> Hello
> My name is Julian
> This is my lifelog
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and digital playground  

Thoughts on Fleets

Twitter launched their version of Stories last week (called Fleets) – some initial thoughts:

  • I think the Stories format fits Twitter better than any other social network because it’s actually quite similar to how Tweets work. Both Stories and Tweets are modularized content. They work as stand-alone micro content (Tweet / Fleet) or can be grouped into a bigger piece of content (Tweet storm / Story) with sub-discussions for each element.
  • The difference between the two formats is that Tweet discussions are public whereas Fleets will drive more usage of private discussions via Twitter DMs. This is a good thing. Twitter DMs are the most underrated part of the site (and probably the best shot any company has at disrupting LinkedIn). I just wish Twitter had improved DMs before driving more users to it.
  • When Instagram launched Stories, it saw that users posted less to the newsfeed – which they reserved for their best / most important photos. I wonder if we’ll see a similar trend on Twitter, but I doubt it. Tweeting photos and videos was never a great user experience, mainly because of the weird way Twitter auto-crops them, so I don’t think we’ll see cannibalization between the two formats.
  • The animation when swiping between Fleets feels clunky. Instagram Stories feel 10x smoother.
  • The creator tools for Fleets are by far the biggest disappointment. Twitter had a real chance to build something new here (personally, I think audio would be a *really* interesting format). Instead, it’s just a very limited version of other Stories features.
  • The Stories bar is great UI real estate for other features: I really hope Periscope will make a comeback. The rumored audio rooms would also fit nicely here.

Nov 27, 2020  ×  Berlin, DE

Chief Notion Officer

I’ve been thinking a lot about corporate knowledge management systems recently.

If we think about a company as an organism, then a knowledge management system is essentially the (collective) brain that keeps that organism alive and running. A corporate knowledge management system should contain every single bit of codifiable information within the company resulting in a library of all projects, processes and procedures.

In an ideal state, it is the single source of truth that helps to inform every individual in the firm about what everyone else is up to. Information should be easy to add (input) as well as easy to search and find (output) resulting in quick knowledge transfer between different employees.

In reality, however, this hardly ever is the case. As anyone who has ever worked at a larger company can attest to, company knowledge bases always end up being a huge mess.

What starts with a neatly organized Confluence wiki, over time morphs into a multi-headed monster consisting of millions of notes and documents that live across Google Docs, Dropbox Paper, Asana and half a dozen different wiki tools. Most docs will be outdated, some will contradict others and the one you are really looking for only shows up on page 14 of your search results.

It seems like things usually start to fall apart once a company surpasses the Dunbar number of 150 employees. This is probably when people start to realize that all the different documents of explicit knowledge they were amassing over the years have been held together with implicit knowledge.

It’s easy to find – and understand – the right documents when you know every other person in the company, but once you’re past that point, you need a system to organize all the data so that people can make sense of it.

The idea behind tools like Notion is to solve this problem by using just one tool for all your different knowledge documents. Instead of Google Docs AND Asana AND Trello AND Airtable, you just do everything in Notion. This reduces complexity because you don’t have to switch and search across different apps. At the same time, Notion forces you to think about a system that makes information easy to find with its folder-like structure and links between different databases.

I’ve never used Notion with more than half a dozen people myself, but from what I’ve heard from people at larger companies, Notion knowledge bases also don’t scale very well beyond a certain number of users. Once too many people start contributing to it, things become bloated and unnavigable.

A friend at Stripe recently suggested – half-jokingly – that we should hire a librarian to organize all our internal data and documentation. The more I think about it, the more I like the idea. Perhaps every company should hire a Chief Notion Officer once it hits 100 employees?!

An alternative approach to Notion is a knowledge management system that can live across different tools and without active manual curation because it’s based on really powerful search. The folder structure of your Google Drive, for example, doesn’t really matter because looking up documents via search is faster and more convenient. Meta search tools like FYI are supposed to offer the same but across different productivity tools.

But again I’m skeptical that this really works beyond a certain amount of users (and thus documents). I remember even Google’s internal search engine doing only a mediocre job of surfacing the most relevant documents (and even if it did you weren’t sure if there wasn’t a better or more up-to-date version of it).

I’m sure we’ll get there eventually, but until then we probably need a mix of automated search and manual human help – which is where Slack comes in. I’ve always thought Slack plus Notion plus Spoke would make a really powerful product (and I’m surprised Slack hasn’t made any major acquisitions in this space).

If you think about it, Slack is basically a search engine powered by humans: Most Slack messages are just questions. It’s 911 for when everything else fails. So if Slack had access to your entire knowledge base, it could answer at least the most commonly asked questions automatically. The rest would still get answered manually by the channel participants. Or your Chief Notion Officer.

Do you have thoughts on this topic? Please leave your feedback here.
Thanks to Jan König for reading drafts of this post.

Nov 20, 2020  ×  Berlin, DE

Is this real life?

In his bestselling book Sapiens, Yuval Harari argues that humans became the dominating species of planet earth because we are the only animal that can cooperate in large numbers. This, he claims, is due to humans’ ability to believe in purely imaginative things and concepts. A company like Google, for example, doesn’t really exist. Sure, there’s the Google.com website and physical Google offices with real Google employees – but the idea of Google as a company is just a fictional concept. It only exists because multiple people believe in it. The same is true for legal systems, nations, religion or money. Every large human cooperation system is based on a fictional idea that only lives in our collective minds.

