• Read 3 books (623 min, -29% MoM) and 38 long-form articles (+36%)
• Listened to 481 songs (-17%) and 9 podcast episodes (627 min, -31%)
• Watched 0 movies (0 min, -100%), 8 soccer games (795 min, +167%) and 4 TV episodes (215 min, -20%)
• Played 1 board game (75 min, +∞) and 1 video game (10 min, +∞)

Algorithms to Live By (Brian Christian & Tom Griffiths)
░░░▓░░░░░░░░░░░ Progress: 24-26%

Big Business (Tyler Cowen)
░▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 10-100%

The Price of Peace (Zachary D. Carter)
▓▓▓▓▓░░░░░░░░░░ Progress: 0-30%

My GPT-3 Blog Got 26 Thousand Visitors in 2 Weeks (Liam Porr)

Antitrust Politics (Stratechery)

This Is Not a Game (Real Life)

The UX of Lego Interface Panels (George Cave)

The Case of the Top Secret iPod (TidBits)

The Internet’s Most Undervalued Company (Not Boring)

History Has Begun with Bruno Maçães (Venture Stories)

Top Artists: Max Richter (78 plays), Sufjan Stevens (594), Trent Reznor & Atticus Ross (57), 2raumwohnung (34), Zoot Woman (21)

Sparked by an interesting Twitter discussion, I’ve spent a lot of time recently thinking about different proof-of-work mechanisms.

When I say proof-of-work, I’m not talking about consensus algorithms like the ones that some crypto currencies use. I’m talking about social networks.

At their core, social networks are primarily about one thing: Building social capital through signaling. As I wrote in Signaling as a Service, signaling can be broken down into three different components:

• Signaling Message
  A hidden status subtext you’re trying to convey about yourself
• Signaling Distribution
  The channel through which you’re communicating your signaling message
• Signaling Amplification
  Ways to boost your signaling message to compete against status rivals

For example: A Patagonia vest signals both a prosocial attitude (“I care about the environment“) as well as wealth (“I can afford to spend $500 on a jacket“). Depending on where you live, it might also signal something about your occupation.

In order to signal these messages to others and build actual social capital you need a signaling distribution channel. One option would be to wear the vest in public where others can see it – but there are obvious physical constraints to the size of the audience you’d be able to reach.

This is where social networks come in.

Their primary role is to distribute signaling messages at scale and transform them into quantifiable social capital (in the form of likes and followers).

As social networks grow, they increase the potential reach of your signaling messages – but they also get crowded with status rivals. This is why social networks typically provide you with a set of signaling amplification tools. These tools help you boost your signaling messages and stand out from the crowd.

In Signaling as a Service I compared signaling amplification to Eugene Wei’s idea of proof-of-work hurdles, which he describes as follows:

But the more I think about it, the less I like the comparison. I actually think that Eugene’s proof-of-work theory only scratches the surface of what social networks actually do.

Let me explain.

Take a look at this very cliché Instagram picture. The photographer clearly put a lot of thought and effort into its composition and applied different filters and editing tools to make it look nicer.

It’s a perfect example of Eugene’s definition of proof-of-work.
Proof-of-creative-work, to be more exact.

Editing your photo helps to amplify your signaling message and sets you apart within Instagram’s status arena (aka the newsfeed). It also adds additional signaling messages to your post: “Look how great a photographer I am” or “I’m a creative person”.

But those are not the main signaling messages you are communicating here. What you really want to tell your followers with this photo is something along the lines of “I’m a world-traveler” and “I’m in a happy relationship” (which in turn are also just signaling proxies for wealth and mating worthiness).

The photo and the location tag are your proof points.

Social networks are therefore not only signaling distribution (and amplification) networks – they also allow users to prove their signaling messages.

The creative proof-of-work is just pretext and helps to boost your post. What’s more important are the additional proof mechanisms that social networks provide. In the case of Instagram those are photos and location tags.

Instagram is essentially “pics or it didn’t happen”-as-a-service.

When new social networks emerge they have to introduce new proof mechanisms to differentiate themselves from existing incumbents. These can either be novel proof-of-creative-work hurdles or completely new proof-of-x mechanisms.

