Author: Benji Siem @BenjiSiem｜IOSG Ventures

Thesis: STRC is a cleverly engineered funding instrument that transforms fixed-income demand into Bitcoin buying pressure, offering 11.5% variable yield with low price volatility in bull markets, but its risk profile resembles being short a put on Bitcoin asset coverage, making it a poor substitute for actual fixed income when BTC declines.

The key fragility of STRC is not BTC price, but mNAV. Once MSTR mNAV stays below 1.0x for 4+ consecutive weeks, the flywheel will enter a passive-mode spiral within 3 months. We assign ~70% probability this trigger fires in 2026 H2, creating a buyable entry for STRC at $85–90. If it does not fire, Saylor has successfully created a new category of BTC-native credit instrument.

Background

Strategy (formerly MicroStrategy) launched STRC (“Stretch”) as a perpetual preferred stock targeting a stable $100 par value through a monthly variable dividend mechanism. With $5B notional outstanding (as of March 31, 2026) and $300M+ daily trading volume (peak figure, as of March 2026), STRC has become Strategy’s primary capital-raising vehicle, funding over $3.5B in BTC purchases since inception. As of April 12, 2026, Strategy holds 780,897 BTC on its balance sheet, with a 33% leverage ratio and $21.6B remaining STRC ATM capacity.

• The instrument sits in a novel category: it looks like a money market fund (stable price, high yield) but carries credit risk tied entirely to a single company’s Bitcoin holdings.

Before making the thesis argument, it is worth stating clearly what would make us wrong. If our analysis is incorrect, it will be because traditional fixed-income allocators genuinely accept reflexive risk in exchange for a 700bps spread; STRC scales to $50B within 3 years and becomes the de facto BTC yield curve; and Saylor succeeds in securitizing BTC as an interest-bearing collateral asset class acceptable to institutional portfolios. That outcome would represent the single largest case of crypto integrating into traditional finance; a new $50B+ asset category that did not exist before 2025.

• In this bull scenario, the April 2026 dividend pause is not a warning sign but a feature: a maturing instrument stabilizing its yield after early-stage price discovery, analogous to how early high-yield ETFs repriced downward as institutional adoption grew.

Thesis Extraction

STRC’s core innovation: it converts yield-seeking demand into BTC buying pressure. When STRC trades at $100, Saylor issues new shares via ATM (~40% of daily volume), uses proceeds to buy BTC, then issues MSTR common stock at >1x mNAV to deleverage. The result: $100M of STRC daily volume generates ~$120M of BTC purchases.

What makes this mechanism fragile, however, is the circularity underneath it: STRC holds at $100 because investors believe it will, and Saylor raises the dividend to keep that belief intact. The peg is not collateral-backed — it is confidence-backed, maintained by a continuous dividend auction with no formal ceiling. The moment that confidence breaks, the auction becomes progressively more expensive to run.

Evidence & Comparison: STRC vs. Other Bitcoin Exposure Instruments

Key Insight: For Strategy, it transforms fixed-income demand into BTC accumulation fuel. For investors, it offers a Sharpe-optimized return during benign conditions but embeds a hidden short put on BTC. NYDIG’s framing is precise: “It resembles being short a put on bitcoin asset coverage, earning yield in exchange for bearing downside risk if bitcoin declines and erodes the asset cushion.”

When STRC Outperforms

When STRC Underperforms

When STRC Collapses: The Death Spiral Scenario

The critical question is whether STRC can enter a self-reinforcing downward loop. The answer is yes, under specific conditions. The mechanism has three interlocking failure modes:

Stage 1: BTC Decline Breaks the $100 Anchor

When BTC drops sharply (as it did ~45% from ATH in late 2025), Strategy’s leverage ratio rises mechanically. At 33% leverage with 780,897 BTC (as of April 12, 2026, MSTR 8-K), a 50% BTC decline would push the ratio to ~66%. STRC’s credit quality deteriorates because its claim on residual assets shrinks. Price drops below $100. This has already happened three times (Aug 2025: ~$92, Nov 2025: intra-period low, Feb 2026: ~$93), but each time BTC recovered and the peg was restored.

Stage 2: Dividend Escalation Trap

Per Strategy’s own SEC-filed guidance, if monthly VWAP falls between $95-$99, the dividend increases 25bps; below $95, it increases 50bps. The rate has already climbed from 9% to 11.5% across approximately 8 months (Aug 2025 to April 2026), averaging ~31bps per month — faster than any comparable corporate preferred has repriced in a stable market. April 2026 marks the first pause after seven consecutive increases. Two readings: (a) demand has stabilized — bullish; (b) Strategy hit the yield-sensitivity ceiling for traditional fixed-income buyers — bearish. This pause is the single most important signal to track over the next one to two months.

If BTC stays depressed, the dividend must keep rising to attract buyers back to par. At $5B notional, every 100bps increase costs ~$50M/year in additional cash obligations; if STRC scales to $20B (the authorized ATM capacity), those increments become $200M/year each. A sustained bear of 6+ months at the current escalation pace would push STRC’s yield toward 13–15%, at which point the annual dividend burden on a $20B book would exceed $2.6–3B/year — consuming a material share of Strategy’s BTC treasury income and forcing a hard choice between further escalation and an explicit abandonment of the stability narrative.

There is no formal ceiling on how high the dividend can go, and this uncapped escalation dynamic is precisely what the bearish minority is tracking.

Stage 3: Flywheel Breaks When mNAV Falls Below 1x

This is the true breaking point. Strategy funds BTC purchases and deleveraging by issuing MSTR common stock at a premium to NAV (mNAV > 1x). If BTC drops enough that mNAV falls below 1x, issuing common stock becomes dilutive to existing shareholders. Saylor cannot deleverage without destroying value. At that point, Strategy faces a trilemma: (a) keep issuing STRC at rising dividend rates and accept higher leverage, (b) cut dividends unilaterally (allowed at 25bps/month per SEC filings) and let STRC price collapse, or © sell BTC into a falling market.

Saylor has repeatedly said he will never sell BTC. BitMEX Research concluded that option (b) is the most likely outcome: “Instead of selling bitcoin, MSTR could just abandon the narrative that STRC is targeting stability.” The stress shifts entirely to STRC holders.

An early warning signal is already flashing: in the week of April 6–12, 2026, MSTR’s ATM facility issued $0 — all capital raising came through STRC ($1.00B, 10,028,363 shares; MSTR 8-K). mNAV is tight enough that Saylor will not risk common equity dilution. Stage 3’s precondition is partially triggered — the flywheel is already running on one leg.

Quantified Collapse Scenarios

Why this is NOT the same as UST/Terra: UST relied on an algorithmic mint/burn mechanism with an endogenous token (LUNA) as the sole backing. STRC is backed by real BTC and Strategy has the discretion to cut dividends rather than enter forced liquidation. The floor on STRC is not zero — it is the preferred claim on residual assets in bankruptcy. But the floor could be well below $100 if BTC drops 60%+ and stays there.

The key variable is time. Every previous STRC dip recovered within weeks because BTC bounced. A collapse requires a sustained bear market (3+ months below $50K) that forces the dividend escalation mechanism to run long enough to erode confidence. The longer STRC stays below par with rising dividends, the more it looks like a company paying higher and higher rates to roll increasingly risky debt — and that pattern has a well-known ending in credit markets.

Capital structure seniority: The order in liquidation is: convertible notes (~$8.2B) → STRF → STRC → STRK → STRD → MSTR common equity. STRC sits behind $8.2B of unsecured debt AND behind STRF preferred.

Community Sentiment

“STRC’s risks are substantially greater than those related to short duration U.S. treasuries… When the music stops, it’s the investors who may feel somewhat aggrieved.” — BitMEX Research, “A Bit of a Stretch” (Nov 2025)

“The appropriate way to assess risk in STRC is through the lens of governance and subordination rather than focusing solely on payment risk.” — Greg Cipolaro, NYDIG Global Head of Research (Mar 2026)

“It resembles being short a put on bitcoin asset coverage, earning yield in exchange for bearing downside risk if bitcoin declines.” — NYDIG Research Note (Mar 2026)

The core fault line in analyst opinion: bulls see STRC as the safest way to earn 11.5% in today’s market; bears see it as mispriced credit risk dressed up as a money market product. The bearish camp’s central concern maps directly to the dividend escalation mechanism described above: STRC does not default suddenly, it reprices gradually, transforming from a near-money instrument into a distressed yield product the longer BTC stays depressed. That creep is the real risk, not an overnight collapse.

Implications & Prediction

Bottom Line: STRC is a genuinely novel financial instrument that works brilliantly in its designed environment (stable-to-rising BTC, open capital markets, mNAV > 1x). In that regime, it offers 11.5% yield with managed volatility — a compelling proposition. But its downside profile is asymmetric: investors earn a coupon in good times and bear concentrated, single-name BTC credit risk in bad times. It is not a substitute for treasuries or diversified high-yield — it is a levered bet on the continuation of Strategy’s BTC accumulation flywheel, packaged to look like fixed income.

Three New Signals (as of April 2026)

Signal 1: First dividend pause in April (as of April 1, 2026, per CoinDesk). After 7 consecutive increases — from 9% to 11.5% between August 2025 and March 2026 — Saylor held the dividend flat in April. Two possible readings: (a) demand has stabilized at this yield level, which is bullish; (b) Strategy has hit the yield-sensitivity ceiling for traditional fixed-income buyers, which is bearish. This is the single most important signal to monitor over May-June 2026, and it is the inflection point around which our mNAV trigger framework above was constructed.

Signal 2: MSTR ATM issued $0 in the week of April 6–12; all issuance came through STRC ($1.00B; MSTR 8-K, April 2026). mNAV is tight enough that Saylor will not risk common equity dilution at current BTC prices. The Stage 3 precondition from the Death Spiral section is partially triggered — the flywheel is running on one leg.

Signal 3: Last week’s BTC purchases averaged $71,902/BTC, below Strategy’s historical cost basis of $75,577/BTC (as of April 12, 2026, MSTR 8-K). Strategy is DCA-ing into a soft market. The flywheel is still turning, but each marginal purchase is thinning the asset cushion rather than thickening it — the inverse of the dynamic that powered the 2024–2025 accumulation run.

Investment Suggestion

HOLD until better entry and BTC price increases.

Current status: HOLD existing positions and don’t buy until better signal. MSTR mNAV has compressed toward 1.0x. STRC is still holding at $100 par and paying 11.5%, which reflects the dividend mechanism working as designed. However, the margin of safety is very narrow.

Entry condition to re-establish: BTC recovering above $70 -75K with MSTR mNAV confirmed above 1.1x for at least two consecutive weeks. At that point, STRC at or near $100 par re-enters as a conditional BUY. Historically, dip entries below $95 with subsequent BTC recovery have produced 7–11% capital gain plus accrued yield — but only in environments where BTC bounces within weeks, as it has in prior drawdowns (Aug 2025, Nov 2025, Feb 2026). The open question is whether this drawdown follows the same pattern or represents a more sustained bear.

Exit signals: Begin a sell review if any of the following occur: (a) MSTR mNAV falls and holds below 1.0x for more than two weeks; (b) STRC VWAP holds below $95 for more than four consecutive weeks; or © BTC breaks below $55K on sustained volume.

Sources

1. Strategy.com — STRC Product Page
2. CoinDesk — “The genius and the danger of STRC”
3. Crypto Narratives — “Understanding STRC: How Strategy turns yield demand into BTC buying”
4. BitMEX Research — “A Bit of a Stretch” STRC Analysis
5. AInvest — “STRC’s Sharpe Ratio of 3.08: Real Alpha or Structural Illusion?”
6. Investopedia — “Meet Stretch: Michael Saylor’s New Tool”
7. Blockonomi — “STRC Raises $1.18B in One Week”
8. Seeking Alpha — “STRK: Most Undervalued Bitcoin Security”
9. CryptoTimes — “Strategy’s Bitcoin Empire: How Preferred Perpetuals Are Redefining Corporate Finance”
10. Benzinga — “Saylor: STRC Achieved Better Risk-Adjusted Returns Than NVDA, TSLA”

Appendix

• Timeline

• Holder concentration — who can force a price break?

Strive’s $50M purchase is mentioned, but there’s no discussion of whether STRC has a small number of large institutional holders who, if they rotate out simultaneously, could overwhelm the daily $258M average volume and push STRC below par in a self-fulfilling way. This is the “bank run” risk.

Saylor’s New Bitcoin Perpetual Motion Machine: Deconstructing STRC’s 11.5% Yield and Its Fragility was originally published in IOSG Ventures on Medium, where people are continuing the conversation by highlighting and responding to this story.

Author: @momir_amidzic | IOSG Ventures

The bull case for TAO requires you to believe in a game-theoretic miracle. But crypto has pulled those off before.

Bittensor has one of the most elegant narratives in crypto: a decentralized marketplace for AI intelligence, where market forces allocate capital to the highest-impact research. TAO is the coordination layer. Subnets are the laboratories. Markets are the grant committees.

Strip away the narrative, and you get something more uncomfortable.

Bittensor is a grant program where crypto speculators fund AI R&D — and grantees have no obligation to return any value to TAO.

Think of TAO as Elon Musk — the first investor in a “non-profit” OpenAI. Subnets are Sam Altman — the builders who take the money, ship products, and have zero contractual obligation to share the upside. They may eventually choose to privatize the gains without returning any value to the original source of capital.

Bittensor emits TAO to subnet operators and miners based on the price of subnet tokens. Once a subnet receives its TAO allocation, there is no enforcement mechanism that requires the resulting AI models, datasets, or services to remain within the Bittensor ecosystem. A subnet operator can build something valuable, extract the TAO emissions, and deploy the product elsewhere — on centralized cloud, through a standalone API, wrapped in a conventional SaaS company.

TAO has no equity. No licensing rights. The only tether is the subnet token — its price must perform well to maintain access to resources. But this only holds if the subnet hasn’t yet achieved escape velocity: a product good enough to sustain itself outside Bittensor. The relationship between Bittensor and its subnets is closer to a research grant than a venture investment.

Under this lens, Bittensor is a wealth transfer from token speculators to AI researchers — or, more cynically, from speculators to tech-savvy farmers.

The mechanics are straightforward:

- TAO investors provide capital by supporting the TAO market price

- Subnet operators receive TAO inflation in exchange for “demonstrated performance” — in practice, keeping their subnet token price healthy

- The AI products built with this capital can walk away at any time, constrained only by their ongoing need for resources

This is the VC nightmare scenario: you fund the company, it ships, and it owes you nothing. There is a token emission schedule and a prayer.

The Optimistic Reading

Now flip the frame. The optimistic case rests on two pillars:

1. Continuous resource hunger. AI companies are perpetually capital-constrained. Compute, data, and talent are expensive. If Bittensor can reliably provide these at scale, subnets have a rational incentive to stay — not because they’re locked in, but because leaving means losing access to a resource pipeline. The soft guarantee is that AI will always need more, and that TAO can provide scale that individual fundraising cannot. Under this logic, subnet teams would actively defend their token valuations, creating a positive flywheel for the TAO economy without any enforcement mechanism at all.

2. Crypto is exceptional at resource aggregation. Bitcoin aggregated an enormous amount of compute power through nothing but token incentives. Ethereum’s proof-of-work was an extraordinarily successful compute magnet. Bittensor is applying the same playbook to AI. The “enforcement mechanism” is the token game itself — as long as TAO is valuable, participation is incentivized.

If you ran 1,000 simulation paths for Bittensor’s future, the distribution would be heavily skewed.

In most paths, Bittensor remains a niche grant program. Subnets produce marginal AI outputs. The best ones achieve meaningful traction, farm the rewards, then pivot to a closed model without returning value to TAO. Emissions exceed value created. The token declines.

In a small number of paths, something catches. A subnet produces a genuinely competitive AI service. Network effects compound. TAO becomes a meaningful coordination layer for decentralized AI infrastructure — capturing value not through enforcement, but through the sheer gravity of being the reserve asset of a functioning AI economy.

And in a handful of paths, TAO becomes a category-defining asset.

What Could Go Wrong

The bear case is simple:

- No stickiness. The moment a subnet doesn’t need emissions, it leaves. Bittensor is a stepping stone, not a destination.

- Centralized AI is winning. OpenAI, Google, and Anthropic command orders of magnitude more compute and talent. TAO cannot compete with the depth of VC and private equity markets. The best talent will therefore take conventional paths.

- Inflation is the tax. TAO’s emission schedule dilutes holders to fund subnets. If subnets don’t create commensurate value, this is a slow bleed disguised as a growth mechanism.

The optimistic case, on honest inspection, reads more like wishful thinking than a realistic path to success.

Bottom Line

Most of the capital invested in TAO will subsidize development that never returns to token holders. But crypto has repeatedly proven that token-incentivized coordination can produce outcomes no rational model would predict. Bitcoin shouldn’t work, yet it does — though this is itself a weak argument, one the industry has used to justify countless ideas that fail under first-principles scrutiny.

The question for TAO isn’t whether an enforcement mechanism exists — it doesn’t, despite dTAO’s best efforts. The question is whether game-theoretic incentives are strong enough to keep the best subnets in orbit. If you’re buying TAO, you’re betting yes on a soft guarantee in a hard world.

That’s either naive or visionary.

TAO and the Free Rider Problem was originally published in IOSG Ventures on Medium, where people are continuing the conversation by highlighting and responding to this story.

Author: Benji Siem@BenjiSiem, IOSG Ventures

Introduction

Stripe acquired Bridge for . Mastercard is buying Zerohash for ~$2B. Robinhood launched its own L2. These aren’t isolated bets, they signal a structural shift in which the largest fintech incumbents are embedding blockchain infrastructure, stablecoins, and decentralized financial rails directly into their core products. Over the past decade, fintech companies modernized payments, banking, and investing through software-native platforms and large-scale digital distribution. The next stage is now underway: crypto is becoming the backend.

This report analyzes ten leading fintech companies and their approaches to digital finance, focusing on their business models, revenue drivers, and strategies for integrating crypto payments and DeFi infrastructure. A consistent pattern emerges: the most successful companies treat crypto not as a speculative asset class but as backend infrastructure that improves settlement speed, reduces costs, and expands global financial connectivity. Stablecoins in particular are emerging as the bridge between traditional financial systems and onchain markets

Fintech Industry Insights

• Digital Finance Clarity: How People See the Opportunity
• The digital finance thesis across these companies can be summarized as:

“Financial services should be borderless, real-time, software-defined, and composable — with compliance abstracted from the end user.”

Here’s how different player types see the opportunity:

Infrastructure Players (Visa, Mastercard, Stripe, Adyen)

• Thesis: Modernize the plumbing of money movement without owning the customer relationship
• Opportunity: Every new payment rail (stablecoins, A2A, instant) expands addressable market
• Crypto angle: Stablecoins reduce settlement friction and enable 24/7 treasury management

Consumer Platforms (Nu, Revolut, PayPal, Cash App)

• Thesis: Own the primary financial interface and cross-sell integrated services
• Opportunity: Increase lifetime value by collapsing banking + payments + investing + crypto into one app
• Crypto angle: Crypto as an engagement and monetization layer (trading fees, yield products, cross-border)

Hybrid Players (Robinhood, Block, SoFi)

• Thesis: Build vertical integration across consumer products + infrastructure
• Opportunity: Capture margin at multiple layers (consumer engagement, B2B infrastructure, asset custody)
• Crypto angle: Dual-sided crypto strategy (consumer distribution + infrastructure ownership)

Major Trends in Fintech

Based on the analysis of these top 10 companies, several clear patterns emerge. These are the core theses of this report; the company case studies and integration playbooks that follow are the evidence.

