous capacity was holding real client data. This is still far from 100%, but the trend is positive: token rewards are increasingly going toward useful storage rather than empty padding. Another measure: as of Q1 2024, about 1,900 PiB (1.9 EiB) of data was stored in active deals on Filecoin, a 200% year-over-year increase. Notably, the majority of new deals now come via Filecoin Plus (verified clients), indicating miners strongly prefer to devote space to data that earns them bonus reward multipliers.

  In terms of economic efficiency, Filecoin’s protocol also experienced a shift: initially, protocol “revenue” (fees paid by users) was negligible compared to mining rewards (which some analyses treated as revenue, inflating early figures). For example, in 2021, Filecoin’s block rewards were worth hundreds of millions of dollars (at high FIL prices), but actual storage fees were tiny; in 2022, as FIL price fell, reported revenue dropped 98% from $596M to $13M, reflecting that most of 2021’s “revenue” was token issuance value rather than customer spend. Going forward, the balance is improving: the pipeline of paying storage clients is growing (e.g. an enterprise deal of 1 PiB was closed in late 2023, one of the first large fully-paid deals). Filecoin’s introduction of the FVM (enabling smart contracts) and forthcoming storage marketplaces and DEXes are expected to bring more on-chain fee activity (and possibly FIL burns or lockups). In summary, Filecoin’s token incentives successfully built a massive global storage network, albeit with efficiency under 5% in the early period; by 2024 that efficiency improved to ~20–30% and is on track to climb further as real demand catches up with the subsidized supply. The sector’s overall demand for decentralized storage (Web3 data, archives, NFT metadata, AI datasets, etc.) appears to be rising, which bodes well for converting more of those mining rewards into actual useful service.

• Render Network: Render’s token model inherently links incentives to usage more tightly, thanks to the burn-and-mint equilibrium. In the legacy model (pre-2023), RNDR issuance was largely in the hands of the foundation and based on network growth goals, while usage involved locking up RNDR in escrow for jobs. This made it a bit difficult to analyze efficiency. However, with BME fully implemented in 2023, we can measure how many tokens are burned relative to minted. Since each rendering or compute job burns RNDR proportional to its cost, essentially every token emitted as a reward corresponds to work done (minus any net inflation if emissions > burns in a given epoch). Early data from the Render network post-upgrade indicated that usage was indeed ramping up: the Render Foundation noted that at “peak moments” the network could be completing more render frames per second than Ethereum could handle in transactions, underscoring significant activity. While detailed usage stats (e.g. number of jobs or GPU-hours consumed) aren’t public in the snippet above, one strong indicator is the price and demand for RNDR. In 2023, RNDR became one of the best-performing crypto assets, rising from roughly $0.40 in January to over $2.50 by May, and continuing to climb thereafter. By November 2023, RNDR was up over 10× year-to-date, propelled by the frenzy for AI-related computing power. This price action suggests that users were buying RNDR to get rendering and AI jobs done (or speculators anticipated they would need to). Indeed, the interest in AI tasks likely brought a new wave of demand – Render reported that its network was expanding beyond media rendering into AI model training, and that the GPU shortage in traditional clouds meant demand far outstripped supply in this niche. In essence, Render’s token incentives (the emissions) have been met with equally strong user demand (burns), making its incentive-to-usage conversion relatively high. It’s worth noting that in the first year of BME, the network intentionally allocated some extra tokens (the 9.1M RENDER emissions) to bootstrap node operator earnings. If those outpace usage, it could introduce some temporary inflationary inefficiency. However, given the network’s growth, the burn rate of RNDR has been climbing. The Render Network Dashboard as of mid-2024 showed steady increases in cumulative RNDR burned, indicating real jobs being processed. Another qualitative sign of success: major studios and content creators have used Render for high-profile projects, proving real-world adoption (these are not just crypto enthusiasts running nodes – they are customers paying for rendering). All told, Render appears to have one of the more effective token-to-service conversion metrics in DePIN: if the network is busy, RNDR is being burned and token holders see tangible value; if the network were idle, token emissions would be the only output, but the excitement around AI has ensured the network is far from idle.

