EP:46 Depreciating Licensing Model in NFT Ownership with Anthony Lee Zhang from UChicago Booth

04/04/21 • 27 min

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NFT is not just about digital art and tokenising assets. There are so many more things we can do with NFTs, like embedding property rights, more efficient mechanism design of property rights allocation and balancing between the various types of tradeoffs.

Today, we have with us Anthony Lee Zhang, assistant professor of Finance at UChicago Booth. He co-authored a paper with Glen Weyl on depreciating licenses (DL). Licenses are basically property rights, and NFT is about tokenising these property rights. (A chapter in the book)

In Depreciating Licenses, Anthony and Glen came up with a simple game theory model of asset ownership. Basically you own a fixed percentage of the property and the remaining percentage is auctioned off or being sold each time period. In this way, it mixes both a full ownership model and full rental model.

This is absolutely fascinating because in property rights allocation, we typically experience the trade off between investment incentive (owning the asset 100%) VS asset allocation (allocating to the best person who values the item the highest right now). With depreciating licenses model, this mixes both and balances the trade off.

Join our NFT discussion channel on discord to ask questions and to learn more: https://discord.gg/6zKyFEyJ

Also check out the Art is Always On Sale model with Simon de la Rouviere: [https://youtu.be/5WitN5bwfr8](https://youtu.be/5WitN5bwfr8)

DL paper by Anthony Lee Zhang and E Glen Weyl: [anthonyleezhang.github.io/pdfs/dl.pdf](http://anthonyleezhang.github.io/pdfs/dl.pdf)

Reach out to Anthony on Twitter: [https://twitter.com/AnthonyLeeZhang](https://twitter.com/AnthonyLeeZhang)

Join our NFT discussion channel on discord to ask questions and to learn more: https://discord.gg/6zKyFEyJ

Also check out the Art is Always On Sale model with Simon de la Rouviere: [https://youtu.be/5WitN5bwfr8](https://youtu.be/5WitN5bwfr8)

DL paper by Anthony Lee Zhang and E Glen Weyl: [anthonyleezhang.github.io/pdfs/dl.pdf](http://anthonyleezhang.github.io/pdfs/dl.pdf)

Reach out to Anthony on Twitter: [https://twitter.com/AnthonyLeeZhang](https://twitter.com/AnthonyLeeZhang)

Previous Episode

EP45, The Economics of Betting on NFT | Polygon (MATIC) NFT Infrastructure

This month is on NFT. We chatted about the economics of NFT, and the economics of NFT flipping last week. A few days ago, an art piece was sold at $69 million by Beeple (https://onlineonly.christies.com/s/first-open-beeple/beeple-b-1981-1/112924)! Of which, $9m is in fees. My mistake, that $9m is to Christie and not the network's transaction.

In any case, transaction fees are still a problem. Not only is it expensive to buy an NFT (we mentioned this 2 weeks ago), but also expensive to MINT NFT. That might be causing the huge inflation in prices, since the fixed cost to mint the NFT is already high.

Hence, I want to talk about Polygon ($Matic) today. Up to now, when the bottlenecks of the Ethereum network have not been fully resolved, Layer 2 solutions are showing their remarkable advantages. Formerly #Matic Network, #Polygon has great ambitions to deploy all Layer 2 solutions on themselves.

NFTs are moving to Polygon because it is cheaper and faster. Also, the level of security on Layer 1 of Ethereum is good but not a necessary function. In this episode, we look at the 4 layers of Polygon and understand the quick dive into the token design!

Donate here: https://gitcoin.co/grants/2054/economics-design-education