Expansion of Friend.tech's Economic Model: What Kind of Price Curve Does SocialFi Need?

IntermediateDec 31, 2023
This article analyzes the economic model of Friend.tech and examines the potential influences and scenarios for the economic model of SocialFi.
Expansion of Friend.tech's Economic Model: What Kind of Price Curve Does SocialFi Need?

I. Price Curve Comparison & The Cost of Changing Slope

Since October, the competitive landscape of SocialFi has become increasingly clear, with some competitors gradually fading from the market. Reflecting on the development of Friend.tech, its economic model, particularly the pricing curve, played a crucial role. Specifically, Friend.tech’s pricing curve has several characteristics:

  • Differential positivity ensures that prices rise continuously and increasingly rapidly as the number of participants grows, guaranteeing early adopters profit.
  • A target of 16,000 users achieves a reasonably sustainable community size.
  • With increasing numbers (especially beyond 100–200), the curve steepens, causing higher price volatility and a gradual reduction in sustainability.
  • The far-left part of the curve represents the most profitable buying interval, but this portion is dominated by bots, creating a revenue form akin to “MEV.”

For more details, see:

Detailed Analysis of Friend.tech’s Economic Model: Game Theory, Expected Value, and the Illusion of the Demand Curve

The economic model of Friend.tech appears straightforward:

  1. The price of Keys increases with quantity.

  2. Each transaction incurs a 10% fee, split between the protocol and the Key issuers.

  3. Points will be distributed to users over the next six months.

Competitors like Cipher, PostTech, and NewBitcoinCity have entirely retained FT’s formula. All protocols still construct quadratic functions, preserving characteristics like positive first and second derivatives and a zero third derivative, ensuring the continuation of FT’s FOMO/profit effect.

The curve changes in New Bitcoin City are mainly due to the variation in the base currency and fluctuations in BTC prices. SA and TOMO have made adjustments to the curve form. For instance, SA added a linear and a constant term on top of the quadratic term (K²) and reduced the coefficient of the linear term. This theoretically leads to a flatter overall curve (slower rise) and an increased initial price. However, the change is subtle due to the minimal value of SA’s constant term. TOMO’s modification is simpler, reducing the coefficient of the quadratic term by approximately 73%.

It’s evident that SA and TOMO essentially modified the growth speed of the curve. With the same Key supply, the prices for SA and TOMO are lower, with SA’s price level being about 15%-20% of FT’s and TOMO’s at 37% of FT’s.

Overall, such changes are not particularly innovative. A flatter price curve is a double-edged sword for imitation platforms. On one hand, FT provides a benchmark of value, and it’s logical for Keys on imitation platforms to be priced lower than on FT itself, bringing better acceptance and larger user capacity. On the other hand, a flatter curve means a weaker wealth effect, which has been a key factor in attracting hundreds of thousands of users to FT.

Of course, a steep price curve comes at a cost. The flip side of spiraling upwards is a potential spiraling downwards. In the past week, Friend.tech’s TVL dropped from 27,000 ETH to 21,000 ETH, a decrease of less than 20%, but the resulting price collapse and the consequent betrayals were far more significant.

II. FT’s Grey Rhino: Net Capital Outflow

The bots and high transaction fees in FT are visible issues, and the resulting net capital outflow is killing Friend.tech. As shown in the graph, Friend.tech’s TVL originates entirely from user deposits. The PnL generated from user transactions and royalties earned by issuers, if reinvested instead of withdrawn, still remain within the protocol. However, the “MEV income” earned by bots and the transaction fees collected by the protocol directly result in net capital outflows.

Quantifying the “MEV income” earned by bots is challenging, but a notable example occurred in September when DWF founder AG joined FT. The initial purchase price displayed on the FT frontend was 0.4 ETH, which means bots directly purchased over 80 Keys at an average price of 0.135 ETH each. These Keys were gradually sold over the next 48 hours at prices ranging from 1.1 to 1.5 ETH. Based on this, it’s estimated that bots earned approximately 100 ETH in AG’s Room, with all profits originating from user losses.

Transaction fees are more quantifiable. Data from DUNE indicates that as of October 25, the cumulative transaction fees belonging to the project’s developers totaled 13,840 ETH. With a peak TVL of 27,000 ETH, users have deposited at least 40,000 ETH. Ignoring bot MEV income, net withdrawals from KOL royalties, and net outflows from scam accounts, FT has already earned over 30% of the users’ principal in just three months.

When TVL rises, users do not feel the impact as much. However, when TVL starts to decline or even plateau, the effects become markedly severe. Protocol skimming, bot MEV income, net withdrawals from KOL royalties, and net outflows from scam accounts are all non-trade related losses. If we conservatively estimate the latter three items at 5,000 ETH, then the total user deposit amounts to 45,000 ETH.

Previous articles mentioned that the nominal value of Keys is about three times the real TVL. Therefore, when TVL was 27,000 ETH, the nominal value of Keys was approximately 81,000 ETH, giving users an average profit of 80%. However, when TVL dropped to 21,000 ETH, the total nominal value of all Keys fell to 63,000 ETH, reducing the average user profit to 40%. It’s evident that the return rate of Keys inherently carries leverage. If TVL continues to fall to 15,000 ETH, the total nominal value will equal the total invested capital, and considering transaction costs and bid-ask spreads, users will enter a state of overall loss.

