Valuing blockchain networks today already seen For anyone who lived the first internet era. In the 1990s, analysts, investors and founders struggled to apply financial models familiar to a radically unknown technology. Companies with a little more than a website and a pitch deck were evaluated by hundreds of millions, sometimes billions, on the basis of something as intangible as “eyelashes”.
It did not end well. And yet, with hindsight, these chaotic first years offered precious lessons: technology evolves faster than finance, and evaluation models must possibly adapt to the form of innovation.
Today, we are faced with a similar dilemma in the blockchain space. Despite increasing adoption, the maturation of infrastructure and undeniable cultural and economic impulse, there is still no largely accepted or standardized means of evaluating a blockchain network. And the few models we have, although useful in the sense, remain defective or incomplete.
To understand where we could go, it is worth seeing how we arrived here.
The first wave of evaluation on the Internet: eyeballs, not gains (mid -1990s 2000)
In the middle at the end of the 1990s, the Internet was a border. Investors did not know what “success” for digital companies would look like, so they relied on everything they could measure: viewed pages, impressions of banners, unique visitors or monthly active users (MAUS). These gross attceptions for attention have become de facto measures for value. The logic was simple: if millions of people aimed at your site, monetization would eventually follow.
The evaluations have skyrocketed. Startups like PEDS.com (see image), Webvan and Etoys have noted hundreds of millions of people on the promise of domination. But income was a reflection afterwards, and profitability was a punchline. When the Dot-Com bubble broke out in 2000, it has become clear that attention without monetization is a bad base for the value of the company.
Post-Crash realignment: income and margins (2001-2005)
After the break -up of the first internet bubble, the feeling of investors has radically changed. The market required proof, not just vision. From 2001, companies had to generate significant income, show raw margins and evolve towards profitability.
This period saw a ruthless weeding of unsustainable models. Only companies with real products, real customers and realistic finances have survived. Amazon, for example, began to move the concentration of investors of the abstract future potential to real operational performance. Its ability to show high -level consistent growth and to improve the discipline of margins has helped to rebuild trust.
Ebay has become a paragon of clarity: a profitable company based on transactions with an evolutionary model. These survivors have taught investors to assess internet companies more such as traditional companies, with states of income.
The rise of the SaaS and the unitary economy (2005-2015)
In the mid -2000s, a new model emerged, the software as a (SaaS) service, and with it came a new language of evaluation. Rather than relying on unpredictable advertising or retail margins, SaaS companies have offered predictable recurring income flows, a change of play for the founders and financiers.
This era gave birth to measures such as:
- Annual recurring income (arr) and monthly recurring income (MRR)
- Customer acquisition cost (CAC) and life value (LTV)
- Churn, net retention and the 40th rule (growth + margin ≥ 40%)
These unitary savings have enabled a sharper overview of the business and operational scalability of a company. Investors have started to assess growth efficiency and recurring income, by rewarding companies with sustainable and high margin models and high collaboration of customers.
Saas companies could be not profitable, but only if their measures tell a clear story: acquire customers at a lower cost, keep them for years and develop Wallet’s sharing over time. This approach has become the backbone of modern technological assessment and remains a dominant objective today.
The era of the platform: Network effects and the value of the ecosystem (2015-present)
In the 2010s, companies like Facebook, Google, Uber and Airbnb redefined what online value looked like. These are not only companies, they were platforms. Their power lies in the aggregation, data control and the effects of the network that made them more and more dominant, the more they grew.
The evaluation models have evolved accordingly. Analysts began to measure:
- Network effects (increased value with each new user)
- Depth of the ecosystem (third-party developer activity, markets, plug-ins)
- User engagement and data locking
Companies were now rewarded not only for income, but for the construction of infrastructure on which others depended. It was a qualitative change, valuing strategic positionNot just cash flows.
Today’s internet giants: AI profit, efficiency, efficiency and moats
In the 2020s, technological assessment matured. Public investors are now focusing on operational efficiency, profitability and available cash flows. Growth is at all costs; The “40 rule” is underway. (It indicates that the growth rate of a company plus the available cash rate should be equal or exceed 40%).
Companies are appreciated according to sectoral performance: SaaS has its own criteria, other e-commerce others, fintech still others. Meanwhile, intangible assets such as owners’ models, data ownership and infrastructure moats are increasingly central to the price of technological managers.
In short, the evaluation has become both more specialized and more rational, adapted to what really stimulates the value in each digital sector.
What it means for the blockchain
Despite all these progress, blockchains remain in the evaluation limbo. We see attempts to apply traditional measures, such as the DCF (cash flow at reduced prices), validator income or protocol costs, but they often lack the point. This is the equivalent of Amazon’s assessment in 1998 by its shipping costs.
Blockchains are public infrastructure, not private companies. Many are based on grants or token emissions that inflate income but do not reflect real demand. In addition, as decentralized systems, they are not designed to extract profits, but to allow coordination without authorization and an economic activity without confidence.
Other evaluation methods have emerged – each offering part of the puzzle:
- The MSOV models (value monetary store) are worth a chain by the way its token is marked out or deposited in DEFI. Useful, but static.
- Onchain’s GDP aims to measure economic production between applications and channels. Smart in theory, but difficult to normalize and easy to distort.
None of these models appeared to be dominant, complete or widely accepted. And the appearance of the blockchains data layer is always absent in any assessment framework.
A new objective: assessment of speed and flow
To move forward, we need models that reflect what blockchains TO DO. This is why I proposed an evaluation framework based on speed and flow, a measure of the way in which money and assets move in a blockchain economy. It focuses on use models, transaction loops and capital reuse, more similar to economic circulation than static measures, and it is parallel to the more mature internet platform, the last border of digital economy assessments.
This model examines:
- Rotation and speed table
- Loans of defi, trading, guarantee
- NFT Trading Dynamics (purchases, fees)
- Bidirectional layer of layer to layer
- Tokenization volumes of real assets (purchases, fees, assessments)
- Real capital training and reusability between applications
- Average exchange costs for assets and transactions guaranteeing, adjusting or punching assets and transactions
This approach offers a native and resilient means of measuring the value of the blockchain. It is concentrated not only on what is in the system, but what moves and the movement is the most clear sign of confidence, utility and relevance, just as the speed of real money is a commonly accepted measure of the vitality of an economy.
Conclusion: Building the model that the future merit
The Internet has taught us that each technological change requires a new financial objective. The first models will always be awkward, but the worst error is to stay with frames that are no longer suitable.
Blockchains are still looking for their legitimate assessment account.
The evaluation executives of the future will be built and not inherited. And just as the first Internet investors had to invent new tools to understand what they saw, the blockchain world must now do the same.
If we get things correctly, we will not just appreciate blockchains, we will unlock a more in -depth understanding of their economic and social potential.
