I’m working on a post gaming out the scenarios for DeFi to scale and as I was writing it I came up with this 2×2 exploring the properties required for a few common crypto use-cases. It doesn’t fit in that post but I’m curious what you guys make of it so posting it here.
In the figure, I suggest that certain use-cases are only attractive once sufficiently high throughput or liquidity has been achieved.
Speculating: buying something very early speculating on its possible success down the line does not require high liquidity or throughput. When I GPU-mined, I did not expect a market to emerge for months or years (much less a liquid one). Or when investing in startups, VCs don’t expect to transact until there’s a “liquidity event.” This is probably the direction yield farming is going.
Gaming: game economies tend to have huge catalogues of assets, most of them very illiquid. Because the assets aren’t often used as investments, players are tolerant of slippage but they want responsiveness because latency takes them out of the experience.
Investing: when investors deploy capital to investments, they are expecting those investments to play a specific role in their portfolio (most commonly beat an index). They also need to manage risk, which makes liquid assets more attractive because they can get in and out of the asset easier if something changes. (This is in large part why the biggest institutional investors talk about BTC and don’t care about anything else). But because investors are buying and holding until conditions change, they do not need high throughput as they do not plan to get in out with high frequency.
Trading: traders want liquidity and throughput to decrease their costs and increase their profits. Low liquidity means slippage and low throughput tends to mean higher transaction costs not to mention risks that the prices get away from them as their transactions are confirming.
Lots of implications you can start to draw from this chart if you buy the premise. Curious what you all think. Leave a comment with your feedback and predictions.
From Tony Sheng: Source link