Welcome to the Block & Mortar newsletter! Every week, I bring you the top stories and my analysis on where business meets web3: blockchain, cryptocurrencies, NFTs, and metaverse. Brought to you by Q McCallum.
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The investment thesis
Last week, before third-party apps stopped working on That Social Media Platform, I happened upon this tweet:
House of Gucci’s biggest competitor isn’t Prada
It’s House of Fortnight, who sold $20B+ of virtual goods over the last four years
NFTs bout to move wayyy beyond just pfps
-@colleenklein on @theempirepod
Twenty billion dollars? In virtual goods? Tell me more …
That tweet was an excerpt of the Empire podcast episode where Colleen Sullivan and Peter Johnson – VCs at Brevan Howard – offered their 2023 predictions for web3.
I know, I know. Just two weeks ago, I explained why those “next year’s predictions” lists can be iffy (and then proceeded to rattle off one of my own). To show why this one’s different, let’s start with three points:
- A decision is a bet on a future outcome.
- An investment is a decision backed by money.
- Investors – at least, those that survive – base their decisions on a carefully researched investment thesis that outlines where they put their money and why.
When investors talk about the future, they’re not throwing out careless ideas. They’re shedding light on their investment thesis. And here, Sullivan and Johnson made it clear that they’d done their web3 homework.
The full 90 minute episode is worth a listen. Maybe even a couple of listens. If you’re in a hurry, here are two parts that stood out:
NFTs (40:00 mark): Deep into the discussion of brands and NFTs, someone (I think it was co-host Santiago Roel?) briefly draws parallels between web3 NFTs and Web 2.0 cookies. Both can be used for unique identification and personalization. That paired well with a point Sullivan had raised, on how loyalty programs could review a wallet’s contents to identify a big spender. One NFT – say, a loyalty pass or a piece of artwork – will tell you something about the buyer, sure. Each additional NFT in that wallet opens up a new dimension of insight, creating a well-rounded picture of the wallet-holder.
Just as a restaurant host can spot a fancy watch and an expensive outfit, a web3 brand could spot a collection of high-end NFTs in the wallet. Maybe even NFTs With Benefits. And unlike the restaurant scenario, a blockchain-based check would reveal how long those items had been in the customer’s possession. Have they owned these for a long time? Or did they just borrow them for the night, to make a good impression?
To be clear: I don’t advocate mistreating the person in the latter case. I just note that this data point can lead to an interesting market segmentation: a brand could spot a rising star and offer them a fast-track to elite status. Airlines have certainly played that game. “Want more of this VIP treatment? Sign up here and meet these goals within three months.”
(There are also plenty of privacy issues for brands to consider here. It will be interesting to see how many Web 2.0 bad habits – including We’ve Never Met, But We Know A Lot About You – companies bring to web3.)
Web3 adoption (1:17:00): The conversation turned to web3 adoption close to the end of the episode. Johson expects that a lot of this will happen indirectly. People won’t necessarily crave blockchain-based apps or services, he posits, but they will find value in some products that just happen to be built on blockchain technologies.
I agree with his assessment. I’ve seen this play out over the years across webapps, cloud, and AI. Most consumers don’t care what technology drives a product they use; they just want it to work. (It’s easy to miss this when your job is in technology, because you are surrounded by people who do – and should – care. But let’s remember that tech work involves a relatively small part of the population.)
Johnson’s take also makes sense when you consider that companies – even tiny startups – can invest the time and effort required to learn the lower-level pieces, while that’s a bigger ask for end-users. This is the same idea behind Wordpress.com, SquareSpace, and cloud computing providers.
A wallet by any other name…
It’s one thing for brands to check a wallet to spot a high-roller. Quite another for the wallet holder to turn their crypto purchases into a public, social experience.
That’s the plan behind the new wallet from The Easy Company:
The platform, which was demonstrated to TechCrunch over Zoom, had a similar layout to social media apps like Instagram through elements like showcasing of NFTs, where users can swipe to view both their own NFTs or NFTs of people they “watch,” like Instagram stories.
I say: why not? Words, photos, and crypto purchases are all forms of expression. Is there really that much of a difference between a Twitter account full of your musings and a crypto wallet full of your art collection? In both cases, you’re showing the world what’s on your mind. And giving everyone a chance to interact with you based on what they see.