TikTok is a good example for proof-of-creative-work innovation. The app provides creators with a powerful set of video editing tools that have opened a whole new level of creativity.

The cost to participate in TikTok’s status game is a lot higher than Instagram’s (compare a well-made dance choreography on TikTok to your median Instagram travel post) – but its powerful feed algorithms also make discovery easier and thus reward users faster and with more social capital.

TikTok doesn’t add any new proof points beyond its novel creative work hurdle though. You can signal and prove your creativity but you could achieve the same by uploading your video to Instagram.

Strava, on the other hand, introduced an entirely new proof mechanism: Proof-of-physical-activity. By using your phone’s GPS sensor (or a 3rd-party fitness tracker), users can actually prove how much and fast they ran or cycled. In contrast to Instagram photos, Strava’s proof mechanism is a lot harder to fake (though there are exceptions).

What’s great about Strava is that it reinforces a behavior that’s actually good for you: While the status game that initially got you into the app might be zero sum, the actual physical exercise you have to put in to compete has a very positive, compounding effect.

The question is: What other social networks should we build that could have similar positive feedback loops? And what are their proof mechanisms?

Let’s start with the two examples in this tweet.

I love the idea of a Strava for Cooking – but I’m very skeptical that it can be built. Why? Because the necessary proof mechanisms don’t exist.

The primary metric you optimize on when cooking is taste. But how would you measure or quantify taste? The closest proxy to taste that we have is optics: How good does the meal that you cooked look? This can easily be proved with a photo .. but that’s a proof-of-work mechanism that Instagram already offers (including filters to make your food look nicer). As long as no one comes up with a better proof mechanism for cooking, I think it’s unlikely that we will see a successful social network in the space.

I’m more optimistic about Strava for Learning.

While the activity of learning itself might be hard to quantify, you can measure the outcome of learning: knowledge. Has anyone built a multiplayer version of Anki yet? Flash cards would be a perfect proof-of-knowledge mechanism and could easily be turned into a game where you compete against friends.

Similar to physical activity in the Strava example, learning is not something that most people enjoy doing. As TikTok founder Alex Zhu points out, education goes a little against human nature. In combination with a strong enough signaling mechanism however, you can get users to participate. It’s kind of the opposite of Chris Dixon’s famous “Come for the tool, stay for the network” strategy. Come for the status, stay for the tool.

A related product I’d love to see is Strava for Reading. Imagine an eBook reader that not only tracks how much time you spend reading but also *what* you are reading. Based on these proof-of-(reading)-work mechanisms you could build streaks or GitHub-contributions-like visualizations that incentivize users to read more (and more regularly).

You could even build leaderboards for different topics based on the content of the books and articles you read. Or think about a score that indicated how balanced your reading behavior per topic was (to incentivize users to read takes on political topics from different perspectives).

Unfortunately, I think it’s unlikely that we will see a product like I described anytime soon. The world’s largest bookstore, most popular eBook reader, and biggest social network for books are all owned by a company that has very little competency in design and user-facing product innovation.

(Side note: Amazon’s monopoly on books might be the most underrated sub-optimal equilibrium in tech.)

Another app that would be interesting is a social investing app. Think “Robinhood but as a social network”. It seems like investing is already quite a social activity – just look at communities like r/wallstreetbets. As patio11 pointed out, Robinhood already feels more like a game than a finance app.

So why not build an investing app that opens with a feed of all your friends’ investments and their returns over time? Instead of sharing screenshots on Reddit and Instagram you could prove your investments right in the app.

Note that an app like this would not be about signaling wealth. It’s about signaling being right and the ability to prove it. This is probably an even stronger and more engaging mechanism than signaling wealth – and the reason why I’m still bullish on prediction markets.

Perhaps a well-designed, consumer-friendly prediction market app would be the ultimate proof-of-x social network. Strava for being right.

While we are on the topic of being right: Do you agree with my thoughts in this post? What other social networks and proof-of-x mechanisms would you like to see?

Please leave your comments here.

Thanks to Dan Romero, Des Traynor, Jan König, Max Cutler and Zack Hargett for reading drafts of this post.