What Harari doesn’t discuss in his book is the extreme other end of this cognitive ability: Conspiracy theories. I’ve been fascinated by Jon Glover’s recent essay on QAnon, in which he compares conspiracy theorizing to alternate-reality games. Participating in QAnon conspiracies, he says, feels like playing a real-life multiplayer game based on secret insider knowledge.

Social media has made conspiracy theorizing so addictive and immersive that the line between story and reality can become incredibly blurry.

“A lot of these groups are like cults […] They have beliefs that border on religiosity … And when you contradict them, it’s like telling them Jesus isn’t real.”

The religion analogy is interesting because it’s a perfect example of why fact checking as a countermeasure is useless. Google, Facebook & co have all introduced fact checks and fake news labels to combat conspiracy theories. It’s naive to think that they will work.

Think about it: Science (which, you could argue, is also a form of fact checking) has been around for centuries trying to debunk most religious beliefs – and yet religion still plays a major role in Western society. If entire education systems teaching millions of people about science haven’t worked, why do you think adding a small fact check disclaimer below a YouTube video would?

In fact – as you would expect from a perfect alternate-reality game – fact checks (and how to circumvent them) have actually long been part of the game.

It’s worth pointing out that science is also just another belief system. We laugh about flat earthers, but how many people can actually explain why the world is round in a scientifically correct way? Most of us don’t know science, we believe in science.

What should give us hope though is the fact that many people believe in *both* science and religion despite their contradictions. This means that multiple realities can co-exist even when they are at odds with each other.

We don’t live in just one reality – we switch between different realities (and play different characters within them). It’s a bit like Westworld, where guests can explore different theme parks: Westworld, Shogunworld, Warworld, etc.

Similar to Westworld, it’s increasingly becoming more difficult to distinguish between what’s real and what isn’t. As Aaron Z. Lewis points out in his brilliant essay You Can Handle the Post-Truth, we have created a fragmented reality with hyper-realistic CGI influencers, bots, deepfakes, AI pretending to be humans and humans pretending to be AI. We don’t live in a single timeline with a single history, but in a variety of “contradictory reality bubbles“.

Bruno Maçães paints a similar picture in his excellent book History Has Begun. America, he believes, is in the process of transforming into a new, post-liberal society, distinct from current Western civilization. It’s a society that has not only been heavily shaped by television but one where reality and fantasy overlap.

This transformation has been in the making for a while: Kennedy had the aura of a movie star and leveraged his image through the medium of television. Nixon created the first political soap opera with the Watergate scandal. And with Reagan an actual movie star moved into the White House.

Trump is the ultimate culmination of this trend. His entire presidency feels scripted. His tweets end with cliffhangers. A House of Cards screenwriter would not have been able to come up with a better story.

Reagan and Arnold Schwarzenegger used the social capital and entertainment skills they acquired as actors to appear more likable and competent as politicians, but at least they tried to be politicians. Trump, on the other hand, uses politics as another stage for his acting performance.

“Americans see the world as an action movie” Maçães writes. I think this became especially apparent during the current covid-19 crisis and the most recent wildfires in California. People in my social media timelines seemed only superficially worried. Instead, their posts contained an underlying sense of excitement about real life finally catching up with the science fiction aesthetics of Blade Runner and Akira.

Perhaps this is Hollywood’s greatest achievement: It gets us excited about our dystopian future. The world might be ending, but at least it’s an ending that’s entertaining to watch.

If Hollywood created the fantasy worlds that reality is catching up with today, who is creating the fantasy worlds of tomorrow?

Maçães thinks the answer is Silicon Valley, which he describes as “a fantasy land where engineering talent and capital come together to power the serious project of creating new worlds out of nothing”. It’s one of the most idiosyncratic descriptions of how startups work that I have read. VCs are the new Hollywood studios; founders are the directors and actors.

A founder’s job is essentially to create the most compelling narrative of what their company will look like in 10 to 20 years time. It’s not lying, it’s telling pre-truths. Being contrarian just means that you came up with a novel fantasy plot no one else had thought of yet.

Sometimes founders are able to re-create the fantasy narratives of their pitch decks. Sometimes you end up with Theranos.

And even when you do end up with Theranos, at least you get material for an exciting new Netflix series. Perhaps VCs should buy the movie rights to the startups they invest in as a hedge against their biggest portfolio failures?

The concept of the tech industry as a creator of fantasy worlds immediately reminded me of a conversation I had with my friend Max recently. His theory is that it’s not the lack of tech talent or venture capital that explains why Europe hasn’t been able to create a tech ecosystem on par with the US. It’s the absence of religiosity that has kept Europe from creating its own Google or Facebook. The US is able to create larger companies because it’s able to believe in larger and more ambitious narratives.

Silicon Valley is not just creating new fantasy worlds, it is building tools that allow others to create their own fantasy worlds. Enter social media.

If TV has taught us to think of ourselves as characters in the story of our lives, then social media has allowed us to actually write and edit the script and build fictional characters. Social media is essentially the democratization of virtual world building.

As I wrote in Signaling-as-a-Service, Twitter, Snapchat and Facebook are just massive virtual status arenas that allow us to build social capital through signaling. Some of that social capital might be built on top of real stories and actual achievements, but most of it is not based on reality. Every time you are applying an Instagram filter, you are already changing reality.

