The driving force behind the evolution of the crypto market is token economic innovation, with algorithmic breakthroughs based on smart contracts playing a key role in the past decade. Early token extensions based on Bitcoin were fairly limited, and the lack of technology and narratives at the time created barriers for token issuance. As Ethereum’s smart contract ecosystem began taking off, some began exploring how to merge smart contracts with token issuance models. The Bonding Curve, one of the first algorithmic innovations on-chain, has profoundly impacted token economics and token engineering. In this article, we’ll examine its core concepts and reflect on its role by analyzing several practical use cases.

From Fixed Supply to Dynamic Supply

Early token issuance models were marked by centralization and chaos. Projects could be launched with nothing more than a basic whitepaper and a pitch deck, making rounds at roadshows and receiving massive token investments in return. But what happened after that? Centralized token models eventually led to price crashes, and without regulation, the resulting market competition collapsed.

Looking back at these token issuance models, many believe that certain features of these early approaches limited the market’s potential for growth:

Centralization: Token issuance and trading mostly occurred through centralized exchanges.

Single Asset: There was limited interconnectivity between ecosystems, and individual blockchains usually only handled single-asset circulation (except for USDT, which circulated via Bitcoin’s OMNI layer).

Liquidity Restrictions: The widespread use of Proof of Work (PoW) systems caused long block confirmation times, limiting on-chain transfers and reducing liquidity.

Fixed Supply Issuance: Projects using fixed-supply tokens could only be distributed through initial allocations or consensus layers. This rigid tokenomics couldn’t adapt to changing market conditions, leaving room for manipulation by project teams and contributing to overinflated token values. These issues were part of the reason why the market couldn’t achieve sustained growth.

In 2017, former Consensys social engineer Simon de la Rouviere proposed a “curated market” system that allows groups to coordinate around a shared goal (and interests) and benefit from the value they co-create. Built on Ethereum’s smart contract framework, this system introduced interoperability between protocols at the base layer. At its core was the idea of “automated coordination,” enabling people interested in the marketization of a particular asset to automatically create that market on-chain. The continuous token model, based on the Bonding Curve, was born from this idea of creating a frictionless, intermediary-free participation model.

Simon outlined several key features of a continuous token model:

Tokens are minted using ETH (or other tokens) at prices set by a pre-defined algorithm.

The token’s price depends on how many tokens are already in circulation (for example, the price of a token = supply²).

The purpose of these tokens is to be “burned” during network operations or services. As tokens are used, supply decreases, lowering the minting cost and ensuring that tokens aren’t only used for distribution.

This model shows that the Bonding Curve offers a new, decentralized token issuance approach, providing applications with a more flexible way to manage supply. Next, we’ll explore several real-world use cases to analyze how these algorithms function in practice and discuss potential future applications of the Bonding Curve.

Curation

One of the primary use cases for the Bonding Curve, as Simon initially envisioned, is in curation. In previous curation systems, problems like poor organizational coordination and insufficient information were common. Let’s take a closer look at two projects.

Ocean Protocol

Ocean Protocol is a decentralized data-sharing protocol designed to facilitate the open exchange of AI data. The token economics in this system aim to maximize the availability of relevant data and services. In traditional curated markets, participants mainly signal their involvement by buying and selling assets. Ocean takes this a step further by linking these transactions to actual service provision, creating what it calls a curated proofs market.

In this market, each dataset is represented by its own “droplet” bonding curve. On the curve, users can stake tokens to earn block rewards (by staking on specific datasets and enhancing their usability) or they can unstake their tokens, with staked “droplets” acting as indicators of user attention.

From the project’s storyline to its token economic model, we see that the project needs a steady initial influx of tokens to ensure that datasets gain equal recognition in the early stages of curation. As the dataset’s usability increases, it becomes more costly for latecomers to join, creating a barrier to excessive concentration of consensus on a single dataset. After 500 “droplets,” the overall cost of minting tokens increases linearly.

In simple terms, users who recognize the value of a dataset early can buy into it through the Bonding Curve and profit later, fulfilling the curation function. However, this curve is still somewhat rudimentary in the curation process because there is a delay between buying/selling tokens and the AI datasets they represent, and the availability of these datasets isn’t always guaranteed, so additional mechanisms are needed to screen for usable data.

Angel Protocol

Delphi Digital, a well-known research institute, developed a token economic model for Angel Protocol, a charitable donation protocol built on Terra, using a Bonding Curve. Angel Protocol features three main participants: donors, charitable organizations, and charity supporters (HALO token stakers who act as curators in the charitable market). The goal is to combine charitable donations with the Bonding Curve to improve the long-term sustainability of the charities involved.

In this model, tokenomics are designed to incentivize actions like curation, donations, and governance, encouraging stakeholder participation over time. Inspired by The Graph (another project that uses Bonding Curves for curation), Delphi created a token curation registry. This system allows users to participate in staking pools and interact with specific charity curves to mint shares in those charities. The curve determines the exchange rate between HALO tokens and charity shares, with curators aiming to maximize their returns by supporting the most beneficial charities. The Bonding Curve helps allocate profits between token trades, with additional shares either distributed or burned.

From a value flow perspective, profits generated from the charity donation fund are split between share distributions (90%) and protocol fees (10%). Of the shares, 75% go to charitable organizations, while 25% is reinvested into the donation fund to ensure long-term cash flow sustainability. Protocol fees are shared between the DAO (the protocol’s governance body) and HALO stakers.

Bonding Curves provide token stakers with multiple income streams (including passive participation, protocol earnings, and even early governance rights) while offering a strong mechanism for charitable causes. Curators can ensure that only the most deserving charitable organizations are featured in the market, and the Bonding Curve helps establish a sustainable economic framework.