• Read 7 books (874 min, +4% MoM) and 28 long-form articles (+22%)
• Listened to 583 songs (+7%) and 13 podcast episodes (908 min, +128%)
• Watched 1 movie (141 min, +∞), 1 soccer game (45 min, -93%) and 5 TV episodes (270 min, -45%)
• Played 0 board games (0 min, -100%) and 0 video games (0 min, -100%)

Designing Games (Tynan Sylvester)
░░░░░░░░░░░░▓▓▓ Progress: 87-100%

Dune (Frank Herbert)
░░░░░░░░░▓▓▓▓▓▓ Progress: 63-100%

Order Without Design (Alain Bertaud)
░▓░░░░░░░░░░░░░ Progress: 3-16%

Algorithms to Live By (Brian Christian & Tom Griffiths)
▓▓▓▓░░░░░░░░░░░ Progress: 0-24%

Underground (Haruki Murakami)
▓▓░░░░░░░░░░░░░ Progress: 0-12%

Very Important People (Ashley Mears)
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 0-100%

Big Business (Tyler Cowen)
▓░░░░░░░░░░░░░░ Progress: 0-10%

The Garden of Forking Memes (Aaron Z. Lewis)

The Freud Moment (Alex Danco)

The TikTok War (Stratechery)

Charlie Songhurst: Lessons from Investing in 483 Companies (Invest Like The Best)

What Dan Romero Thinks About Basically Everything (Venture Stories)

The Tyler Cowen Production Function (North Star Podcast)

Top Artists: Slut (85 plays), Sufjan Steven (54), Hans Zimmer (53), Trent Reznor (34), Tocotronic (33)

At the beginning of each year, I release a public list of my goals and rules for the upcoming 365 days. Here’s a mid-year review of the 24 goals I set myself for 2020.

• Publish 52 blog posts ⚠️
  Behind target: I published 23 articles since the start of the year. I’ll have to carve out more writing time in the next few months to get to my end-of-year goal.
• █████ ██ ███████ ██ ███ ✅
• Read 20 books ✅
  On track: I completed 12 books so far.
• Watch less TV ❌
  Failed: I spent 125 hours watching TV last year. This year I’m already at 120 hours (74 hours of which are The Sopranos).
• Swim a total distance of 120km ⚠️
  Behind target: Due to covid-19 I only swam 36km so far this year. I’ll have to swim at least 3.3km per week from now on to still hit my target. Unlikely.
• Go for a swim at least once a week ❌
  Failed: Covid-19 broke my 183 week swim streak.
• Back exercise every second day ✅
  On track: Completed exercise on 70.16% of all days so far.
• ██████ ████ ████ ✅
• ████████ ████ ⚠️
• ██ ████████████ ❌
• Ship a redesign of this blog ⚠️
  Behind target: Started working on first wireframes.
• Finish work on my daily uniform ⚠️
  Behind target: Not started.
• Conduct a 2020 Quantified Self Project ✅
  On track: You can see my current setup here.
• Publish my 2019 Quantified Self Report before end of Jan ❌
  Failed: Finished about 40% of the work and still aim to publish the report before the end of the year.
• Build a Personal CRM system
  Removed: My idea was to build my own personal CRM system, but I’ve since discovered Clay.
• Limit meat consumption to 24 days ✅
  On track: I ate meat on 12 occasions this year so far.
• No alcohol if I have to work the next day ✅
  On track (with 2 or 3 exceptions).
• Visit 1 country I haven’t been to before ⚠️
  Behind target: Covid-19 makes it unlikely that I’ll achieve this goal.
• Explore more new places ⚠️
  Behind target: 25% of my Swarm check-ins should be places I’ve never visited before. Due to covid-19 I’m currently slightly behind that target (~20%).
• Keep phone screen time below 1h per day ❌
  Failed: My average phone screen time is currently way above 2h per day.
• Meditate 1h per week ❌
  Failed: I keep failing to make meditation an actual habit.
• Do a 3 day silent retreat ⚠️
  Behind target: Unlikely to happen due to covid-19.
• █████████ ████ ████████ ⚠️
• ██████████ █ ✅

• Read 3 books (840 min, +16% MoM) and 23 long-form articles (-15%)
• Listened to 544 songs (-19%) and 8 podcast episodes (397 min, -36%)
• Watched 0 movies (0 min, -100%), 10 soccer games (660 min, +65%) and 9 TV episodes (495 min, -59%)
• Played 2 board games (305 min, -18%) and 1 video game (15 min, same)