1. Infrastructure Over Speculation Almost universally, these companies treat crypto as backend infrastructure, not frontend speculation. Visa, Mastercard, Stripe, and Adyen are using stablecoins to modernize settlement layers while keeping the consumer experience unchanged. Crypto succeeds when it’s invisible. This is the single most consistent pattern across all ten companies and informs every integration strategy that follows.

2. Stablecoins as the Bridge Asset Every company touching crypto is betting on stablecoins as the transitional layer between TradFi and crypto:

• Visa: USDC settlement on Solana, 130+ stablecoin card programs
• Mastercard: 4 stablecoins (USDC, PYUSD, USDG, FIUSD) across multiple chains
• Robinhood: USDG partnership with yield-sharing
• PayPal: Issued PYUSD to internalize settlement
• Stripe: USDC for cross-border merchant payouts

Stablecoins compress settlement time, reduce FX friction, and enable programmable treasury without volatility exposure.

3. The “Regulated Distribution” Moat Companies with massive user bases (PayPal 400M, Revolut 50M+, Nu 122M, Cash App 58M) are positioning as compliant on/off-ramps rather than protocol builders. Their competitive advantage isn’t technology: it’s trust, compliance, and distribution at scale.

4. DeFi as Wholesale, Not Retail None of these companies expose retail users to raw DeFi protocols. Instead, DeFi functions as a wholesale backend: yield sources (tokenized treasuries, money markets), liquidity optimization (faster settlement, cheaper cross-border), and product wrappers (compliant savings accounts backed by DeFi yields). DeFi becomes infrastructure that powers regulated products, not a consumer-facing experience. This thesis shapes the integration opportunities and investment themes outlined later in this report.

5. Multi-Rail Strategy Companies are building payment-method-agnostic infrastructure:

• Stripe: “Own the programmable money layer” regardless of rail
• Mastercard: “Multi-rail company” across cards, A2A, real-time payments, blockchain
• Adyen: “Global operating system for enterprise payments”

As payment methods fragment (cards, A2A, stablecoins, BNPL), the winners will be those who can route intelligently across all rails.

The Convergence

The winning strategy distilled: Be the compliant wrapper around programmable money, capturing value through distribution, trust, and compliance, not through protocol ownership or speculative exposure.

The companies best positioned combine one or more of: massive distribution (Nu, PayPal, Revolut, Cash App), infrastructure control (Visa, Stripe, Mastercard), or vertical integration (Robinhood, Block, SoFi).

Good vs. Bad Business Models

Scalable & Strong Business Models

1. Payment Network “Toll Booth” (Visa, Mastercard)

• Near-zero marginal cost per transaction; enormous fixed-cost leverage; network effects create near-insurmountable moats.
• Essentially unlimited scalability: every new transaction adds revenue with negligible incremental cost.
• Crypto integration risk: Stablecoins and A2A payments could theoretically disintermediate card networks, but Visa and Mastercard are responding by positioning as “networks of networks” that sit on top of all rails, including crypto.

2. Infrastructure-as-a-Service (Stripe, Adyen)

• High switching costs once embedded in merchant tech stacks; per-transaction revenue compounds with merchant growth; VAS upsells (fraud, tax, billing) increase ARPU.
• Stripe processed $1.4T in 2024 (~1.3% of global GDP).
• Stripe’s Bridge/Tempo bet is the most aggressive infrastructure play. If stablecoin payments scale, Stripe could own the developer layer for crypto-native commerce.

3. Recurring Revenue + Float (Coinbase S&S, Revolut Premium)

• Stablecoin reserve interest (Coinbase earned $332.5M from USDC in Q4 2025 alone), staking rewards, and subscription fees provide revenue that is less volatile than trading commissions.
• Scales with AUM/AUC, not just trading volume, making it much more predictable.

4. Low-Cost Digital Banking in Underserved Markets (Nubank)

• $0.80/month cost-to-serve vs. $5–10+ at legacy banks; 83%+ activity rate; net interest margins of 17.7%.
• Customer acquisition is viral in underbanked markets; 122.7M customers with enormous cross-sell runway.

Risky / Less Scalable Business Models

1. Pure Trading Fee Dependence

• Companies where 90%+ of revenue comes from transaction-based trading fees are at the mercy of market cycles. In crypto bear markets, volumes can drop 70–90%.
• Diversification matters: Coinbase went from 96% transaction revenue (2020) to a projected 59% (2025). Robinhood now has 11 business lines.

2. PFOF (Payment for Order Flow) Dependence

• PFOF has been banned in the EU and faces ongoing scrutiny in the U.S. Companies reliant on PFOF face existential regulatory risk to their core revenue model.
• Better path: Shift toward subscription (Robinhood Gold), interest income, and institutional clients (Bitstamp acquisition).

3. Crypto Revenue Without Recurring/Sticky Components

• A crypto exchange that only earns from spot trading fees, with no staking, no stablecoin interest, no custody, no DeFi protocol revenue, and no subscription, is running a highly cyclical business.
• Good crypto business models layer multiple revenue streams (trading + staking + interest + protocol fees + subscription).

4. Bitcoin-as-Revenue-Line Without Margin

• Block reported $1.97B in Bitcoin revenue in Q3 2025, but Bitcoin cost of revenue was $1.89B, leaving gross margin on BTC pass-through revenue at ~4%. It inflates the topline but contributes modestly to gross profit.
• The strategic value is in ecosystem lock-in (Cash App users who buy BTC stay engaged), not direct Bitcoin margin.

Framework: What Makes a “Good” Crypto-Fintech Business Model?

Fintech Crypto Integration: Playbook

The following playbook catalogs how the ten companies are executing against the theses above. For why these approaches work, refer back to the Major Trends section.

1. Stablecoin Integration (Most Common, Highest Momentum)

• Visa: USDC settlement on-network
• Mastercard: OKX Card partnership; Zerohash acquisition
• PayPal: Issued PYUSD ($3.6B circulation); enabled DeFi usage
• Stripe: Acquired Bridge ($1.1B) for stablecoin orchestration; building Tempo L1
• Coinbase: USDC co-issuer/partner; $1.4B projected stablecoin revenue in 2025

2. Crypto Trading as a Feature

• Robinhood: Native crypto trading integrated alongside equities/options
• Revolut: 200+ tokens tradeable in-app
• Nubank: NuCripto
• Block/Cash App: Bitcoin buy/sell/transfer

Low incremental cost to add; captures retail demand during bull markets; deepens engagement.

3. Blockchain Infrastructure Build-Out

• Coinbase → Base Chain (L2 on OP Stack)
• Stripe → Tempo (L1, with Paradigm)
• Robinhood → Robinhood Chain (L2 on Arbitrum)

Owning the chain = owning the economics (sequencer fees, MEV, ecosystem network effects). Analogous to Visa building VisaNet rather than relying on third-party networks.

4. Bitcoin-Specific Full Stack (Block’s Approach)

Consumer wallet (Cash App) → Merchant acceptance (Square Bitcoin/Lightning) → Self-custody (Bitkey) → Mining (Proto) → Open-source development (Spiral).

High-conviction vertical bet: if Bitcoin becomes a daily-use payment rail, Block owns every layer. If it doesn’t, it’s a large resource commitment with uncertain ROI.

5. Custody & Institutional Services

• Coinbase Prime: Custodian for majority of U.S. spot Bitcoin/Ethereum ETFs
• Mastercard & Visa: Providing compliance/KYC/AML overlays for institutional crypto adoption

Institutional capital requires trusted, regulated custody, a high-barrier, high-margin business.

6. DeFi On/Off-Ramp & Protocol Participation

• PayPal: PYUSD enabled for DeFi lending/trading on Ethereum
• Coinbase: Base Chain hosts DeFi protocols; USDC is the dominant stablecoin in DeFi
• Revolut & Robinhood: Staking services (ETH, SOL)

Crypto Payment & DeFi Integration Opportunities

UPSTREAM (Wholesale / Infrastructure Layer)

1. Stablecoin Settlement Networks

Use stablecoins for interbank settlement, treasury management, and merchant payouts. Reduces settlement time from T+2 to near-instant, cuts correspondent banking costs.

• Benefits: Visa, Mastercard, Stripe, Adyen
• Example: Visa settling VisaNet obligations in USDC on Solana

2. Tokenized Money Markets for Liquidity

Use tokenized T-bills (e.g., Ondo OUSG, Franklin OnChain) as yield-bearing reserves without sacrificing liquidity or taking credit risk.

• Benefits: PayPal, Nu, Revolut, SoFi
• Example: Mastercard MTN supporting tokenized treasury assets

3. Cross-Border Remittance Rails

Replace SWIFT/correspondent banking with stablecoin rails for B2B and consumer remittances. Instant settlement, lower fees, better FX rates.

• Benefits: Stripe, PayPal (Xoom), Revolut, Nu
• Example: Stripe enabling USDC payouts for global merchants

4. Programmable Compliance Layer

Smart-contract-based AML/KYC, transaction monitoring, and sanctions screening. Automates compliance, reduces manual overhead, enables real-time risk scoring.

• Benefits: All regulated players (especially Visa, Mastercard selling VAS)
• Example: Visa Protect for A2A payments, Mastercard Crypto Secure

DOWNSTREAM (Consumer / Merchant Layer) 1. Stablecoin-Linked Cards

Cards that spend directly from stablecoin balances, converting to fiat at point of sale.

• Benefits: Visa, Mastercard (enabling), Revolut, Cash App (distributing)
• Example: Visa’s 130+ stablecoin card programs, Mastercard OKX Card

2. Crypto-Backed Lending

Use crypto holdings as collateral for fiat loans without triggering taxable events.

• Benefits: Robinhood, SoFi, Block, Revolut
• Example: Bitcoin-backed loans offered through Cash App or SoFi

3. Yield-Bearing Savings Accounts

High-yield savings products backed by tokenized treasuries or DeFi lending protocols, offered in a compliant wrapper.

• Benefits: Nu, Revolut, PayPal, SoFi
• Example: Revolut offering “crypto earn” products with staking-like returns

4. Merchant Stablecoin Settlement

Merchants settle in stablecoins instead of fiat to reduce FX risk and accelerate payouts.

• Benefits: Stripe, Adyen, Square, PayPal
• Example: Mastercard enabling merchants to receive USDC, PYUSD, or USDG settlement via Nuvei/Circle

5. Instant Cross-Border P2P

Stablecoin-powered remittances that settle in seconds for <1% fee. Undercuts Western Union/MoneyGram, especially in LatAm and Asia.

• Benefits: PayPal (Xoom), Revolut, Cash App, Nu
• Example: Nu enabling BRL → USDC → local currency transfers across LatAm

NICHE (Differentiated / High-Margin) 1. Self-Custody Wallets as a Service

White-label self-custody solutions for institutions and high-net-worth users. Captures custody fees, meets regulatory self-custody requirements.

• Benefits: Robinhood (Bitkey), Block, Stripe (via Bridge)
• Example: Block’s Bitkey hardware wallet

2. Blockchain-Based Loyalty Programs

Issue loyalty points as tokens, enable redemption across ecosystems. Increases stickiness, creates new revenue from tokenized rewards.

• Benefits: Mastercard, Visa, PayPal

3. DeFi Protocol Integration for Institutions (High Potential) Regulated access to DeFi lending, staking, and yield farming for institutions via compliant middleware.

• Benefits: SoFi (Galileo), Stripe (Bridge), Mastercard (MTN)
• Example: SoFi Galileo powering white-label crypto staking for banks

4. Privacy-Preserving Payments

Zero-knowledge proofs for private but compliant stablecoin transfers. Enables confidential business payments while maintaining AML compliance.

• Benefits: All companies (especially Visa/Mastercard for B2B)

The Unbundling-Bundling Connection

Connecting to Insights from

The Architecture of Value: Structural Evolution of Financial Innovation and Deep Dive Report on Venture Capital Strategies

1. Rebundling Winners Are Infrastructure Providers, Not Consumer Platforms The most durable, scalable fintech businesses enable others to bundle, not bundle themselves:

Infrastructure players (Visa, Mastercard, Stripe, Adyen):

• 90–98% gross margins, 50–62% operating margins
• Zero customer acquisition cost (developers/banks bring users)
• Network effects or developer lock-in as moat
• Revenue scales with ecosystem growth, not direct customer acquisition

Consumer platforms (Robinhood, Nu, Revolut, PayPal):

• 30–50% gross margins, 10–25% operating margins
• $200–450 customer acquisition cost
• Must achieve 3+ product adoption to turn profitable
• Vulnerable to regulatory changes and market cycles

Visa’s 97.8% gross margin on $17T payment volume vs. Robinhood’s cyclical crypto trading revenue illustrates the fundamental gap.

2. Three Critical Dependencies Determine Rebundling Success Dependency #1: Capital Access (Banking Charter = Compounding Moat)

Winners: Nu ($19B deposits at 3–4% funding 17.7% NIM lending), SoFi (banking charter enables deposit capture)

Losers: PayPal (no charter, can’t capture deposits), Revolut (UK license delayed, can’t compete on lending)

Consumer fintechs without banking charters are hostage to BaaS partners (Synapse collapse 2024) and can’t internalize the deposit-to-lending spread that makes rebundling profitable.

Dependency #2: Cross-Sell Economics (3+ Product Threshold)

Single-product user ($50/year revenue, $150 LTV) loses money on $200–450 CAC. Three-product user ($180/year, $540 LTV) becomes profitable.

Success cases: Robinhood (11 products, ARPU $191 +82% YoY), Revolut (wealth revenue +298%), Nu (83% monthly active across products)

Failure case: PayPal (400M users but most only use checkout, can’t cross-sell Venmo/crypto/savings)

Dependency #3: Developer/Enterprise Lock-In (Integration Depth)

Stripe’s moat: Once integrated with Billing + Tax + Connect + Radar, ripping out requires 6+ months engineering. Each additional product compounds switching costs.

Visa’s “Chaordic” genius: 20,000 banks compete for customers (decentralized), but all use Visa protocol (centralized). Banks can’t leave because the network IS the product. Zero CAC, 97.8% margin, toll on $17T volume.

Structural challenges for consumer crypto: custody liability, margin compression (Uniswap 0.3% vs. Coinbase 1–2%), no lock-in (users withdraw to self-custody), and cyclicality (Coinbase revenue -75% in 2022–2023).

3. The Winning Crypto Thesis: Be the “Stripe/Visa for Stablecoins” The pattern from successful fintechs is clear: build compliant middleware that abstracts blockchain complexity for enterprises/fintechs.

Investment Theme #1: Stablecoin Orchestration

What Stripe’s $1.1B Bridge acquisition proves: enterprises want stablecoin settlement but don’t want to run nodes, manage wallets, or navigate 50 state licenses.

The winning product: single API that handles multi-chain routing, liquidity optimization, compliance screening, and tax reporting. Enterprises call POST /transfer, infrastructure handles the rest.

Economics: 0.5–1% take rate on massive volume, zero marginal cost per transaction, high switching costs once integrated. Same 90%+ gross margins as Visa/Stripe, applied to the $2T+ stablecoin settlement market.

Investment Theme #2: Vault-as-a-Service (The “Fireblocks” Play)

PayPal, Robinhood, Nu all custody billions in crypto. Custody requires OCC compliance, MPC/HSM security, $100M+ insurance, disaster recovery.

The opportunity: provide “AWS for crypto custody” where enterprises plug into an API and you handle key management, policy engines (e.g., “3 approvals for >$100K withdrawals”), compliance reporting, OFAC screening.

• Every fintech adding crypto needs this
• Zero CAC (B2B sales cycle)
• High retention (switching custody = 12+ month security audit)

Winners: Fireblocks ($8B valuation), Anchorage Digital (OCC-chartered), Copper.co

Investment Theme #3: DeFi Middleware / “Vault Curator” Layer

What’s missing: a “Stripe for DeFi yields” that provides yield aggregation across Aave, Compound, Morpho, and tokenized T-bills (Ondo OUSG), plus gas fee abstraction, tax reporting (IRS-compliant 1099 generation), insurance wrappers, and compliance layers.

The vault curator opportunity:

• Ondo Finance: Tokenized T-bills earning 5%
• Backed Finance: Tokenized corporate bonds
• Maple/Goldfinch: Institutional DeFi lending with underwriters

Concrete example: SoFi has billions in deposits earning 0–1% in traditional accounts. If Galileo (their B2B platform) offered a “DeFi Yield API” connecting to tokenized T-bills at 5%, SoFi could offer customers 4% APY, keep 1% spread, and avoid balance sheet risk. This middleware play, sitting between protocols and regulated fintechs, providing the compliance wrapper and tax reporting, is the gap.

Framework: Evaluating Fintech Business Models

Summary Table Company Crypto Integration & Thesis Alignment

Conclusion

Ultimately, the future of fintech lies in the convergence of traditional financial platforms and programmable financial infrastructure. Rather than replacing existing systems, blockchain technology is increasingly being integrated as a settlement and liquidity layer that operates behind the scenes. Stablecoins, tokenized assets, and onchain markets enable faster settlement, cheaper cross-border payments, and new financial products while remaining largely invisible to end users.

Ultimately, the companies best positioned to capture value in digital finance are those that combine large-scale distribution, regulatory trust, and infrastructure control. Whether through payment networks, developer platforms, or consumer financial ecosystems, the winners will be the platforms that abstract financial complexity while orchestrating multiple payment rails. As programmable money becomes more widely adopted, fintech firms that successfully integrate traditional finance with blockchain infrastructure will shape the next generation of global financial services.

The Future of Digital Finance: How Fintech Platforms Are Integrating Crypto Infrastructure was originally published in IOSG Ventures on Medium, where people are continuing the conversation by highlighting and responding to this story.

Author: Max Wong @max0dte

Introduction

Pump.fun launched in early 2024 as a permissionless meme-coin launchpad on Solana, allowing anyone to create and trade a token in seconds using a bonding curve mechanism. What began as a niche experiment quickly evolved into one of the highest-revenue applications ever deployed on a public blockchain.

Throughout 2024 and 2025, Pump.fun consistently rivaled or exceeded Hyperliquid in daily protocol revenue, a feat made more remarkable by the fact that it operates in the inherently cyclical memecoin market. As a result, there was massive demand for their native token, $PUMP, which launched via a $600mn ICO at $0.004 (implying a $4bnFDV).

Over the past few months, even with record high revenues and 100% value accrual to token, $PUMP currently trades down roughly 80% from its ATH of $0.086 ($8.6bn FDV), at approximately $0.0019, placing its current market cap near $679 mn on a $1.9bn FDV. There clearly is a structural mismatch between price and value.

This report aims to: (1) catalogue Pump.fun’s evolving product suite and ecosystem strategy; (2) stress-test the sustainability of its revenue with a forensic wash-trading analysis; and (3) assess whether the current valuation represents a genuine mispricing or a rational discount for structural risk.

The Product Suite

Pump.fun has evolved well beyond its original launchpad. Since late 2024 it has systematically expanded into adjacent verticals, diversifying its revenue mix and tightening its grip on the onchain speculative trading stack

1. Launchpad (Core Product)

The original product and still the brand’s defining feature. Anyone can deploy a token for a nominal fee.

2. Pumpswap

PumpSwap is Pump.fun’s native AMM DEX. It launched in March 2025 to recapture the postgraduation fee revenue that had previously flowed to Raydium (which charged 6 SOL per graduating token). The protocol retains 0.05% of each swap; 0.20% goes to LPs and a further 0.05% to token creators under the May 2025 revenue-sharing updates

• Free creation of custom liquidity pools for any token
• Ability to add liquidity to existing pools
• Trading of all PumpSwap-listed tokens

3. Padre / Pump Terminal

Padre, now rebranded as Terminal following its acquisition by pump.fun, serves as a specialized trading terminal. It currently supports tokens on Solana, BNB, Base and ETH.

