For most of crypto’s history, credit was something to be avoided. Lending systems were designed to eliminate risk rather than price it, relying almost exclusively on overcollateralization and reflexive demand to function. Yield, when it appeared, was a byproduct of leverage and incentives, not durable cash flows.

That is starting to change.

Over the past year, a new class of protocols has emerged that run credit explicitly onchain. Instead of trying to engineer risk away, these systems generate yield by financing real economic activity: lending to specific counterparties, underwriting real-world risk, or funding assets with predictable revenue. The mechanics differ, but the common thread is simple: yield is increasingly tied to who is borrowing, why they need capital, and how losses are handled when things go wrong.

To understand why this shift matters, it helps to separate stablecoin yield into its underlying components. Despite the diversity of products and narratives, much of the new onchain yield emerging today comes from a small number of economic sources.

This exists wherever there is a structural mismatch between who can lend cheaply and who needs capital. In many markets, traditional lenders are constrained by regulation, risk-weighting, or operational and compliance friction, even when the underlying assets generate predictable cash flows. The result is a higher cost of capital driven by regulatory and operational constraints.

Stablecoins efficiently step into these gaps. Protocols like USD.ai finance infrastructure assets like GPUs that banks are poorly positioned to underwrite, while others like Daylight target energy and infrastructure financing more broadly. In these cases, yield compensates investors for supplying capital where legacy balance sheets cannot, or will not, operate.

This is where yield is explicitly compensation for default risk. Capital is lent to identifiable borrowers, and losses are explicitly planned for, rather than treated as tail events to be engineered away.

What matters most in these systems is not the headline rate, but how losses are allocated. Who absorbs first loss? How much protection exists above them? And how quickly can capital exit when conditions change? Overcollateralization was the dominant answer in earlier DeFi systems, but it is no longer the only one.

Protocols like Wildcat extend undercollateralized credit to institutional counterparties based on reputation and relationship-driven risk assessment, while 3Jane focuses on smaller operators and merchants with measurable cash flows. In both cases, yield is inseparable from borrower quality and loss structure.

These systems generate yield by absorbing volatility that others prefer to avoid. Participants earn premiums in exchange for taking on exposure to specific categories of risk.

This is structurally different from lending. Returns tend to be uneven, with long periods of stability punctuated by drawdowns. Returns depend less on steady utilization and more on whether losses arrive independently or all at once, which is why leverage can dramatically amplify downside.

Reinsurance protocols like Re (auto, homeowners, small business) and OnRe (catastrophe) both sit in this category, but with very different volatility profiles depending on the risks they underwrite. The distinction between them is not whether yield is “real,” but which risks are being priced, and how transparent those risks are to capital providers.

What’s interesting about this moment isn’t that assets are being brought onchain. Tokenization by itself is not new, and it’s rarely the hard part. The harder problem is designing credit systems around those assets that can scale, survive stress, and allocate losses clearly.

The real innovation is not the wrapper, but the structure. How underwriting is done. Where first loss sits. How liquidity is gated. How quickly risk becomes visible to capital providers. Two protocols can both advertise “real-world yield” while one behaves like senior secured financing and the other like a volatility-selling strategy.

Seen this way, the recent wave of “stablecoin yield” and “real-world assets” is less about a single onchain credit narrative and more about a growing range of distinct designs. Yield now reflects how credit is structured, including who borrows, who absorbs losses, and how capital moves under stress. Different protocols express different balance sheets onchain, each with its own assumptions about risk, trust, and time.

That is the shift. Crypto is no longer just moving dollars or wrapping assets. It is starting to design credit.

What makes this moment interesting is not just that credit is being rebuilt onchain, but that crypto is beginning to expand the frontier of what credit systems can do. Blockchains, stablecoins, decentralized exchanges, and cryptographic primitives are converging into a stack that allows capital to move, price risk, and settle globally in ways that traditional finance simply does not offer.

This is why emerging technologies like zkTLS matter. Credit depends on facts: revenues, utilization, balances, insurance coverage. zkTLS makes it possible to verify those facts without fully revealing them or routing them through centralized intermediaries, allowing real economic activity to be underwritten onchain while preserving privacy and minimizing trust assumptions. Protocols like 3Jane use this to enable unsecured lending based on cash flows rather than collateral, while others apply similar primitives to infrastructure financing, institutional credit, or insurance underwriting, as seen in USD.ai, Wildcat, and Re. The common thread is the expansion of what can be verified, priced, and financed onchain.

More broadly, these systems point toward a future where sophisticated credit products are no longer gated behind institutional balance sheets or geographic boundaries. Retail capital can participate directly in financing infrastructure, underwriting risk, or extending credit to real businesses, with transparency and control that rarely exists in traditional markets. If this trajectory holds, onchain credit will not just compete with existing financial rails. It will materially widen the frontier of finance itself.

Credit is where financial systems are forced to confront reality. Defaults happen. Liquidity disappears. Risk concentrates in uncomfortable places. For most of its history, crypto avoided these constraints by design, leaning on overcollateralization and reflexive demand to scale quickly without confronting loss. What’s different now is that onchain systems are beginning to engage with these realities directly, not as failures to be patched over, but as inputs to be priced and structured.

If crypto can build credit systems that survive stress, allocate losses transparently, and expand access to sophisticated financial activity beyond traditional gatekeepers, then everything else follows. Not because yield is higher or rails are faster, but because the system is doing something genuinely new. Onchain credit is hard. That’s precisely why it matters.

Special thanks to Armen Ter-Avetisyan and Conor Moore for helping me with this post.

