Beyond the Multiple: Why Token-Aware Analysis Is the Next Frontier

Last week, three Wall Street firms made the same call in the same week. Analysts at Benchmark, TD Cowen, and Mizuho published buy-equivalent ratings on Bitdeer, DeFi Technologies, Strive, and Gemini. The buy recommendations argued that the market is applying trading-business multiples to platforms that have already shifted toward AI infrastructure, capital markets utilities, and structured financial operations. (The Block)

The convergence is worth paying attention to. Wall Street is not late to crypto, however they are still testing the best ways to analyze crypto protocols. The analytical frameworks being applied now are stepping closer to alignment with the businesses being analyzed. The more interesting question it raises: does this same convergence extend to protocols and to the tokens that power them?

What Wall Street Is Getting Right

The frameworks being applied here are the right ones. Infrastructure comps, EV/Revenue multiples, take-rate analysis; these are standard fundamental tools, and they travel into crypto analysis well.

Mizuho's Dan Dolev pointed to a specific signal: Gemini's transaction revenue held essentially flat despite a greater than 50% decline in trading volumes. That kind of take-rate expansion and composition shift, away from spot trading toward structurally more stable revenue streams, is exactly the read a seasoned equity analyst makes on an exchange evolving into a data and clearing business. Dolev argued the trajectory mirrors how traditional finance incumbents evolved from pure exchanges into data, clearing, and workflow businesses. That comp is precise and it's correct.

The Bitdeer case makes the same point from a different angle. Benchmark's Mark Palmer argued that the company's approximately 3.0 gigawatt global power portfolio may prove materially more valuable than current market pricing implies as hyperscalers and AI infrastructure companies face mounting power constraints. Reframing a mining operation as a scarce power infrastructure asset is the right analytical call given the demand dynamics in AI compute.

These are the right analytical instincts and have been applied correctly. The next step is extending that same rigor one layer deeper, into the token itself.

The Token Layer

Equity analysis was built for single-function instruments. A share of stock confers ownership and a claim on earnings. A bond pays a fixed return. A commodity tracks supply and demand for a physical good. Each instrument does one thing, and the analytical frameworks built around them reflect that.

A token doesn't work that way, especially in DePIN. The same instrument can simultaneously bootstrap supply-side behavior by incentivizing independent infrastructure operators to join the network, settle demand-side transactions between service consumers and providers, govern protocol upgrades through voting rights, and serve as a financing vehicle for early-stage capital formation. In practice, tokens often serve dual roles: as an economic coordination tool inside the protocol, and as a financing instrument for early-stage capital. That duality understates the full picture because in many DePIN protocols, the token is doing considerably more than two jobs at once. (See more here and here)

This isn't a critique of Wall Street methodology. The analysts applying infrastructure comps to Bitdeer and take-rate analysis to Gemini are doing rigorous work. The issue is structural: those frameworks were designed for instruments that do one thing, applied to companies that issue instruments that do several. The commonly accepted analytical toolkit doesn't yet have a native extension for what the token is actually doing inside the protocol.

That extension is what token-aware analysis builds.

How Token Mechanisms Work; And What They’re Actually Doing

The most common misconception about token mechanisms is that they create value directly. They are typically designed to engineer scarcity conditions that the market then prices.

Burn-and-mint models, first pioneered by Factom in 2016 and later popularized by Helium, represent one of the clearest expressions of this mechanic. In this model, new tokens are minted to reward contributors, while tokens are burned when users consume services. The result is a self-adjusting system where token supply is reflexively tied to real-world usage. As utilization grows, burn begins to exceed mint, circulating supply tightens, and the market prices the resulting scarcity. The protocol doesn't set the price; it sets the inputs that feed into price discovery.

This is a meaningful distinction for analysis. The protocol controls the supply-side levers: emissions curves, burn rates, staking locks, vesting schedules, slashing conditions. Each of those is a variable that can be modeled. What cannot be modeled from protocol mechanics alone is the market's response. Understanding what the protocol is feeding into the market is a necessary precondition for any serious valuation work.

This is further complicated by the range of mechanisms in play across DePIN protocols today. Mechanisms range from baseline issuance and bonding curves to staking, slashing, and fee sinks. Different protocols make different design choices, and those choices produce materially different supply trajectories even at identical utilization levels. Two protocols generating the same revenue can have completely different circulating supply dynamics depending on how their token mechanisms are structured, which means the same revenue multiple can imply very different things about long-term token value.

Token-Aware Requirements

The starting point is the same fundamental discipline Wall Street applies. Revenue analysis, cost structure, competitive positioning, demand trajectory. The difference is what comes next.

Token-aware analysis requires an additional layer that equity research hasn't had to build before. Emissions trajectories model how token supply enters circulation over time and at what rate. Circulating supply forecasts account for vesting schedules, staking locks, and the pace at which early allocations reach the market. Stakeholder incentive mapping traces whether the token mechanisms actually align the behavior of operators, consumers, developers, and investors, or whether they create structural tensions that emerge later. Demand-side burn dynamics model the conditions under which protocol usage begins to meaningfully offset emissions.

The question token-aware analysis is trying to answer isn't just "what multiple applies here." It's "at what utilization level do supply dynamics shift, and what does that imply for the token over a two to three year horizon." That is a fundamentally different modeling exercise than applying a peer group average to an EV/Revenue estimate. This requires understanding the protocol mechanics from the inside, not just the revenue line from the outside.

The next frontier is protocol-level analysis, where the instrument is genuinely novel and the frameworks are still being built.