The value of Lunyr Tokens (LUN)

in #ethereum7 years ago (edited)

This article continues off from here.
https://steemit.com/ethereum/@arnpham/introducing-lunyr-and-the-future-of-crowdsourced-knowledge

Today let’s take a closer look at Lunyr Tokens (LUN) and see where they get their value. First off, LUN are used for advertising on the Lunyr platform. Contributors obtain them by creating and peer reviewing content.

LUN are valuable for businesses. Since the content on the knowledge base covers a range of topics, the LUN have general-purpose utility for businesses to advertise on the platform. Businesses are in a great position to buy LUN because they’re more likely than consumers to hold plenty of disposable resources to compete in bidding wars and drive up the demand for LUN.

The value of LUN correlates well with the growth of the knowledge base. As the knowledge base grows in the beginning, it eventually reaches a crossover point where the demand for advertising exceeds the supply. The demand for advertising drives up the value for LUN. Then, seeing the economic opportunity, more contributors enter the ecosystem and add and review content which attracts more readers.

Powerful network effects take over and rapidly increase the value of the ecosystem along with the value of LUN. Because contributors are rewarded for their efforts, we reach the crossover point quickly.

The value of the ecosystem and LUN does not stop at the knowledge base. Lunyr has a longer term vision to create a knowledge base API, ultimately driving up the value of the ecosystem. In the future, Lunyr may also create other products and services, some of which may increase the demand of LUN.

Will the newly created LUN decrease the value of LUN? On the contrary, it actually makes LUN more valuable in the long run. One viewpoint is that creating LUN causes the LUN value to decrease. However, this view forgets to take into account that each time new LUN are rewarded to contributors, more valuable content exists and the value of the ecosystem grows. So what actually occurs is these newly created tokens put a downward pressure on the value, but also fuels content creation, which creates a long term upward pressure.

It’s also important to realize that the effective inflation decreases from 3% towards 0% over time. New LUN are created annually at a fixed rate of 3% of the initial token supply. For example, if the initial token supply is 1,000,000 LUN, then 30,000 LUN are created annually. In the first year, the inflation is 3.0%, but in the second year it’s 2.9%, and so on towards 0%. There’s also the loss of LUN from losing private keys, forgetting passwords, and accidentally sending LUN to an unowned account. The loss of LUN grows with the size of the ecosystem.

The LUN issuance model was primarily designed to help us overcome the chicken and egg problem. Contributors won’t join the network unless the rewards are valuable, readers won’t join unless they can find valuable content, and advertisers won’t buy ads unless advertising is valuable enough. Sounds like headache right?

Thankfully, there’s an elegant solution. If we look at the problem, we can see that the limiting factor comes down to the amount of valuable content to readers. Once we establish a sufficient amount of content contributions, readership will continue to grow until network effects take over. Eventually the LUN from advertising component of the LUN pool should be sufficient on its own to grow the ecosystem.

Overall, the ecosystem was designed to grow value in the long term. The knowledge base is a collaborative effort, and by joining its ecosystem now you can take part in building something that can bring tremendous value to the global community.
To purchase LUN through the Lunyr crowdsale, see https://medium.com/lunyr/lunyr-crowdsale-parity-instructions-489b5bb1afbf

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Flagged: probable scam. Whitepaper is full of buzzwords and wild claims backed by nothing but handwaving. For instance the dev team claims that the content will be machine-readable and accessible by API for AI applications, but doesn't even appear to know about RDF (which Wikipedia uses) and ignores every related question on Slack.

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