The social aspect has other perks:
The wallet also allows users to link their social identity from other sites and curate their profiles with their own NFTs from multiple wallet accounts and blockchains. There’s a rating system called Signal, which allows users to review anything from NFT collections to marketplaces and platforms — it also allows the community to flag possible fraud in an effort to increase safety.
Sounds like a good idea to me. Social ratings can make for a powerful system of trust, and trust improves liquidity in a marketplace. (Just ask eBay. They pioneered the idea of letting buyers and sellers rate each other.)
Speaking of trust and fraud in crypto, that’s the subject of the next segment:
The real data scientist shortage
Here’s a palate-cleanser to break up this issue’s NFT coverage. We can now talk about the other technology that’s far too popular: machine learning (ML).
It’s OK. I’m allowed to poke fun. My other job is in ML.
Part of that job involves explaining ML to executives. I prefer to emphasize the practical aspects of machine learning. And one point I really drive home is: ML modeling is mostly … pattern-matching.
That’s it. That’s the tweet. You feed an algorithm data, it looks for patterns, it bakes those patterns into a model that you can use for predictions.
This notion of “looking for patterns” applies to a variety of scenarios, such as:
- assigning labels (“this document is about cats”; “this is a picture of a dog”)
- grouping things (“all of these documents seem to be similar”)
- finding outliers (“this document is not like the others”)
That last point is a pillar of cybersecurity and fraud detection. Given that crypto has experienced its share of fraud, it seems like this would be an area of deep interest to data scientists and machine learning engineers.
Or, maybe not? According to a recent piece in The Defiant:
Most ML platforms are driven by data scientists, and here lies one of the key challenges as far as the implementation of this technology within the cybersecurity world goes. While web3 has attracted many developers, it hasn’t been able to attract a lot of data scientists so far.
This is unfortunate, given that there is so much data readily available for analysis, opening the door for many research opportunities for solving real-world problems. In this regard, the industry needs to make web3 more appealing to data scientists, something that can be done by educating that cohort about the underlying technology as well as providing incentives to make this niche more appealing.
This matches my experience. I know of just a few people who share my interests in both ML/AI and web3. Many data scientists I’ve met only know web3 from the ugliness they see in the news: algorithmic stablecoins blowing up, NFT phishing attacks, or crypto miners’ impact on the environment. Understandably, they don’t like what they see.
(This is not unique to crypto. Data scientists have expressed a similar distaste for the traditional finance space, or tradfi, because they associate it with stiff corporate culture and the 2008 mortgage crisis. I will make no comment about the former.)
It’s easy for them to miss that web3 is a data-first field. The lower-level blockchain is a system for recording transactions, after all, and much of web3 is built on top of that. There’s no shortage of analysis and modeling work. How will this message reach data practitioners?
Employers will likely lead the charge here. As companies adopt web3 solutions for loyalty programs and promotional activities, they’ll no doubt want to analyze the new streams of data coming in. Talented data teams could use that data to unlock insights and release new products.
Similarly, tradfi institutions already employ teams of data scientists, ML engineers, and quants. As banks wade into crypto, they’ll bring people who already have experience in financial modeling and fraud detection.
This might be a rare case in which companies, not their tech teams, bring a new technology to the table.
All down the drain
Switching back to NFTs as I close out this issue…
Yuga Labs, the company behind the Bored Ape Yacht Club NFT collection, is hard at work on its Otherside metaverse property. You know, the one that made a big splash in May and then bogged down the Ethereum network. The one that’s still not ready.
Yet they’ve somehow found the time to … make a video game?
Yuga Labs […] unveiled plans to gamify the process of minting NFTs.
The startup today announced an upcoming online game in which players must navigate a sewer to claim rewards in the form of NFTs.
From Jan. 17, players will be able to claim a free “Sewer Pass” — giving them access to a course named “Dookey Dash.”
I’ll conveniently skip over the name as I compliment the idea. The web3 space is in dire need of more education, and gamification can help people learn a new skill. The right experience can be so much fun that practice does not feel like practice. So I’ll hand it to Yuga Labs for using gamification to teach people how to mint NFTs.
Having said that … I still want to know: when will Otherside launch?
The wrap-up
This was an issue of Block & Mortar.
Who’s behind Block & Mortar? I'm Q McCallum. I've spent the past two decades in the emerging-tech space. And I'm very interested in web3 use cases.
Credit where it's due. Big thanks to Shane Glynn for reviewing early drafts. Any mistakes that remain are mine.
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