Designing Games (Tynan Sylvester)
░░░░░░░░▓▓▓▓░░░ Progress: 53-87%

Dune (Frank Herbert)
░░▓▓▓▓▓▓▓░░░░░░ Progress: 11-63%

Order Without Design (Alain Bertaud)
▓░░░░░░░░░░░░░░ Progress: 0-3%

Marc Andreessen on Productivity (The Observer Effect)

Why Figma Wins (Kevin Kwok)

The End of OS X (Stratechery)

John Collison: Growing the Internet Economy (Invest Like The Best)

Top Artists: Endel (183 plays), Sufjan Steven (37), Travi$ Scott (25), Portugal. The Man (19), Paul Kalkbrenner (17)

• Read 4 books (725 min, -45% MoM) and 27 long-form articles (+23%)
• Listened to 675 songs (+12%) and 11 podcast episodes (623 min, +68%)
• Watched 1 movie (73 min, +∞), 4 soccer games (400 min, +∞) and 22 TV episodes (1217 min, -35%)
• Played 2 board games (370 min, +∞) and 1 video game (15 min, -92%)

Designing Games (Tynan Sylvester)
░░░░░░▓▓░░░░░░░ Progress: 42-53%

Dominion (Tom Holland)
░░▓░░░░░░░░░░░░ Progress: 12-16% (stopped reading)

The Plot Against America (Philip Roth)
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 0-100%

Dune (Frank Herbert)
▓▓░░░░░░░░░░░░░ Progress: 0-11%

Contemplating calendars (Devon Zuegel)

Doordash and Pizza Arbitrage (Ranjan Roy)

The Rise of TikTok (Turner Novak)

Communication in World 2.0 (Daniel Gross)

Alex Danco: Funding the Future (North Star Podcast)

Tobi Lutke – Building a Modern Business (Invest Like the Best)

Shishir Mehrotra – The Art and Science of the Bundle (Invest Like the Best)

Top Artists: Radiohead (108 plays), Mikel (75), Max Richter (41), Hans Zimmer (30), Nine Inch Nails (26)

(Thoughts on Ecommerce, Pt 2)

This is the second part of my essay on the state and future of ecommerce. In part one we discussed the current status quo of online shopping and looked at the different models and value chains behind Amazon and Shopify.

In this part, I’ll explain what I believe is currently missing in the online shopping experience and what Shopify and Amazon (or someone else) should build.

Let’s kick things off with a chart that Benedict Evans tweeted recently:

The easy way to read this chart is that consumers are becoming less interested in finding the cheapest options and are instead searching for the best options. I find that difficult to believe.

A perhaps more interesting interpretation is that Google’s “cheap” search queries are declining because users already know where to find the cheapest option: On Amazon (and other vertically specialized search engines like OTAs).

As we discussed in last week’s article, Amazon – like Google – is primarily a search engine. But since all its search results – unlike Google’s – are products, it’s easy to rank them by price. If you already know what you want, there’s no point in searching on Google first. Your shopping journey starts and ends on Amazon.

But what about the “best” option?

Amazon’s default search results are “Featured”, which factors in a variety of criteria (purchase frequency, availability, reviews, …) to show you the most relevant products. But that’s not the same as the best. (Side note: It actually turns out that the most relevant results also happen to be the most profitable for Amazon). You can also choose to rank results by customer reviews, but those scores don’t feel very trustworthy either.

As a result, non-price driven Amazon purchase journeys initially start on other sites which help users figure out what the best product for them is (through curation and reviews). This is similar to the Shopify model, which relies on discovery channels such as Instagram and Pinterest to drive users to its stores.

In contrast to Shopify though, there is not one – or even a few -dominating channels. Discovery is spread across many, many different websites, which Amazon rewards with its affiliate program. The fact that Amazon just drastically reduced its affiliate fees is perfect evidence of how little negotiating power these individual sites have in this value chain, despite their collective importance.

The high number of affiliate partners also explains why people are still using Google to search for “best” options. Not only do consumers need to figure out what the best product is – they first need to figure out what the best product review site is.