It’s not just that we bend reality in our social media narratives, we also play different characters. As Chris Poole already pointed out years ago, we all have multiple (online) identities. There is not just one reflection of yourself – identity is prismatic. Twitter-Julian (armchair intellectual) is not the same as Instagram-Julian (hobby photographer) or Facebook-Julian (high-school drinking buddy). Google Circles and Facebook Lists always got this wrong: They let us change who we shared with, but not who we shared as.

This is why social networking is not a winner-take-all market. We need different channels for our different, contradicting online personas.

The problem is not that we live in multiple realities or that these realities are sometimes at odds with each other. What’s problematic is that we sometimes get so immersed in one virtual world, that we forget about all the other realities – which brings us back to the problem of online conspiracies.

In Christopher Nolan’s Inception, Dom Cobb uses a spinning tractricoid top that tells him if he is awake or still dreaming. You can think of the mechanisms I describe in Proof of X as social media’s equivalent of the spinning top. As influencers rent grounded private jets to pretend living a billionaire lifestyle, social networks introduce new proof-of-work hurdles to make sure our status games remain grounded in truth. Proof of reality.

It feels like some of the new virtual realities we have created need more than that. A kill switch that automatically brings us back to base reality.

So if you have reached this point of my essay, perhaps now would be a good time to close your browser window and enjoy real life. Or at least the closest simulation you have thereof.

Thanks to Aaron Z. Lewis, Jan König and Max Cutler for reading drafts of this post. If you have thoughts on this essay, please leave them here.

Sep 25, 2020  ×  Vienna, AT

A Meta-Layer for Notes

What’s the digital equivalent of sticky notes?

01 Hey

This was originally supposed to be a blog post about Hey. I wanted to write a longer essay about Basecamp’s new email tool and test if the app actually lives up to its hype.

After playing around with it for a few weeks, my conclusion is this: Hey’s most interesting aspect is not its radical approach to email – but its fresh approach to note taking!

We have long treated notes as a distinct silo in our productivity stack, when we should have integrated them right into our workflows instead. While email might need an overhaul, I see a way bigger opportunity in rethinking digital note taking.

So instead of my Hey review, let’s talk about notes and my idea for a radically new kind of note taking app.

02 A Closer Look at Notes in Hey

Hey has two interesting notes features.

The first are so-called Thread Notes. These are basically emails to yourself within an email thread that only you can see. You might have seen similar internal notes features in shared inbox tools like Zendesk or Front. Thread Notes in Hey are effectively the single player version of those.

I’d find Thread Notes super useful in combination with snoozed emails: “Show me this email again in [insert time] and remind me of [insert note]”

This feels like a way better workflow than adding a note in a separate reminder, to-do, CRM, or note taking app.

a) Because there’s no need for context/app switching.
b) You might not even remember that you took a note related to an email when it resurfaces in your inbox a few weeks later.

To-do and reminder apps (and calendars!) work great for tasks that are tied to a specific day or time. But many tasks – and especially notes – are not dependent on time. Their relevance is based on other trigger points. Only when certain conditions are met, should these notes resurface: “If [insert event] is true, then show [note]”

In the case of our email, the note becomes relevant in [insert snooze time] or whenever the recipient replies to the email thread. The fact that many tasks have external dependencies (which are usually linked to an email thread) is one of the reasons I believe that your email inbox should also be the place where you manage your to-dos. You shouldn’t need a separate to-do app.



The second note feature in Hey are Inbox Notes.

As the name suggests, these notes are added to individual emails in your inbox. Similar to Thread Notes, you can use them to quickly jot down things you need to remember, but they also help you to highlight specific emails.

Thread Notes and Inbox Notes feel similar, but they serve two slightly different use cases. Thread Notes work more like reminders (“Don’t forget X when you reply”), whereas Inbox Notes feel more like bookmarks that highlight the most important messages in a long list of emails.

Together, they remind me of one of my all-time favorite note taking tools: Post-it Notes.

03 Post-it Notes

I’m a huge fan of physical note taking and there are two writing tools that I use every single day: A physical notebook (for longer thoughts, including first drafts of my blog posts) and post-it notes (for all kinds of quick notes).

(Disclaimer: When I say “post-it notes” I’m referring to all types of sticky notes, not just those sold by 3M.)

Post-it notes serve two of the same functions that Hey’s note features offer: highlights and reminders.

One of the reasons I still read a lot of non-fiction in physical book form is because it’s easier to bookmark and annotate passages that I quickly want to find again later. Similar to Thread Notes, sticky note bookmarks help me highlight the most important items in a long list.

Apart from helping you find important passages in a book later on, sticky note bookmarks also allow you to add additional context to the section you highlighted (e.g. *why* you bookmarked a particular section or thoughts you had about it).

You could write down notes like this in a separate notebook, but then you’d lose the connection to the source they are based on. What makes post-it notes so interesting is the spatial relationship between the notes and their respective context.

It’s this spatial relationship that also make post-it notes great reminders.

Post-it note reminders are similar to Hey’s Thread Notes in that they are triggered not based on time but on events that don’t have a (forecastable) deadline. They are essentially like notifications that appear when you look at specific objects.

