Prediction: in the near future, every luxury good (handbags/clothes, wine, cars, etc) will have an NFT associated, and Instagram will integrate and allow linking NFT's to photos. I post a photo of me with a fancy bottle of scotch, and the NFT to prove I actually bought it
70% of the point of buying luxury stuff is to instagram about it. Now, at the moment, a nontrivial % of ferrari instagrams are "hey look at this ferrari on the street let's pretend we own it". NFT's would solve this
Now, why couldn't I just borrow a friend's ferrari NFT and instagram using it? Well, if I use the code, my friend can't also use it, and again, 70% of the point of the thing is to post it on instagram...
(At least) two possible designs actually:

1. Each code has a limited # of tag uses by design
2. Each code has an unlimited amount, but I can click to see all other photos using the same code. Easy to tell whether someone has borrowed a Ferrari
@instagram feature request! (Though probably won't work until more luxury firms have NFT integration)
@instagram From discussion with @RiedBN, realized another benefit of this: luxury goods are naturally durable, which is bad bc it creates a second-hand market and lowers primary market prices

But Instagram-linked-NFTs, being effectively single-use, effectively makes luxuries single-use and thus non-durable. Once a good has been single-use-instagrammed, much of its status value is gone
As some pointed out, there's an element of "those in the know, know" and these things don't work if they're too obvious

But one can lean into this also with clever product design: perhaps do a "quiet/soft launch" so there's a way to check authenticity of an instagram, but it's not announced broadly. A couple digits at the bottom of the pic or something. Like the gold serials on common projects
Signal-boosting @tmychow's idea as a way to "hide in plain sight" an authenticity signal

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More from @AnthonyLeeZhang

27 Jul
I participate a bit in the econ RA market on the hiring side, and I also hang out occasionally on the @Academic_Econ discord and talk with ppl looking for positions. Strikes me that labor markets (probably all markets!) are shrouded in a perpetual N-way "fog of war"
Each participant in the market sees a small bubble of info, that's available to and relevant to them. No single person in the market actually has the big picture at any particular point in time!
Applicants know the set of positions hiring, but not the screening criteria/hiring bar/etc. I know my own criteria and some colleagues', but other schools could be very different. I see roughly who we win against and who we lose to, but only in a small ball of programs around us
Read 10 tweets
27 Jul
Tl;dr: independent seller claims that:

- Seller launched product on amazon
- Amazon identified most profitable product, sold data to competitor
- Some product rankings shenanigans
Without commenting on the specifics of this case. This seems like a pretty tricky regulation/antitrust issue! Amazon also creates basics products, which directly compete with their sellers. Does Amazon have an unfair advantage, bc they have data from fulfilling sellers' orders?
One could argue grocery stores have a similar "unfair advantage" since, e.g. Walmart observes cookie sales, and can choose which kind of cookie is more profitable to sell. Is this also an unfair advantage?
Read 14 tweets
25 Jul
Conjecture: there is a decent (though not perfect) correlation between how well an industry/area/etc is doing, and how nice the median person in the area is
My guess at the reason for this is that people tend to be nicer when they are in environments of relative abundance, that feel positive-sum: on average, helping a stranger will be good for myself at some point
When people sense that growth is slowing down, there is not enough for everyone, perhaps the aggregate mentality shifts to be more zero-sum: my neighbor is generically my competitor, and helping them will tend to hurt myself slightly on average
Read 6 tweets
25 Jul
Interesting alternative property rights mechanism: when you buy a bottle of Laphroaig (who seem not to be on Twitter), you get this cool little certificate Image
When you enter it on their website (which only works on IE, chrome breaks it) you get this big certificate, which entitles you to a square foot of land at their distillery. They now owe me rent of a dram of Laphroaig per year. Yay! I am now a landowner! Image
Ownership rights are conveniently bundled with survey apparatus for verifying the dimensions of the plot of land, as well as weather-appropriate equipment for fending off attacks from wild geese, stoats, and otters while attempting to access the land Image
Read 7 tweets
22 Jul
Here's a set of questions. It seems a large % of people believe Tether is insolvent, which I define as: Tether does not actually have enough USD to pay $1 for each USDT, if everyone redeemed at once
Questions:

1. Why is a USDT still worth $1, if Tether is insolvent? Why isn't it worth like $0.9, or the market's best guess at how much USD assets Tether actually holds?
2. Couldn't a hedge fund or someone "attack" Tether to exploit the fact that Tether is insolvent?
Here is what I think about 1. The key is that USDT cannot trade below $1, _as long as Tether allows you to redeem 1 USDT for $1_. The argument is super simple! If ever someone was willing to sell a USDT for like, $0.99, you could just buy it from them and redeem it for $1!
Read 25 tweets
22 Jul
In tech firms, from what I've heard, it's rare for very early stage startups to have many data scientists. You need some product guys, hackers, and sales/marketing people. Make something, try to sell it, pivot if it fails, repeat until you succeed or run out of money
At this stage, I guess data science isn't needed because success or failure is obvious. Or sufficiently obvious not to need p-values. You have users, or you don't. You also realistically don't have big enough N's to actually run experiments even if you wanted to
Data scientists seem to enter later when firms are more mature. Changes are more incremental, and they're run through A/B tests, datasci folks pore over metrics, before deciding whether or not to shift. I think this is an intrinsically slower process
Read 9 tweets

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