Tokenization in 2026: Where Adoption Is Compounding

Decasonic
2 days ago
13 min read

Updated: 2 days ago

The Next Stage of Tokenization Adoption is AI Native

-- Justin Patel, Venture Investor at Decasonic

Capital is on-chain. That's solved. Tokenized U.S. Treasuries crossed $10 billion. Tokenized gold hit $6 billion. Private credit on-chain exceeds $18 billion. Non-stablecoin tokenized RWAs are approaching $40 billion. All this growth has accelerated within the past 12-24 months.

Compounding usage is where this market is won. Capital is on-chain, yet participation is still concentrated across a narrow issuer and holder base. The next phase is expanding from issuance success to embedded repeat usage.

Adoption is real today and poised to get much larger over the coming years. The investable signal lives in repeat behavior and integration depth, not issuance headlines. AI drives tokenization adoption today and tomorrow.

At Decasonic, we invest at pre-seed and seed in teams building tokenization infrastructure and applications where AI materially improves underwriting, risk, distribution, or servicing. This piece is how we see the market right now: where adoption in tokenization is compounding, where it's stalling, and what we're looking to fund.

The Adoption Numbers

Beyond our standard underwriting, we start with market structure, not just headline AUM. We use RWA.xyz’s split between Distributed Asset Value (DAV) and Represented Asset Value (RAV). DAV tracks tokenized assets that can move wallet-to-wallet. RAV tracks assets represented on-chain but still largely confined to issuer/platform rails. That distinction matters because it separates open, reusable capital from value that remains operationally siloed.

Currently, DAV is $24.83B and RAV is $373.77B, implying roughly a 6% / 94% split. The category is scaling, but most value is still represented rather than broadly distributed.

From there, we evaluate adoption quality through three lenses:

Asset growth quality
Is growth coming from repeat allocators and organic inflows, or concentrated one-off deposits? Durable categories show diversified and recurring capital formation.
Usage intensity
Are holders actively transacting, posting collateral, rebalancing, earning, and redeeming? Idle balances are a weak PMF signal; repeated workflow usage is a strong one.
Integration depth
Are assets embedded in lending, margin, treasury, and settlement systems across venues, or confined to a single platform context? Integration is what converts tokenization from issuance into utility.

AUM is directionally useful, but it is not a verdict. Adoption compounds when capital is reusable, behavior is repeatable, and integrations deepen over time. That is the lens we apply across each category below.

Where adoption is compounding in Open Finance and where AI steps in

Tokenization in financial markets is developing on multiple curves, not one. Treasuries are mature and compounding via collateral reuse. Gold is expanding on macro demand plus on-chain utility. Private credit is scaling with strong quality dispersion. Equities are early and structurally fragmented, with CLARITY Act passage as the key catalyst. The common thread: durable financial tokenization products create reasons to hold and reuse, not just buy and park.

Treasuries and cash-like funds

Tokenized Treasuries are the most mature segment for a reason: the underlying asset is simple, the yield is transparent, and the institutional demand is structural. BlackRock's BUIDL and Circle's USYC have each crossed $1.5 billion in AUM, competing through product design rather than marketing. BUIDL distributes yield; USYC accumulates it. That distinction matters more than it appears. Collateral systems prefer accumulating structures because they integrate more cleanly into automated margin workflows without operational handling of payouts.

The deeper signal is what's being built on top. Ethena's USDtb and Ondo's OUSG both use BUIDL as reserve collateral, effectively making it the backbone of an emerging on-chain cash layer. JPMorgan has framed tokenized money market funds as the evolution of stablecoins: programmable cash equivalents with faster settlement and native collateral utility. The regulatory environment is reinforcing this trend. The GENIUS Act created a federal framework for stablecoin issuers, and the CLARITY Act (now working through the Senate) would extend regulatory clarity to the broader digital asset market, including the tokenized instruments that sit between stablecoins and traditional securities. DTCC secured SEC authorization to tokenize Treasuries and Russell 1000 equities on the Canton Network, with a minimum viable product targeted for 2026. That's core market plumbing, not a pilot.

Where AI Steps In: The category now benefits most from operational intelligence. AI systems for collateral routing, venue selection, and margin optimization can increase reuse velocity and rebalance frequency. Durable growth shows up in repeated collateral loops across protocols, not static balances tied to one counterparty. We’re interested in teams building automated collateral orchestration and treasury yield optimization layers.

Commodities

Tokenized gold crossed $6 billion in market cap, adding $2 billion since January alone. Tether's XAUT ($3.6 billion) and Paxos' PAXG ($2.3 billion) control roughly 97% of the segment, backed by over 1.2 million ounces of vaulted bullion. PAXG recorded a record $248 million inflow in January. 2025 trading volume hit $178 billion, with $126 billion in Q4 alone, surpassing all but one U.S. gold ETF.