The second problem I see is that reviews only work for a handful of product categories. You can only rank and compare products if they have a strong utility. For example, you can determine what the best TV is by looking at screen resolution or HDR support. These features are easy to measure and compare.

But how would you decide what the best pair of sneakers is? Or the best handbag? You could look at build quality or materials, but those attributes are neither easy to quantify nor do they have an actual influence over what people perceive as the best.

So how do you determine what the best option is when utility isn’t the decisive factor in the purchase decision?

A good framework to understand how consumers make purchase decisions is René Girard’s Mimetic Theory. René Girard was a French anthropologist and philosopher who has gained quite a following among people in tech in recent years, largely driven by the influence of his ideas on Peter Thiel.

The core idea behind mimetic theory is that human development is based on imitation. What sets humans apart from other species is our ability to learn by observing and copying others. According to Girard, this includes watching and imitating what other people desire.

This is not something most of us are aware of. We think we make autonomous purchase decisions based on objective facts (“These shoes are waterproof”) or personal preferences (“I like the way these sneakers look”).

In reality though, Girard argues, there is never a direct relationship between subject (the consumer) and object (the product). Instead, the relationship is always triangular between the subject, the object and a so-called mediator – someone the subject is drawn to and wants to imitate.

In other words: We don’t actually want the object itself. What we really want is to be like the person we admire. The object is just a means to an end.

The person we are trying to imitate might be a celebrity, but it could also be one of “the cool kids at school” or someone you discovered on Twitter or Instagram.

As a consequence, there isn’t a “best sneaker”. What you perceive as “the best” isn’t based on objective attributes, it depends on who you are trying to imitate.

As Alex Danco brilliantly summarizes in his essay on Girard, many advertisers already leverage mimetic desire in their campaigns:

This is also why influencer marketing works so well and why Instagram has become the perfect discovery channel for Shopify.

As we discussed in last week’s essay, Instagram is both a blessing and a curse for Shopify. On the one hand, it is the perfect discovery channel for the type of products that are typically sold by Shopify merchants: visually appealing objects you didn’t even know you wanted (fashion, homeware, furniture, etc). On the other hand, too much reliance on Instagram can become dangerous. A demand aggregator always has the upper hand over a supply aggregator as evidenced by the high tax Shopify D2C brands have to pay to Instagram in the form of ads.

Nevertheless, further integrating with Instagram is probably a good idea for Shopify. Instagram’s user behavior is a prime example of mimetic desire. Users can scroll through the life of the person they want to imitate to get an idea of what they should desire.

Shopify already announced a deeper integration with Instagram and Facebook last week, now shops can sell directly on Instagram. The ideal feature, however, would allow users to buy objects straight from the feed of their favorite influencers.

While brands will still be important (for signaling, among other things), I suspect that a lot of stores will become commoditized over time. Ecommerce will become more modularized as transactions shift from both retailers and D2C brands to individual influencers.

It’s not hard to imagine a future with a separate Instagram profile tab that lists all the products a user recommends. The user becomes the window display – the actual store is just an API in the background.

Similarly, should Shopify decide to make its Shop app an actual discovery platform, it should build its recommendation feed around influencers – not shops.

Rather than an algorithmic feed with random products, the app should feature collections of products that certain people use or recommend. Apps like Svpply and Kit have tried to build similar product recommendation services, but none of them have ever gained mainstream adoption. Yet I’m still convinced that there is a market for a stand-alone app that does curated product discovery.

Amazon is not a product discovery platform, it’s a search engine. It works best when you already know what you want to buy. When you search for “Sapiens”, Amazon will give you a variety of options to buy Yuval Harari’s bestseller (audiobook, Kindle, hardcopy, etc). Perfect.

If you don’t have a specific book in mind yet, however, and just want to discover a history book, Amazon becomes useless. It will show you a list of every SKU available that fits the history book description, but no real guidance on which book you should pick.

But what if you could filter and rank search results by mimetic desire?

Instead of a seemingly random list of books, Amazon should now only show me reading recommendations from people I admire. Who these people are could easily be derived from Twitter data, for example (users I follow + whose tweets I engage most with).

