Tokenization Platforms Are Emerging Across Many Asset Classes

Start with Economic Value, Not the Asset

In our previous post, we argued that tokenization is only useful if it creates real economic value. In this post, we move beyond the legal and economic implications of the three Structural Models of Tokenized Assets (issuer liability, ownership, and derivative) and go a layer deeper to explore how that value gets created.

While the conversation around the tokenization of real-world assets (RWAs) almost always begins with asset classes, this is a misunderstanding of the landscape we see emerging. The successful companies in the space are not winning because they picked the "right" asset class to tokenize. They are winning because they're identifying a specific friction inside an asset class, building the infrastructure to remove it, and then expanding outward from that wedge after gaining sufficient market share.

If this insight is correct, it will change how we think about this space, how we think about who is going to win, and how we think about building companies.

What Should Work (In Theory)

Before jumping in to what is happening in the market, it's worth grounding the discussion in how the market today thinks about which asset classes should scale onchain. Keyrock and Securitize recently published The $400T Future of Tokenized Assets report, and at the heart of it sits a readiness framework scoring asset classes on standardization, liquidity, valuation frequency, redemption speed, regulatory clarity, and onchain demand. It's the most widely-cited lens for a discussion on which assets are "ready" to be tokenized.

When scoring assets against those criteria, the hierarchy is fairly intuitive. Treasuries, which are standardized, liquid, easy to price, and already central to global financial markets, sit at the top. Listed equities and commodities follow. Credit and alternative assets score lower due to complexity, illiquidity, and operational friction.

The takeaway is straightforward: by the lens outlined in the report, some assets are structurally easier to tokenize than others. If the pace of tokenization was decided by ease alone, we would expect treasuries and government debt to dominate the space.

What Is Actually Working (In Practice)

But when looking at the market, the picture is far less clean.

We mapped tokenization platforms based on the amount of assets they have brought onchain. The goal was simple: understand where traction is actually emerging.

What stands out is dispersion.

Companies with meaningful traction are spread across a wide range of asset classes, including treasuries, private credit, commodities (primarily gold), private markets, equities, and more niche categories. There is no single asset category that is clearly pulling away from the rest.

If the readiness framework is directionally right, why isn't the market converging?

Because asset "readiness" alone does not determine success. The current level of dispersion is not noise. It is a signal that the market is still forming.

In practice, platforms are not just selecting assets that are easy to tokenize. They are building the infrastructure required to make assets tokenizable in the first place.

Figure is the clearest example. HELOCs are not the most obvious starting point if you are optimizing purely for liquidity or standardization. And yet, Figure has built one of the most scaled platforms in the market by going deep in that category. It did so by controlling the stack, from origination to standardization to capital markets infrastructure. In effect, Figure manufactured tokenizability rather than inheriting it. This is an important shift.

In some cases, there may actually be more value in tokenizing assets that are harder to work with, precisely because they carry higher friction and higher costs. In these instances, we see platforms choose to underwrite and own the asset that spurs deeper capital formation. We saw this with Figure and Maple and now emerging platforms like Valinor and OatFi are following suit with asset-backed credit. Public equities already trade efficiently for most U.S. users. The incremental benefit of tokenization there may be narrower, at least in the near term, but assets that are harder to distribute, finance, or administer may offer more room for improvement.

From Wedge to Platform

Once you understand this insight, the pattern across the market becomes clearer.

Companies are not starting as horizontal "tokenization platforms." They are starting with a specific wedge where they have a structural advantage, going deep, and then expanding outward.

Figure began with HELOCs and is now expanding across primary mortgage and other capital markets products. Ondo evolved from structured onchain yield products into tokenized cash equivalents and is now extending into broader public market exposure. Securitize began as a securities issuance and compliance layer and now spans private and public asset tokenization more broadly. Different starting points, similar trajectory.

The market is not converging on a single "winning" asset class. Instead, multiple wedges are being explored in parallel. Companies don't start as a platform. They become one.

Asset characteristics still matter, and the readiness framework helps explain why some categories are easier to get off the ground. But those characteristics are not sufficient on their own. Teams can and do overcome structural limitations by building the right infrastructure around an asset.

And the most effective approach so far has not been breadth, but depth. The companies gaining traction are those that dominate a specific category first, then expand. They are building the infrastructure and distribution that make assets work onchain in the first place. These companies won't just tokenize a single asset class. They will build the platforms that multiple asset classes can run on.

If you are building toward that future, we would love to hear from you.