Just like photography has become much easier to create in the last 100 years, liquid NFTs will surely bring down price points of tokenized digital content but will also open expansive new markets.
1/The next major problem set in #DeFi is #NFTLiquidity.
It has become very clear that, like fungibles, nonfungibles are their own financial asset class.
One path to see this is to envision the future of NFTs as “liquid
Just like photography has become much easier to create in the last 100 years, liquid NFTs will surely bring down price points of tokenized digital content but will also open expansive new markets.
👉🏻 In particular, because of tokenization, *the NFT liquidity problem reduces to the NFT price discovery problem.*
This is a core mechanism that today is underutilized in the market, and one that — we will soon see — applies to all illiquid assets.
And the fundamental problem with each of these mechanisms is capital efficiency: in each mechanism, participants need to spend *at least* $N to value something at $N.
But one super cool approach uses peer prediction oracles created by @nick_emmons and @UpshotHQ. https://t.co/HleEMPpgcq
It\u2019s no secret that pricing NFTs is really difficult, so we built an NFT appraisal product powered by peer prediction.https://t.co/uWFZq4aeGs
— Upshot (@UpshotHQ) December 23, 2020
Prices can then be reported on-chain through @UpshotHQ’s mechanism.
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We’ve spent the last ten months building #CitizenBrowser, a project that aims to peek inside the Black Box of social media algorithms, by building a nationwide panel to share data with us. Today, we are publishing our first story from the project. /1
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You can learn more in our methodology, where we describe how we did this and what steps we took to ensure that we preserved the panelists' privacy. https://t.co/UYbTXAjy5i /3
Personally, this project is the culmination of years of experiments trying to figure out how to collect data from social media platforms in a way that can lead to meaningful reporting. I’ve described a couple of highlights below 👇 /4
My first attempt was in 2016 at Propublica, when I was working with @JuliaAngwin . We were interested in seeing if there was a difference in the Ad interests FB disclosed to users in their settings and the interests they showed to marketers. /5
.@corintxt crunched the numbers and found that after Facebook flipped the switch for political ads, partisan content elbowed out reputable news outlets in our panelists’ news feeds. https://t.co/Z0kibSBeQZ /2
You can learn more in our methodology, where we describe how we did this and what steps we took to ensure that we preserved the panelists' privacy. https://t.co/UYbTXAjy5i /3
Personally, this project is the culmination of years of experiments trying to figure out how to collect data from social media platforms in a way that can lead to meaningful reporting. I’ve described a couple of highlights below 👇 /4
My first attempt was in 2016 at Propublica, when I was working with @JuliaAngwin . We were interested in seeing if there was a difference in the Ad interests FB disclosed to users in their settings and the interests they showed to marketers. /5