Gold pushing toward $5,000 is a tailwind, but utility is still the real driver. Tokenized gold delivers 24/7 settlement, fractional access, and direct integration into DeFi lending and margin workflows. Investors now use it as on-chain safe-haven collateral without leaving crypto rails. That behavior has moved from edge case to scalable pattern.

The friction is concentration. Two issuers controlling 97% of the market introduce counterparty risk that cuts against the decentralization thesis. Custody transparency still relies on periodic attestations rather than real-time proof of reserves. Cross-chain liquidity is expanding to Arbitrum, Solana, and BNB but remains Ethereum-heavy. Distribution without intelligence rarely compounds.

Where AI Steps In: The next phase requires AI-driven risk telemetry, real-time reserve monitoring, and automated rebalancing across chains. What compounds is on-chain gold with measurable collateral reuse frequency. What breaks is price-driven inflows without utility beyond holding. We're watching for teams building reserve verification and cross-chain liquidity infrastructure for tokenized commodities.

Private credit and alternatives

Private credit is the largest tokenized RWA category by value, accounting for over $18 billion of the roughly $36 billion total market, up more than 74% in twelve months. The appeal is straightforward: private credit yields of 8-14% are attractive relative to public debt, and the traditional market (now $3 trillion and growing) suffers from exactly the problems tokenization addresses: Illiquidity, opaque reporting, and fragmented servicing.

Platforms like Maple Finance, Centrifuge, and OpenTrade have moved well beyond proof-of-concept. Anemoy's JAAA fund reached $1 billion in AUM providing on-chain CLO exposure. Superstate's USCC accumulated $440 million through a crypto-carry strategy. These aren't wrappers. They're products with distinct risk profiles and investor bases.

But quality varies significantly. The underwriting rigor, servicing transparency, and default recovery processes differ widely across platforms. As Percent's 2026 outlook noted, the market is becoming less forgiving. Investors are applying tighter scrutiny to reporting quality and repeatable risk management. Maple's Sidney Powell argues that on-chain defaults will ultimately make credit markets safer through auditable infrastructure, but that thesis only holds when underwriting is rigorous and surveillance is continuous.

Where AI Steps In: AI is the operating layer that determines whether tokenized credit scales or stalls. Automated underwriting and always-on covenant surveillance are the unlock for scaling volume without taking a quality hit. Teams that instrument borrower and repayment behavior in real time will iterate faster than teams that only ship wrappers. What compounds is transparent performance data, measurable recovery outcomes, and repeat high-quality borrower cohorts. What breaks is opaque packaging that moves the same illiquidity on-chain without improving risk visibility. We are focused on backing AI-native credit scoring, real-time portfolio monitoring, and automated servicing infrastructure.

Tokenized stocks

Tokenized equities surged from under $30 million to over $700 million in 2025, a 50x expansion, and are approaching the $1 billion mark in early 2026. Backed Finance (acquired by Kraken) grew xStocks AUM to roughly $186 million within five months. Securitize announced compliant natively tokenized stocks launching Q1 2026: real shares on the issuer's cap table, not synthetic wrappers.

The SEC drew a clear line in January: only issuer-sponsored tokenized securities convey true equity ownership. Third-party products typically provide synthetic exposure or custodial entitlements without voting rights, dividend claims, or cap table recognition. There are currently four versions of "tokenized Tesla," none representing actual shares, none fungible with each other.

The DTCC no-action letter authorizing tokenization of Russell 1000 equities and major index ETFs for a three-year pilot (expected H2 2026) could shift this from fragmentation to infrastructure. Nasdaq's proposal to trade tokenized securities on its exchange would add another layer.

The broader regulatory backdrop matters here too. The CLARITY Act, which passed the House in July 2025 with strong bipartisan support (294-134), aims to resolve the jurisdictional ambiguity between the SEC and CFTC by defining when a digital asset is a security versus a commodity and creating registration pathways for exchanges, brokers, and custodians. The Senate Agriculture Committee advanced its companion bill on a 12-11 party-line vote in late January, while the Senate Banking Committee's markup remains delayed over disagreements around stablecoin yields and DeFi treatment. The bill still needs to clear both committees, be reconciled with the House version, and reach a full Senate vote before the November 2026 midterms. Passage is plausible but not guaranteed, and the timeline pressure is real.

Where AI Steps In: For tokenized equities specifically, the CLARITY Act matters because it would establish the legal classifications and compliance frameworks that institutional participants need before they can engage at scale. Clearer rules for custody, trading, and token classification would remove the ambiguity that currently forces every tokenized equity product to navigate a patchwork of no-action letters and enforcement precedent. Until that framework exists, tokenized stocks remain more optionality than adoption. AI-driven compliance automation and rights reconciliation across fragmented issuance models are the operational layer this category needs before it can compound. What we are looking to fund here is cross-venue reconciliation and compliance infrastructure for tokenized equities.