For most of crypto’s history, credit was something to be avoided. Lending systems were designed to eliminate risk rather than price it, relying almost exclusively on overcollateralization and reflexive demand to function. Yield, when it appeared, was a byproduct of leverage and incentives, not durable cash flows.

That is starting to change.

Over the past year, a new class of protocols has emerged that run credit explicitly onchain. Instead of trying to engineer risk away, these systems generate yield by financing real economic activity: lending to specific counterparties, underwriting real-world risk, or funding assets with predictable revenue. The mechanics differ, but the common thread is simple: yield is increasingly tied to who is borrowing, why they need capital, and how losses are handled when things go wrong.

To understand why this shift matters, it helps to separate stablecoin yield into its underlying components. Despite the diversity of products and narratives, much of the new onchain yield emerging today comes from a small number of economic sources.

This exists wherever there is a structural mismatch between who can lend cheaply and who needs capital. In many markets, traditional lenders are constrained by regulation, risk-weighting, or operational and compliance friction, even when the underlying assets generate predictable cash flows. The result is a higher cost of capital driven by regulatory and operational constraints.

Stablecoins efficiently step into these gaps. Protocols like USD.ai finance infrastructure assets like GPUs that banks are poorly positioned to underwrite, while others like Daylight target energy and infrastructure financing more broadly. In these cases, yield compensates investors for supplying capital where legacy balance sheets cannot, or will not, operate.

This is where yield is explicitly compensation for default risk. Capital is lent to identifiable borrowers, and losses are explicitly planned for, rather than treated as tail events to be engineered away.

What matters most in these systems is not the headline rate, but how losses are allocated. Who absorbs first loss? How much protection exists above them? And how quickly can capital exit when conditions change? Overcollateralization was the dominant answer in earlier DeFi systems, but it is no longer the only one.

Protocols like Wildcat extend undercollateralized credit to institutional counterparties based on reputation and relationship-driven risk assessment, while 3Jane focuses on smaller operators and merchants with measurable cash flows. In both cases, yield is inseparable from borrower quality and loss structure.

These systems generate yield by absorbing volatility that others prefer to avoid. Participants earn premiums in exchange for taking on exposure to specific categories of risk.

This is structurally different from lending. Returns tend to be uneven, with long periods of stability punctuated by drawdowns. Returns depend less on steady utilization and more on whether losses arrive independently or all at once, which is why leverage can dramatically amplify downside.

Reinsurance protocols like Re (auto, homeowners, small business) and OnRe (catastrophe) both sit in this category, but with very different volatility profiles depending on the risks they underwrite. The distinction between them is not whether yield is “real,” but which risks are being priced, and how transparent those risks are to capital providers.

What’s interesting about this moment isn’t that assets are being brought onchain. Tokenization by itself is not new, and it’s rarely the hard part. The harder problem is designing credit systems around those assets that can scale, survive stress, and allocate losses clearly.

The real innovation is not the wrapper, but the structure. How underwriting is done. Where first loss sits. How liquidity is gated. How quickly risk becomes visible to capital providers. Two protocols can both advertise “real-world yield” while one behaves like senior secured financing and the other like a volatility-selling strategy.

Seen this way, the recent wave of “stablecoin yield” and “real-world assets” is less about a single onchain credit narrative and more about a growing range of distinct designs. Yield now reflects how credit is structured, including who borrows, who absorbs losses, and how capital moves under stress. Different protocols express different balance sheets onchain, each with its own assumptions about risk, trust, and time.

That is the shift. Crypto is no longer just moving dollars or wrapping assets. It is starting to design credit.

What makes this moment interesting is not just that credit is being rebuilt onchain, but that crypto is beginning to expand the frontier of what credit systems can do. Blockchains, stablecoins, decentralized exchanges, and cryptographic primitives are converging into a stack that allows capital to move, price risk, and settle globally in ways that traditional finance simply does not offer.

This is why emerging technologies like zkTLS matter. Credit depends on facts: revenues, utilization, balances, insurance coverage. zkTLS makes it possible to verify those facts without fully revealing them or routing them through centralized intermediaries, allowing real economic activity to be underwritten onchain while preserving privacy and minimizing trust assumptions. Protocols like 3Jane use this to enable unsecured lending based on cash flows rather than collateral, while others apply similar primitives to infrastructure financing, institutional credit, or insurance underwriting, as seen in USD.ai, Wildcat, and Re. The common thread is the expansion of what can be verified, priced, and financed onchain.

More broadly, these systems point toward a future where sophisticated credit products are no longer gated behind institutional balance sheets or geographic boundaries. Retail capital can participate directly in financing infrastructure, underwriting risk, or extending credit to real businesses, with transparency and control that rarely exists in traditional markets. If this trajectory holds, onchain credit will not just compete with existing financial rails. It will materially widen the frontier of finance itself.

Credit is where financial systems are forced to confront reality. Defaults happen. Liquidity disappears. Risk concentrates in uncomfortable places. For most of its history, crypto avoided these constraints by design, leaning on overcollateralization and reflexive demand to scale quickly without confronting loss. What’s different now is that onchain systems are beginning to engage with these realities directly, not as failures to be patched over, but as inputs to be priced and structured.

If crypto can build credit systems that survive stress, allocate losses transparently, and expand access to sophisticated financial activity beyond traditional gatekeepers, then everything else follows. Not because yield is higher or rails are faster, but because the system is doing something genuinely new. Onchain credit is hard. That’s precisely why it matters.

Special thanks to Armen Ter-Avetisyan and Conor Moore for helping me with this post.