Friend.tech's Complex Economics: The Key to Success or a Ponzi Scheme?

Table of Contents

In the world of social finance, Friend.tech has made quite a splash with its unique economic model. Some hail it as a revolutionary social product, while others label it as an elaborate Ponzi scheme. In this article, we will delve into the intricacies of Friend.tech's economic model, exploring its key features, the mathematics behind its price dynamics, and the implications for users and investors.

I. Creating a Successful Ponzi Social Product

Understanding the Friend.tech Economic Model

Friend.tech's economic model appears deceptively simple, but it holds several key elements that set it apart from traditional financial systems. To grasp its inner workings, one must look through the lens of its creators and understand their motivations.

The Role of Ponzi Attributes

Friend.tech embraces a unique approach, incorporating certain Ponzi attributes into its design. These attributes aim to kickstart the platform, generating a sense of excitement and interest among users.

Key Price Increase Mechanism

At the core of Friend.tech's economic model lies the mathematical model (S²) / 16000, which underpins the increase in Key prices as the platform gains more users. This mechanism creates a dynamic where each new participant accelerates the rate of price growth.

The Economy Cycle

Optimists see Friend.tech as a social platform, while pessimists view it as a gambling platform. Both perspectives play a role in the platform's development, as users and Key issuers engage in transactions that drive its economy.

Revenue-Sharing Model

Friend.tech's economic model introduces a revenue-sharing model that distributes fees equally between the protocol and Key issuers, promoting engagement in the early stages when service offerings may be inconsistent.

Point Airdrops

Point airdrops serve to stimulate demand and blur the lines between users' speculative, usage, and investment motivations.

II. Examining Transaction Friction

In Friend.tech, the economic model's success and functionality are intrinsically tied to transaction friction, particularly the 10% transaction fee. This section delves into the complexities of this fee structure and its implications for participants on the platform.

The 10% Transaction Fee

Friend.tech's economic model revolves around a 10% transaction fee, which impacts every participant engaging in key transactions. This fee structure is a fundamental aspect of the platform's design and plays a crucial role in its economic dynamics.

Consider this scenario: you enter Friend.tech with 1.1 ETH. When you purchase a Key, you are subject to a 10% fee, which means that with your 1.1 ETH, you can buy a Key worth 1 ETH. Your Room's Value now stands at 1 ETH. However, when you decide to sell the Key, you will incur another 10% fee, resulting in an actual liquidation value of 0.9 ETH. From the moment of purchase, the 10% transaction fee becomes an unavoidable reality, leaving you with a 19.2% loss. You'd need a 22% increase in Key price just to break even. This 19.2% loss isn't challenging to calculate, but it becomes the second red herring in Friend.tech's model, undermining the initial optimism of participants.

Book Value (BV) vs. Expected Value (EV)

To comprehend the economic intricacies of Friend.tech, it's essential to understand the distinction between book value (BV) and expected value (EV). These concepts provide a lens through which participants can evaluate their investments and returns more accurately.

Let's illustrate this with examples: Imagine Alice, Bob, and Cora pooling their resources to buy a cow, a duck, and an egg. They agree that the first one to exit takes the cow, the second the duck, and the last the egg. All three initially believe they have equal rights to the cow, but, in reality, their rights are equal only in principle. There are six possible outcomes, with each of them taking one of these outcomes: 1) cow, duck, egg; 2) cow, egg, duck; 3) egg, duck, cow; 4) egg, cow, duck; 5) duck, cow, egg; 6) duck, egg, cow. As a result, Alice's true ownership of the cow, duck, and egg stands at 1/3 for each, indicating that the initial belief in equal rights was an illusion. In this context, the EV is the mathematical expectation of their rights summed up.

In the Friend.tech context, this concept translates to recognizing that each new participant dilutes the EV of previous holders, introducing an essential dynamic. This core aspect of Friend.tech's economic model serves two primary purposes:

1. Confuse BV and EV to create an illusion of wealth: The platform's design may lead participants to overestimate their ownership or potential gains.

2. Utilize the EV of later participants to provide profits to earlier users: Friend.tech's model redistributes wealth from latecomers to early adopters, making the platform attractive for the latter group.

Implications for Investors

As participants engage with Friend.tech, evaluating their investments requires a nuanced understanding of the relationship between BV and EV. These concepts come into play when deciding whether to buy or sell Keys, as well as when assessing the expected returns and potential losses.

Investors should be mindful of the fact that purchasing Keys in the flatter part of the curve results in not only paying a 10% fee upon buying but also a 10% forward fee upon selling, leading to an immediate loss of approximately 70% of their EV. Thus, the Room Value shown by Friend.tech appears overly optimistic. To account for this, a more cautious approach is to evaluate the Value of the Key held by multiplying the Room value by around 30% (EV).

This approach underscores why many participants appear to have realized substantial paper returns over time, as their perceived gains were intertwined with the platform's intricacies, including BV, EV, and transaction friction.

The complexities surrounding transaction friction and the interplay between BV and EV underscore the need for investors to make informed decisions, evaluate potential returns accurately, and navigate the platform's economic dynamics effectively. Friend.tech's economic model presents a unique set of challenges and opportunities, requiring participants to engage with a keen understanding of these principles.

III. The Limits of Growth

Friend.tech's unique economic model, blending elements of social finance with a touch of Ponzi dynamics, presents intriguing challenges and limitations as it strives for sustainable growth. In this section, we delve into the intricacies of these constraints and explore the delicate balance Friend.tech must maintain to avoid potential pitfalls.