A post-it note on your front door, for example, is like a notification that pops up when you’re about to leave the house: “Before you go, don’t forget to [insert note]”. A shopping list on your fridge is a data request notification that surfaces when you are most likely to have new items to add to your list.

Together, post-its essentially become a notes layer that augments the real world. Instead of a physical notebook that lists all your notes and tasks in chronological order, post-it notes are scattered around your house but tied to specific places or objects where they are most relevant.

The question is: Why isn’t there a digital note taking tool that works like this?

04 A Spatial Note Taking Layer

There are dozens of great note taking apps out there: Evernote, Google Keep, Apple Notes, Workflowy, Notion, Roam … the list goes on and on. Every one of these tools has its own unique angle on note taking, but they all have one thing in common: They are stand-alone apps.

This strikes me as suboptimal. Neither the creation nor the consumption of notes should be treated as separate workflows.

As John Palmer points out in his brilliant posts on Spatial Interfaces and Spatial Software, “Humans are spatial creatures [who] experience most of life in relation to space”. Post-it notes are so powerful because they have a spatial relationship to their context.

Many notes shouldn’t live in a dedicated note taking app that you explicitly have to open and search. Notes should emerge automatically whenever and *wherever* they are most relevant.

As long as note taking remains separated, users constantly have to switch back and forth between different applications, which is not ideal. It reminds me of the recent discussion around productivity and collaboration – which have historically also been treated as two separate, isolated workflows:

The platonic flow of productivity should minimize time spent not productive, with collaboration as aligned and unblocking with that flow as possible. By definition, any app that requires you to switch out of your productivity app to collaborate is blocking and cannot be maximally aligned. It’s fine to leave your productivity app for exceptions and breaks. But not ideal when working.

The same applies to notes. You shouldn’t have to switch apps and context to take or consume notes. It should stay within the same workflow!

(Side Note: You could argue that note taking is essentially single-player collaboration where you communicate with your future self – but that’s a whole new discussion I’ll save for another blog post.)

Natively built in note taking features like email notes in Hey feel like a good step in the right direction – but email is just one distinct silo in your productivity stack. Imagine you had to buy different sets of post-it notes for every single room or object in your house.

What we need instead is a spatial meta layer for notes on the OS-level that lives across all apps and workflows. This would allow you to instantly take notes without having to switch context. Even better yet, the notes would automatically resurface whenever you revisit the digital location you left them at.

Let’s look at a few examples.

05 Examples

One use case that immediately came to mind when I thought about spatial notes is bookmarking.

Most of us don’t use just one bookmarking app for everything. We use different bookmarking apps or bookmarking features depending on the type of object we want to save for later: Podcasts are usually saved in a dedicated podcast app, for example. Articles are bookmarked in Pocket, books on Goodreads, songs on Spotify, places on Foursquare, products on Amazon … you get my point.

Bookmarks are great to remember *what* you want to revisit later – but not *why* you saved something in the first place. I would love to be able to add notes to my bookmarks directly in each app so that I have some context on why these objects are important when I return to them later.

Ideally, these notes wouldn’t just show up in the one place I originally left them, but across all apps and websites that reference the (semantic) object I bookmarked. A note attached to a book I want to read in Goodreads, for example, should also emerge when I see that book in my Amazon search results – or when someone mentions it in my Twitter timeline.

People are a similar type of semantic object you could tie notes to. Instead of a stand-alone CRM tool, you would leave a note attached to a person straight from your current workflow (e.g. your email client). That note would then automatically re-surface whenever the person it references becomes relevant again:

  • When you’re in an email thread with them
  • When you add them to a calendar event
  • When you’re visiting their LinkedIn page
  • When you look them up in your phone book
  • etc


Another use case for spatial notes are instructions on how to use specific software features or improve workflows. These could be quick reminders to add permissions to new calendar events or to use Filtered Views in Google Sheets. You could also use these notes to train users on keyboard shortcuts.

You could imagine employers shipping corporate laptops with pre-installed notes to make it easier to transfer (previously tacit) knowledge and thus improve the onboarding process for new hires.

06 Closing Notes

I could go on and on about potential use cases for a spatial note taking app. The possibilities are endless – but blog posts shouldn’t be. So I’ll end things here.

A final note before you leave: I’d love to hear your thoughts on this whole idea. What would you use a spatial note taking tool for? Let me know what you think in this Twitter thread!

Thanks to Kevin Yien, Matthew Achariam, Max Cutler and Nathan Baschez for their detailed feedback on drafts of this post.

If you liked this post, you might also enjoy the following essays:

Sep 04, 2020  ×  Berlin, DE

Proof of X

01 Intro

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:

Almost every social network of note had an early signature proof of work hurdle. For Facebook it was posting some witty text-based status update. For Instagram, it was posting an interesting square photo. For Vine, an entertaining 6-second video. For Twitter, it was writing an amusing bit of text of 140 characters or fewer. Pinterest? Pinning a compelling photo. You can likely derive the proof of work for other networks like Quora and Reddit and Twitch and so on. Successful social networks don’t pose trick questions at the start, it’s usually clear what they want from you.

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.

02 A closer look at proof mechanisms

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.

Full disclosure: I actually took this picture from Unsplash. No influencers were harmed during the production of this blog post.

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.

If you look closely, you’ll notice additional hidden signaling messages in the form of Allbirds sneakers and what’s most likely a Patagonia vest → proof-of-ownership

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.