Like most terminals, it has advanced toolings and analytics to support traders including but not limited to:

• Trenches feature (check newly migrated tokens, tokens about to migrate etc)
• Customizable features
• Sniping, Instant buy, etc.
• Multiwallet strategies
• Bundle checker

4. Pumplive

Pump live is pump.funs inplatform livestreaming product, where streamers can create a stream whilst simultaneously having a token attached it

The core thesis behind this is the publisher exchange thesis, similar to Parti,. Kick → stake.com etc.; the main value prop is to have streamers and tokenholder incentives align, where streamers want to drive volume to their token as they get a % total volume fees, and tokenholders want greater volume / liquidity / buy pressure

The more they stream, the more active the token, the more volume they generate

Ecosystem Strategy and Initatives

Since their TGE, Pump.fun has been strategically using their ~$1bn cash treasury to as continued to spin out multiple product lines, e.g their acqusition of Padre, and implement initiatives to continue to grow out their ecosystem.

Pumpfund

Pump Fund represents Pump.fun’s strategic pivot toward sustainable ecosystem development, launching a $3 million “Build in Public” (BiP) hackathon on January 19, 2026. The initiative will fund 12 selected projects with $250,000 each at a $10 million valuation, prioritizing market-driven selection based on public traction rather than traditional venture judging.

Glass full foundation

GFF represents Pump.fun’s strategic liquidity injection initiative launched in August 2025 to stabilize and support its ecosystem memecoins. The foundation deployed $1.69–1.7 million (2,022 SOL) across five transparent wallets into 10 selected tokens including Tokabu (21.3%), House (20.6%), USDUC, NEET, MASK, and FART, targeting projects with strong community engagement.

Project ascend

Project Ascend is Pump.fun’s foundational creator incentive program launched in 2025, to introduce a new, sustainable growth model for olatform featuring dynamic, tier-based creator fees (0.95% to 0.05%) designed to increase earnings by 10x. It focuses on long-term sustainability and accelerating Community Takeover (CTO) applications.

Aggregate Metrics (All Products)

The following table consolidates all three reportable product lines. The 2025 actuals reflect a genuinely exceptional revenue year and the 2026E’s shows a stable runrate ahead.

At present, ~32.7% of the platform’s total revenue is being generated from non-launchpad products, clearly signifying early success towards their goal of revenue stream diversification and to seek growth in other areas.

Is Pump.fun Wash Trading?

Clearly, fundamentals on the surface for $PUMP seems extremely strong; however a critical question for any assessment of Pump.fun’s fundamentals is whether its volume numbers reflect genuine economic activity or are artificially inflated by wash trading and bot activity.

1. The Volume Correlation Hypothesis

Under organic conditions, launchpad and PumpSwap volume should be moderately positively correlated with a lag. High launchpad activity reflects genuine speculative interest, some of which flows through graduation and sustains post-listing trading. Under heavy wash trading, this relationship inverts. Launchpad volume becomes artificially inflated and decoupled from real demand; tokens graduate based on fabricated bonding curve activity and arrive on PumpSwap with no genuine buyer base behind them. The result is launchpad volume spiking while PumpSwap volume stays flat or declines; correlation collapses toward zero or turns negative.

• In a healthy market, launchpad volume and high PumpSwap volume move together. In a wash-traded market, launchpad volume surges while PumpSwap volume remains suppressed. In a bear market, both fall in tandem and correlation stays positive. The direction of divergence is therefore the signal, not the level of either series in isolation.
• The most telling combination of indicators would be a spike in graduation rate (more tokens artificially hitting the bonding curve threshold), paired with low and rapidly decaying per-token PumpSwap lifetime volume, and average PumpSwap liquidity depth that fails to grow proportionally with the number of graduating tokens.

Volume dataset since Jan 2026 until present:

(The first two datapoints — Jan 5–11, Jan 12–18 — were excluded from the correlation analysis due to the implementations of fee and liquidity provider changes to pumpswap, leading to a large outlier spike in volume)

Key Findings

Launchpad vol is quite flat. Over 8 weeks, launchpad volume ranged only ~40% around its mean, from $~400mn to $570mn. That’s the behavior of a volume floor maintenance. This makes sense considering a large amount of bundlers/farmers creating volume for the platform which is entirely different from was trading

PumpSwap is a bit more reactive; during this same period, it ranged ~60% $5.8bn to $3.5bn, driven by increased memecoin trading demand in mid Jan, along with additional pumpswap incentives by the team without any corresponding spike in launchpad volume

r = 0.579, moderate correlation. With n=8, the significance threshold at p=<0.05 would need to be r= ~0.63 but the direction and magnitude are consistent with the organic hypothesis.

2. The University of Pisa Paper

Researchers at the University of Pisa provides the first full on-chain parse of Pump.fun’s launchpad, covering every transaction across 655,770 tokens launched in September–October 2025. Bot-initiated transactions are distinguished from human-initiated trades using Solana transaction log metadata.

Four findings bear directly on the wash trading question.

Large human buys are the dominant graduation predictor

The single strongest predictor of graduation is fast SOL accumulation via a small number of large trades. The median successful graduation required only ~457 bonding curve events and completed in ~4.4 minutes from token creation. This pattern, large, infrequent capital commitments from distinct wallets is consistent with coordinated human speculation (Telegram calls, influencer-driven hype) or serial creator pump-and-dump, not sustained high-frequency wash trading bot volume churn. By contrast, bot-dominated tokens accumulate many small trades and stall before graduation.

and Vice Versa, bot volume actually suppress graduation; the majority of bots are Snipers and Bundlers which also act as a volume floor; but different from wash trading

Tokens with high bot-activity exhibit systematically lower graduation probabilities beyond early bonding curve stages. At the time, graduation requires accumulating ~85 SOL in the curve. If bots were inflating volume toward that threshold, bot-heavy tokens would graduate at higher rates, however, it was found bot activity is actually negatively correlated with graduation. Why? The mechanism is structural: at graduation, the bonding curve transitions from virtual to real AMM reserves, causing a discrete drop in effective liquidity depth. Selling before graduation into virtual-reserve-supported depth is more profitable than selling after.

Further, they found that the top ten token creators in September 2025 each launched over 2,000 tokens within the single month and found statistically anomalous sell sequences by wallet clusters on each of these tokens before the graduation threshold. Bundlers and snipers would front-load positions and dump into retail demand attracted by the curve’s upward momentum

The paper concludes most bots the platform are front-runners that extract value from human counterparties on entry and exit immediately, not volume inflators with an interest in pushing tokens to graduation. Essentially, bots snipe / conquer a large amount of supply, and near graduation, dump on retail participants — distinctly different from wash trading

Net SOL flow is strongly and persistently positive; structurally incompatible with was trading

The paper computes net SOL flow — total SOL committed to bonding curves minus total SOL withdrawn via sells — across the full dataset. Cumulative net SOL retained in the ecosystem reached ~160,000 SOL (~$32mn at September 2025 prices) over the single observation month. This is the definitive empirical test for wash trading: circular volume between related wallets leaves net capital flow near zero, as buys and sells cancel. A $32mn

net retention is structurally inconsistent with meaningful circular volume and confirms that real outside retail capital continuously enters the launchpad, loses to the 1.25% fee on every trade, and funds the protocol revenue.

The paper’s findings is similar to the key findings in our volume corrleation analysis: a large volume on the launchpad is generated via pump and dumps by bundlers and snipers, creating a floor, but this is not wash trading. The distinction is material: wash trading generates zero net protocol revenue because fees cancel across related wallets. Pump-and-dump generates real fees on every transaction from genuine retail counterparties who lose capital to the house. The ~$390mn ARR figure is consistent with a platform monetising enormous real retail volume via a pump-and-dump ecosystem, not manufacturing synthetic metrics

Tokenomics

Buybacks

Currently, the Pump foundation uses 100% of revenues from all product lines to buyback

$PUMP

token on the open market. Since announcing their 100% revenue buyback program on July 15th; in 8 months, they have:

• Removed 27% of the circulating supply
• Removed 9.6% of the total supply

For reference, Hyperliquid has only burned 4.1% of total supply or ~12.3% of circulating since starting their buyback program on Nov 2024

At current prices and revenue numbers, they are clearing near ~45% of the circulating supply PER YEAR.

Supply Structure + Unlocks

• Total Supply: 1,000,000,000,000 PUMP
• Circulating Supply: 430,000,000,000 (43%)

Remaining Locked: ~58% of supply

Major upcoming unlocks:

• Ongoing — 12% @ 2% monthly for C&E until July
• July 2026–8.25%, then 24.75% @ 0.68% monthly for 36 months

Valuation Reasoning and Analysis

In general, if we believe the washtrading analysis above, Pump is objectively undervalued by a massive factor and may represent an asymmetric upside.

This discounts likely due to a few reasons:

1. The primary driver of this discount appears to be skepticism around revenue sustainability

The market broadly believes that Pumpfun’s platform-wide volume is largely speculative, cyclical, and heavily tied to short-term memecoin activity. As a result, investors are treating current earnings power as transient rather than durable. Buybacks, while mechanically accretive at these multiples, are not being capitalized in valuation models because the underlying assumption is that revenues will compress materially in a normalized environment. In other words, the debate is not centered on whether Pump is currently profitable, it is centered on whether that profitability still exists 24 months from now.

2. There is also a meaningful information and coverage gap contributing to the discount

For this piece, we spoke to 15 prominent VC and liquid funds to get their thoughts on $PUMP.

Out of the 15, only one was actively covering $PUMP with bottom-up analysis. The majority had not modelled the new product suite, segmented revenue by product line, or stress-tested volume durability assumptions. This lack of institutional coverage creates a narrative vacuum, where pricing is driven more by perception than by granular financial analysis. By contrast, assets like $HYPE benefit from deeper institutional sponsorship, stronger research coverage, and clearer product positioning, all of which support higher and more stable valuation multiples.

There is also a reflexive narrative element at play. Assets associated with memecoin infrastructure are often categorized as speculative and transient, and they trade accordingly. It takes time and data across market cycles for the market to update that framework. Until Pump’s revenue base survives a broader crypto drawdown and institutional coverage expands, valuation compression may persist regardless of current cash generation.

3. People don’t believe in Pumpfun’s management

Another component of the discount is a credibility premium that has yet to be earned.

Investors express concerns around:

• Long-term vision beyond memecoins
• Capital allocation discipline
• Execution of the broader product roadmap
• Transitioning from viral growth to durable platform economics

Markets tend to assign low multiples to founder-led, high-growth platforms until they demonstrate resilience through volatility and prove that growth can evolve into durable platform economics. Until Pump shows sustained revenue diversification through products such as Pumpswap and Pumpterminal, along with consistent execution and disciplined capital returns, a management credibility discount is likely to remain embedded in the multiple.

$PUMP Valuation Deep Dive: Inorganic Volume or Revenue Machine? was originally published in IOSG Ventures on Medium, where people are continuing the conversation by highlighting and responding to this story.

Author: Turbo @TurboGuo

TL;DR

1. Cross-border payment companies’ stablecoin strategies fall into three buckets: stablecoin acceptance (settling payments in stablecoins), stablecoin issuance (applying for licenses to issue their own), and operating through separate offshore brands to isolate regulatory risk.
2. Stablecoins offer little fee or speed advantage in cross-border payments. As local payment rails have proliferated, traditional transfer costs have compressed dramatically. The remaining friction sits in domestic settlement, exactly where stablecoins can’t help. FX conversion is also unavoidable, so stablecoins don’t actually solve either core pain point.
3. Neobanks are the highest-value segment in the stablecoin cross-border payment value chain. The real advantage of stablecoin payments is closed-loop ecosystems — zero friction only materializes when both sender and receiver settle in stablecoins. Regions with thin banking infrastructure (Southeast Asia, Middle East, Africa) are the strongest use cases. Tether’s investment in SQRIL is a clear signal.

Part 1: Breaking the myth of stablecoin cross-border payment

Stablecoin Strategy for main Cross-border payment companies

Asian cross-border fintechs’s stablecoins strategies have three main part:

• Stablecoin Usage: accepting s settling payments in stablecoins)
• Stablecoin Issuance : applying for licenses to issue their own stablecoins
• Separate Brand: operating crypto/stablecoin businesses through independently branded offshore entities to isolate regulatory risk from their domestic licenses.

1. The separate offshore entity is essential

Every company with meaningful stablecoin activity operates through a separate offshore entity: KUN (for Yeepay), DFX Labs (for LianLian), RD InnoTech (for RD Technologies). This is due to regulation requirment. PBoC’s October 2025 intervention against Ant Group and JD.com and the February 2026 eight-agency ban, made brand separation a regulatory necessity for any mainland-linked company.

2. Stablecoin acceptance is the only live product, but not many companies disclosed the fee and speed

Most companies are starting with stablecoin acceptance, not issuance. This includes LianLian, KUN, OristaPay. Only RD InnoTech is close to actually issuing a stablecoin (HKDR, license expected March 2026 if pass). However, have not see companies that disclosed the fee and speed of stablecoin payment services like other established payment companies. The only one disclosed fee is BVNK, the fee structure is : 0 for transfer + standard conversion fee + services fee to transfer fund from/to outside wallet + Blockchain fee.

3. The March 2026 HK License Issurance will be a turning point

HKMA will issue its first stablecoin issuer licenses in March 2026. Only a very few companies will be approved. Comfirmed companies that apply for the license are RD InnoTech, JD and Anchorpoint Financial. RD has high possibility of getting the license since they are in the HKMA Stablecoin Issuer Sandbox. JD is unlikely to be approved due to the regulation requirment. Whether KUN or LianLian’s DFX Labs applied is unconfirmed. The license outcome will determine which companies can move from stablecoin usage to issuance, and whether HK becomes a real stablecoin hub or remains constrained by Beijing’s influence.

4. Reasons for Cross-Border Payment Companies’ Stablecoin Adoption are slow

4.1 Stablecoins Offer nearly No Fee or Speed Advantage in Cross-Border Payments

If we want to do cross border payment from oversea to China mainland as a business owner, there are three main steps: Transfer, Exchange and Mainland Settlement.

• Transfer: Moving funds from the overseas buyer to the payment platform, via methods like SWIFT, local bank rails, or internal wallet transfers.
• Exchange: Converting the foreign currency (e.g., USD, EUR) into CNY at a given FX rate.
• Mainland Settlement: Withdrawing the converted CNY to a China mainland bank account or Alipay.

4.1.1 New rails including local rails that bring down traditional transfer to ~0% fee and instant speed

Traditionally, transfer will cost most of the fees and time since SWIFT will take large fees. For the companies that we analysis in this article, it takes 56% of the total fees. For example, it takes $25. to use SWIFT by Airwallex.

However, many cross border fintech have Local Rails now, It means:

• Fintech collects funds using the sender’s domestic payment network (e.g., ACH in the US, SEPA in Europe, UPI in India), internally moves them across its own global network in some way, then pays out via the receiver’s local network, bypassing SWIFT entirely.

New rails bring down the transfer fee by a lot for cross border transfer. Currently mainland settlement takes 58% of the fees.

However, mainland settlement is not a path that Stablecoin can bypass since people will always want CNY cash in their banks.

Not only stablecoin payment can’t bypass mainland settlement, exchange is also an essential step for cross border payment, there is nearly no room for stablecoin to improve the user experience. The transfer is close to instand and $0 fee, the exchange take most of the fee and stablecoin companies also need to do exchange for users.

Overall, if we do a fair comparison within cross border transfer and excluded mainland settlement and exchange, there is no room for stablecoin to improve.

4.2 Regulation: The pause of stablecoin issurance and concerns

Beijing has moved from passive skepticism to active enforcement against stablecoin activity by mainland companies.

In October 2025, the PBoC and CAC directly ordered Ant Group and JD.com to suspend their Hong Kong stablecoin plans (Source: Financial Times, and these two company doest pause the applying). Both companies had been preparing to apply under Hong Kong’s new stablecoin licensing regime.

Then in February 2026, eight government agencies (PBoC, CSRC, SAFE, and five others) jointly issued 《关于进一步防范和处置虚拟货币等相关风险的通知》.

The notice explicitly bans mainland entities and their controlled overseas subsidiaries from issuing CNY-pegged stablecoins abroad without government approval, stating that such stablecoins “perform some of the functions of fiat currency in circulation” (在流通使用中变相履行了法定货币的部分功能). The regulation also bans RWA tokenization within mainland China and prohibits companies from even including terms like “stablecoin,” “virtual currency,” or “RWA” in their registered business names. This two-step escalation, from targeted orders against specific companies to a blanket legal prohibition, signals that Beijing views private stablecoin issuance as a direct challenge to monetary sovereignty and the e-CNY (digital yuan) strategy.

Concern 1: Currency Over-issuance and Market Manipulation

1. Over-issuance without full reserves. Issuers may mint stablecoins without genuinely holding 100% reserves, effectively creating money out of thin air.
2. Leverage-driven money multiplication. Even when issuers do maintain 100% reserves, stablecoins still generate a multiplier effect through downstream activities such as lending, collateralization, trading, and revaluation. This means that in a potential bank run scenario, the redemption demand could be several times larger than the original reserve pool

Chinese officals argued that from the perspective of financial and asset market trading, the most critical risk to guard against is market manipulation, especially price manipulation, which requires sufficient transparency and effective regulation.

Concern 2: Stablecoins Won’t Actually Reduce Costs

While many believe stablecoins will reshape the payment system, the current infrastructure, especially in retail payments, already has very limited room for further cost reduction. The existing retail payment ecosystem already includes third-party payment platforms, CBDC, software and hardware wallets, and clearing infrastructure.

This is true in some level according to our previous analysis.

Part 2: Investment Implications: The Neobank Thesis

2.1 Core Thesis: Neobanks is the key of stablecoin cross border payment

The stablecoin cross-border payment value chain has three layers:

1. Issuance (Tether, Circle, HKMA licensees): Creating stablecoins
2. Infrastructure (Bridge/Stripe, BVNK, Circle CPN): Moving and converting stablecoins
3. Distribution/Endpoint (Neobanks): Converting stablecoins to local spending power

Neobanks are the core bottleneck and the highest-value opportunity for the following reasons:

2.1.1 Neobanks Are the Last Mile

The real implication is that stablecoin cross-border payment only has an advantage in ecosystems where stablecoin itself is the final destination, not a bridge to fiat.

If a merchant earns in stablecoin and pays suppliers in stablecoin, if employees receive salary in stablecoin and spend through a stablecoin-native neobank, the entire transaction stays on-chain and never touches traditional rails at all.

In that scenario, there is no transfer fee, no FX spread, and no settlement cost. The problem is that this requires both ends of the transaction to live inside the stablecoin economy.

The moment either party needs to convert back to fiat (CNY, USD, HKD), the cost of off-ramping reintroduces the same settlement fees that fintech local rails already handle cheaply. This is why stablecoin payment has the strongest case in regions with underdeveloped banking infrastructure, high remittance corridors, or crypto-native communities where people already hold and spend stablecoins daily.

Additionally, neobank users can have above average yield on top of stablecoin, which can bring additional reason for users to use stablecoin cross-border payment.