Search results are now ranked by my personal memetic score. I can also see at first glance why each particular book in the list is relevant for me. Not only would this feature improve Amazon’s search results, it would also turn the site into more of a discovery platform.

Given their respective value chains, Amazon and Shopify both have an interest in becoming better at discovery. Technology companies have a tendency to (try to) solve discovery with automated recommendation engines, but that’s not how we make purchase decisions.

Algorithms are not the reason why we buy things, no matter how good they are. Mimetic desire is.

This is why curation is underrated – not because it is actually better than algorithmic suggestions, but because it is perceived as being better.

If this essay has inspired you to imitate me and my desires, feel free to follow me on Twitter. It would a be great honor to become your mediator.

Thanks to Gonz Sanchez, Kent de Bruin and Max Cutler for reading drafts of this post.

(Thoughts on Ecommerce, Pt 1)

With much fanfare and many hot takes on Twitter, Shopify launched one of their “most significant products ever” a few days ago: a consumer-facing shopping app, simply called Shop.

Many have been interpreting this as a massive shift in Shopify’s strategy to compete more directly with Amazon.

I’m not so sure that’s the case.

Inspired by the launch of Shop, I decided to write a two-part essay on ecommerce. The first part – the article you’re reading right now – looks at the current state of online shopping. It explains the business model behind Shopify (and Amazon) and how Shop fits into that strategy.

The second part – which I’ll release next week – is an outlook on the future of ecommerce. It describes what I believe is currently missing in the shopping experience and what Shopify should build next.

Let’s dive into it.

To start things off, let’s first take a look at how Shopify and Amazon operate – because while both of them are ecommerce companies, their strategies are actually fundamentally different.

(Disclaimer: This section is essentially a brash copy summary of Ben Thompson’s excellent analysis on the same topic)

Shopify is first and foremost an infrastructure company. It provides a platform on which merchants can build their own stores to have a direct relationship with their end customers. This is why the type of business Shopify enables is often referred to as “direct to consumer” (D2C).

Amazon, on the other hand, is more of an aggregator. Alongside its own supply, Amazon lets any merchant sell their products on the Amazon.com website. But it is always Amazon which owns the customer relationship – never the merchant. The supply side becomes commoditized.

It’s not just the two companies’ strategies that are different – they also serve two completely different types of shopping behavior.

Any internet service can broadly be categorized based on two types of user actions: Pull and push.

Google is the perfect example of a Pull service. Users are actively looking for a particular piece of information or an answer to a specific question (e.g. “how to make pancakes?” or “are koalas bears?“). Google’s search engine lets you pull that information.

Facebook, on the other hand, is a typical Push service. The user behavior is a lot more passive since you don’t have to actively ask for information. Instead, Facebook automatically pushes the most relevant content into your newsfeed.

Amazon is essentially the Google of ecommerce. It’s primarily a search engine and works best when you already have an idea of what you want to buy. Amazon is not great at discovery though. It doesn’t show you things you didn’t even know you were interested in.

So who, then, is the Facebook of ecommerce?

That question is a little more difficult because there’s not one clear answer. Instead of one dedicated platform for product discovery, we have seen social networks like Instagram and Pinterest slowly morph into product discovery channels. And the fact that they are not pure ecommerce apps, but insert products between organic content, is probably exactly why they perform so well.

The types of products that work particularly well on Instagram are things that are visually appealing. You don’t see ads for HDMI cables or windshield phone mounts on Instagram – those are typical Amazon products.

Instead, it’s products like jewelry, cosmetics, fashion or home accessories that do well on Instagram – and those are classic Shopify D2C brands.

Shopify is powering the infrastructure of the “Facebook for ecommerce”, but it doesn’t own or control the entire channel. And that’s a risk.

Shopify’s model actually looks a bit more like this:

While some of that Instagram traffic might be organic, the most significant chunk of customers is coming from auction-based ads (due to Instagram’s no-link-policy). This means that as competition increases (and it always does once one D2C brand in a particular segment sees some success, because there aren’t any real barriers to entry), ad prices go up.

As a result, many of Shopify’s merchants aren’t really direct-to-consumer brands, they are more like direct-to-consumer-but-with-Instagram-in-the-middle-eating-all-of-their-margin brands.