03 Implications for new social networks

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?

04 Strava for X

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.

05 A Closing Ask

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.

Aug 06, 2020  ×  Berlin, DE

What Shopify and Amazon can learn from Mimetic Theory (Thoughts on Ecommerce Pt 2)

01 Intro

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.

02 Cheap Options vs. Best Options

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?

03 Mimetic Desire

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:

Advertisers understand this principle really well: you’re not trying to convince somebody that they want Bud Light or a Ford F150; you’re telling them they ought to desire membership to a particular peer set, and the way to become a part of that group is to drink Bud Light and drive an F150. It’s why Abercrombie can advertise their clothes with models that aren’t actually wearing any of those clothes; the clothes aren’t the point.

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

04 What Shopify Should Build

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.

05 How Amazon Could Leverage Mimetic Theory

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.

06 Closing Thoughts

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.

May 28, 2020  ×  Berlin, DE

Thoughts on Ecommerce (Pt 1): The Status Quo

01 Intro

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.

02 How Shopify and Amazon operate

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.

03 Pull vs Push Commerce

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.

04 Demand and Supply Aggregation

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.

05 Shopify’s Options

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.

May 17, 2020  ×  Berlin, DE

AirPods as a Platform

01 Intro

One of the favorite activities of tech analysts, VCs and similar Twitter armchair experts is to predict what the next big technology platform might be.

The usual suspects that come up in these conversations are VR/AR, crypto, smart speakers and similar IoT devices. A new contestant that I’ve seen come up more frequently in these debates recently are Apple’s AirPods.

Calling AirPods “the next big platform” is interesting because at the moment, they are not even a small platform. They are no platform at all. They are just a piece of hardware.

But that doesn’t mean they can’t become platform.

02 What is a platform?

Let’s first take a look at what a platform actually is.

At its core, a platform is something that others can build on top of. A classic example would be an operating system like iOS: By providing a set of APIs, Apple created a playground for developers to build and run applications on. In fact, new input capabilities such as the touch interface, gyroscope sensor and camera allowed developers to create unique applications that weren’t possible before.

Platforms are subject to network effects: More applications attract more users to the platform, while more users in turn attract more developers who build more apps.

It’s a classic flywheel effect that creates powerful winner-takes-all dynamics. This explains why there are only two (meaningful) mobile operating systems – iOS and Android.

It also explains why everyone is so interested in upcoming platforms – and why Apple might be interested in making AirPods a platform.

03 Why AirPods aren’t a platform

In their current form, AirPods are not a platform. They don’t provide any unique input or output functionalities that developers could leverage. Active Noise Cancellation and Transparency Mode are neat but not new or Airpods-exclusive features – other headphones offer exactly the same. In either case, developers don’t have any control over them and thus can’t build applications that use these functionalities.

Some say that AirPods will give rise to more audio apps because they are “always in” which in turn will lead to more (and perhaps new forms of) audio content. That might be true – content providers are always looking for alternative routes to get consumers’ attention – but, again, it does not make AirPods a platform. You can use any other pair of headphones to use these audio apps as well.

If Apple wants to make AirPods a platform, it needs to open up some part of the AirPods experience to developers so that they can build new things on top of it.

04 On Siri & Voice Platforms

The most obvious choice here is Siri, which is already integrated into every pair of AirPods.

In contrast to other voice assistants like Alexa and Google Assistant, Apple has never really opened up Siri for 3rd-party developers. If they did, it would create a new platform that could have its own ecosystem of apps and developers.

But I’m not convinced that this is Apple’s best option.
Let me explain why.

Opening up Siri wouldn’t make AirPods a platform, it would make Siri a platform. This might sound like a technicality, but I think it’s an important difference. As Jan König brilliantly summarized in this article, voice isn’t an interface for one device – it’s an interface across devices. It’s more of a meta-layer that should tie different products together to enable multi-device experiences.

This means Apple has little interest in making Siri an AirPods-exclusive. Voice-based computing works best when it’s everywhere. It’s about reach, not exclusivity. This is part of the reason why Google and Amazon excel at it.

At the moment, Siri’s capabilities are considerably behind those of Google Assistant and Alexa. Again, this isn’t overly surprising: Google’s and Amazon’s main job is finding the right answers to users’ questions. The required ML capabilities for a smart assistant are among the core competencies of these two companies.

But even Amazon and Google haven’t really figured out the platform part yet, as indicated by the lack of breakout 3rd-party voice applications. It seems like the two platforms are still looking for their product-market-fit beyond being just cheap speakers that you can also control with your voice.

This is partly because the above-mentioned use case of voice as a cross-device layer isn’t something developers can build with the current set of APIs.

The other big reason I see is that people are mistaking voice as a replacement for other interfaces. Movies like Her paint a future where human-computer-interaction primarily occurs via voice-powered smart assistants, but in reality, voice isn’t great as a primary or stand-alone interface. It works best as an *additional* input/output channel that augments whatever else you are doing.

Let me give you an example: Saying “Hey Google, turn up the volume” takes 10x longer than simply pressing the volume-up button on your phone. It only makes sense when your hands are busy doing other things (kitchen work, for example).

The most convincing voice app I have seen to date was at a hackathon where a team used the StarCraft API to build voice-enabled game commands. Not to replace your mouse and keyboard but to give you an additional input mechanism. Actual multitasking.