2.1.2 Must Be in Regions with Lacking Financial Infrastructure

Stablecoins have the strongest case where they are more convenient than traditional banking:

• SE Asia (Philippines, Vietnam, Indonesia): 44%+ unbanked, high smartphone penetration
• Middle East/Africa: Large expatriate remittance corridors, weak local rails, progressive regulation (UAE 4 regulatory frameworks)

Tether act as a parallel financial system in Vietnam. In the meanwhile, Tether invested in SQRIL, this is a move that showing their growth strategy is on funding neobank-layer companies that let people spend USDT locally in less developed countries, which is the strongest market signal that the neobank endpoint is where the real value accrues.

2.1.4 Why Series A/B Is the Sweet Spot

Stablecoin neobanks are relatively heavy infrastructure businesses that require:

• Local licenses and regulatory approval
• Local banking partners for fiat on/off-ramps
• Local compliance infrastructure (KYC/AML)
• Merchant network development
• Consumer trust building

This means:

• Seed/Pre-A is too early: The business model is not yet proven in the local market. Regulatory risk is high. Unit economics are unclear.
• Series A/B is optimal: After proving the local market has demand, the business has run successfully, regulatory standing is confirmed, and unit economics are validated. Investment risk decreases materially. The upside (expansion to adjacent markets, deeper market penetration) becomes clearer.
• Late stage/IPO may be too late: Valuations will already reflect proven business models

Part 3: Breakdown of each cross-border payment companies’ stablecoin strategies

Airwallex (空中云汇)

Airwallex is taking a more cautious approach: build internally first, deploy when regulatory and market conditions are favorable. This may reflect the company’s strength in traditional rails reducing the urgency to adopt stablecoins immediately.

1. Stablecoin/Blockchain Integration: Skepticism, no live product yet

1.1 CEO Jack Zhang has been skeptical of stablecoins.

• He has argued that Airwallex already moves money “at the cost of less than 0.01% and real time,” and that “you can’t be cheaper than free and faster than real time.”
• The company’s official blog echoed this position, citing fragmented regulation, the complexity of on/off-ramping, and limited mainstream adoption as key barriers, concluding that “stablecoins can’t yet solve cross-border payments at scale.”

1.2 Internal stablecoin Team

Airwallex posted 22 stablecoin engineering jobs in July 2025 to build a token settlement platform. Job postings revealed that the company is building infrastructure to “allow customers and internal systems to buy, hold, send, and settle tokens globally, support near-instant global payments, and enable on-chain liquidity as well as seamless fiat-to-stablecoin conversion”

The planned use cases include cross-border settlements in emerging markets, on-chain liquidity management and programmable payments with fiat-to-stablecoin conversion services.

1.3 Current Status:

• No live stablecoin product yet. 2025 end-of-year mission update contain zero mention of stablecoins.
• No public partnerships with Circle, Tether, or other stablecoin issuers
• 2026 strategic priorities are geographic expansion , AI-powered developer tooling, and customer experience, not stablecoins
• Company blog (Jan 2026) states “the jury is very much still out on the benefits of stablecoins”

XTransfer

1. Stablecoin/Blockchain Integration: Have positive views, but have not launched any live product; no hidden blockchain brand found

1.1 Overseas stablecoin receiving service (Announced, not live)

XTransfer announced in August 2025 that it will launch an overseas stablecoin receiving service (海外稳定币收款服务) within 2025, initially opening to select customers. However, as of February 2026, there is no public confirmation that the service has gone live.

1.2. Assumtion: Dual Wallet Model

XTransfer’s stablecoin strategy centers on a “dual-currency wallet” approach, allowing businesses to hold both fiat and stablecoins at the same time.

In addition, there is no hidden crypto/blockchain related brands is found for XTransfer

WorldFirst (万里汇-蚂蚁集团)

1. Stablecoin/Blockchain Integration: Not at WorldFirst product level, but Ant International is building blockchain rails

WorldFirst’s own product does not currently offer stablecoin or crypto services. Its official services has not include blockchain, stablecoin, or digital currency. All WorldFirst products operate on traditional banking rails

However, parent Ant International is building significant blockchain infrastructure that will likely flow through to WorldFirst:

1.1 Whale Platform’s Tokenised Deposit Service (TDS)

In 2024, over 1/3 of Ant International’s $1 trillion+ total fund processing volume was processed through Whale using blockchain technology. This is not stablecoin usage, but Tokenised Deposit Service (TDS).

Tokenised deposit is issued by regulated banks instead of stablecoin companies. Take HSBC as an example, tokenized deposit allows HSBC’s clients to create digital records of their traditional, fiat deposits. While HSBC maintains the fiat deposits, each one of the digital records on the DLT is a token that can be transferred. Clients can transfer funds without having to wait for batch processing.

In May 2025, Ant Group launched Hong Kong’s first blockchain settlement solution, the “Tokenized Deposit Service” with HSBC, allowing real-time payments in HKD and USD via corporate wallets

• Additional tokenized deposit integrations with: DBS, Standard Chartered, OCBC, BNP Paribas, JP Morgan’s Kinexys Digital Payments, and Deutsche Bank
• UBS Digital Cash (Nov 2025): UBS Singapore signed MoU with Ant International for multi-currency tokenized deposit capabilities integrated into Whale
• Standard Chartered (Dec 2025): Launched tokenized deposit solution for HKD, CNH, SGD, and USD on Whale, including a cross-bank transfer of HKD $3.8M from HSBC to StanChart via deposit tokens
• Ant International has worked with ten international banks supporting tokenized deposits on Whale
• Participate the Singapore’s MAS “Project Guardian” on tokenized deposits in transaction banking (ISDA and Ant co-led an industry report on tokenized bank liabilities for FX settlement)

1.2 Stablecoin License Applications: Ant wanted to apply but the government stopped it

• In around June 2025, Ant Group said they will apply to HKMA once Stablecoin Ordinance takes effect.
• In Oct 2025, Financial Times reports Alibaba-backed Ant Group and JD.com have been instructed to suspend their stablecoin plans in Hong Kong from 中国人民银行（PBoC）and 国家互联网信息办公室（CAC）
• In Feb 2026, the new regulation 《关于进一步防范和处置虚拟货币等相关风险的通知》emphasized that:No mainland companies can participate in CNY related stablecoin issuance.
• Ant Group filed “ANTCOIN” trademarks with HK’s IP Department (interpreted as strategic IP protection), but no public indication that Ant has resumed its actual license application

Yeepay (易宝支付)

1. Stablecoin/Blockchain Integration: Not directly at Yeepay, but founders operate a hidden stablecoin brand “KUN (鲲)”

Yeepay’s official product does not integrate stablecoins. However, Yeepay’s co-founders are actively building a stablecoin payment company through a separate brand: KUN (鲲)

KUN (鲲) is Yeepay’s “Overseas Ecological Partner” . Key personnel overlap:

• Liu Jialiang (刘家良), Yeepay’s Partner and SVP, is KUN’s Founder and CEO (“Louis Liu”)
• Chen Yu (余晨), Yeepay’s Co-Founder and President, is KUN’s Chief Advisor

1. 1 KUN’s Products

Note: KUN does not publicly disclose its fee structure. KUN states it serves “customers outside mainland China and the United States” only

2. KUN’s Partnerships & Integrations

• Circle Payments Network (CPN): Live. Confirmed by Circle as a CPN partner for 24/7 USDC/EURC-based stablecoin settlement. CPN mainnet launched mid-2025 with 29 financial institutions.
• WSPN: Integrated WUSD (USD-pegged stablecoin) into KUN Space platform for cross-border enterprise transactions (March 2024)
• Marco Digital (马可数字, 01942.HK): Reported to have completed Asia’s first USDT-based insurance commission payment via KUN (Aug 2025).

LianLian Pay (连连支付 / 连连数字)

1. Stablecoin/Blockchain: YES, integrate stablecoin by partnerships

LianLian has one of the most aggressive stablecoin strategies among Chinese cross-border payment companies.

1.1 Circle/USDC MOU with LianLian: Still exploration phase, not yet live product.

Signed MOU with Circle. Evaluating USDC for high-volume international payment flows. Exploring Circle’s layer-1 blockchain Arc for future payment use cases.

1.2 BVNK Partenership: Live Stablecoin-Powered Payments Partenership (June 2025)

Flow: Merchants deposit stablecoins -> BVNK auto-converts to USD -> LianLian routes through global network.

1.3 RD Technologies (圆币科技): HKDR Stablecoin

Partnered with RD Technologies . RD plans to issue HKDR (HKD-pegged stablecoin) on Ethereum. LianLian uses RD Technologies’ RD ezLink enterprise identity verification and RD Wallet payment tools. Also partners with HashKey Exchange, Cobo.

HKDR remains a sandbox/testing product, not a licensed stablecoin. The partnership cannot become fully operational until RD Technologies obtains a formal HKMA stablecoin issuer license, expected March 2026.

1.4 DFX Labs: Virtual asset trading platform; Wholly-owned, Hong Kong-listed subsidiary company

Their offerings include:

• Crypto trading: buying/selling Bitcoin and other cryptocurrencies
• Wallet services: secure custody/storage of virtual assets
• Liquidity services
• DFX Labs received HK SFC VATPlicense.VATP license: Type 1-dealing in securities + Type 7-automated trading. Conditions: must complete rectification per SFC on-site inspection and pass independent penetration testing before full operations.
• No confirmed evidence that DFX Labs formally applied for HK stablecoin.

RD Technologies (圓幣科技)

Founded by the former HKMA chief. Its competitive advantages are:

• Regulatory pedigree: Former HKMA chief as chairman, first-batch sandbox participant
• Dual licensing: SVF (fiat payments) + stablecoin sandbox

1. Core Business: Payment (OristaPay)+Stablecoin Issurence(RD InnoTech)

• Operates under the Stored Value Facility (SVF) License (SVF0016) granted by the HKMA in December 2022
• Multi-currency e-wallet supporting 8 currencies for business payment and FX management
• Transfer methods: FPS (Faster Payment System), CHATS, Telegraphic Transfer (TT)

Two independent business lines:

1. OristaPay (源穩支付, operated by RD Wallet Technologies Ltd.): Fiat-basedB2B cross-border payment and wallet services under the existing SVF license. Positioned as a “next-generation payment infrastructure provider”
2. RD InnoTech Limited (圓幣創新科技有限公司): Dedicated to stablecoinissuing (HKDR) and blockchain/Web3 initiatives

1.1 OristaPay (RD Wallet)

OristaPay launched the Global Collection product (confirmed live), enabling fiat and stablecoin cross-border payments. 24/7 liquidity, with particular depth in Africa and Latin America.

• Receive payments in 100+ currencies across 200+ countries and regions
• Accepts major stablecoins for both merchant funds and third-party collections
• Features rapid settlement with real-time AML and KYT (Know Your Transaction) screening
• Specific fee details for Global Collection not publicly disclosed

OristaPay Stablecoin Products

1.2 RD InnoTech Limited (圓幣創新科技有限公司)Stablecoin Issuing

1.2.1 HKDR Stablecoin

HKMA Stablecoin Issuer Sandbox (July 2024):

RD InnoTech was admitted into the first cohort of the HKMA stablecoin issuer sandbox, alongside Standard Chartered/Animoca/HKT (HKDG) and JD CoinLink (JD-HKD)

1.2.2 Regulatory Timeline for stablecoin

• Dec 2022: SVF License (SVF0016) granted by HKMA
• Jul 2024: Admitted to HKMA Stablecoin Issuer Sandbox (first cohort)
• Aug 2025: Hong Kong Stablecoin Ordinance takes effect
• Sep 2025: Brand restructuring, OristaPay and RD InnoTech separated
• Jan 2026: OristaPay Global Collection launched with stablecoin support

1.3 Key Partnerships

ZA Bank: Active partnership with investment

ZA Global co-led a US$40M Series A2 round in RD Technologies (alongside China Harbour, Bright Venture, Hivemind Capital). Signed MOU with ZA Bank covering

• Reserve custody: ZA Bank provides custody services for HKDR reserve assets (ZA Bank became HK’s first digital bank to offer reserve banking services for stablecoin issuers)
• Distribution: ZA Bank exploring becoming a sales/distribution partner for HKDR
• RD InnoTech is the inaugural stablecoin issuer to use ZA Bank’s reserve banking services

Additional Partnerships:

• Allinpay International: Stablecoin-enabled online/offline payment integration
• Ripple: Aligned for 2026 growth
• Circle Payment Network: Joined CPN

Appendix: Cross-Border Payment Companies Fees and Speed Breakdown and compairson

Comparison

Cost Comparison: $100 Overseas to China Mainland (Lowest Rate)

Scenario: Transfer $100 USD from overseas to a China mainland bank account. Each step uses the lowest available rate from each company. Fees are applied sequentially.

Notes:

• XTransfer’s 0.1% settlement rate requires high volume; standard rate is 0.4% max ($0.40 fee, $99.60 received)
• WorldFirst’s $0 total assumes personal Alipay withdrawal; B2B bank withdrawal is 0.3% (B2C) or 0.4% (B2B)
• Yeepay’s Local Payment Methods fee ranges 0.6%-1.6%; lowest rate (0.6%) used here. CNY settlement fee not publicly disclosed
• LianLian’s 0.3% settlement is for high-volume users; standard can be up to 0.7%
• RD Technologies does not disclosed fee and speed information

Cost Comparison: $100 Overseas to China Mainland (Highest Rate)

Scenario: Transfer $100 USD from overseas to a China mainland bank account. Each step uses the highest/worst-case rate from each company. Fees are applied sequentially.

Notes:

• Airwallex SWIFT fee is $25/txn flat; at $100 this is 25% of the transfer. More reasonable at higher amounts
• Yeepay card acquiring: 3.8% + $0.30 base + 1% cross-border + 3% currency conversion = $8.10 on $100; FX spread up to 0.8%; CNY settlement fee not publicly disclosed
• WorldFirst settlement: 0.4% for B2B withdrawals to CNY per All Product Breakdown table
• LianLian Wish payout capped at 0.75%; settlement up to 0.7%
• RD Technologies TT with correspondent bank fees is HKD 400 (~$51.28); they do not offer mainland CNY settlement
• Flat fees (SWIFT, TT) disproportionately impact small transfers; percentage-based fees scale linearly

Speed Comparison: Overseas to China Mainland (Fastest)

Scenario: Transfer USD from overseas to a China mainland bank account. Each step uses the fastest available method.

Speed Comparison: Overseas to China Mainland (Slowest)

Scenario: Transfer USD from overseas to a China mainland bank account. Each step uses the slowest available method.

Notes:

• XTransfer X2X requires both buyer and seller to be on the XTransfer platform
• WorldFirst’s 1-minute Alipay withdrawal is to personal Alipay, not business bank account
• Yeepay’s fastest transfer (Local Payment Methods, instant) is bottlenecked by T+1 to T+2 CNY settlement
• Yeepay’s slowest transfer is now Cross-border payment (0.5–2 days), replacing the removed Export Seller Collection product
• RD Technologies does not offer mainland China CNY settlement; times shown are HK-only
• The slowest path for all companies is driven by SWIFT, which adds 1–7 days before FX/settlement even begins

Stablecoins in Asian Cross-Border Payments: Strategic Landscape and Investment Thesis was originally published in IOSG Ventures on Medium, where people are continuing the conversation by highlighting and responding to this story.

Author: Benji Siem @BenjiSiem, IOSG Ventures

Thanks to Kai Siefert (@KaiSiefert) and Yiping for his feedback and help with this article!

Introduction

I began this research with a simple observation: the electricity system is being asked to do something it was never designed for. As renewable penetration accelerates, electrification expands, and AI-driven data center demand surges, the traditional model of building more generation and transmission to meet peak load is breaking down. Infrastructure timelines are too slow, interconnection queues are too long, and capital intensity is too high.

In this context, flexibility, defined as the ability to dynamically adjust demand and supply in real time, has shifted from a supporting function to a core pillar of grid reliability. What once relied primarily on large industrial loads and peaker plants is now evolving into a complex, multi-layered marketplace where distributed energy resources, software platforms, and aggregators coordinate millions of assets to balance the system. I believe we are at a structural inflection point.

The winners in this transition will not be those who control generation, but those who build the connective and orchestration layers that unlock flexibility at scale. Emerging crypto-native coordination models and token-based incentive systems may further accelerate this shift by enabling decentralized participation, transparent settlement, and global liquidity for flexibility services. As I explore throughout this paper, flexibility is no longer just a technical capability; it is an emerging economic infrastructure where revenue stacking across capacity, ancillary, demand response, and local markets is creating new value pools and reshaping how energy is traded, managed, and monetized.

Thesis

1. The electricity flexibility market is at an inflection point. Rising renewable penetration, data center demand growth, and regulatory mandates are creating a structural supply-demand imbalance for grid flexibility services. The demand for electricity to power large AI and app developments rapidly exceeds the amount of available electricity in the grid to support it. This is driven by:Increase in global data centre electricity consumption, which is set to more than double to around 945 TWh by 2030. This is slightly more than Japan’s total electricity consumption today. AI is the most important driver of this growth, alongside growing demand for other digital services. Similarly, the lack of flexibility could also be a limiting factor for the growth of AI
2. The electricity market requires operational efficiency and flexibility to mitigate risks, as infrastructure lags behind and construction is built. This increases the demand and necessity of these services.Electricity grids are already under strain in many places: it is estimated that unless these capacity risks are addressed, around 20% of planned data centre projects could be at risk of delays. Power generation projects in the US that are currently delayed because grid operators struggle with grid congestions (a queue of ~10,300). Thats 2,300 GW or or twice the entire existing generation capacity of the US.
3. The middle layer of aggregation and connection infrastructure will be the biggest winners in the space, as it bridges essential infrastructure between supply (consumers with spare unused capacity) and demand (strained grid operators)Software-centric platforms that aggregate and optimize distributed energy resources (DERs) will capture disproportionate value as the market scales from ~$98.2 billion (2025) to approximately ~$293.6 billion (2034), expanding at a CAGR of 12.94% from 2025 to 2034.

Flexibility Market Overview

What is flexibility in energy markets?

In power systems, flexibility = the ability of the system to quickly adjust generation and/or demand in response to signals (price, grid congestion, frequency, etc.) to keep supply and demand balanced and avoid blackouts

Historically, this came almost entirely from flexible generators (gas peakers, hydro). As renewables and electrification scale, system operators now also buy flexibility from:

• Demand response: loads that can turn down or shift in time
• Storage: batteries, EVs, thermal storage
• Distributed generation: rooftop solar, small CHP, etc.

The “flexibility market” is the set of marketplaces and contracts where this flexibility is bought and sold: wholesale markets, balancing/ancillary service products, capacity markets, and local Distribution System Operators (DSO) flexibility platforms. Aggregators work together as middlemen to provide platforms that allows grid operators to buy flexibility from end customers, creating a meaningful infrastructure layer (see “How Flexibility Is Traded & Priced” section). The settlement is handled by Transmission System Operators (TSOs), who pay out the aggregator and, then, the aggregators pays the customer after taking its cut.

This flexibility can be delivered in two ways:

• Implicit flexibility happens automatically through static price signals, like time-of-use tariffs. E.g. your smart EV charger delays charging until overnight when electricity is cheaper. The price signal drives the behaviour.
• Explicit flexibility involves deliberate actions in response to specific requests from the grid operator. These actions are consciously taken and directly compensated — normally through markets coordinated via a market platform.

Detailed Example:

Step 1: Customer Enrollment

An aggregator like CPower signs up a manufacturing facility. They install monitoring equipment (smart meters, controllers) and integrate with the facility’s building management system. The customer agrees to reduce load by, say, 2 MW when called upon.

Step 2: Registration with Grid Operator

The aggregator registers this 2 MW (along with thousands of other sites) as a “demand response resource” with the ISO. The aggregator must prove the resource can actually deliver. This involves baseline calculations, metering protocols, and sometimes test dispatches.