Where adoption is compounding in the Open Internet and where AI steps in

Tokenization doesn’t stop at financial instruments. The same primitives that make Treasuries and credit composable on-chain also apply to non-financial assets. In the Open Internet, tokens are increasingly used to coordinate attention, culture, information, and access.

A useful framing comes from Nikita Bier’s point that he wants crypto to proliferate on X, but incentive systems that drive spam, raids, and harassment are “not the way.” That is the core design challenge in this category: tokenized systems compound when incentives increase signal quality, and they decay when incentives reward engagement spam.

The Open Internet categories share a common thread with Open Finance: tokenization compounds when it creates repeat behavior, not just one-time transactions. But the adoption curves look different. Financial tokenization is driven by yield, collateral efficiency, and institutional mandate. Non-financial tokenization is driven by community, identity, access, and information. The categories gaining traction (community tokens with real IP, prediction markets with institutional-grade accuracy, gaming economies with genuine retention) have all moved past speculation into utility that generates repeat engagement.

Community tokens and digital collectibles

The 2021 NFT bubble burned billions in speculative capital and left 96% of collections effectively dead. That crash was necessary. What survived is a smaller, sharper market where the winning projects are building actual businesses on tokenized IP rather than selling static JPEGs.

Pudgy Penguins is the clearest example. What started as an 8,888-piece PFP collection became a global consumer brand with plush toys in Walmart and Target, a mobile game (Pudgy Party) surpassing one million players, and the PENGU token on Solana with a ~$440 million market cap. The project's evolution from collectible to tokenized IP with retail distribution, gaming, licensing, and community governance is the template for what durable adoption looks like in this category. PENGU isn't just a speculative asset. It functions as a coordination layer across a physical and digital product ecosystem.

The broader pattern holds beyond any single project. The global digital collectibles market is projected to grow by $84 billion from 2025 to 2029 at roughly 30% CAGR. The market has quietly rebranded from "NFTs" to "digital collectibles" and "membership tokens," reflecting the shift from speculation to utility.

This is also where the culture debate lives. Community tokens like BONK and PENGU, and ecosystem tokens tied to projects like Azuki and Doodles, represent a non-consensus view: that tokenizing culture, community identity, and social coordination is a legitimate and large category of tokenization. The Asian market in particular has leaned into this thesis, with high NFT adoption rates and strong community-driven token ecosystems (more on Asian Adoption). Whether you frame these as "memecoins" or "community equity," the underlying behavior is the same: groups of people using tokens to coordinate around shared identity, access, and governance.

Where AI Steps In: AI is already reshaping discovery and curation in digital collectibles. Marketplaces use recommendation engines to surface relevant collections. Fraud detection models identify wash trading and counterfeit assets. The next wave is AI-driven dynamic NFTs that evolve based on user interaction, and AI-powered creator tools that lower the barrier to producing high-quality tokenized content. What compounds is IP-backed digital ownership with repeat engagement across products. We're interested in teams building AI-native tools for creator economies, community coordination infrastructure, and tokenized IP management.

Prediction markets (tokenized information)

Prediction markets are tokenization applied to information itself. Every contract is a tokenized claim on an outcome: tradeable, composable, and settled on-chain. The category has exploded. Total notional trading volume reached over $44 billion in 2025, a fourfold increase year-over-year.

This isn't a niche crypto phenomenon anymore. Prediction markets are recognized as legal financial products at the federal level in the United States. The category has graduated from "experimental" to "infrastructure for pricing uncertainty.” Our previous blog post on prediction markets can be found here.

The forecast accuracy is notable. These markets consistently outperform polls and traditional forecasting methods, which is why institutional participants and algorithmic traders are entering the space.

Where AI Steps In: AI is the natural multiplier here. Market creation, liquidity provision, resolution verification, and fraud detection are all AI-native problems. The teams building AI-powered market creation and resolution infrastructure will define the next phase. What compounds is high-frequency, high-accuracy prediction infrastructure with growing institutional participation. What breaks is event-driven speculation that disappears between election cycles. We'd fund AI systems for automated market creation, intelligent liquidity provision, and oracle infrastructure for prediction markets.

Gaming and virtual economies

Gaming represents one of the highest-volume applications of non-financial tokenization. On-chain gaming NFTs account for 38% of total NFT transaction volume in 2026, making it the single largest use case by activity. In-game items, characters, land, and currencies tokenized as NFTs or fungible tokens create player-owned economies where assets are portable, tradeable, and composable across platforms.

The shift from "play-to-earn" to "play-and-own" reflects the same maturation pattern seen in financial tokenization: durable adoption requires utility, not just speculation. The winning model combines genuine gameplay with tokenized ownership that players value for reasons beyond price appreciation.