Summary

Through the above analysis, we can summarize the role of Bonding Curves in the curation field:

Natural token-based ranking: Market-driven token prices provide insights into user preferences and the status of curated assets within the system.

Early market incentives: Dynamic supply creates real-time pricing incentives, giving early participants a valuable advantage in future protocol use cases.

Healthy value flow: Each purchase corresponds to tangible asset storage, with organic asset appreciation and potential distributions providing positive cash flow for the protocol.

Overall, the Bonding Curve offers an ideal market environment for curation applications and plays a central role in driving protocol growth curves.

Algorithm Control

The Bonding Curve, as an innovation in on-chain mechanisms, has become a core part of the algorithm in several protocols. Below, I will analyze two examples from the realms of on-chain insurance and stablecoins.

Nexus Mutual

Nexus Mutual, one of the pioneers in on-chain insurance, introduced a mutual insurance alternative that provides services for purchasing and underwriting insurance within the protocol. Members can contribute funds to the mutual pool in exchange for NXM tokens, staking their NXM to assess underwriting risks and earn rewards.

A key parameter in the protocol is the Minimum Capital Floor (MCF), which relates to the protocol’s overall fund ratio, commonly referred to as MCR%. For the sustainable development of mutual insurance on-chain, there needs to be a correlation between the equity token (NXM in this case) and the total equity within the protocol, enabling organic growth. Initially, MCF was determined through governance. However, in November 2019, the community voted to automate MCF regulation. On days when the MCR% exceeded 130%, the MCF would automatically increase by 1%.

The team modelled this change, and under a fixed MCF, the overall growth curve was relatively slow. However once the MCF began increasing linearly, the growth rate accelerated. This showcases the appeal of the compound Bonding Curve—when multiple protocol indicators align with token growth, it drives rapid token appreciation.

Fei

FEI was a once-popular algorithmic stablecoin that integrated lessons from past on-chain innovations. When users bought or sold FEI on-chain, the algorithm would adjust the token’s peg.

To create a protocol-controlled value (PCV) and accept new demand, the Bonding Curve became the perfect solution due to its mathematical fairness. Specifically, prices outside the buffer zone could be balanced by minting through the Bonding Curve, which operates as a one-way buy-in curve. For general PCV financing and deployment, additional funds could be raised via Bonding Curves priced in other tokens, and then deployed directly across on-chain protocols. For example, when the protocol launched, it established a unique curve based on the Uniswap ETH-FEI liquidity pool and later added liquidity for multiple DeFi protocols. Each Bonding Curve links to the liquidity of a specific protocol, and this flexible design allows PCV to be creatively deployed and integrated with future DeFi protocols.

Unfortunately, due to the limited use cases for its stablecoin, FEI’s unique mechanisms eventually trapped users, leading to what was referred to as a “water prison.” Ultimately, the protocol merged with Rari Capital but suffered a hack, ending in disappointment. However, before this, FEI collaborated with Ondo Finance to launch Liquidity-as-a-Service (LaaS), partially realizing its vision. The Bonding Curve, as a major factor in PCV construction, significantly contributed to the growth of DeFi that year.

Summary

One of the key strengths of the Bonding Curve is that users can directly benefit from early growth, and when the curve is integrated with other protocol indicators, it creates synergistic effects, amplifying growth. In Nexus Mutual, as staking value increases, token growth becomes exponential. In FEI, the Bonding Curve supports stable protocol inflows while promoting collaboration with other DeFi protocols. Moreover, the “purely on-chain governance” introduced by the Bonding Curve is inherently sustainable—smart contracts don’t rug-pull themselves.

Does buying mean growth?

As the subtitle suggests, does buying always lead to growth? Let’s look at Friend.tech and pump.fun. Both have used the Bonding Curve expertly, but what happened in the end? One applied the curve to social networks and the other to memes. While both achieved significant success in their respective fields, sustainability and externalities seem to have vanished. It feels like we’re repeating the mistakes of the past.

Why? Let’s revisit the characteristics of projects that use Bonding Curves solely as a token issuance tool:

Chaotic issuance: The open curve market has led to scattered consensus since everyone wants to be the initial issuer. Look at the success rate of pump.fun launches, and you’ll understand.

No Value Flow: For projects where token issuance is the only use case, discussing value flow becomes meaningless.

Let’s return to an age-old question: the crypto space is always chasing the next billion users, but finding real use cases has always been fraught with challenges. The irony is that we’re once again falling into the same traps of past issuance models, even though token economics were created to avoid this.

If we list crypto’s strengths, token economics is undoubtedly one of the most important. Real-world use cases are the breakthrough point for tokenomics.

Here are some potential use cases I’d like to highlight:

Fairer (natural) governance: Buying and selling governance indicators might be more intuitive than direct voting (similar to prediction market logic).

Decentralized asset backing: For NFTs or other tokens, Bonding Curves can ensure decentralized distribution and automate value generation. Applied to real-world assets (RWA), this could ensure collateral rates.

Protocol growth: What happens if you combine TVL, yield, or points with a Bonding Curve? Growth on the curve would surely trigger a flywheel effect in the underlying metrics.

Of course, the possibilities for tokenomics go beyond this, and I look forward to seeing more innovative use cases in the future.

Disclaimer:

1. This article was reposted from [Foresight News] under the original title “Breaking the Impossible Triangle: The Ideals and Reality of Web3 Games.” Copyright belongs to the original author, [Pzai]. If there are any objections to this repost, please contact the Gate Learn team, and they will handle it promptly according to the relevant process.

2. Disclaimer: The views and opinions expressed in this article are those of the author and do not constitute investment advice.

3. Other language versions of this article were translated by the Gate Learn team and may not be copied, distributed, or plagiarized without proper reference to Gate.io.