There’s already a trend of FT’s “33 Consensus” breakdown spreading to Tomo. If the high extraction by the protocol and bots continues, collapsing FT and other SocialFi platforms is just a matter of time. Moreover, with declining return rates, the disintegration trend will accelerate. There were hopes that Friend.tech would address the issues of protocol skimming and bot activities, but currently, no changes seem to be happening. Recent changes in the scoring rules have objectively led to increased transaction friction due to users engaging in point farming. Furthermore, founder 0xRacer has extracted high transaction fees earned from his Keys.

III. How Can the Curve Be Improved?

Further consideration suggests that if we maintain the pricing formula in the form P = K²/C + D (where C and D are constants), the following factors need consideration in setting the pricing formula:

  • Curve Growth Speed & Price:

Faster growth speeds induce FOMO, primarily achieved by increasing the constant C. Competitors generally reduce growth rates for a smoother curve. However, this approach is mainly to maintain a “low price” for Keys, as copycat platforms will have a significant TVL gap with FT. Therefore, a lower price for the same holder base is more reasonable.

  • Community Capacity

The curve growth rate also determines the maximum number of people the community can accommodate. For a higher capacity, the curve needs to be flatter, which can be achieved by increasing C, setting a piecewise function with latter sections being flatter, and calculating the ratio of P to FT-Key P for the same X value.

  • MEV Value at the Left End of the Curve

To address the “MEV problem” caused by bots, options include increasing the D intercept to ensure an initial price greater than 0 (Tomo has set a low D value):

(1)Adding a flatter or horizontal curve at the left

(2)Fixed-price IDO (pre-sale system, differing from first-come-first-serve)

(3)Allowing room owners to pre-purchase.

From the perspective of curve shape, there are two main approaches for improvement. The first approach involves directly changing the parameters C and D. This is currently the most common method of enhancement, where altering the constant D can also address the issue of MEV (Maximal Extractable Value) to some extent.

The second approach is to implement piecewise functions. This method allows for different parameters in varying price ranges, achieving specific goals. For example, setting a flatter or even horizontal curve in the first half of the graph can combat MEV or facilitate IDO (Initial DEX Offering) launches. Notably, the IDO model is particularly effective in addressing Bot MEV and launch failures, as prominently observed on the Tomo platform. However, this approach comes with trade-offs. Using a flatter curve at the left end can significantly diminish the wealth effect at the opening, and it’s crucial to consider the quantity of supply on the left end. Excessive supply might deplete potential buyers or the wealth effect.

IV. Beyond KOLs, What Else Can Key Embody?

An undeniable reality is that the “services” or “information” provided by most Room Owners are insufficient to justify the value of Key, often leading to its overvaluation. This issue stems from the speculative and score-boosting demands on Friend.tech, which overshadow the actual utility needs. Both FT and its imitators have made curve choices based on business objectives.

Most people perceive Key merely as a social token, but in reality, it can represent any asset. Friend.tech offers a concept: integrating the issuance and trading of assets into “Social” as “Fi,” thus completing the SocialFi loop. For FT and most imitators, Key represents the personal brand or reputation of a KOL. However, this doesn’t limit SocialFi’s scope. Even based on FT, Key can encapsulate any asset, such as equity or tokens of Web3 projects (which is already happening), where Key represents Tokens or shares. Alternatively, utilizing FT for IDO, Key could represent investment shares or future claims (which might soon be implemented).

Currently, FT and its imitators offer overly simplistic functions, falling short in addressing certain derivative demands. Another strategy is to introduce “Asset Issuance” into existing Web3 social/content products (like DeBox, CrossSpace, etc.). DeBox, for instance, positioned as the most native DAO governance platform, has already built a social platform encompassing chat, updates, and community features based on DID. It provides functions like voting, proposals, Token authorization checks, and trading. With a strong user base, social connections, information, management tools, and transactional capabilities, DeBox, boasting 1.5 million registered users and over 100 million daily messages, is highly scalable and naturally suited to integrate effective asset issuance solutions and economic models and price curves that match its business type.

DeBox Interface

The assets here include but are not limited to specific content, decentralized groups, or even MEMEs with no substantial meaning but shared group intent. A range of social tools and infrastructure services for these assets can complete the value loop of Key.

Finally, the major difference between Fi and Ponzi lies in whether the assets exist and have value, a fact we must never overlook.

Loki, Researcher at ABCDE

About ABCDE

ABCDE is a VC focusing on leading investments in top Crypto Builders, founded by Huobi Cofounder Du Jun with over ten years in the Crypto industry, and BMAN, a former Internet and Crypto entrepreneur. The co-founders of ABCDE have established companies in the Crypto sector worth billions of dollars from scratch. As entrepreneurs ourselves, we understand entrepreneurs better. We have built a comprehensive ecosystem for ABCDE’s Builders, including a listed company (1611.HK), exchange (Huobi), SAAS company (ChainUP), media (CoinTime.com), and a developer platform (BeWater.xyz).

Twitter:https://twitter.com/ABCDLabs

Website:www.ABCDE.com

Disclaimer:

  1. This article is reprinted from[ABCDE]. All copyrights belong to the original author [Loki]. If there are objections to this reprint, please contact the Gate Learn team, and they will handle it promptly.
  2. Liability Disclaimer: The views and opinions expressed in this article are solely those of the author and do not constitute any investment advice.
  3. Translations of the article into other languages are done by the Gate Learn team. Unless mentioned, copying, distributing, or plagiarizing the translated articles is prohibited.
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