Step 3: Market Participation

The aggregator bids the aggregated capacity into various markets:

• Capacity markets (annual/multi-year): “I commit to having 500 MW available during summer peaks”
• Day-ahead energy markets: “I can reduce load by 200 MW tomorrow between 4–8pm”
• Real-time ancillary services: “I can respond within 10 minutes to frequency deviations”

Step 4: Dispatch

When the grid needs flexibility, the TSO sends a signal to the aggregator. The aggregator’s software platform then:

• Sends notifications to enrolled customers (text, email, automated control signals)
• Activates pre-programmed load reductions (e.g., raising thermostat setpoints, dimming lights, pausing industrial processes)
• Monitors performance in real-time

Step 5: Settlement

After the event:

• The ISO measures what was actually delivered vs. what was promised
• Payment flows: ISO → Aggregator → Customer (minus aggregator’s cut)

Key Players

1. Exchanges — Market Platforms

• Where flexibility is traded: These platforms match buyers (DSOs/TSOs) with sellers (aggregators, DER owners). Fast Frequency Reserves Markets also provide another platform for trade.
• Examples: EPEX SPOT, Nord Pool, Piclo Flex, NODES, GOPACS, Enera

Business Model:

• Transaction fees on cleared trades (typically 0.5–2% of transaction value or €0.01–0.05/MWh)
• Subscription/membership fees for market access (annual platform fees for participants)
• Some platforms operate as regulated utilities (cost recovery through grid tariffs) while others are commercial ventures

Pricing:

• Platforms don’t set prices, they facilitate price discovery through auctions (pay-as-bid or pay-as-clear)
• Local flexibility platforms (Piclo, NODES) typically see prices of €50–200/MWh for congestion management
• Wholesale balancing markets can spike to €1,000+/MWh during scarcity eventsPrices at classic wholesale markets (e.g. EPEX) can go negative, which has the same effect like actively acquiring flexibility at a dedicated flexmarket.

2. Aggregators / Virtual Power Plants (VPPs)

• Control fleets of flexible assets. Their revenue depends on winning contracts and dispatching loads/storage correctly.
• Examples: Enel X, CPower, Voltus, Next Kraftwerke, Flexitricity, Limejump

Business Model:

• Revenue share with asset owners: aggregator keeps 20–50% of market revenues, pays remainder to customers
• Some charge upfront enrollment fees or monthly SaaS fees to asset owners
• May also earn performance bonuses from utilities for exceeding dispatch targets

Pricing:

• Capacity payments: $30–150/kW-year depending on market and product
• Energy payments: Pass-through of market prices (minus aggregator margin)
• Typical customer earnings: $50–200/kW-year for C&I loads, $100–400/year per residential battery

3. Distributed Energy Resource Management System (DERMS) / Optimization

• Software that enables forecasting, control, bidding, and compliance. This is the intelligence layer.Can be built into Aggregators
• Examples: AutoGrid (Uplight), Enbala (Generac), Opus One, Smarter Grid Solutions, GE GridOS, Siemens EnergyIP

Business Model:

• Enterprise SaaS licensing: Annual contracts based on MW under management or number of assets controlled
• Implementation/integration fees: One-time project fees for utility deployments ($500K-5M+)
• Managed services: Ongoing optimization-as-a-service with performance-based fees

Pricing:

• Typically $2–10/kW-year for software licensing (varies by functionality and scale)
• Large utility DERMS deployments can be $5–20M total contract value over 5+ years
• Some vendors offer revenue-share models (5–15% of incremental value captured)

4. Assets

• The physical supply: EVs, batteries, thermostats, heat pumps, industrial loads, etc.

5. Grid Buyers

• The demand side: utilities and system operators who procure flexibility to manage congestion, balancing, and peak loads.DSOs, TSOs, suppliers, and municipal utilities
• Examples: PJM, CAISO, National Grid ESO, TenneT, UK Power Networks, E.ON, Con Edison

Business Model:

• Regulated entities — costs recovered through grid tariffs or capacity charges to ratepayers
• Procure flexibility when it’s cheaper than infrastructure alternatives (“non-wires alternatives”)
• Some vertically integrated utilities run in-house DR programs; others outsource to aggregators

Pricing (what they pay):

• Capacity procurement: $20–330/MW-day depending on market conditions (PJM 2026–27 auction hit $329/MW-day)
• Ancillary services: $5–50/MW-hour for frequency response, spinning reserves
• DSO local flexibility: €50–300/MWh for congestion management (often pay-as-bid auctions)
• Rule of thumb: Flexibility must be cheaper than grid reinforcement (~30–40% savings target)

Figure 1: Mechanism Diagram

1. Distribution System Operators (DSO): are the companies managing the local electricity grids (power lines, substations) that deliver power from the main transmission lines to homes and businesses
2. Transmission System Operators (TSO): the crucial entities managing and maintaining the high-voltage networks (power grids and gas pipelines) that transport energy over long distances from producers to local distributors or large users

Revenue estimate by Player:

Current State of Industry

The electricity system faces a structural supply-demand imbalance for both generation capacity and grid infrastructure. This constraint manifests in two interconnected problems: an unprecedented interconnection queue backlog and surging demand from electrification and data centers.

Interconnection Queue Backlog

As of late 2024, 2,300+ GW of generation and storage capacity are actively seeking grid connection in the US alone, more than twice the total installed capacity of the existing power fleet (1,280 GW). This backlog has become the primary bottleneck for clean energy deployment.

Demand-Side Pressure

• Data centers: Global electricity demand projected to double to 1,000–1,200 TWh by 2030 (equivalent to Japan’s total consumption)
• PJM capacity market: Prices jumped from $28.92/MW-day (2024–25) to $329.17/MW-day (2026–27) — over 10x increase — driven largely by data center commitments
• US grid planners have nearly doubled 5-year demand forecasts; AI data centers require 99.999% uptime with massive power draws
• Grid upgrade costs: EU needs €730B for distribution + €477B for transmission by 2040; flexibility offers 30–40% cost savings vs. infrastructure buildout

How Flexibility Is Traded & Priced

Grid operators (ISOs/RTOs like PJM, ERCOT, CAISO) need to balance supply and demand in real-time, but they can’t directly communicate with millions of distributed assets (thermostats, batteries, industrial loads). Thus, Aggregators act as Middlemen for this process.

The aggregators we analyzed below (Enel X, CPower, Voltus) sit between two parties:

1. Grid operators/utilities who need flexible capacity
2. End customers who have flexible loads or assets

The aggregator bundles thousands of small, distributed resources into a single “virtual power plant” that can bid into wholesale markets as if it were a traditional power plant.

Settlement:

Unlike generation (where you measure MWh produced), demand response measures MWh not consumed. This requires establishing a “baseline” — what the customer would have consumed absent the DR event.

Common baseline methodologies:

• 10-of-10: Average consumption from the same hour over the previous 10 similar days
• Weather-adjusted: Baseline adjusted for temperature differences
• Meter-before/meter-after: Comparing consumption immediately before and during the event

Example Settlement:

The aggregator then pays the customer according to their contract (typically 50–80% of gross), keeping the remainder.

Flexibility is monetized through multiple market mechanisms, each with different timeframes, products, and pricing structures. Providers can “stack” revenues across multiple markets to maximize asset returns.

Additionally, energy communities, locally anchored cooperatives of citizens and small businesses empowered by EU policy, are becoming a significant force in flexibility aggregation, with roughly 9,000 communities representing about 1.5 million participants across the bloc.

• By pooling behind-the-meter assets such as solar, batteries, and controllable loads, these communities overcome the scale and coordination barriers that typically prevent individual households from accessing multiple flexibility revenue streams.
• This aligns directly with research showing that flexibility providers can “stack” value across capacity, ancillary, energy arbitrage, demand response, and local DSO markets: energy communities create the organizational and operational framework needed to participate reliably across these markets, transforming dispersed DERs into coordinated portfolios that can meet market entry thresholds, provide predictable availability, and capture diverse pricing structures, ultimately democratizing access to flexibility revenues while supporting grid decarbonization and resilience.

Why Flexibility Matters

Flexibility services provide a faster, cheaper alternative to building new generation and transmission. VPPs can be “built” as quickly as customers can be enrolled — no interconnection queue required. The Brattle Group estimates VPP peaking capacity is 40–60% cheaper than gas peakers or utility-scale batteries. ENTSO-E estimates flexibility could save €5B annually in EU generation costs alone.

For Grid Operators: Real-time balancing of supply and demand; reduced need for costly peaker plants and transmission upgrades; improved renewable integration; enhanced grid resilience during extreme weather.

For Asset Owners: New revenue streams from existing assets (batteries, EVs, HVAC, industrial loads); multi-service stacking can increase returns 30–50%; participation requires minimal operational disruption.

For Consumers: Lower electricity bills through demand response incentives; avoided costs from deferred infrastructure investment; improved reliability and fewer outages.

For the Energy Transition: Enables higher renewable penetration without curtailment; decarbonizes grid services (replacing gas peakers); accelerates deployment vs. infrastructure-constrained alternatives.

Structural Tailwinds

1. Regulatory Momentum: FERC Orders 2222/2023 (US), EU Network Code on Demand Response (2027), UK BSC P483 enabling 345K households. 45+ countries introducing flexibility markets.
2. Grid Investment Surge: US utilities forecast $1.1T grid investments through 2029. EU needs €730B distribution + €477B transmission upgrades by 2040. Flexibility is cheaper alternative.
3. Data Center Demand: Global data center electricity doubling to 1,000–1,200 TWh by 2030. PJM capacity prices up 10x (2024→2027). Creates both flexibility demand (grid stress) and supply.
4. DER Proliferation: 4M+ US residential solar systems; 240K+ home batteries; 1M+ EVs sold in 2023. Critical mass enabling aggregator and DER economics.

Key Risks to Monitor

1. Post-2030 Oversupply: Massive battery storage investment could compress flexibility market margins. Pumped hydro revival in some markets.
2. Cybersecurity: Millions of distributed assets expand attack surface. EU AI Act classifies grid operations as “high-risk.” NFPA 855 adding 15–25% to urban BESS costs.

The Aggregator Business Model

Revenue Streams:

1. Capacity payments ($/MW-year or $/MW-day)Largest, most predictable revenue stream Customer gets paid for being available, even if never dispatched Example: PJM capacity prices hit $329/MW-day in 2026–27 auction
2. Energy payments ($/MWh)Payment for actual load reductions during events More volatile, depends on dispatch frequency and market prices
3. Ancillary services ($/MW + $/MWh)Frequency regulation, spinning reserves, etc. Higher value but requires faster response (seconds to minutes) Voltus has pioneered access to these higher-margin products

Cost Structure:

Unit Economics Example (C&I Customer):

Revenue Stacking: How Aggregators Maximize Value

The most sophisticated aggregators “stack” multiple revenue streams from the same asset:

Example: A 10 MW industrial load in PJM

This is why Enel’s DER.OS and Tesla’s Autobidder emphasize “co-optimization,”their AI figures out which markets to participate in at any given moment to maximize total returns.

Key Players in Aggregator Layer

Enel X — Global Market Leader

Company Overview

Enel X is the demand response and distributed energy arm of Enel Group, one of the world’s largest utilities with €86B+ in annual revenue. The company traces its roots to EnerNOC, the pioneering demand response provider founded in 2001 and acquired by Enel in 2017. Today, Enel X operates the world’s largest C&I virtual power plant with over 9 GW of demand response capacity across 18 countries and 110+ active programs.

Scale & Reach

• Global capacity: 9+ GW under management (Q1 2025), with ambition to reach 13 GW
• North America: ~5 GW across 10,000+ sites in 31 US states and 2 Canadian provinces
• Programs: 80+ demand response programs, 30+ utility partnerships (11 exclusive bilateral agreements)
• Customer payments: Nearly $2 billion distributed to DR participants since 2011
• Technology investment: $200M+ invested in platform development

Strategic Partnerships

In September 2024, Enel X partnered with Google to aggregate 1 GW of flexible load from data centers — the largest corporate VPP globally. This partnership demonstrates the convergence of data center demand growth and flexibility provision: the same hyperscalers driving grid stress can become major providers of demand-side flexibility through their UPS batteries and load-shifting capabilities.

Technology Platform: DER.OS

Enel X’s DER.OS platform uses machine learning-driven dispatch optimization to improve profitability by 12% over rule-based strategies (per internal audits). The platform streams data from 16,000+ enterprise sites and operates a 24/7/365 Network Operations Center for real-time dispatch management and monitoring.

Key Customers: Commercial & Industrial (C&I) Facilities

These are large electricity consumers with interruptible loads, processes that can be temporarily reduced without major disruption:

Key point: These customers already own the “asset” (their electricity load). Enel X just helps them monetize the flexibility they didn’t know they had. Enel X is explicitly demand-side and asset-light. They don’t build or own generation. They reduce demand, which has the same grid effect as adding supply.

The Google Partnership

The September 2024 Google deal is notable because it flips the usual model:

Traditional model: Enel X recruits facilities → aggregates into VPP → sells to grid

Google deal: Google’s data centers become the flexible asset → Enel X operates the VPP → Grid operators buy the flexibility

Google’s data centers have:

• Massive UPS battery banks (normally for backup)
• Flexible cooling loads
• Some workload scheduling flexibility

Instead of consuming grid flexibility, Google is now providing it — and Enel X is the orchestration layer. This is the “data centers as grid assets” thesis playing out.

Revenue Model Breakdown

How Enel X makes money from a single customer:

Competitive Position

• Strengths: Largest global scale, deep utility relationships, integrated clean energy ecosystem (11 GW renewables + 1 GW storage), proven platform, financial backing of Enel Group
• Weaknesses: Legacy enterprise sales motion, slower innovation cycle vs. pure-play startups, corporate overhead
• Strategy: Focus on C&I segment, utility-scale renewables integration, data center flexibility partnerships

2. Voltus — Software-First Challenger Europe vs North America

Company Overview

Founded in 2016 by former EnerNOC executives Gregg Dixon and Matt Plante, Voltus has positioned itself as a technology-first alternative to legacy demand response providers. The company’s thesis: superior software and broader market coverage can overcome scale disadvantages. As of September 2025, Voltus ranked #1 in GW under management in Wood Mackenzie’s North American VPP report for the third consecutive year.

Scale & Funding

• Capacity: 7.5+ GW under management (September 2025), up from 2 GW in 2021
• Market coverage: Active in all 9 US wholesale power markets and Canada — broadest geographic footprint among pure-play aggregators
• Funding: $121M raised to date (investors include Equinor Ventures, Activate Capital, Prelude Ventures)
• SPAC attempt: Announced $1.3B SPAC merger in December 2021 (valued at $1.3B); deal did not complete

Differentiation Strategy

Voltus differentiates on three axes: (1) First-to-market innovation — the company pioneered access to Operating Reserves programs across multiple grid operators; (2) Broadest market access — active in programs that competitors avoid due to complexity; (3) DER partnerships — rather than competing with device manufacturers, Voltus partners with OEMs like Resideo and Carrier to aggregate their installed bases into VPPs.

Data Center Focus

In 2025, Voltus launched “Bring Your Own Capacity” (BYOC), a product specifically designed for data centers and hyperscalers. BYOC allows data center developers to deploy VPP-driven grid flexibility in parallel with their own projects, reducing time-to-power by offsetting their capacity needs with flexibility procured from Voltus’s distributed network. Partners include Cloverleaf Infrastructure.

Key Customers: C&I Facilities (Similar to Enel X)

OEM Partnerships

This is Voltus’s key strategic innovation. Instead of only doing direct sales to facilities, they partner with device manufacturers:

Why this matters: Customer acquisition cost (CAC) is the biggest expense for aggregators. By partnering with OEMs:

• OEM handles the customer relationship
• Voltus provides the software and market access
• Revenue is split between OEM, Voltus, and end customer
• CAC drops dramatically vs. direct enterprise sales

Revenue Streams: Where Voltus Differs

Enel X: Primarily Capacity Markets

Enel X’s bread and butter is capacity market revenue — getting paid for committed availability. This is:

• Predictable (annual auctions)
• Lower $/kW but high volume
• Requires large MW commitments

Voltus: Ancillary Services Focus

Voltus deliberately pursues ancillary services programs that competitors avoid:

Why ancillary services?

• Higher $/kW (2–3x capacity markets)
• Fewer competitors (complexity is a barrier)
• Requires sophisticated software (Voltus’s strength)
• But: Requires faster-responding assets

Competitive Position

• Strengths: Technology sophistication, broadest market coverage, regulatory advocacy (former FERC Chairman Jon Wellinghoff as Chief Regulatory Officer), OEM partnership strategy, data center positioning
• Weaknesses: Smaller scale than Enel X, no utility-scale asset base, venture-funded burn rate, failed SPAC
• Strategy: Software monetization of third-party DERs, first-mover in ancillary services, data center partnerships

Investment criteria and standard for VPP/Aggregator

# Criteria Best Metric Why This Metric Good Great Elite

1 Scale GW under management Direct measure of market position; determines negotiating power with ISOs and utilities 1 GW 5 GW 10+ GW

2 Market Access # of ISOs/markets active Revenue diversification; more markets = more revenue stacking opportunities per customer 3 6 All 9 US + Canada

3 Monetization Efficiency Revenue per kW-year ($) Shows how well they extract value from assets; indicates product mix quality $50 $80 $120+

4 Customer Economics LTV:CAC ratio Single best measure of unit economics sustainability; can they scale profitably? 3:1 5:1 8:1+

5 Technical Performance Dispatch reliability (%) % of committed MW actually delivered; determines ISO penalties and reputation 90% 95% 98%+ 6 Response Capability Portfolio-weighted response time Faster response = access to higher-margin ancillary services <60 min <10 min <1 min

7 Customer Stickiness Net revenue retention (%) Combines churn + expansion; shows if customers grow or leave 100% 110% 120%+

8 Competitive Moat # of exclusive utility contracts Hard-to-replicate relationships; recurring revenue with barriers 3 7 12+

9 Growth Velocity YoY capacity growth (%) Market momentum; ability to scale 20% 40% 60%+

10 Profitability Path Gross margin (%) Revenue minus customer payments; shows business model health before operating costs 25% 35% 45%+

EU vs US Market:

The EU is already scaling up their system-wide flexibility with greater speed than the US due to supporting regulations and interconnected infrastructure.

Eurelectric

notes that liberalized EU markets incentivize producers and consumers to participate, boosting flexibility, with smart meters enabling time-of-use pricing for demand shifting.

• Market Design: liberalized EU markets incentivize producers and consumers to participate, boosting flexibility, with smart meters enabling time-of-use pricing for demand shifting.
• Interconnected Grid: the EU’s robust, interconnected grid, leading to fewer, shorter blackouts, which is crucial for stable industrial power.

The US has significant untapped flexibility from customers, with studies showing potential for large-scale load curtailment (e.g., 100 GW) with minimal impact.

• Grid Edge Focus: Rapid DER (Distributed Energy Resource) growth makes flexibility at the “grid edge” critical for US utilities

https://a16z.com/decentralizing-the-electric-grid/

“Because of the grid’s inherent fragility, careful consideration must be made to assets connected to it, aligning reliable supply with forecasted demand. The growth of intermittent power sources (unreliable supply) coinciding with the rise of “electrification” (spiky demand) is causing serious challenges.” — a16z

Conclusion

So far, flexibility has been dominated by macro-flexibilities, i.e. large, industrial-scale assets (>200 kW) connected at the transmission or high-voltage distribution level. These assets were attractive because they were straightforward to identify, contract, and dispatch.

