Valuating Blockchain networks today feels like déjà vu For anyone who has lived in the early era of the Internet. In the 1990s, analysts, investors and founders fought to apply family financial models to a radically unknown technology. Companies with little more than a website and a launch cover were valued in the hundreds of millions, sometimes billions, based on something as intangible as “eyeballs.”
It didn’t end well. And yet, in retrospect, those first chaotic years offered valuable lessons: technology evolves faster than finance, and valuation models must eventually adapt to the form of innovation.
Today, we face a similar dilemma in the Blockchain space. Despite the growing adoption, the maturation infrastructure and undeniable cultural and economic impulse, there is still no widely accepted or standardized way to assess a blockchain network. And the few models we have, although directionally, remain defective or incomplete.
To understand where we could go, it is worth visiting how we got here.
The first wave of internet assessment: ocular balloons, non-profits (mid-1990-2000)
In the middle of the 1990s, the Internet was a border. Investors did not know what the “success” would be like for digital companies, so they relied on what they might measure: page views, BANNER adpressions, unique visitors or monthly active users (MAU). These raw representatives for care became de facto metrics for value. The logic was simple: if millions of people visit their site, monetization would eventually follow.
The valuations shot. Startups such as Pets.com (see image), Webvan and Etoys raised hundreds of millions on the promise of domain. But income was a late occurrence, and profitability was a phrase. When the bubble DOT-COM EXPLES in 2000, it was clear that attention without monetization is a poor basis for business value.
Realineration after shock: income and margins (2001-2005)
After the first internet bubble, the feeling of investors changed dramatically. The market demanded evidence, not just vision. As of 2001, companies were expected to generate significant income, show gross margins and move towards profitability.
This period saw a ruthless disremed of unsustainable models. Only companies with real products, realist customers and finance survived. Amazon, for example, began to change the focus of investors of the abstract future potential to real operational performance. Its ability to show constant first -line growth and improve margin discipline helped rebuild trust.
Ebay became a clear model: a profitable business based on transactions with a scalable model. These survivors taught investors to evaluate Internet companies more such as traditional companies, with income states that imported.
The Rise of Saas and Unit Economics (2005-2015)
In the mid -2000s, a new model, the software as a service (SAAS) emerged, and with it came a new valuation language. Instead of depending on unpredictable advertising or retail margins, Saas companies offered predictable recurrent income currents, a change of play for both founders and financiers.
This era gave rise to metrics such as:
- Annual recurring income (RR) and monthly recurring income (MRR)
- Customer acquisition cost (CAC) and life for life (LTV)
- Rotation, net retention and the rule of 40 (growth + margin ≥ 40%)
These unitary economy allowed a more clear vision of the health and operational scalability of a company. Investors began to assess the efficiency of recurrent growth and income, rewarding companies with sustainable and high margin models and a strong customer adhesion.
Saas companies may not be profitable, but only if their metrics told a clear story: to acquire customers at low price, keep them for years and expand the participation of the wallet over time. This approach became the backbone of modern technological assessment and remains a dominant lens today.
The era of the platform: network effects and ecosystem value (2015-present)
For the 2010 decade, companies such as Facebook, Google, Uber and Airbnb redefined how the online value was seen. These were not just business, they were platforms. Their energy lies in aggregation, data control and the effects of the network that made them more and more dominant the more they grew.
Assessment models evolved accordingly. Analysts began to measure:
- Network effects (value that grows with each new user)
- Ecosystem depth (third -party developer activity, markets, accessories)
- User participation and data lock
The companies were now rewarded not only by income, but by the construction of the infrastructure on which others depended. This was a qualitative change, valued strategic positionNot only cash flow.
Today’s Internet giants: profits, efficiency and graves of AI
In the 2020s, the technological assessment matured. Public investors now focus on operational efficiency, profitability and free cash flow. Growth at all costs is out; The “40 rule” is in (says that the growth rate of a company plus the free cash rate should be the same or exceed 40%).
Companies are assessed according to the specific performance of the sector: SAAS has its own criteria, others, even others. Meanwhile, intangibles such as patented models, data ownership and infrastructure mouths are increasingly central to the way in which technological leaders have a price.
In summary, the assessment became more specialized and more rational, adapted to what really drives value in each digital sector.
What this means for blockchain
Despite all this progress, block chains remain in the valuation limbo. We see attempts to apply traditional metrics, such as DCF (discount cash flow), validator income or protocol rates, but often they lose the point. This is the equivalent of valuing Amazon in 1998 for its shipping costs.
Block chains are public infrastructure, not private companies. Many trust tokens subsidies or emissions that inflate income but do not reflect true demand. In addition, as decentralized systems, they are not designed to extract profits, but to allow coordination without economic permission and activity without trust.
Other assessment methods have emerged, each that offers part of the puzzle:
- MSOV (Store of Value) models of MSOV value a chain for how your token is affirmed or deposited in Defi. Useful, but static.
- Ochain’s GDP aims to measure economic production between applications and chains. Intelligent in theory, but difficult to normalize and easy to distort.
None of these models have become dominant, integral or widely accepted. And the appearance of the blockchains data layer still lacks in any framework.
A new lens: value speed and flow
To advance, we need models that reflect what really blockchains really do. That is why I have proposed an assessment framework based on speed and flow, a measure of how money and assets move through a blockchain economy. It focuses on use patterns, transactions loops and capital reuse, more similar to economic circulation than static metrics, and has parallels with the most mature era of the Internet era, the last border of the valuations of the digital economy.
This model examines:
- Rotation and speed of stablecoin
- Defi loans, trade, guarantee
- NFT Trading Dynamics (Purchases, Royalties)
- Bidirectional layer asset flows
- Real asset tokenization volumes (purchases, royalties, appreciations)
- Real capital formation and reuse in all applications
- Half exchange of rates to collaborate, resolve or join assets and transactions
This approach offers a native and resistant shape to measure the blockchain value. It focuses not only on what is in the system, but what moves and movement is the clearest sign of trust, utility and relevance, as well as the speed of real money is a commonly accepted measure of the vitality of an economy.
Conclusion: Build the model that the future deserves
Internet taught us that each technological change demands a new financial lens. The first models will always be clumsy, but the worst mistake is to continue with the frames that no longer fit.
Blockchains is still looking for your legitimate assessment narrative.
The assessment frames of the future will be built, they will not be inherited. And as the first Internet investors had to invent new tools to understand what they were seeing, the world blockchain must now do the same.
If we do it well, we will not value block chains with greater precision, we will unlock a deeper understanding of its economic and social potential.
