Overvaluation in Cryptocurrencies: How the Network Effect Becomes an Illusion

The Paradox of Economic Fundamentals

The total market capitalization of (cryptocurrencies excluding Bitcoin) has reached $1 trillion, a value that warrants critical analysis when compared to real user metrics.

Considering a generic user base of 400 million people, the valuation per user reaches $2,500. If we narrow the analysis to only 100 million monthly active users, the value jumps to $9,000 per person. Even more surprisingly, considering the 40 million users actively on-chain, the metric hits $23,000 per individual.

Meta (Facebook) has 3.1 billion monthly active users with a market capitalization of $1.5 trillion, establishing a value per user between $400 and $500. Meta remains the most sophisticated monetization model in the consumer tech sector.

The gap is clear: the market is pricing each crypto user with a premium ranging from 5 to 50 times that of Meta, despite Meta demonstrating higher loyalty, more solid revenue generation capacity, and significantly deeper engagement.

The Misinterpretation of Metcalfe’s Law

The crypto community has adopted Metcalfe’s Law (V ≈ n²) as the main justification for current valuations. However, this interpretation ignores the fundamental assumptions of the formula.

For the model to be applicable, specific conditions must be met:

• Users must generate meaningful interactions within the network
• The ecosystem must possess “sticky” characteristics that reduce migration
• The captured value must consolidate within the protocol layer
• Switching costs must be high and difficult to overcome
• Scale must build permanent competitive moats

Most crypto networks do not meet these fundamental criteria. Network effects, in their classical theoretical form, assume that as nodes (n) increase, value grows quadratically. But this only occurs when the network has “stickiness” features comparable to WeChat, Visa, or iOS.

The coefficient k in the formula V = k·n² represents crucial indicators: monetization efficiency, trust level in the ecosystem, depth of user participation, ability to generate loyalty, resistance to migration costs, and overall ecosystem maturity.

For Facebook and Tencent, k is measured between 10⁻⁹ and 10⁻⁷ due to the massive scale of the network itself. Applying these same parameters to the crypto sector:

• With 400 million users: k ≈ 10⁻⁶
• With 100 million users: k ≈ 10⁻⁵
• With 40 million on-chain active users: k ≈ 10⁻⁴

This reveals an counterintuitive market assumption: each crypto user is valued as if they possess greater potential value than a Facebook user, despite lower willingness to return, reduced value extraction capacity, and more fragile engagement.

How the Network Effect Reverses

When user networks grow excessively, phenomena opposite to those observed in traditional platforms occur in crypto environments:

• Experience deterioration: Increasing user base often leads to network congestion and spikes in transaction costs
• Volatility of rewards: Fees fluctuate wildly without stable supply and demand logic
• Fragmentation of development: The open-source nature of the code creates reduced incentives for developers, who disperse across numerous alternative projects
• Inefficient liquidity: Short-term financial incentives drive capital shifts between platforms, creating cycles of inflow and outflow

Contrary to this model, when Facebook integrated tens of millions of new users, the platform’s experience never deteriorated. Service quality remained stable and predictable.

Scalability Solutions Do Not Address the Core Issue

Innovations in scalability (rollup, sidechains, parallel execution) have indeed reduced congestion. However, this technical optimization does not tackle the fundamental issue: the intrinsic weakness in value capture capacity.

Improving throughput eliminates transactional friction but does not create additional composite value. The structural conflict remains:

• Liquidity remains vulnerable to migration to competing platforms
• Developers can move following changing economic incentives
• Users abandon when economic benefits diminish
• Code remains forkable, reducing the exclusivity of technical solutions
• Layer 1s continue to struggle in aggregating the value they generate

The Fee Narrative Reveals the Reality

If an L1 blockchain genuinely had a robust network effect, it should capture the vast majority of the value created, just like iOS, Android, and Visa.

The reality is quite different:

• L1s account for 90% of total crypto market capitalization
• The share of fees captured has plummeted from 60% to 12% in the current cycle
• DeFi generates 73% of total fees
• Yet, DeFi is valued at no more than 10% of the entire industry

This paradox clearly exposes the disconnect between valuation models and economic reality. The market continues to adhere to the “fat protocol” theory, assuming that value concentrates at the base layer. But empirical data suggests otherwise: L1s are overvalued, application layers remain undervalued, and the final value will converge toward user aggregation layers (exchanges, wallets, interfaces).

Direct Comparison of User Loyalty

Unlike Facebook, where users are bound to the platform through social networks, established habits, and digital identities, crypto users exhibit radically different dynamics.

Facebook built in its first decade:

• Daily usage rituals that generate psychological dependence
• Authentic social connections that increase the psychological cost of migration
• Fully integrated personal identity within the platform
• A sense of community that grows with network scale

Cryptocurrencies, on the other hand, remain primarily anchored to speculation. This generates:

• Rapid acquisition cycles during price rallies
• Even faster abandonment during market corrections
• Lack of consolidated usage habits
• No proportional experiential improvement with user growth

Unless cryptocurrencies transform into “invisible infrastructure” — an underlying technological layer functioning without user awareness — the network effect will hardly generate a self-reinforcing cycle. It’s not a matter of time and maturity; the core issue lies in the essential nature of the product itself.

Emerging Signs of Maturity

Ethereum shows the first indicators of a robust network:

• Incremental stability in liquidity
• Gradually concentrated developer ecosystem
• Visible improvements in fee capture
• Growing loyalty among institutional user base

Solana is positioned in the preparatory phase toward these characteristics, while most public chains remain significantly distant from these maturity parameters.

Logical Valuation Based on Network Effect

If crypto users exhibit:

• Lower loyalty compared to their historical crypto peers
• Greater complexity in monetization
• Higher abandonment rates during recession cycles

then their unit value should be lower than that of Facebook users, not 5, 20, or even 50 times higher.

Current valuations have already priced in network effects that have not yet materialized. The market is pricing as if these effects are already consolidated and powerful, when in reality they remain fragile, emerging, and incomplete — at least for now.