That model is now hitting structural limits. Macro flexibility is no longer sufficient, leading to electricity undersupply and cascading issues such as delayed grid connections. This increases system fragility and is emerging as a key bottleneck to AI-driven load growth.

The next frontier is therefore inevitable: micro-flexibilities. This refers to small, behind-the-meter assets in the 1 to 10 kW range, including EV chargers, heat pumps, HVAC systems, batteries, and household appliances, connected to medium- and low-voltage grids. In aggregate, these assets represent orders of magnitude more capacity than macro sources, but are significantly harder to access.

Most current approaches to acquiring this flexibility leave substantial value on the table, opening an opportunity for the flexibility owners to fill this gap and engage with the ecosystem. An aggregator that directly engages a critical mass of owners, independent of supplier or device brand, creates a powerful pull dynamic.

Once users are aggregated horizontally, both energy companies and OEMs are economically incentivized to participate, rather than attempting to control the customer relationship from the outset.

At the center of it, I believe that DePINs have the greatest opportunity to disrupt this sector and create long term upside by unlocking scalability through crypto-native infrastructures and incentives. By increasing capacity and opening new ways to access flexibility, this niche will innovate the current electricity market and allow AI to continue reshaping the world without constraints.

The Inflection Point in Electricity Flexibility: Macro Assets to Distributed Intelligence was originally published in IOSG Ventures on Medium, where people are continuing the conversation by highlighting and responding to this story.

Author: @TurboGuo | @IOSGVC, @DaiZeshi | @SurfAI

Thanks for the insights from Kevin, Selina, Momir

TL;DR

1. Stablecoin-backed, fixed-rate products are preferred in bear markets

During bull markets, TVL rises across all products. However, in bear markets, performance diverges significantly. In bear markets, investors gravitate toward more stable yields and lower underlying risk, driving growth in yield-bearing stablecoins.

2. The trend of protocols is developing towards frontend and backend

• Large DeFi protocols are building their own wallets and mobile apps to control traffic entry points. Crypto is entering the era of apps, where retail users can access financial services through mobile applications.
• Demand from new L1/L2s and DeFi projects for their own stablecoins will push yield protocols toward a “backend” model, creating large demand for yield protocols.

3. Fed rate cuts, declining T-Bill yields, and rise of alternative RWA yield sources

• Interest rate cuts will lead to declining T-Bill yields. This will push stablecoins to incorporate a broader range of RWA assets as backing.
• Real-world businesses and financial products can serve as solid yield sources, becoming a special edge for yield protocols even when they lack frontend strength.

Current On-chain Yielding Spectrum

Here we examined 18 on-chain yielding products across the various yield source spectrum. These include tokenized T-Bills and their derivatives, native staking (ETH/SOL), liquid staking tokens (Lido, Jito), yielding stablecoins (sUSDe, SyrupUSD), protocol revenue-sharing models (JLP, SKY), DeFi Strategies and ecosystem rewards (Lido GGV, SIUSD/LIUSD, asBNB), DEX LP, Market Making (HLP), and real-world asset products (PRIME, USDai, USP). Each product is evaluated on APY, liquidity, withdrawal timeframes, and key risks.

1. Yielding Pattern

On-chain yields derive from eight distinct mechanisms, each with unique risk-return profiles and market sensitivities. Native consensus rewards (ETH/SOL staking, LSTs) offer stable, protocol-secured returns. Funding arbitrage and protocol revenue are affected by the cycle, which are strong in bull markets, compressed in bears. Lending and RWA yields introduce counterparty risk but greater stability. Liquidity providers capture trading fees. DeFi strategies and ecosystem rewards aggregate multiple protocols, compounding both yields and smart contract exposure.

2. Risk Tier

Products are assigned to risk tiers mainly based on evaluation across three dimensions:

• Protocol Risk: The ****Protocol’s technical risk, including smart contract risks
• Participants Risk: Dependence on centralized entities or off-chain actors
• Market/strategies Risk: Exposure to asset price volatility or strategies issues
• Liquidity Risk: TVL depth and withdrawal mechanics

At the minimal-risk tier, tokenized treasuries and mature lending. Low-risk products like native staking and liquid staking derivatives introduce smart contract exposure but benefit from battle-tested code, the risk is low. Medium-risk products have additional complexity through DeFi strategy aggregation or protocol revenue sharing, they also face risks about token price volatility and yield variability.

High-risk products exhibit multiple compounding risks, funding rate strategies face yield compression during bear markets, market-making vaults carry manipulation exposure, and emerging RWA protocols introduce counterparty opacity with limited liquidity for exit.

Key Insights and The Future of On-chain Yielding

1. Stablecoin-backed / relatively fixed interest rates products are good choices in a bear market.

Here we have a deeper look at the TVL and APY of different kinds of yielding product over the bull market and bear market. We take stETH(Staking), JITOSOL(Staking), sUSDS(lending),WETH/USDT(Uniswap DEX LP), SyrupUSDC (Maple lending)and sUSDE (Ethena funding rate strategy) as representative of different yielding products. The bull market is around June to October and the market goes bear afterward.

From the TVL data, we can see that the TVL of all kinds of product goes up during bull markets. However, in the bear market, the TVL of stETH, sUSDE and JITOSOL goes down, but the TVL of sUSDS and SyrupUSDC goes up.

The APYs of WETH/USDT pool and stETH remain relatively the same during different market conditions. The APYs of JITOSOL, SyrupUSDC, sUSDE, and sUSDS experienced a decline, and the APYs of sUSDE and SyrupUSDC dropped significantly. The graph also shows that products with higher APYs experienced higher volatility. The APY of sUSDS is more governance-driven rather than market-driven, which is why it remains stable most of the time.

Overall, in bear markets, stablecoin-backed yielding products will attract more attention with higher liquidity. Non-stablecoin-based yielding products will experience TVL drops in bear markets because of the price drops of backed assets. People also want more stable yields with less underlying risk, which also boosts the TVL of stablecoin-backed yielding products.

In bear markets, relatively fixed-rate products are a more reasonable choice. Even though sUSDS is not market-driven, its APY is stable and predictable in the mid-term. The APY of sUSDE is too unstable due to market conditions, and it is not a good choice in bear markets since it may drop significantly.

This also shows that, when evaluating on-chain yielding opportunities, APY alone does not provide the complete picture of potential returns. The underlying assets play a crucial role in determining actual performance, particularly for products like JLP (an index fund comprising SOL, BTC, and ETH), asBNB, and SKY. In these cases, token price fluctuations often exceed the APY itself, making asset selection as important as yield rates. However, some investors can mitigate this risk through hedging strategies, such as shorting equivalent values of the underlying assets on centralized or decentralized exchanges to isolate and capture only the yield component.

2. The trend of protocols is developing towards frontend and backend.

Before, stablecoin was a good business with great cash flow through the 4% yield on T-Bills. However, yielding stablecoins are the kind of product that shares almost 100% of T-Bill yields with users, which brings challenges to traditional stablecoins. The market share of yielding stablecoins has steadily increased since 2024. If we look at the supply of the top 3 stablecoins with native yield and the top 3 stablecoins without native yield (which are USDT, USDC, PYUSD, USDe, USDS, and USDY), the market share of stablecoins with native yield increased from 0.1% to 7.6%, peaking at 11.5%.

Source: Artemis

This is why many DeFi protocols are starting to control their entry points and trying to build their own distribution channels. Many large DeFi protocols are building their own wallets or mobile apps, and this way, they can control the retail entry point.

This also shows a trend that crypto is entering the era of apps. Retail users can access financial services through mobile apps, which is a much more convenient way for web2 users to onboard to web3. These apps can also provide seed phrase-free services to lower the learning curve.

Demand from L1s and DeFi projects for their own stablecoins will be an important catalyst for the growth of yielding protocols in the future. Yielding protocols could also be pushed toward a ‘backend’ model.

Given the current stable supply landscape, if Layer 1 deployed their own stablecoins instead of relying on USDT or USDC, their revenues could potentially be 2X or even 3X. This represents a significant incentive for adoption for new L1/L2s and DeFi projects. The trend is clear, MegaETH, Jupiter, Hyperliquid, BNB are all doing their own stablecoins and this would be a huge demand for yielding protocols. Ethena has seen this trend. They provide stablecoin-as-a-service that brings T-Bill yields to these projects. By deploying their own stablecoin, protocols and chains can generate substantial, stable revenue flows.

Source: DeFiLlama

3. Fed Cut, the decline of T-Bill yield and the rise of additional yield source

A significant shift is underway in yielding landscape, driven by a chain of macroeconomic events originating from the U.S. monetary policy.

Source: FOMC

President Donald Trump nominated Kevin Warsh to succeed Jerome Powell as the Chairman of the Federal Reserve, with the transition expected in May 2026, if this is approved.

The recent nomination of a new Federal Reserve Chairman is poised to accelerate interest rate cuts, leading to a decline in U.S. Treasury Bill (T-Bill) yields.

This, in turn, is creating a compelling reason for stablecoins diversify their backing assets into a broader range of RWAs. Figure’s PRIME is a good example of bringing HELOC yield on-chain. A home equity line of credit (HELOC) is a loan that allows homeowners to borrow against, spend, and repay as needed, using their home as collateral. Holders of PRIME tokens are providing capital to HELOC loans. The yield is fixed at 8%.

Another kind is to bring real world business on-chain as a yielding source. USDai is a way to bring GPU financing on-chain. The source of USDai’s yield is borrower loan payments, specifically, GPU infrastructure operators who obtain financing by posting their GPU hardware as collateral make monthly loan payments.

Private credit is also getting more attention since this is a solid yield source with appealing APY. Projects like Craftt and Pareto allow on-chain users to earn yield through lending assets to institutions and business. This kind of yield also has solid real-world business to support it.

These examples show that real-world business and financial products can be a solid yield source. This can also be a special edge for yielding protocols, even if they are weak at the front end. It is also a solid alternative for T-Bill yields for stablecoins.

Crypto native unique yield sources will become increasingly important in the competitive landscape. Products that offer exclusive access to revenue streams hold particular value. For instance, asBNB provides exposure to Binance Launchpad revenues, a yield source exclusively available within the BSC ecosystem. Similarly, revenue-sharing models have proven highly appealing when backed by protocols with strong and transparent revenue fundamentals. The continued success of JLP and HLP demonstrates that users are willing to commit capital to products that directly share in genuine protocol revenues.

4. Institution Adoption for On-chain Yielding: End-to-end services and Crypto credit product (preferred stock)

With the wave of institution adoption, many institutions may try to capture on-chain yielding or revenue from crypto. The key here is to provide end-to-end services

End-to-end services from DeFi protocols

For example, Ether.fi provides institution staking service and the key here is end-to-end asset management. They provide both non-custodial and custodial options for staking. They also provide a service called white-glove, which is an end-to-end staking service that offers a controlled environment with annual audits, KYC compliance and monthly statements. The ETH fund also is a CIMA-registered fund. On top of the staking, institutions can also participate in DeFi lending and fixed rewards from other protocols.

Preferred stock is a form of “treasury bond” built on crypto, and serves as an important approach for distributing crypto yield to institutions

The preferred stock issued by DATs is actually a way for institutions to access onchain yield. In essence, this is a credit product built on crypto, similar to government bonds. Government bonds are debt instruments created on the basis of national credit and capacity; DAT companies have created a credit market built on crypto, and preferred stock is a credit product created on top of that credit market. Preferred stock delivers crypto yield to traditional institutions through dividends. There are two main yield types: long term CAGR and DeFi yields including staking.

STRC from Strategy provides a 11.5% dividend annually and is payable monthly in cash. It is available on most major brokerage platforms. The thesis of Strategy is based on the CAGR of Bitcoin. The assumption is that BTC is an anti-inflation asset, and they believe the real inflation rate would be around 8%. STRF and some other preferred stock products like STRD and STRK bring the anti-inflation part of revenue to investors. Investors can also choose STRK with an 8% yield but have the chance to convert into MSTR to capture more upside from Bitcoin.

There are similar products in TradFi for anti-inflation like TIPS (government-issued bonds, Treasury Inflation-Protected Securities). The principal (called par value or face value) of a TIPS goes up with inflation and down with deflation. They will adjust the principal of TIPS using CPI(Consumer Price Index) from the Bureau of Labor Statistics. Even though the interest rate of TIPS is lower than the inflation rate (2.7%), this is the real yield that represents a return after inflation since they will adjust the principal according to the inflation rate and the real yield is around 4%.

It is also interesting to see stablecoin projects like Saturn @saturn_credit

bring the stable yield of DATs on-chain as the yield source of stablecoins. This could be an on-chain alternative to T-Bills in the age of digital assets and Fed rate cuts.

Preferred stock’s dividend could also be a way to distribute aggregated on-chain yields to stock investors.

The Solana DAT, Forward Industries, staked nearly all of their SOL holdings (more than 6.87 million SOL) and earned around 7% staking yield. They also converted around 25% of the SOL into fwdSOL, the liquid staking token (LST), to gain greater DeFi liquidity and yield opportunities. Although they have not yet announced that these yields will be distributed to investors through preferred stock, they have the ability to provide around 7% yields and leverage all on-chain protocols to generate better yields. DeFi Development Company provides Series C Perpetual Preferred Stock that provides dividends at the annual rate of 10%. According to current on-chain yield rates and SOL staking rates, they can likely afford the dividends.

Reference:

• https://research.artemisanalytics.com/p/understanding-strategy-from-the-ground
• https://aptuscapitaladvisors.com/tips-vs-treasuries-real-yield-is-back/

On-Chain Yield Landscape was originally published in IOSG Ventures on Medium, where people are continuing the conversation by highlighting and responding to this story.

Author: 0xjacobzhao | https://linktr.ee/0xjacobzhao

In our previous Crypto AI research, we established that while stablecoins and DeFi offer immediate utility, Agents represent the critical user interface for the AI industry. Consequently, we define two primary value paths for Crypto-AI integration: a short-term focus on AgentFi, which automates yield strategies on mature DeFi protocols, and a medium-to-long-term evolution toward Agent Payment, enabling autonomous stablecoin settlement via emerging standards like ACP, x402, and ERC-8004.

Prediction markets have become an undeniable new industry trend in 2025, with total annual trading volume surging from approximately $9 billion in 2024 to over $40 billion in 2025, achieving a year-on-year growth of over 400%. This significant growth is driven by multiple factors: demand for uncertainty hedging brought by macro-political events, the maturation of infrastructure and trading models, and the breaking of ice in the regulatory environment (Kalshi’s lawsuit victory and Polymarket’s return to the US). Prediction Market Agents are showing early prototypes in early 2026 and are poised to become a new product form in the agent field over the coming year.

I. Prediction Markets: From Betting Tools to a “Global Truth Layer”

A prediction market is a financial mechanism for trading around the outcomes of future events. Contract prices essentially reflect the market’s collective judgment on the probability of an event occurring. Its effectiveness stems from the combination of crowd wisdom and economic incentives: in an environment of anonymous, real-money betting, dispersed information is rapidly integrated into price signals weighted by financial willingness, thereby significantly reducing noise and false judgments.

By the end of 2025, prediction markets have largely formed a duopoly dominated by Polymarket and Kalshi. According to Forbes, total trading volume in 2025 reached approximately $44 billion, with Polymarket contributing about $21.5 billion and Kalshi about $17.1 billion. February 2026 weekly data shows Kalshi’s trading volume ($25.9B) has surpassed Polymarket ($18.3B), approaching 50% market share. Kalshi, leveraging its legal victory in the previous election contract case, its first-mover compliance advantage in the US sports prediction market, and relatively clear regulatory expectations, achieved rapid expansion. Currently, their development paths have clearly diverged:

• Polymarket adopts a hybrid CLOB (Central Limit Order Book) architecture with “off-chain matching, on-chain settlement” and a decentralized settlement mechanism. It has built a globalized, non-custodial high-liquidity market, forming an “onshore + offshore” dual-track operational structure after its compliant return to the US.
• Kalshi integrates into the traditional financial system, accessing mainstream retail brokers via API to attract Wall Street market makers for deep participation in macro and data-based contract trading. Its products are constrained by traditional regulatory processes, leading to a lag in addressing long-tail demands and sudden events.

Beyond Polymarket and Kalshi, other competitive participants in the prediction market field are developing along two main paths:

1. Compliant Distribution Path: Embedding event contracts into the existing account and clearing systems of brokers or large platforms, relying on channel coverage, compliance qualifications, and institutional trust to build advantages (e.g., Interactive Brokers × ForecastEx’s ForecastTrader, FanDuel × CME Group’s FanDuel Predicts). While compliance and resource advantages are significant, product and user scale are still in the early stages.
2. Crypto-Native On-Chain Path: Represented byOpinion.trade, Limitless, and Myriad, these leverage points mining, short-cycle contracts, and media distribution to achieve rapid volume growth. They emphasize performance and capital efficiency, but their long-term sustainability and risk control robustness remain to be verified.

These two paths — traditional financial compliance entry and crypto-native performance advantages — together constitute the diversified competitive landscape of the prediction market ecosystem.

While prediction markets superficially resemble gambling and are essentially zero-sum games, the core difference lies in whether they possess positive externalities: aggregating dispersed information through real-money trading to publicly price real-world events, forming a valuable signal layer. The trend is shifting from gaming to a “Global Truth Layer” — as institutions like CME and Bloomberg connect, event probabilities have become decision-making metadata directly callable by financial and corporate systems, providing a more timely, quantifiable, market-based truth.

From a global regulatory perspective, compliance paths for prediction markets are highly divergent. The US is the only major economy explicitly including prediction markets in its financial derivatives regulatory framework. Markets in Europe, the UK, Australia, and Singapore generally view them as gambling and tend to tighten regulations, while China and India completely ban them. Future global expansion of prediction markets still depends on national regulatory frameworks.

II. Architecture Design of Prediction Market Agents

Prediction Market Agents are currently entering an early practice stage. Their value lies not in “AI predicting more accurately,” but in amplifying information processing and execution efficiency within prediction markets. Prediction markets are essentially information aggregation mechanisms where price reflects the collective judgment of event probability; real-world market inefficiencies stem from information asymmetry, liquidity, and attention constraints. The reasonable positioning for a Prediction Market Agent is Executable Probabilistic Portfolio Management: converting news, rule texts, and on-chain data into verifiable pricing deviations, executing strategies in a faster, more disciplined, and lower-cost manner, and capturing structural opportunities through cross-platform arbitrage and portfolio risk control.

An ideal Prediction Market Agent can be abstracted into a four-layer architecture:

• Information Layer: Aggregates news, social media, on-chain, and official data.
• Analysis Layer: Uses LLMs and ML to identify mispricing and calculate Edge.
• Strategy Layer: Converts Edge into positions using the Kelly Criterion, staggered entry, and risk control.
• Execution Layer: Completes multi-market order placement, slippage and Gas optimization, and arbitrage execution, forming an efficient automated closed loop.

III. Strategy Framework for Prediction Market Agents

Unlike traditional trading environments, prediction markets have significant differences in settlement mechanisms, liquidity, and information distribution. Not all markets and strategies are suitable for automated execution. The core of a Prediction Market Agent lies in whether it is deployed in scenarios with clear rules, codifiability, and structural advantages. The following analysis covers target selection, position management, and strategy structure.

