X2earn’s Product Methodology: Frequency, Capital, and Work
1. X2earn: One of the Easiest Web3.0 Paradigms to Understand
2. X2earn Product Methodology (Assumptions Based on 2022 Study)
3. Which Scenarios are Suitable for X2earn?
4. Conclusions to the Above Scenario
01 X2earn: One of the Easiest Web3.0 Paradigms to Understand
If Web3 is an obscure concept, then X2earn is the most down-to-earth product form. Illustrated by A16Z in their blog, people’s future work prospect is to get steady benefits from participating in the DAO and crypto network. In this paradigm, the Web2.0 concepts of users, products, and PMF (Product-Market-Fit) are shifted into its Web3.0 version with participants, networks, and PCRF (Participant-Cots-Reward-Fit). As shown in the following figure:
Let’s discuss X2earn more broadly. Satoshi Nakamoto’s peer-to-peer cash system is a standard X2earn model. When you participate in the validation of calculations and contribute to the computing power, you have rewarded in the mining process. The most important participants of the network are the “miners,” who appear even before the “users.” Network nodes multiply as participants flock to the web because of the profitability and the flywheel effect. The participants eventually reap huge benefits.
Therefore, it is essential to maintain a reasonable cost/reward return ratio.
02 X2earn Product Methodology (Assumptions Based on 2022 Study)
2.1 X2earn is a Factor Market
We summarized over 50 X2earn products in the market. The products have a shared pattern: the network bootstraps with its token and rapidly expands its market capitalization to incentivize participants (to invest work or capital) until the network effect and monetizes the gamification, fee-based services, or externality economic activity.
For participants, X2earn has created a new “factor market” where players contribute capital and work to create value and receive practical economic benefits.
2.2 X2earn Product Design Life Cycle
1. Phase of Market Cap Expansion
The period of attracting participants. The volume of participants determines the ceiling of the network. Monthly Activate User (MAU) is the most direct and clear indicator. If a product’s MAU can reach 1 million, it can be described as a “phenomenon”; if MAU reaches 10 million, it can be regarded as a potential contender for Ethereum!
2. Phase of Market Cap Contraction
Market cap inflation will inevitably end. Early-stage participants will often leave the market after taking profits and just waiting and watching. We need to focus on whether there are long-term loyal participants who stay when the market cap contracts and what is the value of their contribution (per player transaction)?
3. Phase of Consolidation
A long and unsettling phase. The network is consolidating for actual demand from users. We need to focus on how the network will evolve and how to develop more ecological products and retain participants and users.
4. Phase of Secondary Growth
It is characterized by explosive growth, the second time to reflect the value of the network. For the game, it is the player’s initiative to pay; For products with the business model, it is being actively paid and used by external users; For a Meme or currency, it is the external economic activity taking shape.
5. Phase of Death Spiral or Turning-Zombie
It is possible that the network did not find a business model and sufficient positive externalities and entered a “death spiral” or “zombie state”.
In short, successful projects continue to attract participants and failed projects lose them.
03 Which Scenarios are Suitable for X2earn
3.1 The Participant-Cost-Reward-Fit Triangle
If we were to design X2earn from the participant’s perspective, a rational player would consider three dimensions, including:
“How often do I get work confirmation and rewards?”
“How much principal do I need to invest?”
“What kind of work do I need to devote?”
Frequency: the rate at which participants complete tasks and receive rewards. Example: The frequency of participation in a bitcoin miner is approximately every 9 minutes, and the frequency of a run-to-earn model is the interval between each run, which may be several hours a day.
Capital: The financial cost of participating in the network, such as a mining machine, the capital of a liquidity provider, and access to NFTs.
Work: The work cost of participating in the network. Examples include playing games, physical work-outs, and creative jobs requiring trained skills. In our research, higher frequency by default relates to more standardized work that can be mechanized or orchestrated at a massive scale; lower frequency relates to more individualized work that requires specialized effort, knowledge, and credentials.
3.2 Classification of Frequency, Capital and Work
The different trade-offs of this triangle can produce eight scenarios, and they are the following:
Let’s see which ones are easier to design as X2earn models.
- High Frequency, High Capital, and High Work
This type includes Bitcoin and all mining networks. There are Chainlink, the Graph and Render Network in the new generation, etc.
Pros: It creates and captures economic value, and has the opportunity to scale as a “fat protocol” which accrues enormous value.
Cons: It is highly involuntary and likely to develop into an arms race. Participants who have the greater computing power and capital power always prevail. Organized institutions are favored, but retail participation is low.