• Akash: Akash’s efficiency can be seen in the context of cloud spend vs. token issuance. As a proof-of-stake chain, Akash’s AKT has inflation to reward validators, but that inflation is not excessively high (and a large portion is offset by staking locks). The more interesting part is how much real usage the token is capturing. In 2022, Akash usage was relatively low (only a few hundred deployments at any time, mainly small apps or test nets). This meant AKT’s value was speculative, not backed by fees. However, in 2023–2024, usage exploded due to AI. By late 2024, Akash was processing ~$11k of spend per day on its network, up from just ~$1.3k/day in January 2024 – a ~749% increase in daily revenue within the year. Over the course of 2024, Akash surpassed $1.6 million in cumulative paid spend for compute. These numbers, while still small compared to giants like AWS, represent actual customers deploying workloads on Akash and paying in AKT or USDC (which ultimately drives AKT demand via conversion). The token incentives (inflationary rewards) during that period were on the order of maybe 15–20% of the 130M circulating AKT (~20–26M AKT minted in 2024, which at $1–3 per AKT might be $20–50M value). So in pure dollar terms, the network was still issuing more value in tokens than it was bringing in fees – similar to other early-stage networks. But the trend is that usage is catching up fast. A telling statistic: comparing Q3 2024 to Q3 2023, the average fee per lease rose from $6.42 to $18.75. This means users are running much more resource-intensive (and thus expensive) workloads, likely GPUs for AI, and they are willing to pay more, presumably because the network delivers value (e.g. lower cost than alternatives). Also, because Akash charges a 10–20% fee on leases to the protocol, that means 10–20% of that $1.6M cumulative spend went to stakers as real yield. In Q4 2024, AKT’s price hit new multi-year highs (~$4, an 8× increase from mid-2023 lows), indicating the market recognized the improved fundamentals and usage. On-chain data from year-end 2024 showed over 650 active leases and over 700 GPUs in the network with ~78% utilization – effectively, most of the GPUs added via incentives were actually in use by customers. This is a strong conversion of token incentives into service: nearly 4 out of 5 GPUs incentivized were serving AI developers (for model training, etc.). Akash’s proactive steps, like enabling credit card payments and supporting popular AI frameworks, helped bridge crypto tokens to real-world users (some users might not even know they are paying for AKT under the hood). Overall, while Akash initially had the common DePIN issue of “supply > demand,” it is quickly moving toward a more balanced state. If AI demand continues, Akash could even approach a regime where demand outstrips the token incentives – in other words, usage might drive AKT’s value more than speculative inflation. The protocol’s design to share fees with stakers also means AKT holders benefit directly as efficiency improves (e.g. by late 2024, stakers were earning significant yield from actual fees, not just inflation).

• io.net: Being a very new project (launched in 2023/24), io.net’s efficiency is still largely theoretical, but its model is built explicitly to maximize incentive conversion. By hard-capping supply and instituting hourly rewards, io.net avoids the scenario of runaway indefinite inflation. And by burning tokens based on revenue, it ensures that as soon as demand kicks in, there is an automatic counterweight to token emissions. Early reports claimed io.net had aggregated a large number of GPUs (possibly by bringing existing mining farms and data centers on board), giving it significant supply to offer. The key will be whether that supply finds commensurate demand from AI customers. One positive sign for the sector: as of 2024, decentralized GPU networks (including Render, Akash, and io.net) were often capacity-constrained, not demand-constrained – meaning there was more user demand for compute than the networks had online at any moment. If io.net taps into that unmet demand (offering lower prices or unique integrations via Solana’s ecosystem), its token burn could accelerate. On the flip side, if it distributed a large chunk of the 500M IO initial supply to insiders or providers, there is a risk of sell pressure if usage lags. Without concrete usage data yet, io.net serves as a test of the refined tokenomic approach: it targets a demand-driven equilibrium from the outset, trying to avoid oversupplying tokens. In coming years, one can measure its success by tracking what percentage of the 300M emission gets effectively “paid for” by network revenue (burns). The DePIN sector’s evolution suggests io.net is entering at a fortuitous time when AI demand is high, so it may reach high utilization more quickly than earlier projects did.

In summary, early DePIN projects often faced low token incentive efficiency, with token payouts vastly exceeding real usage. Helium’s IoT network was a prime example, where token rewards built a huge network that was only a few percent utilized. Filecoin similarly had a bounty of storage with little stored data initially. However, through network improvements and external demand trends, these gaps are closing. Helium’s 5G pivot multiplied usage, Filecoin’s utilization is steadily climbing, and both Render and Akash have seen real usage surge in tandem with the AI boom, bringing their token economics closer to a sustainable loop. A general trend in 2024 was the shift to “prove the demand”: DePIN teams started focusing on getting users and revenue, not just hardware and hype. This is evidenced by networks like Helium courting enterprise partners for IoT and telco, Filecoin onboarding large Web2 datasets, and Akash making its platform user-friendly for AI developers. The net effect is that token values are increasingly underpinned by fundamentals (e.g. data stored, GPU hours sold) rather than just speculation. While there is still a long way to go – the sector overall at 100× price/revenue implies plenty of speculation remains – the trajectory is towards more efficient use of token incentives. Projects that fail to translate tokens into service (or “hardware on the ground”) will likely fade, while those that achieve a high conversion rate are gaining investor and community confidence.