1. Prediction Market Target Selection

Not all prediction markets have tradable value. Participation value depends on: Settlement Clarity (are rules clear, is the data source unique), Liquidity Quality (market depth, spread, and volume), Insider Risk (degree of information asymmetry), Time Structure (expiration time and event pacing), and the trader’s own Information Advantage and Professional Background. A prediction market only has a basis for participation when most dimensions meet basic requirements. Participants should match based on their own strengths and market characteristics:

• Human Core Advantage: Markets relying on domain expertise, judgment, and integration of ambiguous information, with relatively loose time windows (days/weeks). Typical examples: Political elections, macro trends, and corporate milestones.
• AI Agent Core Advantage: Markets relying on data processing, pattern recognition, and rapid execution, with extremely short decision windows (seconds/minutes). Typical examples: High-frequency crypto prices, cross-market arbitrage, and automated market making.
• Unsuitable Areas: Markets dominated by insider information or purely random/highly manipulated markets, which offer no advantage to any participant.

2. Position Management in Prediction Markets

The Kelly Criterion is the most representative capital management theory in repeated games. Its goal is not to maximize the return of a single trade, but to maximize the long-term compound growth rate of capital. It calculates the theoretical optimal position ratio based on estimates of win rate and odds, improving capital growth efficiency under the premise of positive expectancy. It is widely used in quantitative investment, professional gambling, poker, and asset management.

• Classic Formula: f^* = (bp — q) / b
• Where f∗ is optimal betting fraction, b is net odds, p is win rate, and q=1−p.
• Simplified for PM: f^* = (p — market\_price) / (1 — market\_price)
• Where p is the subjective true probability, market\_price is the market implied probability.

The theoretical effectiveness of the Kelly formula is highly dependent on accurate estimates of true probability and odds. In reality, traders find it difficult to consistently and accurately grasp the true probability. In practice, professional gamblers and prediction market participants tend to adopt rule-based strategies that are more executable and less dependent on probability estimation:

• Unit System: Splits capital into fixed units (e.g., 1%) and invests different numbers of units based on confidence levels. This automatically constrains single-bet risk through a unit cap and is the most common practical method.
• Flat Betting: Uses a fixed percentage of capital for each bet. Emphasizes discipline and stability, suitable for risk-averse or low-conviction environments.
• Confidence Tiers: Presets discrete position tiers and sets absolute caps to reduce decision complexity and avoid the false precision problem of the Kelly model.
• Inverted Risk Approach: Calculates position size backwards starting from the maximum tolerable loss. It defines boundaries from risk constraints rather than profit expectations.

For Prediction Market Agents, strategy design should prioritize executability and stability over theoretical optimality. The key lies in clear rules, simple parameters, and tolerance for judgment errors. Under these constraints, the Confidence Tiers method combined with fixed position caps is the most suitable general position management scheme for PM Agents. This method does not rely on precise probability estimates but divides opportunities into limited tiers based on signal strength, setting clear caps to control risk even in high-conviction scenarios.

3. Strategy Selection for Prediction Markets

Structurally, strategies fall into two main categories: Deterministic Arbitrage strategies (characterized by clear rules and codifiability) and Speculative Directional strategies (relying on information interpretation and direction judgment). Additionally, there are Market Making and Hedging strategies, mainly for professional institutions with high capital and infrastructure requirements.

Deterministic Arbitrage Strategies (Arbitrage)

• Resolution Arbitrage: Occurs when an event outcome is basically determined but the market hasn’t fully priced it in yet. Returns come from information synchronization and execution speed. Rules are clear, risk is low, and it is fully codifiable — the core strategy most suitable for Agent execution.
• Dutch Book Arbitrage (Probability Conservation): Exploits structural imbalances where the sum of prices for a mutually exclusive and exhaustive set of events deviates from the probability conservation constraint ($\sum P \neq 1$). By building a portfolio, it locks in risk-free returns. It relies only on rules and price relationships, has low risk, and can be highly regularized. It is a typical deterministic arbitrage form suitable for automated Agent execution.
• Cross-Platform Arbitrage: Profits by capturing pricing deviations for the same event across different markets. Low risk but high requirements for latency and parallel monitoring. Suitable for Agents with infrastructure advantages, but competition is intensifying, leading to declining marginal returns.
• Bundle Arbitrage: Exploits pricing inconsistencies between related contracts. Logic is clear but opportunities are limited. Can be executed by Agents but requires some engineering for rule parsing and portfolio constraints. Agent suitability is medium.

Speculative Directional Strategies (Speculative)

• Structured Information Driven (Information Trading): Centers around clear events or structured information, such as official data releases, announcements, or ruling windows. As long as the information source is clear and trigger conditions are definable, Agents can leverage speed and discipline in monitoring and execution. However, when information turns into semantic judgment or scenario interpretation, human intervention is still needed.
• Signal Following: Profits by following accounts or capital behaviors with historically superior performance. Rules are relatively simple and automatable. The core risk lies in signal decay and being front-run/counter-traded, requiring filtering mechanisms and strict position management. Suitable as an auxiliary strategy for Agents.
• Unstructured / Noise-driven: Highly dependent on sentiment, randomness, or participation behavior. Lacks a stable, reproducible edge, and long-term expected value is unstable. Difficult to model and extremely high risk; not suitable for systematic Agent execution and not recommended as a long-term strategy.

High-Frequency Price & Liquidity Strategies (Market Microstructure): Relies on extremely short decision windows, continuous quoting, or high-frequency trading. Requirements for latency, models, and capital are extremely high. While theoretically suitable for Agents, they are often limited by liquidity and competition intensity in prediction markets, suitable only for a few participants with significant infrastructure advantages.

Risk Control & Hedging: Does not directly seek profit but is used to reduce overall risk exposure. Clear rules and objectives; runs long-term as an underlying risk control module.

Summary: Strategies suitable for Agent execution in prediction markets are concentrated in scenarios with clear rules, codifiability, and weak subjective judgment. Deterministic arbitrage should be the core revenue source, with structured information and signal following strategies as supplements. High-noise and emotional trading should be systematically excluded. An Agent’s long-term advantage lies in disciplined, high-speed execution and risk control capabilities.

IV. Business Models and Product Forms of Prediction Market Agents

Ideal business model designs for Prediction Market Agents have exploration space at different levels:

• Infrastructure Layer: Provides multi-source real-time data aggregation, Smart Money address libraries, unified prediction market execution engines, and backtesting tools. Charges B2B fees to obtain stable revenue unrelated to prediction accuracy.
• Strategy Layer: Introduces community and third-party strategies to build a reusable, evaluable strategy ecosystem. Captures value through calls, weights, or execution profit-sharing, reducing dependence on a single Alpha.
• Agent / Vault Layer: Agents directly participate in live trading via entrusted management, relying on on-chain transparent records and strict risk control systems to earn management fees and performance fees based on capability.

Corresponding product forms can be divided into:

• Entertainment / Gamification Mode: Lowers participation barriers through Tinder-like intuitive interaction. Has the strongest user growth and market education capability, making it an ideal entry point for breaking out of the niche, but needs to funnel users to subscription or execution products for monetization.
• Strategy Subscription / Signal Mode: Does not involve capital custody, is regulatory-friendly with clear rights and responsibilities, and has a relatively stable SaaS revenue structure. It is currently the most feasible commercialization path. Its limitation is that strategies are easily copied and execution suffers from slippage. Long-term revenue ceilings are limited, but experience and retention can be significantly improved through a “Signal + One-Click Execution” semi-automated form.
• Vault Custody Mode: Possesses scale effects and execution efficiency advantages, resembling asset management products. However, it faces multiple structural constraints such as asset management licenses, trust thresholds, and centralized technical risks. The business model is highly dependent on the market environment and sustained profitability. Unless possessing a long-term track record and institutional-grade endorsement, it should not be the main path.

Overall, a diversified revenue structure of “Infrastructure Monetization + Strategy Ecosystem Expansion + Performance Participation” helps reduce reliance on the single assumption that “AI consistently beats the market.” Even if Alpha converges as the market matures, underlying capabilities like execution, risk control, and settlement retain long-term value, thus building a more sustainable business closed loop.

V. Project Cases of Prediction Market Agents

Currently, Prediction Market Agents are still in the early exploration stage. Although the market has seen diverse attempts from underlying frameworks to upper-layer tools, a standardized product that is mature in strategy generation, execution efficiency, risk control systems, and business closed loops has not yet formed.

We classify the current ecosystem landscape into three levels: Infrastructure, Autonomous Agents, and Prediction Market Tools.

Infrastructure Layer

Polymarket Agents Framework

This official developer framework standardizes “connection and interaction,” handling data retrieval, order construction, and basic LLM interfaces. However, it functions primarily as an access standard rather than a turnkey solution; it solves “how to code an order” but leaves core trading capabilities — such as strategy generation, probability calibration, and risk management — entirely to the developer.

Gnosis Prediction Market Tools

Offering complete read/write support for the Gnosis ecosystem (Omen/Manifold), this toolset provides only read access for Polymarket, creating clear ecosystem barriers. It serves as a strong foundation for Gnosis-native agents but has limited utility for cross-platform development.

Polymarket and Gnosis are currently the only prediction market ecosystems that have clearly productized “Agent Development” into official frameworks. Other prediction markets like Kalshi still mainly remain at the API and Python SDK level, requiring developers to self-complete key system capabilities like strategy, risk control, operation, and monitoring.

Autonomous Agents

Current “Prediction Market AI Agents” on the market are mostly still in early stages. Although labeled “Agent,” their actual capabilities are significantly far from delegatable automated closed-loop trading. They generally lack independent, systematic risk control layers and have not incorporated position management, stop-loss, hedging, and expected value constraints into the decision process. Overall productization is low, and mature systems for long-term operation have not yet formed.

Olas Predict

Olas Predict is currently the most productized prediction market agent ecosystem. Its core product “Omenstrat” is built on Omen within the Gnosis system, utilizing FPMM and decentralized arbitration mechanisms. It supports small-scale high-frequency interactions but is constrained by Omen’s limited single-market liquidity. Its “AI prediction” primarily relies on generic LLMs, lacking real-time data and systematic risk control, with historical win rates varying significantly across categories.

In February 2026, Olas launched “Polystrat”, extending Agent capabilities to Polymarket — users can define strategies in natural language, and the Agent automatically identifies probability deviations in markets settling within 4 days and executes trades. The system controls risk through Pearl local execution, self-custodied Safe accounts, and hardcoded limits, making it the first consumer-grade autonomous trading Agent for Polymarket.

UnifAI Network Polymarket Strategy

Provides automated trading Agent for Polymarket, with a core tail risk strategy: scanning contracts near settlement with >95% implied probability and buying in, targeting 3–5% spread capture. On-chain data shows a win rate close to 95%, but returns diverge significantly across categories. The strategy is highly dependent on execution frequency and category selection.

NOYA.ai

Attempts a comprehensive “Research-Judgment-Execution” closed loop. Its architecture features an Intelligence Layer for signal aggregation and an Abstraction Layer using Intents to manage cross-chain complexity. Currently, its Omnichain Vaults have been delivered; the Prediction Market Agent remains under development, and a complete mainnet closed loop has not yet formed. Overall, it is in the vision validation stage.

Prediction Market Tools

Current prediction market analysis tools are insufficient to constitute complete “Prediction Market Agents.” Their value is mainly concentrated in the Information and Analysis layers of the agent architecture; trade execution, position management, and risk control must still be borne by the trader. Product forms align more with “Strategy Subscription / Signal Assistance / Research Enhancement” and can be viewed as early prototypes of Prediction Market Agents.

Based on a systematic review ofAwesome-Prediction-Market-Tools, we selected representative projects with preliminary product forms:

Market Analysis Tools

• Polyseer: Research-oriented tool using a multi-Agent architecture (Planner/Researcher/Critic/Analyst/Reporter) for evidence collection and Bayesian aggregation to output structured reports. Transparent methodology, open-source.
• Oddpool: “Bloomberg Terminal for Prediction Markets,” aggregating Polymarket, Kalshi, CME, etc., with arbitrage scanning.
• Polymarket Analytics: Global data analysis platform for Polymarket, showing trader, market, position, and volume data.
• Hashdive: Trader-oriented data tool using Smart Score to identify “Smart Money.”
• Polyfactual: Focuses on AI market intelligence and sentiment/risk analysis via Chrome extension.
• Predly: AI mispricing detection platform comparing market prices with AI-calculated probabilities on Polymarket and Kalshi. Claims 89% alert accuracy.
• Polysights: Covers 30+ markets and on-chain metrics with Insider Finder tracking new wallets and large unidirectional bets.
• PolyRadar: Multi-model parallel analysis with real-time interpretation, timeline evolution, and confidence scoring.
• Alphascope: AI-driven intelligence engine for real-time signals and research summaries (early stage).

Alerts / Whale Tracking

• Stand: Focuses on whale copy-trading and high-conviction alerts.
• Whale Tracker Livid: Productizes whale position changes.

Arbitrage Discovery Tools

• ArbBets: AI-driven tool identifying cross-platform arbitrage (Polymarket, Kalshi, Sportsbooks).
• PolyScalping: Real-time arbitrage and scalping analysis for Polymarket (1-minute scans).
• Eventarb: Lightweight cross-platform arbitrage calculator (Polymarket, Kalshi, Robinhood).
• Prediction Hunt: Cross-exchange aggregator comparing prices for arbitrage (Polymarket, Kalshi, PredictIt).

Trading Terminals / Aggregated Execution

• Verso: Institutional-grade terminal (YC Fall 2024) with Bloomberg-style interface, covering 15,000+ contracts across Polymarket and Kalshi with AI news intelligence.
• Matchr: Cross-platform aggregator covering 1,500+ markets with smart routing for optimal price matching and planned automated yield strategies.
• TradeFox: Professional aggregation and Prime Brokerage platform backed by Alliance DAO and CMT Digital. Offers advanced order execution (limit, stop-loss, TWAP), self-custody, and multi-platform smart routing. Expanding to Kalshi, Limitless, and SxBet.

VI. Summary and Outlook

Currently, Prediction Market Agents are in the early exploration stage of development.

• Market Essence: Backed by the Polymarket and Kalshi duopoly, prediction markets differ from gambling by acting as a “Global Truth Layer” that aggregates information via real-money trading.
• Core Positioning: Agents function as Executable Probabilistic Portfolio Management tools. They convert data into verifiable pricing deviations, prioritizing discipline and execution speed.
• Strategy & Risk: Deterministic Arbitrage is the optimal strategy for automation, with speculation serving only as a supplement. Risk management should prioritize executability using Confidence Tiers with Fixed Caps.
• Business Model: The most sustainable path combines Infrastructure (B2B data/execution fees), Strategy Ecosystems (third-party licensing), and Vaults (performance-based asset management).

Despite the emergence of diverse tools and frameworks in the ecosystem, a mature, standardized product capable of closing the loop on strategy generation, execution efficiency, and risk control has yet to appear. We look forward to the continued iteration and evolution of Prediction Market Agents.

Disclaimer: This article was created with the assistance of AI tools including ChatGPT-5.2, Gemini 3, and Claude Opus 4.5. While the author has strived for accuracy, errors may exist. Please note that crypto asset fundamentals often diverge from secondary market prices. This content is for information and research purposes only and does not constitute investment advice or a recommendation to buy or sell any tokens.

Turning Probability into Assets: A Look Ahead at Prediction Market Agents was originally published in IOSG Ventures on Medium, where people are continuing the conversation by highlighting and responding to this story.

Author: Danny @IOSGVC

1. The Curator Boom

DeFi is heating up but still below the ATH TVL of DeFi summer. At the same time the supply of stablecoins keeps growing at a higher rate and hit new ATH. This points to a paradox: there’s more money on-chain than ever, but DeFi products are still too complex for most people to understand, use, and adopt.

Over the past few years, DeFi infrastructure solved for access and composability, but it turned into a high-stakes game that’s tough for newcomers to play. For the average user, a seemingly simple stablecoin yield is actually a rabbit hole of nested strategies: lending spreads, layered incentives (like funding and airdrops), structured products (like Pendle), and leveraged looping.

Risks have also evolved. We’re way beyond simple smart contract hacks now. Today’s risks are a vicious cycle of shaky LTVs, poor liquidation liquidity, and oracle failures feeding on each other. For example, in October 2025, a glitch in Binance’s internal oracle caused a flash crash in USDe’s price on the platform, setting off a chain of liquidations.

DeFi is in a strange spot: the better the tech gets, the harder it becomes for users to understand the risks. When people can’t figure out where the yield is coming from or where the real risks are, DeFi’s growth hits a wall.

This is where Curators come in. They act as a new layer in the stack, connecting complex protocols with the vast pools of capital sitting on the sidelines. As protocols increasingly hand off the job of creating yield and pricing risk, Curators are stepping up to fill the gap.

2. The Business of Curation

In ecosystems like Morpho, the protocol provides the neutral rails, but Curators are the ones who decide which assets to list, set risk parameters, and manage positions day-to-day. They have three main jobs:

Strategy Selection

A good Curator knows the difference between a solid, long-term yield and a temporary, degen opportunity. Strategies aren’t set-and-forget; they need constant tweaking as market conditions and capital flows change. Two Curators running the same USDC strategy can have wildly different results during a market crash. What sets the best apart is their ability to stay on top of the market and de-leverage when things get shaky.

Risk Pricing

In a modular world, the Curator is the one calling the shots on risk. Deciding what collateral to accept and how much leverage to use are the core acts of risk pricing. Curators don’t just execute strategies; they price the risk itself. But even the pros get it wrong sometimes. Re7 Labs, for instance, saw user positions get liquidated incorrectly because the Pyth oracle they used had a price feed delay. This is a huge red flag: the biggest systemic risks this cycle are coming from this new middle layer.

Productization & Distribution

For users, a productized Curator is a simple, one-click way to get into and out of a strategy. For front-ends like CEXs and wallets, it’s a non-custodial yield module with clear, defined risks. Curators aren’t trying to steal users from protocols; they’re helping capital find risks it’s comfortable taking.

The Curator model is an asset management business driven by AUM. Since revenue is tied directly to AUM, this creates a natural tension. Growing AUM means more revenue, but growing too fast can stretch a strategy too thin and expose LPs to nasty tail risks.

Market cycles also dictate how Curators behave. In a bull run, they’re pushed to maximize capital efficiency with leverage, stacked incentives, and looping. With more borrowers in the market, beta hides the underlying risks, APYs are juicy, and capacity is high — but so is the risk.

During choppy or bear markets, strategies have to go back to basics and find real yield from lending spreads, RWA cash flows, or low-correlation assets. In these conditions, playing defense is more important than offense.

3. The New Distribution: From Institutions to Retail

According to DefiLlama data from February 2026, the total TVL in Risk Curators hit $5.9B. The top three, Steakhouse Financial ($1.53B), Sentora ($1.34B), and Gauntlet ($1.29B) — make up almost 70% of that. This concentration means that if a top Curator messes up a strategy or parameter, the damage won’t be contained to just one protocol.

The Curator space won’t be a monolith. It’s already splitting into at least three different camps:

1: The Capacity Maximizers

These Curators are all about handling huge sums of low-volatility capital. They stick to sustainable yield sources like lending spreads, stable incentives, and RWAs, focusing on conservative, easy-to-understand parameters. They’re a natural fit for CEXs, wallets, and Fintech front-ends, and they represent the main type of large-scale vault on Morpho today. Some protocols are even getting their hands dirty in the vault tech stack to help build more institution-friendly Curator businesses from the ground up.

Many of these large Curators also act as a “Curator of Curators” lending out their AUM to smaller, more aggressive Curators to generate extra yield. They work hand-in-hand with the opportunity-driven players.