Design ideas: To introduce more mathematical difficulty and randomness in the algorithm of the validation work so that the asymmetric advantage of prominent participants does not grow disorderly, leading to a lower “Gini Coefficient”. Although this kind of network is the ultimate goal of most product designers, it is not suitable to be used at the beginning because it inhibits the participation of many participants, and it is easy to turn the product into a game controlled by a few people. Therefore, designers should lower the barrier in the early stage and gradually improve it later.
2. High Frequency, High Capital, and Low Work
This category is represented by POS networks, staking-related products, liquidity mining, and networks that use proof of assets as a barrier to entry.
Pros: Participants do not have to devote much work, the efficiency of capital is amplified, and it is a very efficient way to raise capital and liquidity for the network.
Cons: There is a risk of being held captive by whales. As the revenue diminishes, the whale withdraws its capital for benefits, causing the network’s value to fall and enter a death spiral or zombie state.
Design ideas: To introduce more participants in the network, increase participants’ loyalty (using POAP for governance and participation rewards), and long-term sinking costs (e.g., introducing a voting rights mechanism). Introducing more workload and work is an inevitable direction that needs more attention.
3. High Frequency, Low Capital, and High Work
Typical models in this category are play-to-earn, move-to-earn, speak-to-earn, learn-to-earn, drive-to-earn, share-house-to-earn, etc.
Pros: Participants don’t need to be rich, as long as they have the labor to pay for the rewards.
Cons: Work is not easily quantifiable, and its business model needs to be found.
Design suggestions: To introduce more anti-cheating technologies such as intelligent hardware and oracles and work on economic value creation. For example, product managers can design a more complex and randomized gamification business ecology. While from another perspective, they should also explore the economic value of externalities.
4. High Frequency, Low Capital, and Low Work
Representative models in this category are sleep-to-earn, read-to-earn, share-to-earn, comment-to-earn, etc.
Pros: The bar is relatively low, resulting in a substantial potential group of participants.
Cons: The profile of participants could be very diverse given the work is generic (anyone can “like”, “comment”, “share”, etc.), and the value of work generated is low, as is the capital contribution. It is easy to become a low-value network.
Design ideas: To raise capital or work requirement of participants and find vertical areas where such actions (“X”) are more meaningful. Appropriately increase the capital investment so that labor participants will not cheat or else forfeit the capital.
5. Low Frequency, Low Capital, and High Work
The low frequency of high work means that the nature of work is more specialized and difficult. The threshold for participation in the project is higher, requiring players to master specific technical skills. Representative models are: research-to-earn, code-to-earn, create-to-earn etc.
Pros: Precise users, highly skilled labor, and the existence of a business model.
Cons: the participants are elitist, making it difficult to scale up, and the tasks are challenging to quantify and reward.
Design ideas: To split a complex skill task into several simple tasks and then combine them.
Instead of linking all the stock analysts to make a research-2-earn network, the product manager should break up the research into two parts. One part is a read-2-earn which is only responsible for collecting information, and the other part is comment-2-earn, which gives investment advice, simplifying the complex research labor into two different modules, speeding up the settlement frequency, and simplifying the process.
6. Low Frequency, High Capital, and High Work
This model is uncommon, similar to Venture DAO, where participants jointly contribute money, make investment strategies, and share dividends.
Difficulties: Confirmation and incentive frequency is very low, the workload is difficult to measure, and work tasks are extremely non-standardized, leading to difficulty in reaching consensus. So it isn’t easy to form a scale.
In the last two “low frequency, high principal, low work” and “low frequency, low principal, low work” scenarios, the value of participants’ contribution is too low, and we don’t see the value of exploring such a scenario at the moment.
4.1 Choose Higher-Frequency Models
Frequency is the most significant force in the design of X2earn networks. If the confirmation and reward frequency of the network is too low, there are two fatal problems:
l Participants could not give timely feedback. They would migrate to other competing products;
l Due to the low frequency, it is difficult for network administrators to detect cheating and attacks timely, leaving the chance for the liars to take advantage of their unpreparedness to destroy the network at one time.
4.2 Splitting & Recombining Complex Tasks
If we see that a project can be done with an x-2-earn model, then we must split up the workflow of the project: where high-frequency and quantifiable actions can be turned into x-2-earn network models; and non-quantifiable, complex, and personalized actions retained for centralized business as a particular participant in the network.
X-2-earn is a factor market, so it is crucial to consider the efficiency and low friction of the market.
4.3 Design the “Work Lego”, not Just the “Money Lego”
We firmly believe that the next wave of X-2-earn networks should be designed around work for the following reasons：
l Networks are more lasting. Human nature is relatively easy to accept the loss of money, but difficult to let go of “wasted effort”.
l Networks are more valuable. Countless amounts of work can create goods and services.
l Networks can create positive externalities. It is easy to create a more affluent upstream and downstream industrial chain.