Seth Ginns & Anthony Pecore: Why Liquid Assets Win The Tokenization Super Cycle (Full Breakdown)

Seth Ginns’s central argument was that liquid assets may be stronger candidates for tokenization than private or pre-IPO instruments. Around-the-clock trading could be especially valuable for equities and Treasuries, where major events can happen while conventional markets are closed. He pointed to oil trading around the Iran war as an example of weekend developments drawing liquidity into markets. The potential advantage is not simply putting ownership records on a digital ledger, but enabling assets and collateral to move when traditional market hours would otherwise constrain activity.

Ginns also described a shift in institutional interest toward crypto businesses with revenue growth, token value capture, and reasonable multiples. He sees demand for early-stage companies operating in secular growth industries, rather than a focus on meme coins. Businesses using crypto rails, he argued, can pair strong top-line growth with profitability and capital efficiency. That combination makes them more legible to allocators evaluating operating businesses, while still leaving their investment case tied to crypto infrastructure and token economics.

Anthony Pecore cited Franklin Templeton’s Benji tokenized money market fund as an example of tokenization already operating in asset management. Live since 2021, Benji began as an experiment backed by leadership that saw digital ledger technology as a potential disruption to the industry. Pecore said the fund was developed with the SEC and that its ownership records are entirely on-chain, without a separate secondary book. Institutions are beginning to view Benji as useful collateral, and he described tokenized money market funds as becoming table stakes for asset managers.

Pecore’s broader point was that tokenization could change how capital moves, not just how assets are issued. Capital flowing into protocols and on-chain applications could expand the addressable market and potentially lead investors to re-rate total value locked and assets under management. He sees the technology as a way to make finance more capital-efficient, productive, and programmable. But that outcome depends on building the liquidity layer for 24/7 trading and collateralization, as well as orderly use and unwinding of leverage over weekends. What products ultimately emerge remains uncertain.

Ginns emphasized composability as a source of possible new financial instruments. Tokenized equities could be divided and recombined around profit streams, yield, or other dynamics, allowing investors to trade individual company profit pools. He suggested that these market valuations might influence how management directs capital, favoring businesses whose profit pools receive higher valuations. In the longer run, market signals could help drive autonomous management and capital allocation, although this was presented as a prediction rather than an established outcome.

Ginns also predicted that investment research combining sector knowledge with crypto expertise could emerge within one or two years. Evaluating a crypto-enabled business requires understanding both its end market and competitive dynamics and the role of crypto in its model. AI could let analysts review a company in 10%–20% of the time and raise productivity tenfold, but he did not suggest that faster analysis eliminates the need for differentiated information. Proprietary human and analog data, he said, would remain an investment advantage.