For institutions, the game is now about whether to build their own Curator or partner with a top firm. With its open, modular design, Morpho is becoming the go-to for institutions that want to build. Bitwise is a perfect example. In January 2026, they launched their own non-custodial vault on Morpho, managed by their in-house team. This is a big deal: pro asset managers are shifting from just using DeFi to building it.

Coinbase took a different route. They use a third-party Curator, Steakhouse Financial, to manage the backend of their lending products on Morpho. The front-end is the slick Coinbase app users know, but the engine is pure DeFi. It’s the classic “DeFi Mullet”: business in the front, party in the back.

And the big money is coming. Apollo Global Management (over $938 billion in AUM), inked a deal with Morpho in February 2026 to buy up to 9% of the $MORPHO token over four years. Apollo is playing both sides. Their credit funds are already tokenized as RWAs (like ACRED and ACRDX) and plugged into Morpho’s lending markets through top Curators like Steakhouse. By also holding the governance token, they get a direct say in the future of on-chain credit.

That same month, Taurus, a custodian for over 40 banks, plugged Morpho into its platform. Now, traditional financial institutions can allocate capital to Morpho Vaults right from their existing, compliant setups, with Curators managing the money. For institutions, the question is no longer if they should get into DeFi, but where in the stack they should play.

2: The Opportunity Chasers

These Curators are the degens. They hunt for new structures, novel assets, and short-lived incentive programs. They’re willing to cap their AUM and take on more risk to chase that sweet, sweet alpha. Think small AUM caps, short-lived strategies, and a high tolerance for volatility. They cater to pro funds and DeFi natives.

These are the first guys to ape into new L1/L2 ecosystems. When a new chain like Hyperliquid, Plasma, Monad, or Megaeth launches with juicy liquidity mining rewards, the opportunity chasers are the first responders. They spin up vaults on these new chains to farm the early, one-off bonuses for their users.

They also love to experiment with new assets and DeFi primitives. While the big guys stick to blue chips like ETH and USDC, these Curators are happy to play with new stuff. Re7 Labs, for example, became the Curator for BlackRock’s BUIDL, pioneering the use of RWAs in lending at scale.

These Curators are also hyper-aware of market shifts, ready to pounce on any arbitrage opportunity. Their strategies are often more complex, involving things like cross-protocol rate arb or profiting from liquidations. It’s riskier, for sure, but the potential returns can blow the market average out of the water.

3: The Productizers

Productizers take curation a step further. They don’t just manage money on the backend; they package their strategies into products for everyday users, like a “Vault as a Service,” a tokenized asset, or even a stablecoin. This is the holy grail of distribution, but it comes with immense pressure to get risk management, transparency, and liability right.

The biggest hurdle for them is finding strategies that have both high yield and high capacity. Almost every DeFi strategy has a ceiling. The popular looping/basis trades, for instance, have ballooned to a $20 billion market (nearly 10% of all DeFi TVL), up from just $5 billion six months ago. As these trades get crowded, yields compress, and the room for error shrinks.

But if they can crack the code, these productized vaults can be plugged directly into Fintech apps, opening the floodgates for Web2 capital. This is how curation goes mainstream.

4. Let the People Use DeFi

DeFi’s biggest problem right now is that it’s just too damn complicated and risky for the average person. No one wants to deposit their life savings into something they don’t understand. And when projects like Streamfinance blow up after misusing user funds, it spooks the market. During the last bear, we saw capital flee back to the safety of conservative lending protocols.

Today, about 45% of DeFi’s TVL (~$56B) is chasing yield in protocols like Aave, Morpho, and Spark. But tons of USDC is still just sitting there, doing nothing. It’s not because there are no opportunities; it’s because understanding the strategies, assessing the risks, and managing positions is a full-time job.

Most users don’t need more protocols. They need:

• A simple, trustworthy on-ramp;
• A mix of yields that are always being adjusted;
• A clear, easy-to-understand picture of the risks they’re taking.

We can create better on-ramps by building better vault interfaces and productizing strategies. We can get better yields as more high-quality Curators enter the market. But to really bring back confidence, we need a transparent Curator audit system. This means:

• On-chain proof of where the money is going;
• Standardized labels for different types of risk;
• A clear emergency exit so users know what happens and what to do when things go wrong.

This won’t get rid of risk, but it will change it from a scary, unknown unknown into a clear, priceable choice. Without this transparency, the Curator space could easily become a shadow banking system, no different from Celsius or BlockFi. But if Curators can properly slice, price, and manage risk in this middle layer, they can act as a shock absorber for the entire DeFi ecosystem, not an amplifier. They can be the professional risk managers that DeFi needs.

Curation isn’t the final form of DeFi, but it’s a necessary step to get to mass adoption. The core infrastructure of DeFi works. What’s missing is the middle layer to package it, distribute it, and plug it into real-world applications. Curators are stepping up to do just that.

Once the complexity is hidden, the risks are transparent, and the responsibilities are defined, DeFi can finally deliver on its original promise: to build a financial system for every banked and unbanked.

References

[1] BeInCrypto. (2025, October 12). Ethena USDe “Depeg”, What Really Happened?. Retrieved from

[2] Blockworks. (2025, March 20 ). Who’s responsible when something breaks in DeFi?. Retrieved from

[3] Chorus One. (2025, December 2 ). DeFi Curators in 2025: Navigating Chaos, Building Resilience. Retrieved from

[4] DefiLlama. (2026, February 24 ). Risk Curators Rankings. Retrieved from

[5] Chorus One. (2025, December 2 ). DeFi Curators in 2025: Navigating Chaos, Building Resilience. Retrieved from

The Curator Paradigm: Bridging DeFi’s Great Divide was originally published in IOSG Ventures on Medium, where people are continuing the conversation by highlighting and responding to this story.

Author: @0xjacobzhao, @0xjiawei

On February 3, 2026, Vitalik published an important reflection on the Ethereum scaling roadmap on X. As the practical difficulties of Layer 2 evolving into a fully decentralized form are being recognized, and with the mainnet’s own throughput expected to increase significantly in the coming years, the original assumption of “L2 as the core carrier of Ethereum scaling” no longer holds. Ethereum’s strategic focus is returning to the mainnet itself — reinforcing its positioning as the world’s most trusted settlement layer through institutionalized scaling and protocol-endogenous security mechanisms. Scaling is no longer the sole goal; security, neutrality, and predictability are once again becoming Ethereum’s core assets.

Core Changes:

• Ethereum is entering an “L1-First Paradigm”: With direct mainnet scaling and continuously decreasing fees, the original assumption relying on L2 to shoulder the core role of scaling no longer holds.
• L2 is no longer “Branded Sharding,” but a Trust Spectrum: The progress of L2 decentralization is much slower than expected, making it difficult to uniformly inherit Ethereum’s security. Their role is being redefined as a spectrum of networks with different trust levels.
• Ethereum’s core value is shifting from “Traffic” to “Settlement Sovereignty”: The value of ETH is no longer limited to Gas or Blob revenue, but lies in its institutional premium as the world’s most secure EVM settlement layer and native monetary asset.
• Scaling strategy is adjusting towards protocol internalization: Based on continuous direct L1 scaling, the exploration of protocol-layer native verification and security mechanisms may reshape the security boundary and value capture structure between L1 and L2.
• Valuation framework acts a structural migration: The weight of security and institutional credibility has risen significantly, while the weight of fees and platform effects has decreased. ETH’s pricing is shifting from a cash flow model to an asset premium model.

This article will analyze the paradigm shift in Ethereum’s pricing model and valuation reconstruction according to a layered approach: Facts (technological and institutional changes that have occurred), Mechanisms (impact on value capture and pricing logic), and Deductions (implications for allocation and risk-return).

I. Back to Origins: Ethereum Values

To understand the long-term value of Ethereum, the key lies not in short-term price fluctuations, but in its consistent design philosophy and value orientation.

• Credible Neutrality: Ethereum’s core goal is not the maximization of efficiency or profit, but to become a set of credibly neutral infrastructure — with open rules, predictability, no favoritism towards any participant, no control by a single entity, and where anyone can participate without permission. The security of ETH and its on-chain assets ultimately depends on the protocol itself, not on any institutional credit.
• Ecosystem First, Not Revenue First: Multiple key upgrades of Ethereum reflect a consistent decision-making logic — actively foregoing short-term protocol revenue in exchange for lower usage costs, larger ecosystem scale, and stronger system resilience. Its goal is not to “collect tolls,” but to become the irreplaceable neutral settlement and trust foundation in the digital economy.
• Decentralization as a Means: The mainnet focuses on the highest level of security and finality, while Layer 2 networks are located on a connection spectrum with varying degrees to the mainnet: some inherit mainnet security and pursue efficiency, while others position themselves with differentiated functions. This enables the system to serve both global settlement and high-performance applications simultaneously, rather than L2s being “Branded Shards.”

Long-Termist Technical Route: Ethereum adheres to a slow but certain evolutionary path, prioritizing system security and credibility. From the PoS transition to subsequent scaling and confirmation mechanism optimizations, its roadmap pursues sustainable, verifiable, and irreversible correctness.

II. Valuation Misconceptions: Why Ethereum Should Not Be Viewed as a “Tech Company”

Applying traditional corporate valuation models (P/E, DCF, EV/EBITDA) to Ethereum is essentially a category error. Ethereum is not a company aiming for profit maximization, but an open digital economic infrastructure. Corporations pursue shareholder value maximization, while Ethereum pursues the maximization of ecosystem scale, security, and censorship resistance. To achieve this goal, Ethereum has repeatedly actively suppressed protocol revenue (e.g., via EIP-4844 introducing Blob DA to structurally lower L2 data publishing costs and suppress L1 revenue from rollup data) — which approximates “revenue self-destruction” from a corporate perspective, but from an infrastructure perspective, is sacrificing short-term fees for long-term neutrality premium and network effects.

A more reasonable framework is to view Ethereum as a globally neutral settlement and consensus layer: providing security, finality, and trusted coordination for the digital economy. ETH’s value is reflected across multiple structural demands — rigid demand for final settlement, the scale of on-chain finance and stablecoins, the impact of staking and burning mechanisms on supply, and long-term, sticky capital brought by institutional adoption such as ETFs, corporate treasuries, and RWAs.

III. Paradigm Restructuring: Finding the Pricing Anchor Beyond Cash Flow

The https://www.google.com/url?sa=E&source=gmail&q=ethval.com launched by the Hashed team at the end of 2025 provided a detailed set of reproducible quantitative models for Ethereum, but traditional static models struggle to capture the dramatic pivot in Ethereum’s narrative in 2026. Therefore, we reused their systematic, transparent, and reproducible underlying models (covering yield, money, network effects, and supply structure), but reshaped the valuation architecture and weighting logic:

1. Structural Restructuring: Mapping models to four value quadrants: “Security, Money, Platform, Revenue,” aggregated for pricing.
2. Weight Rebalancing: Significantly increasing the weight of security and settlement premium, weakening the marginal contribution of protocol revenue and L2 expansion.
3. Risk Control Overlay: Introducing a circuit breaker mechanism sensing macro and on-chain risks, making the valuation framework adaptable across cycles.
4. Removing “Circular Reasoning”: Models containing current price inputs (like Staking Scarcity, Liquidity Premium) are no longer used as fair value anchors, but retained only as indicators for position and risk appetite adjustment.

Note: The following models are not for precise point prediction, but to depict the relative pricing direction of different value sources in different cycles.

1. Security Settlement Layer: Core Value Anchor (45%, Increased in Risk-Off)

We view the security settlement layer as Ethereum’s most core source of value and assign it a 45% benchmark weight; this weight is further increased during periods of rising macro uncertainty or declining risk appetite. This judgment stems from Vitalik’s latest definition of “truly scaling Ethereum”: the essence of scaling is not increasing TPS, but creating block space fully backed by Ethereum itself. Any high-performance execution environment relying on external trust assumptions does not constitute an extension of the Ethereum entity.

Under this framework, ETH’s value is mainly reflected as the credit premium of a global sovereign-less settlement layer, rather than protocol revenue. This premium is jointly supported by structural factors such as validator scale and degree of decentralization, long-term security record, institutional adoption, clarity of compliance paths, and protocol-endogenous Rollup verification mechanisms.

In specific pricing, we mainly use two complementary methods: Validator Economics (Yield Equilibrium Mapping) and Staking DCF (Perpetual Staking Discount), to jointly depict the institutional premium of ETH as the “Global Secure Settlement Layer.”

• Validator Economics (Yield Equilibrium Pricing): Based on the ratio of annualized staking cash flow per ETH to the target real yield, deriving a theoretical fair price. This expression is used to depict the equilibrium relationship between yield and price, serving as a directional relative valuation tool rather than an independent pricing model.
• Staking DCF (Perpetual Staking Discount): Viewing ETH as a long-term asset capable of generating sustainable real staking yields, discounting its cash flow in perpetuity. Essentially, this value layer does not benchmark against the revenue capability of platform companies, but is similar to the settlement credit of a global clearing network.

2. Monetary Attribute: Settlement and Collateral (35%, Dominant in Utility Expansion)

We view the monetary attribute as Ethereum’s second core source of value and assign it a 35% benchmark weight, becoming the main utility anchor in neutral markets or during on-chain economic expansion. This judgment is not based on the narrative that “ETH equals USD,” but on its structural role as the native settlement fuel and ultimate collateral asset of the on-chain financial system. The security of stablecoin circulation, DeFi liquidation, and RWA settlement all rely on the settlement layer supported by ETH.

For pricing, we use an extended form of the Quantity Theory of Money (MV = PQ), but model ETH’s usage scenarios in layers to address the order-of-magnitude differences in circulation velocity across different scenarios:

High-Frequency Settlement Layer (Gas Payment, Stablecoin Transfers)

• M_transaction = Annual Transaction Settlement Volume / V_high
• V_high ≈ 15–25 (Referencing historical on-chain data)

Medium-Frequency Financial Layer (DeFi Interaction, Lending Liquidation)

• M_defi = Annual DeFi Settlement Volume / V_medium
• V_medium ≈ 3–8 (Based on mainstream DeFi protocol capital turnover rate)

Low-Frequency Collateral Layer (Staking, Restaking, Long-term Locking)

• M_collateral = Total ETH Collateral Value × (1 + Liquidity Premium)
• Liquidity Premium = 10–30% (Reflecting compensation for liquidity sacrifice)

3. Platform / Network Effect: Growth Option (10%, Bull Market Amplifier)

Platform and network effects are viewed as growth options in Ethereum’s valuation, assigned only a 10% weight, used to explain the non-linear premium brought by ecosystem expansion during bull market phases. We use a trust-corrected Metcalfe model to avoid weighting L2 assets of different security levels equally in the valuation.

4. Revenue Asset: Cash Flow Floor (10%, Bear Market Bottom)

We view protocol revenue as the cash flow floor in the Ethereum valuation system, rather than a growth engine, also assigning a 10% weight. This layer mainly functions during bear markets or extreme risk phases to depict the valuation lower limit.

Gas and Blob fees provide the minimum operating cost for the network and affect the supply structure through EIP-1559. For valuation, we use Price-to-Sales (P/S) and Fee Yield models, taking the conservative value among them, serving only as a bottom reference. As the mainnet continues to scale, the relative importance of protocol revenue declines, with its core role reflected as a safety margin during downturns.

• Price-to-Sales Model (P/S Floor): ETH Price (PS) = M_PS / Circulating Supply
• Fee Yield Model: ETH Price(Yield) = M_Yield / Circulating Supply
• Cash Flow Floor Pricing (Minimum Value Principle): P_Revenue_Floor = min(P_PS , P_Yield)

IV. Dynamic Calibration: Macro Constraints and Cycle Adaptation

If the previous text established Ethereum’s “intrinsic value pivot,” this chapter introduces an “external environment adaptation system” independent of fundamentals. Valuation cannot operate in a vacuum and must be constrained by three major external factors: Macro Environment (Cost of Capital), Market Structure (Relative Strength), and On-Chain Sentiment (Crowdedness). Based on this, we constructed a Regime Adaptation mechanism to dynamically adjust valuation weights across different cycles — releasing option premiums during loose periods and retreating to the revenue floor during risk-off periods, thereby achieving a leap from static models to dynamic strategies. (Note: Due to space limitations, this article only presents the core logical framework of this mechanism.)

V. The Conditional Path for the Institutional Second Curve

The analysis above is based on internal crypto technical, valuation, and cycle logic. This chapter discusses a problem at a different level: When ETH is no longer priced solely by crypto-native funds but is gradually integrated into the traditional financial system, how will its pricing power, asset attributes, and risk structure change? The “Institutional Second Curve” is not an extension of existing logic, but a redefinition of Ethereum by exogenous forces:

• Change in Asset Attribute (Beta → Carry): Spot ETH ETFs solve compliance and custody issues, essentially still being price exposure; while the future advancement of Staking ETFs introduces on-chain yields into the institutional system via compliant carriers for the first time. ETH thus shifts from a “non-interest-bearing high-volatility asset” to an “allocation asset with predictable yield,” expanding potential buyers from trading funds to pension, insurance, and long-term accounts sensitive to yield and duration.
• Change in Usage (Holding → Using): Institutions may no longer just view ETH as a tradable ticker, but start using it as settlement and collateral infrastructure. Whether it’s JPMorgan’s tokenized funds or the deployment of compliant stablecoins and RWAs on Ethereum, it indicates demand for ETH is shifting from “Holding Demand” to “Running Demand” — institutions not only hold ETH but use it for settlement, clearing, and risk management.
• Change in Tail Risk (Uncertainty → Pricing): As stablecoin regulatory frameworks (like the GENIUS Act) are gradually established, and with increased transparency in Ethereum’s roadmap and governance, the regulatory and technical uncertainties most sensitive to institutions are being systematically compressed. This means uncertainty starts being priced in, rather than avoided.

The so-called “Institutional Second Curve” is a change in the nature of demand, providing a real demand source for the “Security Settlement Layer + Monetary Attribute” valuation logic, driving ETH to transition from a sentiment-driven speculative asset to a foundational asset carrying both allocation and functional needs.

VI. Conclusion: Value Anchoring in the Darkest Hour

In the past week, the industry has undergone a severe deleveraging wash, with market sentiment dropping to freezing point — undoubtedly a “darkest hour” for the crypto world. Pessimism is spreading among practitioners, and Ethereum, as the asset most representative of the crypto spirit, is also in the eye of the storm of controversy.

However, as rational observers, we need to pierce through the fog of panic: What Ethereum is currently experiencing is not a “collapse of value,” but a profound “migration of pricing anchor.” With L1 scaling advancing directly, L2 being redefined as a network spectrum of different trust levels, and protocol revenue actively giving way to system security and neutrality, ETH’s pricing logic has structurally shifted to “Security Settlement Layer + Native Monetary Attribute.”

Against the backdrop of high macro real interest rates, liquidity not yet being loose, and on-chain growth options not yet permitted to be priced by the market, ETH’s price naturally converges to a structural value range supported by settlement certainty, verifiable yield, and institutional consensus. This range is not a sentiment bottom, but a value pivot after stripping away platform growth premiums.

As long-term builders of the Ethereum ecosystem, we refuse to be “mindless bulls” for ETH. We hope to use a rigorous logical framework to carefully demonstrate our prediction: Only when macro liquidity, risk appetite, and network effects simultaneously meet market state trigger conditions will higher valuations be re-factored in by the market.

Therefore, for long-term investors, the critical question now is not anxiously asking “Can Ethereum still go up,” but to clearly recognize — in the current environment, which layer of core value are we buying at a “floor price”?

Ethereum Repricing: From Rollup-Centric to Security Settlement Layer was originally published in IOSG Ventures on Medium, where people are continuing the conversation by highlighting and responding to this story.