Where AI Steps In: AI-generated game content (procedural worlds, dynamic NPCs, personalized quests) combined with tokenized ownership creates a flywheel: AI produces more content, tokens give players ownership stakes in that content, and engaged players generate data that improves the AI. This is the intersection of generative AI and tokenized economies, and it's early but directionally significant. What compounds is games where tokenized assets have in-game utility that drives repeat sessions. What breaks is yield-farming disguised as gaming. We're watching for teams building AI-native game economies with genuine player retention.

Full synthesis

Tokenization is not one market. It's two overlapping ones, each with multiple adoption curves.

In Open Finance, Treasuries are mature and compounding via collateral reuse. Gold is expanding on macro demand plus on-chain utility. Private credit is scaling with strong quality dispersion. Equities are early and structurally fragmented, with regulatory clarity from the CLARITY Act as the key catalyst.

In the Open Internet, community tokens and digital collectibles are rebuilding on utility after the speculative crash. Prediction markets have graduated to institutional-scale infrastructure. Gaming economies are transitioning from yield-farming to genuine player ownership.

The common thread across both: the categories that compound share three traits. Clear underlying value, usable on-chain infrastructure, and at least one integration that creates a reason to hold, use, or return rather than just buy. Categories without that third trait plateau after the initial issuance wave. The asset gets tokenized, the press release goes out, and then nothing changes about how it's actually used. That pattern repeats across every asset class and application we've studied. It's the single biggest predictor of whether a tokenized product builds durable demand or fades into a line item on a dashboard.

In tokenization, operational intelligence is becoming a competitive moat. The teams that build feedback loops between user behavior, product iteration, and risk management are the ones whose metrics improve quarter over quarter. Everyone else is shipping static wrappers into a market that increasingly rewards dynamic products. AI is the through-line. In Open Finance, it automates underwriting, risk, and collateral management. In the Open Internet, it powers discovery, content generation, market creation, and fraud detection. The teams that use AI as a product layer will define both markets.

The Future: AI-Native Tokenization Drives Enduring Adoption

Tokenization compounds when assets are reusable in real workflows, not just issued on-chain. Putting an asset on a blockchain is now table stakes. The real test is whether that asset gets integrated into collateral systems, lending protocols, margin workflows, and settlement infrastructure in ways that drive repeat transactions. When those loops exist, adoption compounds. Without them, AUM is a snapshot, not a trajectory.

Product-market fit shows up in recurring behavior. One-time issuance can validate initial interest, but durable demand is measured by returning users, repeat collateral activity, ongoing rebalancing, and reliable redemption. Launch-day headlines matter less than what happens in quarters two and three.

AI-native operations are becoming a structural moat. Teams that automate underwriting, risk monitoring, and servicing will out-execute teams that only automate issuance. Most cost and friction sit in operational layers: credit assessment, covenant monitoring, compliance, reporting, and redemption processing. AI applied to those layers improves speed, accuracy, and scalability.

Durability comes from measurable workflow improvement. When tokenized products improve capital efficiency, transparency, and execution quality, usage compounds. When products remain static wrappers with added friction, adoption plateaus.

The next six months will separate products with real operating traction from announcement-driven momentum. The metrics that matter are repeat deposit cohort rates, collateral reuse frequency, redemption SLA and failure rates, transfer completion times between venues, and integration count growth quarter over quarter. Those are the signals we track, and the signals we want founders to show.

Our point of view:

The category has moved beyond proof-of-concept and into an execution phase, where product quality, distribution, and operational rigor drive outcomes. We are seeing the strongest momentum from teams turning tokenization into repeat behavior, with progress visible in repeat transaction rates, integration growth, and operational metrics that improve quarter over quarter.

We are especially focused on the intersection of tokenization and AI, where AI functions as a core product layer across underwriting, risk, distribution, and servicing in measurable, defensible ways.

We view this intersection as an important next wave and are actively deploying into it. Adoption is real, and broader adoption will be earned through repeat behavior, integration depth, and operational intelligence.

If you're building tokenization infrastructure or applications with an AI-native layer that improves underwriting, risk, distribution, or servicing, and you can show compounding usage rather than just issuance volume, we'd like to hear from you. DM me or ping us via our website.

The content of these blog posts is strictly for informational and educational purposes and is not intended as investment advice, or as a recommendation or solicitation to buy or sell any asset. Nothing herein should be considered legal or tax advice. You should consult your own professional advisor before making any financial decision. Decasonic makes no warranties regarding the accuracy, completeness, or reliability of the content in these blog posts. The opinions expressed are those of the authors and do not necessarily reflect the views of Decasonic. Decasonic disclaims liability for any errors or omissions in these blog posts and for any actions taken based on the information provided.