Instagram capturing most of the value is a perfect example of why demand aggregation is always more powerful than supply aggregation. Too much reliance on a powerful gatekeeper like Instagram is a risk for Shopify and its merchants.

Luckily for Shopify though, there are several ways to mitigate that risk.

The first thing to note is that Instagram isn’t the only product discovery channel driving traffic to Shopify stores. As mentioned earlier, there is not one leading contestant for the role of “Facebook for Ecommerce”. Pinterest, YouTube and Twitter are also product discovery engines (among many other services trying to do the same).

One of Shopify’s options is therefore to diversify the portfolio of user acquisition channels its merchants can use. This is why I find Shopify’s Pinterest and Google Shopping partnership announcements from the last few weeks way more interesting than the launch of Shop.

Shop has been touted as Shopify’s own product discovery channel – which would be another way to tackle its current dependence on other platforms. Instead of relying on Instagram traffic, Shopify could simply start aggregating the demand side itself and become more of a marketplace like Amazon.

But if you install and open Shop, you’ll notice that that’s not really what the app does. Instead of a feed with product recommendations, Shop connects you with merchants you have purchased from in the past.

Here is why:

Another option to defend Shopify merchants against ever increasing customer acquisition costs (read Instagram ads) is to simultaneously increase customer lifetime value. This is exactly what Shop is supposed to do.

By connecting you with merchants you have bought from before, Shop will recommend you additional products that you might be interested in from the same sellers. The result is higher post-purchase loyalty and thus higher LTV which makes it easier to justify high initial acquisition costs via Instagram.

That being said, it’s not difficult to imagine a future where Shopify also starts recommending products from other merchants. What exactly this should look like is the topic of part two of this essay, which you can read here.

Do you have feedback or thoughts on this post?
If so, I’d love to hear them!

Thanks to Jan König for reading drafts of this post.

This is a quarterly update and review of new tools and products I have recently added to my personal productivity stack.

Glyphfinder
I used to spend at least five minutes each day on Google just searching for specific unicode characters – especially when I was writing or designing. Until I found Glyphfinder. Glyphfinder is a Mac app that gives you instant access to more than 30,000 characters and emojis. The best part: Super fast and well-designed search that just works. (Whoever is building Alfred 2.0 – good unicode search has to be a key feature!)

Nototo
I stumbled upon this app in John Palmer’s article on Spatial Software and it’s one of the most unique products I’ve seen recently. The best way to describe Nototo is probably as a cross-over of Notion and Minecraft. Instead of writing down notes on a blank canvas like in any conventional note taking app, Nototo gives you a game-like map on which you place and group your thoughts. The idea is that visualizing your notes makes it easier to connect and remember them. As someone who loves working with post-it notes, I definitely subscribe to that idea.

Zelda and Chill
This playlist of Zelda soundtracks remixed with lo-fi hip hop beats is my new go-to productivity music.

• Read 7 books (1325 min, -34% MoM) and 22 long-form articles (-31%)
• Listened to 605 songs (+25%) and 8 podcast episodes (370 min, -49%)
• Watched 0 movies (0 min, -100%), 0 soccer games (0 min, -100%) and 34 TV episodes (1875 min, +33%)
• Played 2 video games (195 min, +457%)

Tools for Thought (Howard Rheingold)
░░░░░░░░░░░▓▓▓▓ Progress: 73-100%

Designing Games (Tynan Sylvester)
░░▓▓▓▓░░░░░░░░░ Progress: 11-42%

The Glass Hotel (Emily St. John Mandel)
░░░░░░░░░░░░▓▓▓ Progress: 78-100%

History Has Begun (Bruno Maçães)
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 0-100%

Fatherland (Robert Harris)
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 0-100%

Dominion (Tom Holland)
▓▓░░░░░░░░░░░░░ Progress: 0-12%

Spatial Interfaces (John Palmer)

Spatial Software (John Palmer)

IT’S TIME TO BUILD (Marc Andreessen)

Nintendo’s Little-Known Product Philosophy (Adam Ghahramani)

Creating Tools For Networked Thought (Venture Stories)

Top Artists: Mikel (97 plays), Sufjan Stevens (47), IAMX (42), Travi$ Scott (40), EOB (32)