Tokenization outlook shifts focus to networks, platforms and fees in crypto finance

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Tokenization outlook in crypto

Highlights

Citrini Research's 79-page thesis asserts that tokenization is integrating traditional assets such as U.S. Treasuries and stocks into onchain infrastructure, encouraged by clearer regulatory paths.
Profit pools from tokenization—issuance fees, trading income, stablecoin demand, lending, and collateral usage—may be fragmented across networks, platforms like Coinbase and Robinhood, and infrastructure providers.
Industry leaders highlight that competition will likely commoditize basic tokenization, with stronger economics accruing to underlying networks like Ethereum, BNB Chain, and Solana as transaction volumes rise.

Research thesis reframes tokenization economics

As reported by CoinDesk, Citrini Research last week laid out a 79-page tokenization thesis, arguing that blockchains are beginning to serve a clearer role in mainstream finance as traditional assets move onto crypto-based infrastructure.

The report says years of development in wallets, exchanges, decentralized finance and stablecoins are starting to connect with investor demand for real-world assets such as U.S. Treasuries, credit, stocks, funds and commodities. In Citrini's framing, tokenization does more than extend trading hours, it can make financial assets behave more like software by allowing them to move across applications, serve as collateral and plug into other products without each platform rebuilding custody and settlement systems.

That shift comes as regulators in the U.S. and other markets are giving tokenized securities a clearer path into financial markets. Citrini's core argument is that the infrastructure built by crypto over more than a decade may now be finding a commercial purpose as conventional assets begin to migrate onchain.

Investors weigh where the profits accrue

The more difficult question for investors is not whether tokenization expands, but which companies, protocols and networks collect the revenue. The article highlights issuance fees, trading income, stablecoin demand, lending activity and collateral usage as possible profit pools, though those economics may be split across very different parts of the market.

Arthur Hayes, co-founder of BitMEX and chief investment officer of Maelstrom, argues that simply recording an already digital security on another ledger does not by itself create much value. Others cited in the piece point to infrastructure providers and distribution platforms, including Coinbase, Robinhood, banks and wealth managers, as potential beneficiaries because of their customer reach and role in access.

Jeff Dorman, chief investment officer at Arca, says the tokenization process itself may become commoditized as competition grows, with stronger economics emerging once assets begin trading, being lent out or used as collateral onchain. Zach Pandl, head of digital asset research at Grayscale, says the simpler investment case may be in the underlying networks that host tokenized assets and process transactions, naming Ethereum, BNB Chain and Solana among current leaders, while Avalanche and Canton may develop more institution-focused roles.

The article also points to a longer-term scenario in which AI agents automate portfolio rebalancing, cash management, borrowing and collateral shifts using programmable financial assets. That prospect could strengthen demand for always-on markets and smart-contract infrastructure, although market participants still describe tokenized assets as very small relative to global finance and say eventual winners may differ from today's leaders.

In our earlier article on a KPMG U.S. CEO survey about AI agents, we noted that many large companies are seeing early efficiency and growth gains, but only a small share consider the technology transformational so far. The survey also highlighted that firms expect to keep hiring, retraining staff, and investing heavily in AI infrastructure as they redesign processes and workflows to make agentic systems effective at scale.

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