05 What Apple Should Build

I’m not against Apple opening Siri for developers. On the contrary, given that AirPods are meant to be worn all the time, a voice interface for situations that require multitasking is actually a very good idea. But voice input should remain the exceptional case. And it shouldn’t be what makes AirPods a platform.

Instead of voice, I’d love to see other input mechanisms that allow developers to build new ways for users to interact with the audio content they consume.

Most headsets currently on the market offer the following actions with one (or multiple) clicks of a physical button:

These inputs were invented a long time ago and there has been almost zero innovation since. Why has no one thought about additional buttons or click mechanisms that allow users to interact with the actual content?

For example, when listening to podcasts I often find myself wanting to bookmark things that are being talked about. It would be amazing if I could simply tap a button on my headphones which would add a timestamp to a bookmarks section of my podcast app. (Or better even, a transcript of the ~15 seconds of content before I pressed the button, which are then also automatically added to my notes app via an Apple Shortcut.)

Yes, you could build the same with voice as the input mechanism, but as we discussed earlier, saying “Hey Siri, please bookmark this!” just doesn’t seem very convenient.

While podcast apps might use the additional button as a bookmarking feature, Spotify could make it a Like button to quickly add songs to your Favorites playlist. Other developers could build completely new applications: Think about interactive audiobooks or similar two-way audio experiences, for example.

This is the beauty of platforms: You just provide developers with a set of tools and they will come up with use cases you hadn’t even thought about. Crowdsourced value creation.

06 Closing Thoughts

(1) The input mechanism I describe doesn’t have to be a physical button. In fact, gesture-based inputs might be even more convenient. If AirPods had built-in accelerometers, users could interact with audio content by nodding or shaking their heads. Radar-based sensors like Google’s Motion Sense could also create an interesting new interaction language for audio content.

(2) You could also think about the Apple Watch as the main input device. In contrast to the AirPods, Apple opened the Watch for developers from the start, but it hasn’t really seen much success as a platform. Perhaps a combination of Watch and AirPods has a better chance of creating an ecosystem with its own unique applications?

(3) One thing to keep in mind is that Apple doesn’t really have an interest in making AirPods a standalone platform. The iPhone (or rather iOS) will always be the core platform that Apple cares about. Instead of separate iPhone, Watch and AirPods ecosystems, think about Apple’s strategy as more of a multi-platform bundle. Even as a platform, AirPods will remain more of an accessory that adds stickiness to the existing iPhone ecosystem.

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

Thanks to Jan König for reading drafts of this post.
Apr 19, 2020  ×  Berlin, DE

Signaling as a Service

01 Intro

One of the best books I have read in the last few years is The Elephant in the Brain by Robin Hanson and Kevin Simler.

The book makes two main arguments:

a) Most of our everyday actions can be traced back to some form of signaling or status seeking

b) Our brains deliberately hide this fact from us and others (self deception)

So we think and say that we do something for a specific reason, but in reality, there’s a hidden, selfish motive: to show off and increase our social status.

You may have heard about a similar concept before called conspicuous consumption. Conspicuous consumption describes the practice of purchasing luxury goods (or services) for the sake of signaling the buyer’s wealth in order to attain or maintain a certain social status.

A classic example of this would be a luxury watch: A Rolex isn’t better at telling the time than a cheap Casio – but a Rolex signals something about its owner’s economic power and thus their social standing.

This is not a new theory, but Simler and Hanson argue that a lot more human behavior can be explained by signaling. Here are a few examples from the book:

  • Consumption
    Signaling does not only explain luxury purchases but also consumption of all sorts of other goods: “Green products” are more about signaling a prosocial attitude than actually helping the environment. Other consumption signals include loyalty to a specific subculture (e.g. band t-shirts), athleticism & health consciousness (athleisure clothing) or intelligence (e.g. Rubik’s Cube).

  • Charity
    There are several indicators that suggest that giving to charity isn’t really about improving the well-being of others: The lack of effective altruism demonstrates that we don’t care much about the actual outcome of our donations and studies show that our charitable behavior is heavily driven by visibility (hardly any donations are anonymous), peer pressure (95% of donations are solicited) and mating motives (donations are higher and more likely when observed by a member of the opposite sex). Charity is about appearing generous rather than actually doing good.

  • Education
    You would think that going to school is about learning and acquiring skills, but then why do students pay tens of thousands of dollars for Ivy League schools when all of the learning material is effectively available online for free? Why do we use grading systems when we know that students learn worse when being graded? The answer, again, is signaling: Education helps with credentialing and signaling to potential employers.

There are many more examples in the book (and I recommend reading the whole thing), the point the authors are trying to make is clear: Almost everything has a signaling component – we are just not aware of it. In fact, Hanson believes that “well over 90 percent” of human behavior can be explained by signaling.

Whether or not you agree with that exact number, I think it’s an interesting thought experiment to look at a specific behavior and think about what the hidden signaling subtext of that behavior might be.

Ever since finishing the book, the signaling behavior I’ve been thinking about the most is the adoption – and more importantly the monetization – of software products and services.

This is what this blog post is about.

02 Components of Signaling

Let’s take a closer look at signaling first.

The way I see it, signaling can be broken down into different components:

  • Signal Message
  • Signal Distribution
  • Signal Amplification

To better illustrate what I mean let’s take a pair of sneakers as an example.