Break-Even Analysis

Breaking even in Friend.tech is a multifaceted endeavor. The criteria for achieving this balance differ significantly depending on the perspective one adopts—book value (BV) or expected value (EV). From a BV standpoint, breaking even may appear straightforward. However, considering the complexities of Friend.tech's economic model, where speculative demand plays a significant role, breaking even becomes a more intricate proposition.

The (N, M) Dynamic

Friend.tech's economic model exhibits an intriguing dynamic as growth progresses. In this model, N represents the total number of participants, and M represents the threshold beyond which rational participants cease to buy Keys. This threshold is the turning point where the equilibrium of supply and demand begins to shift. As N approaches M, buyers after N-M find it increasingly challenging to break even. This phenomenon introduces a cyclic nature to the platform, where rational actors opt to abstain from buying Keys, putting downward pressure on prices.

To better understand this dynamic, we can draw parallels to the "2/3 Game" in game theory. In the context of Friend.tech, when the number of participants is high, the inherent drive for individual gain can lead to mutual suspicion and an environment where the Nash equilibrium defaults to a state where each participant experiences a loss. In other words, the (N, M) dynamic reflects an ongoing challenge that Friend.tech faces, where the pursuit of profit becomes increasingly elusive for latecomers as the platform grows.

Downward Price Pressure

As N approaches M, and participants become more reluctant to buy Keys, the platform experiences downward price pressure. In normal circumstances, this downward movement might not pose a significant issue, but Friend.tech also contends with an abundance of bots. These bots often seize opportunities in the lower price range of the new market, further eroding the expected value (EV) of users.

The interplay between the (N, M) dynamic and bot activity adds a layer of complexity to Friend.tech's growth trajectory. The platform must find a delicate balance to maintain a thriving ecosystem while ensuring that latecomers can still reasonably expect to profit. This balance will be instrumental in addressing concerns about the platform's sustainability and long-term viability.

Reliability of (3, 3)

The (3, 3) model within Friend.tech introduces several complexities that merit closer examination. One significant challenge is the asymmetry often present in (3, 3) agreements. If one participant holds a Key valued at 3 ETH while another holds a Key valued at 0.1 ETH, their contributions to the agreement become highly skewed. The former contributes significantly more to the fees, creating an inherent imbalance in the agreement.

Moreover, in a multi-participant setting, the (3, 3) model becomes increasingly unstable. The emergence of additional participants amplifies this instability, following the principles of evolutionary game theory. This instability results from the natural inclination of individuals to seek strategic advantages when such opportunities arise, a behavior driven by the pursuit of profit.

This situation mirrors another classic model in game theory—the evolutionary game model. In the context of Friend.tech, as the number of participants increases, the likelihood of someone attempting to exploit the system for personal gain also rises. If such attempts result in losses for others, the motivation for further opportunistic behavior intensifies, creating a cascade of suspicion and mistrust among participants. Ultimately, this pattern leads to a state where the only Nash equilibrium is one of collective loss, represented by the (-3, -3) scenario.

It's worth noting that during bullish market cycles, the (-3, -3) scenario tends to be less frequent due to the overall optimism among participants. However, as the platform matures and growth slows, this scenario becomes more pronounced, leading to a steady decline in Key prices. The consequences are amplified by the prevalence of bots that dominate the lower price range of the market, further eroding the expected value (EV) of users.

Navigating the intricate web of the (3, 3) model within Friend.tech is a formidable challenge, particularly in a dynamic ecosystem with a diverse array of participants. Understanding the intricacies of this model is pivotal for those engaging in agreements with other users and for anyone seeking to secure their investments and interests in the platform.

IV. The Future of FT

Utility Demand vs. Speculative Demand

We'll explore the potential for Friend.tech to move beyond its Ponzi-like dynamics by focusing on utility demand. Differentiated services offered by Room owners may play a pivotal role in transitioning the platform from speculation to genuine utility.

High Fees + Bots Are Killing the Game

Despite the promise of Friend.tech's economic model, high fees and rampant bot activity have driven some users away. We'll delve into the economic implications of these issues and the potential for the platform to address them.

Conclusion

Friend.tech's economic model is a subject of fascination and controversy, with its unique attributes and challenges. Understanding the intricacies of this model is essential for users and investors to navigate the platform effectively. As the crypto and DeFi landscape evolves, Friend.tech's journey provides valuable insights into the evolving dynamics of social financial platforms.

About Orochi Network

Orochi Network is a cutting-edge zkOS (An operating system based on zero-knowledge proof) designed to tackle the challenges of computation limitation, data correctness, and data availability in the Web3 industry. With the well-rounded solutions for Web3 Applications, Orochi Network omits the current performance-related barriers and makes ways for more comprehensive dApps hence, becoming the backbone of Web3's infrastructure landscape.

Blog

Friend.tech's Complex Economics: The Key to Success or a Ponzi Scheme?

I. Creating a Successful Ponzi Social Product

II. Examining Transaction Friction

III. The Limits of Growth

IV. The Future of FT

About Orochi Network

The Foundation of Decentralized AI: A Data Provisioning Layer

Understanding Dust Attacks in Web3: A Growing Concern

Advancements in Homomorphic Encryption: Large Look-up Table Evaluation

New Paradigm: Understanding the Intersection of AI and Crypto

Orosign Wallet