A Framework for Investing in NFTs

I will try to keep this post short and to the point.

The world of NFT art is here to stay. It will have its ups and downs, but it is here to stay. And faster than you might expect, it will eclipse the physical art market in terms of capitalization.

That’s because the combination of art, extreme liquidity, and financial incentives will fundamentally change what art is (more on this in a longer post).

Over the longterm, I believe unprecedented amounts of capital will enter the NFT art ecosystem. In the short term, however, my biggest concerns are illiquidity and oversupply. This combination could cause hemorrhagic gaps in liquidity as valuations get ahead of the amount of new capital entering the ecosystem.

It’s ok though, just keep holding.

But, hold what, you might ask? Where should one allocate their capital?

If you have a good framework for evaluating NFTs you will end up holding pieces you’re glad you held vs. pieces you’re sad you held. A good framework allows you to know what to ignore so you can focus your attention and make concentrated bets. That means less FOMO and more wins.

This is especially important in an environment where capital and attention are being spread thin by oversupply.

What’s a good framework?

I’ve ordered the filters by how reliable of predictors they are.

Top-most filter → the most reliable predictor of longterm value

Bottom-most filter → the least reliable predictor of longterm value.

And the most important, over-arching filter should always be:

Is the project likely to become a Schelling point in which capital inflows and attention reinforce themselves over the longterm? e.g. CryptoPunks, Autogylphs, Fidenza, BAYC.

Everything in the framework below will help answer the above question.

Framework

1) Is it a breakthrough project?

Breakthrough winners in new categories will attract capital and attention over the longterm. What is the project the “first of” and will that “first of” actually be an important, massive category over the longterm?

Hint: generative art will be a massive category…

2) Does the collection have credibility / legitimacy?

It’s also important that the person/entity is actively pushing the project forward, building community, and generally increasing the project’s legitimacy. Not just releasing it and abandoning it for another project.

The Chromie Squiggle was the first Art Blocks Curated project, created by Snowfro

Aside: I believe that ArtBlocks (AB) has become a Schelling Point that will experience self-reinforcing inflows of capital and attention. The amount of attention that AB has been able to capture in such a short period of time is truly exceptional. People are “playing” ArtBlocks as intensely and consistently as they play video games. This is a strong signal.

3) Does ownership come with incentives or utility?

Dmitri Cherniak’s “Eternal Pump”

Fun example: there are only 50 of Dmitri Cherniak’s Eternal Pumps. Soon, only holders will have the opportunity to mint his new project, Wrapture. The catch is, if any of the holders sell, he will immediately flood the supply until it hits 666.

The Punks Comic NFT represents ownership in its DAO

Aside: can someone help me make a spreadsheet of creators/projects that are likely to do this or have already done this for holders?

4) What is the project’s collectibility quotient?

The key here is maximizing versatility while preserving ease of categorization. Lets break these two things down:

  • Versatility: tons of surprises, each piece feeling special or unique, and a few ultra rare or magical feeling pieces (grails).
Tyler Hobbs’ “Fidenza’. Mint #53, #90, and #76 respectively
Also Tyler Hobbs’ “Fidenza”. Mint #938, #314, and #367 respectively… all of this is from one algorithm…🤯
  • Categorization: it should be easy to form categories from the commonalities inside a collection. It should be easy to make sense of the variation between pieces, and how that variation pushes pieces into a new category. If the variation between pieces is overly random or seems like chaotic noise, that is not good. Each piece should also be independently recognizable as a part of the same collection.

In other words, a collection needs lots of widely ranging variants with some invariants that provide structure for sense-making. If done right, this combination should lead to tons of easily categorizable surprises. I should never look at a collection and think, “these all look so similar,” and I should also never look at a collection and think, “these are all so random.” It’s tough to balance this and make sure all of the pieces still look great.

Dmitri Cherniak’s “Ringers”

As Tyler Hobbs said in his recent piece:

“With a top-class algorithm, every output will have something new about it, a little surprise that teaches you more about what is possible. You shouldn’t ever get bored flipping through the results.”

Basically, every piece needs to feel special, but you should be able to easily understand why it’s special. In general, collectors want to feel like the piece they bought is rare, special and likely to increase in value over time.

5) How aesthetically pleasing is it?

View Cards by Jeff Davis is the first collection from Art Blocks Playground

In my experience, when I show a random person a Fidenza, they are immediately wowed, even if they know nothing about generative art or NFTs.

6) How Memeable is it?

There will be a dogecoin of NFTs and it will nail a meme that coordinates holders and it will probably have more than 10K supply and it also won’t be so dependent on whales.

7) What’s the total supply?

For some reason, we’ve landed around this equilibria of 1K — 10K pieces, but I think these numbers will change once the number of NFT users explodes past its current size.

There are only 100 photographs in Justin Aversano’s Twin Flames NFT collection

I do believe scarcity will be a decent driver of economic value & memeability. Especially at the extreme ends of the supply spectrum, supply can be a useful predictor. One thing to keep in mind — if a projects supply is too low (i.e. <100 pieces) then it can be hard for the community to reach a critical mass, causing the project’s potential to be stunted.

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Remember, the above filters are ranked in terms of their predictive power. E.g. if a project doesn’t meet the first filter, you can probably ignore it, unless it passes the 2nd/3rd/4th filters with flying colors.

I recommend trying to assess projects that are already successful through this framework to see if it checks out. A good model should explain the success of most projects. And of course, feel free to share any feedback or criticism — that’s how the framework gets better.

-YeastyDough.eth

p.s. Special thanks to Bonafidehan, Crow, Zoink, pistachiojellybean, mikesigo, and fellow members at FingerPrintsDAO for all of the helpful feedback.

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