Axie Infinity – Altlift Case Study
Description of Axie Infinity
Axie Infinity was the first Web3 mobile game that exploded in global popularity, albeit for a very short
time. At its peak, the game reached 2.8 million active players and launched a wave of similar
„play-to-earn“ projects at the turn of 2021/2022.
„Play-to-earn“ games were intended to connect the world of cryptocurrencies with mobile and computer
games, allowing players to „earn by playing“ and easily transfer their assets from one game to another. Earning by playing? A careful reader might be puzzled.
The Axie Infinity ecosystem was not sustainable and resembled a Ponzi scheme. Only a handful
of analysts warned about the problems of this game amidst the rapid growth euphoria.
Figure 1 - Develompent of AXS Token Price and Trading Volume
Our Task
We had been monitoring Axie Infinity for some time due to its high APY, which at least subjectively, did not
correspond to the underlying game.
In managing the Altlift portfolio and due to the high interest of the broader Web3 community, we decided
in July 2021 to use SFC (mathematical) modeling to point out deeper issues in this ever-growing game.
Using the latest available data, we built a complete model of the Axie ecosystem in the Minsky software.
In summary, what happened in the ecosystem:
To play Axie Infinity, a player first had to invest in purchasing 3 game NFT Axies (similar to Pokémon).
Players then competed with others and were rewarded with SLP tokens, which could be used to breed more Axies and sell them to new players who were waiting in line and needed Axies. Initially, there were only 3,112 Axies, but they multiplied in a quadratic sequence with minimal restrictions. In the beginning, there were significantly more players who wanted to play the game and earn by breeding and selling Axies compared to the supply of Axies on the market. However, the model and results clearly showed that as soon as the inflow of new players stops, all demand disappears, and the price drops rapidly because Axies continue to multiply while the number of players stops growing. And the more the price of Axies falls, the less likely it is that any new players will emerge.
Our model included 16 parameters and over 30 differential equations representing the Axie ecosystem.
The model accounted for factors such as each Axie‘s ability to breed only 7 times before becoming sterile and the rising cost of SLP required for breeding. We also monitored the average monthly income from playing and compared it with the average salary in the Philippines, where most players came from.
Figure 2 - Self-Destructive Cycle of the Axie Ecosystem
Outcome
Our model helped StableLabs validate their business case and use the model‘s outputs when
approaching key investors like WOOD & Company.
The model further served to parameterize tokenomics, primarily in setting the presale price level
and the revenue share size for token holders.
The model also helped accurately determine the market share percentage for stablecoins that
the company needs to achieve to prosper and provided benchmarks for the company to monitor.
Throughout our collaboration, we consulted the entire model directly with the StableLabs team
and helped them make key decisions regarding the project‘s fee structure and the utility token.
Model Figure 3 - Model of Axie Infinity
Model Figure 4 - Axie Infinity Model - Model's Prediction
Outcome
Our model could not predict the exact future. We didn‘t know how many players Axie Infinity would reach
or how far the euphoria would take the price of one Axie. However, we knew that as soon as the first weak
growth in new players occurred, it was time to open short positions and sell the rest of our exposure. Thiswas done very successfully. SFC modeling proved to be an ideal partner for stress-testing more complex tokenomics and ecosystems, helping to identify the ecosystem‘s state according to changes
in the growth rate of users and Axies.