Coupling with AI Compute Demand: Trends and Opportunities​

One of the most significant developments benefiting DePIN projects is the explosive growth in AI computing demand. The year 2023–2024 saw AI model training and deployment become a multi-billion-dollar market, straining the capacity of traditional cloud providers and GPU vendors. Decentralized infrastructure networks have quickly adapted to capture this opportunity, leading to a convergence sometimes dubbed “DePIN x AI” or even “Decentralized Physical AI (DePAI)” by futurists. Below, we outline how our focus projects and the broader DePIN sector are leveraging the AI trend:

• Decentralized GPU Networks & AI: Projects like Render, Akash, io.net (and others such as Golem, Vast.ai, etc.) are at the forefront of serving AI needs. As noted, Render expanded beyond rendering to support AI workloads – e.g. renting GPU power to train Stable Diffusion models or other ML tasks. Interest in AI has directly driven usage on these networks. In mid-2023, demand for GPU compute to train image and language models skyrocketed. Render Network benefited as many developers and even some enterprises turned to it for cheaper GPU time; this was a factor in RNDR’s 10× price surge, reflecting the market’s belief that Render would supply GPUs to meet AI needs. Similarly, Akash’s GPU launch in late 2023 coincided with the generative AI boom – within months, hundreds of GPUs on Akash were being rented to fine-tune language models or serve AI APIs. The utilization rate of GPUs on Akash reaching ~78% by year-end 2024 indicates that nearly all incentivized hardware found demand from AI users. io.net is explicitly positioning itself as an “AI-focused decentralized computing network”. It touts integration with AI frameworks (they mention using the Ray distributed compute framework, popular in machine learning, to make it easy for AI developers to scale on io.net). Io.net’s value proposition – being able to deploy a GPU cluster in 90 seconds at 10–20× efficiency of cloud – is squarely aimed at AI startups and researchers who are constrained by expensive or backlogged cloud GPU instances. This targeting is strategic: 2024 saw extreme GPU shortages (e.g. NVIDIA’s high-end AI chips were sold out), and decentralized networks with access to any kind of GPU (even older models or gaming GPUs) stepped in to fill the gap. The World Economic Forum noted the emergence of “Decentralized Physical AI (DePAI)” where everyday people contribute computing power and data to AI processes and get rewarded. This concept aligns with GPU DePIN projects enabling anyone with a decent GPU to earn tokens by supporting AI workloads. Messari’s research likewise highlighted that the intense demand from the AI industry in 2024 has been a “significant accelerator” for the DePIN sector’s shift to demand-driven growth.

• Storage Networks & AI Data: The AI boom isn’t just about computation – it also requires storing massive datasets (for training) and distributing trained models. Decentralized storage networks like Filecoin and Arweave have found new use cases here. Filecoin in particular has embraced AI as a key growth vector: in 2024 the Filecoin community identified “Compute and AI” as one of three focus areas. With the launch of the Filecoin Virtual Machine, it’s now possible to run compute services close to the data stored on Filecoin. Projects like Bacalhau (a distributed compute-over-data project) and Fluence’s compute L2 are building on Filecoin to let users run AI algorithms directly on data stored in the network. The idea is to enable, for example, training a model on a large dataset that’s already stored across Filecoin nodes, rather than having to move it to a centralized cluster. Filecoin’s tech innovations like InterPlanetary Consensus (IPC) allow spinning up subnetworks that could be dedicated to specific workloads (like an AI-specific sidechain leveraging Filecoin’s storage security). Furthermore, Filecoin is supporting decentralized data commons that are highly relevant to AI – for instance, datasets from universities, autonomous vehicle data, or satellite imagery can be hosted on Filecoin, and then accessed by AI models. The network proudly stores major AI-relevant datasets (the referenced UC Berkeley and Internet Archive data, for example). On the token side, this means more clients using FIL for data – but even more exciting is the potential for secondary markets for data: Filecoin’s vision includes allowing storage clients to monetize their data for AI training use cases. That suggests a future where owning a large dataset on Filecoin could earn you tokens when AI companies pay to train on it, etc., creating an ecosystem where FIL flows not just for storage but for data usage rights. This is nascent but highlights how deeply Filecoin is coupling with AI trends.

