Opportunity in the NFT ecosystem
The emergence and evolution of the NFT (non-fungible token) has caught the world by surprise. Due to nascent technology and a primary focus on art and media-based NFTs, the rapid expansion in the ecosystem has led to some substantial shortcomings when it comes to the way we interact with, describe, and store these assets. Currently, the best way for non-technical people to validate the quality of these NFTs is through “memability.” While there’s nothing wrong with that from a cultural value perspective, there should be better technicals for evaluating NFTs, especially when projecting out past culture-based tokens into other asset classes.
Understanding the current challenges with NFTs forms a great foundation to start building a grading system. The main challenges include:
NFT Smart Contracts
Blockchain smart contracts are the fundamental building “block” to all NFTs. To summarize, the smart contract is primarily responsible for recording who owns the NFT and facilitating the transfer of that ownership usually in exchange for money (crypto) between parties.
Fees can be more than the NFTs are worth (current ETH price of $1,685):
- Rarible ERC-721 single collectible: 0.5003 ETH (~$843)
- Rarible ERC-1155 collection: 0.4599 ETH (~$775)
- Rarible Token: approval 0.0065 ETH (~$11), minting 0.0474 ETH ($80)
Environmental impact / Co2 footprint:
- Most tokens are currently minted on Proof of Work chains ie. Ethereum
- PoW requires lots of energy
- The environmental costs may be antithetical to the token’s ethos
All NFTs have some form of associated metadata. This data can be used to describe what the NFT is and if it has any additional associated files or information. For example, an NFTs metadata may contain a link to the associated piece of artwork (Beeple) or even a deed to a piece of property.
- Difficulties of enforcing legal ownership / mitigating copyright infringement
- Standardization across marketplaces and chains
- Difficulties of updating / storing metadata in a permissioned way (see below)
Metadata and related assets can be stored both “on-chain” (in the smart contract) as well as “off-chain” (typically in the form of a URL link to the data). This duality mixed with non-standard methods of implementation creates a varying degree of immutability among NFTs.
Current storage challenges
- Data storage is not efficient and creates “chain bloat”
- Storage and contract fees are potentially increased
- While the tokens are immutable the assets may not be
- The link between asset and token can be a single point of failure
- Platforms store assets in their personal cloud accounts rather than the creator or owners account
Some examples of NFT smart contract implementations which currently store data “off-chain” (mostly) include:
Offchain Storage Options
When it comes to off-chain storage there are a few options.
- Traditional cloud storage (Amazon s3, Google Cloud Storage, etc.). Shortcomings: fully reliant on the platform, less redundant than alternatives, censorship.
- Semi-immutable distributed cloud storage (IPFS, Swarm, Sia/Skynet, ). Shortcomings: difficult to interact with over HTTP, require incentives to maintain the data.
- Immutable distributed cloud storage (Arweave). Shortcomings: not everything should be stored forever, still very early stage.
Putting It All Together
Mapping all of these pieces provides a broader view of NFTs in the context of their surrounding ecosystem. It becomes clear that the quality of an NFT goes well beyond the token itself. Each piece is critical to a well-functioning system that truly meets the objectives of a decentralized digital economy.
A prototype for the NFT grading system: FungyProof 1.0
The first version of FungyProof pulls information about a token that relates to each of the previous stated challenges. While this is far from a perfect system, it provides a starting point to begin the discussion and help educate participants about what they are buying, selling, and creating.
There are a wide variety of use cases for an NFT analysis tool of this kind. A few examples include:
- Choosing the right platform/vehicle for minting tokens based on specific use cases
- Creating curated lists of verified assets by grade/quality/objective properties
- Providing visible best practices and educational materials
- Creating data-based NFT comparisons across multiple blockchain platforms
- Enabling transparent risk assessments for token purchasers