How to Value Crypto Assets in 2026: A Framework Beyond Cash Flows

Crypto assets do not fit into traditional valuation frameworks.

They do not produce stable cash flows, do not represent enforceable ownership, and rarely offer a clear terminal value. Yet, trillions of dollars are allocated to them across institutional portfolios. This creates a contradiction. If these assets cannot be valued using the tools of traditional finance, what exactly are investors pricing?

The problem is not that crypto is hard to value. The problem is that it is approached with the wrong assumptions.

Crypto is not mispriced. It is misunderstood.

Crypto assets are not valued in the classical sense. They are bid based on their ability to attract, retain, and compound capital within a reflexive system. To understand this, valuation must be reframed. Not as intrinsic value, but as a function of monetary gravity.

The framework that follows redefines crypto valuation not as a function of intrinsic worth, but as a question of which systems can attract, retain, and compound capital — and which cannot.

Traditional valuation models rely on enforceable claims on future earnings. Discounted cash flow assumes that an asset produces predictable cash flows and that investors have a legal claim over them. Crypto assets do not satisfy these conditions.

Even when protocols generate revenue through fees, those revenues are distributed across validators, liquidity providers, and network participants rather than accruing cleanly to token holders. There is no shareholder structure, no guaranteed distribution, and no mechanism that ties ownership directly to earnings.

Tokens behave differently. Depending on their technical feature and local regulatory classification frameworks, they may function simultaneously as commodities, capital assets, and monetary instruments. Applying equity frameworks to them does not produce rough estimates. It produces misleading conclusions that appear analytical but lack real explanatory power.

The concept of terminal value also breaks down. Traditional finance assumes persistence and relatively stable business models. Crypto operates in an environment of rapid iteration where protocols evolve, fragment, or become obsolete at a pace that makes long-term projections unreliable.

The more relevant question is not what a crypto asset is worth. It is why capital flows into it and, more importantly, why it stays.

Value in crypto is not derived. It is captured.

Crypto assets do not compete on earnings. They compete on their ability to pull capital toward themselves and retain it. This dynamic can be understood through what can be described as monetary gravity.

Monetary gravity is the force that determines whether capital accumulates around an asset or disperses away from it. It emerges from the interaction of liquidity, economic activity, attention, and value capture. Together, these forces define how value forms and persists in crypto markets.

Liquidity is the base layer of crypto valuation.

Without liquidity there is no price formation. Without price formation there is no narrative. Without narrative there is no adoption. Crypto markets are highly sensitive to the availability of capital, particularly in the form of stablecoins, leverage, and institutional inflows.

Bitcoin provides the clearest example of this dynamic.

Figure 1: Bitcoin vs Crypto Liquidity (Indexed to 100, 2020–2026)

Source: AMINA Bank

When both Bitcoin and crypto-native liquidity are normalised to a common starting point, their trajectories move closely together over time. Periods of expanding liquidity have aligned with sustained price appreciation, while contractions in liquidity have coincided with periods of weakness or consolidation.

This relationship does not prove strict causality, but it highlights a potential structural dependency. Bitcoin does not operate independently of capital flows. It may responds to them, often with amplification.

Liquidity does not follow value in crypto. It defines it.

In traditional systems, increased usage generally translates into increased value. Higher demand leads to higher revenues, which in turn supports higher valuations. In crypto, this relationship is far less stable.

Ethereum illustrates this clearly.

Figure 2: Ethereum Activity vs Supply (Indexed to 100, 2020–2026)

Source: AMINA Bank

Despite substantial growth in on-chain activity, Ethereum’s supply and price have not moved in lockstep with usage. While price has at times risen alongside activity spikes, the correlation is inconsistent and supply remains relatively stable over time.  This creates a visible divergence between usage and value accrual.

The underlying reason is structural. Improvements in scalability reduce transaction costs, which lowers fee pressure on the base layer. The network becomes more efficient and more usable, but the mechanisms that translate usage into scarcity weaken.

This creates what can be described as a scaling paradox. Better technology improves the network while simultaneously reducing the direct economic pressure that might otherwise support token value.

Usage can grow significantly without producing a proportional increase in value.

In a system where the number of assets is effectively unlimited but capital is constrained, attention becomes a critical resource.

Crypto markets operate differently from traditional financial systems in that narratives are not merely reflections of price. They actively shape it. Attention directs capital, and capital reinforces attention in a reflexive loop.

Memecoins provide the clearest example of this mechanism.

Figure 3: Memecoin Volume vs Attention (Indexed to 100, 2020–2026)

Source: AMINA Bank

Trading activity in memecoins closely tracks shifts in attention. When attention rises, participation increases. As participation increases, liquidity deepens and price movements accelerate.

These assets do not derive value from utility in the traditional sense. They derive value from coordination. Attention becomes the mechanism through which capital is directed and concentrated.

In this context, attention is not a secondary factor. It functions as a core input into price formation.

Attracting liquidity and activity is only part of the equation. The more difficult question is where value ultimately settles.

Crypto ecosystems are increasingly modular. Base layers provide security and settlement. Execution layers handle transactions. Applications interface with users and capital.

As blockspace becomes more abundant and transaction costs decline, scarcity shifts away from infrastructure and toward layers that directly engage users and capital. This changes where value is captured.

The assumption that value automatically accrues to the base layer is no longer reliable. In many cases, value migrates toward applications and interfaces that control distribution and user interaction.

Crypto assets do not compete for market share in the traditional sense. They compete for the ability to attract and retain capital within their own ecosystems.

Valuing crypto requires a shift in perspective.

The focus should move away from estimating intrinsic value and toward understanding the forces that drive capital flows. Liquidity determines how much capital enters the system. Economic activity determines where that capital moves. Attention determines what assets capital concentrates around. Value capture determines whether that capital stays.

This framework does not produce a single number. It produces a way of thinking about how value forms in a system that does not behave like traditional markets.

Crypto valuation is not about estimating intrinsic value.

It is about understanding which systems can attract, retain, and compound capital within a reflexive environment.

Liquidity determines how much capital enters. Activity determines where it flows. Attention determines what it concentrates on. Value capture determines who retains it.

The question is no longer what an asset is worth.

The question is whether it can sustain monetary gravity.

In a market defined by uncertainty and rapid change, that is the closest approximation to value.

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