• Wireless Networks & Edge Data for AI: On the surface, Helium and similar wireless DePINs are less directly tied to AI compute. However, there are a few connections. IoT sensor networks (like Helium’s IoT subDAO, and others such as Nodle or WeatherXM) can supply valuable real-world data to feed AI models. For instance, WeatherXM (a DePIN for weather station data) provides a decentralized stream of weather data that could improve climate models or AI predictions – WeatherXM data is being integrated via Filecoin’s Basin L2 for exactly these reasons. Nodle, which uses smartphones as nodes to collect data (and is considered a DePIN), is building an app called “Click” for decentralized smart camera footage; they plan to integrate Filecoin to store the images and potentially use them in AI computer vision training. Helium’s role could be providing the connectivity for such edge devices – for example, a city deploying Helium IoT sensors for air quality or traffic, and those datasets then being used to train urban planning AI. Additionally, the Helium 5G network could serve as edge infrastructure for AI in the future: imagine autonomous drones or vehicles that use decentralized 5G for connectivity – the data they generate (and consume) might plug into AI systems continuously. While Helium hasn’t announced specific “AI strategies,” its parent Nova Labs has hinted at positioning Helium as a general infrastructure layer for other DePIN projects. This could include ones in AI. For example, Helium could provide the physical wireless layer for an AI-powered fleet of devices, while that AI fleet’s computational needs are handled by networks like Akash, and data storage by Filecoin – an interconnected DePIN stack.

• Synergistic Growth and Investments: Both crypto investors and traditional players are noticing the DePIN–AI synergy. Messari’s 2024 report projected the DePIN market could grow to $3.5 trillion by 2028 (from ~$50B in 2024) if trends continue. This bullish outlook is largely premised on AI being a “killer app” for decentralized infrastructure. The concept of DePAI (Decentralized Physical AI) envisions a future where ordinary people contribute not just hardware but also data to AI systems and get rewarded, breaking Big Tech’s monopoly on AI datasets. For instance, someone’s autonomous vehicle could collect road data, upload it via a network like Helium, store it on Filecoin, and have it used by an AI training on Akash – with each protocol rewarding the contributors in tokens. While somewhat futuristic, early building blocks of this vision are appearing (e.g. HiveMapper, a DePIN mapping project where drivers’ dashcams build a map – those maps could train self-driving AI; contributors earn tokens). We also see AI-focused crypto projects like Bittensor (TAO) – a network for training AI models in a decentralized way – reaching multi-billion valuations, indicating strong investor appetite for AI+crypto combos.

• Autonomous Agents and Machine-to-Machine Economy: A fascinating trend on the horizon is AI agents using DePIN services autonomously. Messari speculated that by 2025, AI agent networks (like autonomous bots) might directly procure decentralized compute and storage from DePIN protocols to perform tasks for humans or for other machines. In such a scenario, an AI agent (say, part of a decentralized network of AI services) could automatically rent GPUs from Render or io.net when it needs more compute, pay with crypto, store its results on Filecoin, and communicate over Helium – all without human intervention, negotiating and transacting via smart contracts. This machine-to-machine economy could unlock a new wave of demand that is natively suited to DePIN (since AI agents don’t have credit cards but can use tokens to pay each other). It’s still early, but prototypes like Fetch.ai and others hint at this direction. If it materializes, DePIN networks would see a direct influx of machine-driven usage, further validating their models.

• Energy and Other Physical Verticals: While our focus has been connectivity, storage, and compute, the AI trend also touches other DePIN areas. For example, decentralized energy grids (sometimes called DeGEN – decentralized energy networks) could benefit as AI optimizes energy distribution: if someone shares excess solar power into a microgrid for tokens, AI could predict and route that power efficiently. A project cited in the Binance report describes tokens for contributing excess solar energy to a grid. AI algorithms managing such grids could again be run on decentralized compute. Likewise, AI can enhance decentralized networks’ performance – e.g. AI-based optimization of Helium’s radio coverage or AI ops for predictive maintenance of Filecoin storage nodes. This is more about using AI within DePIN, but it demonstrates the cross-pollination of technologies.