The first component is what I call the signal message. This is whatever (hidden) subtext you are trying to convey. In the case of our sneakers this is probably something along the lines of “I can afford to spend $100 on a pair of shoes” and “I live an active, healthy lifestyle”.


In order to get your signal message across to other people you need some form of signal distribution. This is the second component of my signaling taxonomy.

So how are you going to distribute the signal message of your sneakers? You simply wear them where other people can see them. The obvious constraint here is that your signal distribution is limited to things you can display in public. This is why people are willing to spend hundreds of dollars on shoes but not on socks.

The third component is signal amplification: If everyone is wearing cool sneakers .. how do you make sure yours stand out? You could buy the pair with the most noticeable design or the one with the flashiest colors, for example. These signal amplifiers help you to better compete against status rivals.


Let’s recap: Signaling can be broken down into signal message, distribution and amplification. “Real world” products are great at visualising a signal message due to their physical nature. However, as a consequence there are also physical boundaries to distribution because there are only so many people you can signal to at once.

But what about software?

03 Software’s Signaling Limitations

Digital products have one crucial disadvantage over atom-based products and services: Intangibility. Apps live on your phone or computer. No one can see them except for you.

The signal message of a fitness app is the same as that of a gym membership or athletic wear (strength & fitness display), but the signal is much weaker because you can’t distribute it to anyone.

I believe that this is the main reason why consumer software companies have a harder time monetizing than their physical counterparts.

Here’s another example: eBooks have never caught up with paper books despite being more convenient. On the contrary, physical book sales have remained stable (and in some markets even increased) in recent years. Interestingly though, people spend less time reading them. Their value seems to stem from lying around the house to impress visitors (see also coffee table books) – a benefit digital books simply can’t offer.

Another point of evidence is the lack of luxury software products. People spend absurd amounts of money on jewellery, handbags and cars, but I can’t think of a piece of software with an even remotely similar price tag. Sure, people have tried to sell $999 apps but those never took off.

The app that comes closest to a luxury service that I can think of is Superhuman, which charges its users $30 a month for an email client (which you could also get for free by just using Gmail).

But there’s a difference to other software products: Superhuman has signal distribution built in. Every time you send an email via Superhuman, your recipient will notice a little “Sent via Superhuman” in your signature.

In a similar fashion, apps like Strava use their built in social networks as a signal distribution channel for their premium subscriptions. Users who have upgraded get a little premium badge and appear in exclusive premium leaderboards.

Another interesting way to solve the signal distribution problem is to add a physical product to a software’s premium offering, which allows signaling via casual contact (like fashion products).

Neobanks such as N26 or Revolut reward their premium users with a fancy metal card which doesn’t just look nice but is also noticeably heavier than normal credit cards. There aren’t a lot of other benefits that justify the hefty €17/month price tag these banks charge for their premium tiers – clear evidence that the primary monetization driver is in fact signaling.

04 Signal Distribution as a Service

While many digital products struggle to monetize as well as their real-word counterparts, the Internet has created a whole new kind of signaling utility: Distribution as a service.

Physical products are limited to the amount of people you see in public – but the Internet allows you to reach a virtually infinite number of people at once.

This is the primary value that social networks like Facebook, Snapchat and Instagram provide. These services don’t contain a hidden signal message. All they do is provide signal distribution at scale. Want to increase the number of people who can see your sneakers? Just take a photo and post it on Instagram.

A positive feedback loop of views, likes and comments helps you to quantify how successful your signal distribution has been.


Eugene Wei calls this Status as a Service:

By merging all updates from all the accounts you followed into a single continuous surface and having that serve as the default screen, Facebook News Feed simultaneously increased the efficiency of distribution of new posts and pitted all such posts against each other in what was effectively a single giant attention arena, complete with live updating scoreboards on each post. It was as if the panopticon inverted itself overnight, as if a giant spotlight turned on and suddenly all of us performing on Facebook for approval realized we were all in the same auditorium, on one large, connected infinite stage, singing karaoke to the same audience at the same time.

It’s difficult to overstate what a momentous sea change it was for hundreds of millions, and eventually billions, of humans who had grown up competing for status in small tribes, to suddenly be dropped into a talent show competing against EVERY PERSON THEY HAD EVER MET.

Social networks are subject to network effects: The more users join a network, the more valuable the network becomes. Your incentive to use Facebook increases with the number of people you can distribute your signal message to. This is why social networks are free to use – in order to maximize their signaling potential they need to acquire as many users as possible.

A social network like Path attempted to limit your social graph size to the Dunbar number, capping your social capital accumulation potential and capping the distribution of your posts. The exchange, they hoped, was some greater transparency, more genuine self-expression. The anti-Facebook. Unfortunately, as social capital theory might predict, Path did indeed succeed in becoming the anti-Facebook: a network without enough users. Some businesses work best at scale, and if you believe that people want to accumulate social capital as efficiently as possible, putting a bound on how much they can earn is a challenging business model, as dark as that may be.

While Path did indeed fail as a distribution provider, I would argue that keeping the network’s size small can still have benefits in line with my signaling theory: Deliberately limiting the number of people who can join a network (e.g. by charging a membership fee) creates scarcity which in turns makes the network more interesting. Network membership becomes the signal message. Users pay a membership fee to signal to other members that they are part of the tribe.