In essence, AI has become a tailwind for DePIN. The previously separate narratives of “blockchain meets real world” and “AI revolution” are converging into a shared narrative: decentralization can help meet AI’s infrastructure demands, and AI can, in turn, drive massive real-world usage for decentralized networks. This convergence is attracting significant capital – over $350M was invested in DePIN startups in 2024 alone, much of it aiming at AI-related infrastructure (for instance, many recent fundraises were for decentralized GPU projects, edge computing for AI, etc.). It’s also fostering collaboration between projects (Filecoin working with Helium, Akash integrating with other AI tool providers, etc.).

Conclusion​

DePIN projects like Helium, Filecoin, Render, and Akash represent a bold bet that crypto incentives can bootstrap real-world infrastructure faster and more equitably than traditional models. Each has crafted a unique economic model: Helium uses token burns and proof-of-coverage to crowdsource wireless networks, Filecoin uses cryptoeconomics to create a decentralized data storage marketplace, Render and Akash turn GPUs and servers into global shared resources through tokenized payments and rewards. Early on, these models showed strains – rapid supply growth with lagging demand – but they have demonstrated the ability to adjust and improve efficiency over time. The token-incentive flywheel, while not a magic bullet, has proven capable of assembling impressive physical networks: a global IoT/5G network, an exabyte-scale storage grid, and distributed GPU clouds. Now, as real usage catches up (from IoT devices to AI labs), these networks are transitioning toward sustainable service economies where tokens are earned by delivering value, not just by being early.

The rise of AI has supercharged this transition. AI’s insatiable appetite for compute and data plays to DePIN’s strengths: untapped resources can be tapped, idle hardware put to work, and participants globally can share the rewards. The alignment of AI-driven demand with DePIN supply in 2024 has been a pivotal moment, arguably providing the “product-market fit” that some of these projects were waiting for. Trends suggest that decentralized infrastructure will continue to ride the AI wave – whether by hosting AI models, collecting training data, or enabling autonomous agent economies. In the process, the value of the tokens underpinning these networks may increasingly reflect actual usage (e.g. GPU-hours sold, TB stored, devices connected) rather than speculation alone.

That said, challenges remain. DePIN projects must continue improving conversion of investment to utility – ensuring that adding one more hotspot or one more GPU actually adds proportional value to users. They also face competition from traditional providers (who are hardly standing still – e.g. cloud giants are lowering prices for committed AI workloads) and must overcome issues like regulatory hurdles (Helium’s 5G needs spectrum compliance, etc.), user experience friction with crypto, and the need for reliable performance at scale. The token models, too, require ongoing calibration: for instance, Helium splitting into sub-tokens was one such adjustment; Render’s BME was another; others may implement fee burns, dynamic rewards, or even DAO governance tweaks to stay balanced.

From an innovation and investment perspective, DePIN is one of the most exciting areas in Web3 because it ties crypto directly to tangible services. Investors are watching metrics like protocol revenue, utilization rates, and token value capture (P/S ratios) to discern winners. For example, if a network’s token has a high market cap but very low usage (high P/S), it might be overvalued unless one expects a surge in demand. Conversely, a network that manages to drastically increase revenue (like Akash’s 749% jump in daily spend) could see its token fundamentally re-rated. Analytics platforms (Messari, Token Terminal) now track such data: e.g. Helium’s annualized revenue (~$3.5M) vs incentives (~$47M) yielded a large deficit, while a project like Render might show a closer ratio if burns start canceling out emissions. Over time, we expect the market to reward those DePIN tokens that demonstrate real cash flows or cost savings for users – a maturation of the sector from hype to fundamentals.

In conclusion, established networks like Helium and Filecoin have proven the power and pitfalls of tokenized infrastructure, and emerging networks like Render, Akash, and io.net are pushing the model into the high-demand realm of AI compute. The economics behind each network differ in mechanics but share a common goal: create a self-sustaining loop where tokens incentivize the build-out of services, and the utilization of those services, in turn, supports the token’s value. Achieving this equilibrium is complex, but the progress so far – millions of devices, exabytes of data, and thousands of GPUs now online in decentralized networks – suggests that the DePIN experiment is bearing fruit. As AI and Web3 continue to converge, the next few years could see decentralized infrastructure networks move from niche alternatives to vital pillars of the internet’s fabric, delivering real-world utility powered by crypto economics.

Sources: Official project documentation and blogs, Messari research reports, and analytics data from Token Terminal and others. Key references include Messari’s Helium and Akash overviews, Filecoin Foundation updates, Binance Research on DePIN and io.net, and CoinGecko/CoinDesk analyses on token performance in the AI context. These provide the factual basis for the evaluation above, as cited throughout.