Some examples:

These social networks still rely on some critical mass and network effects, but need to set an artificial limit to the amount of people who can join. If membership isn’t scarce, the membership loses its signal message. The same applies to physical products: Apple will never offer a cheap iPhone to compete with low-end Android devices – it would destroy the company’s signal message that the iPhone is a luxury product.

In contrast to iPhones, there is another limitation that social networks with this strategy face: Like in the before mentioned software examples, signal distribution is limited to the in-group. Signaling however is strongest when you can signal tribe affiliation to both in-group members as well as outsiders. This is also the reason why luxury car manufacturers don’t limit their advertising campaigns to potential buyers but deliberately extend it to people who will never be able to afford the car.

But as we’ve discussed earlier, the intangibility of software makes signaling to the out-group difficult: You would instagram a photo from your Equinox gym, but would you share a screenshot of your MyFitnessPal Premium subscription?

Instead of monetizing network membership, the software products that monetize most successfully have chosen another strategy: Make memberships free and monetize signal amplification instead.

05 Monetizing Signal Amplification

Earlier, we defined signal amplification as product features that help users to increase the strength of the signals they want to convey to stand out of the crowd. In the example of our aforementioned sneakers, flashy colors help to amplify our signal message.

Similar amplifiers exist in the software world, but they often come in the form of a set of tools. Take the Instagram photo editor for example: Applying filters to your photos makes them look nicer and hopefully more noticeable in the app’s status arena – aka the newsfeed.

Eugene Wei calls these amplifiers Proof of Work:

Almost every social network of note had an early signature proof of work hurdle. For Facebook it was posting some witty text-based status update. For Instagram, it was posting an interesting square photo. For Vine, an entertaining 6-second video. For Twitter, it was writing an amusing bit of text of 140 characters or fewer. Pinterest? Pinning a compelling photo. You can likely derive the proof of work for other networks like Quora and Reddit and Twitch and so on. Successful social networks don’t pose trick questions at the start, it’s usually clear what they want from you.

While Instagram, Twitter and the other above-mentioned social networks are free to use, other companies have figured out a clever way to monetize their signal amplifiers. The two companies who have done this most successfully are Tinder and Fortnite.

Let’s start with Tinder.

06 How Tinder Monetizes Signal Amplification

Tinder is a social network for dating – or in other words, a signal distribution network to display your mating worthiness. Like other social networks, Tinder is subject to network effects: The value of the network increases with its size. The obvious strategy therefore is to make memberships free so that as many people as possible can join. (Technically, dating apps are two-sided networks. The value for female members increases with the number of male members and vice versa.)

Tinder’s primary proof-of-work mechanism is to optimize one’s profile picture for a maximum number of swipe rights. But with millions of rivals on the same platform, competing for status with just a few pixels of profile picture real estate becomes a really hard task.

Luckily, Tinder offers a variety of additional signal amplifiers that help you to stand out. The sole purpose of features like Tinder Boost and Super Likes is to outcompete status rivals by giving you preferential signaling treatment. And guess what – they come with a price tag.

Tinder’s entire business model is built on the assumption that people are willing to spend money on signaling. That assumption seems to be correct: Tinder made a staggering $1.2 billion in revenue last year making it one of the most successful apps world wide.

07 Fortnite – The Ultimate Status Battleground

Fortnite has seen even greater levels of financial success: In the last two years combined, the game has brought in more than $4 billion in revenue – and like Tinder, it too monetizes primarily with signal amplification.

For the longest time, the monetization model of games used to be – and for many still is – one-time upfront payments which then allowed you to play the game for as long as you wanted to.

That business model changed with the emergence of mobile games on iOS and Android. Instead of charging players upfront for access, mobile games are typically free to play. However, in order to progress faster and win the game, users will eventually have to pay for small upgrades with in-app purchases.

Similar to these traditional mobile games, Fortnite is also free to play. As a multiplayer game that many play with their real-life friends, this strategy makes a lot of sense – the network becomes more valuable the more people join.

In contrast to mobile games however, Fortnite is also free to win. None of the in-app purchases available impact the core gameplay. You can’t buy more powerful weapons or stronger armor that give you an advantage over other players.

That’s because the core gameplay isn’t the core signaling layer – and thus also doesn’t offer the greatest monetization potential.


Fortnite is more than just a game. It’s more like a giant virtual theme park, or the closest thing we have to a metaverse even. People don’t just come for the battle royale game – they come to meet and hangout with friends.

But if The Elephant in the Brain has taught us anything it’s that you don’t just meet people for fun. You are engaging in a constant battle for attention and status. Signaling is the meta game that Fortnite provides – and monetizes.

Fortnite’s monetization model is based on cosmetics: The skin your character wears; the looks of your glider and the tools you use; the way your character dances (emotes) – all of these are signaling amplifiers with different signal messages to uniquely express yourself in the game. And you have to purchase them.

Fortnite has pulled off what so many other software products have been struggling with – monetizing a purely digital product whose value is not based on utility or entertainment but solely on the one thing we all secretly care so much about: Signaling.

08 Summary

While the physical nature of material goods and services is perfect to visualize hidden signaling messages, there are natural limitations to distribution and amplification.

Software perfectly complements physical goods by distributing their signal messages at scale. Maximizing scale, however, prevents it from monetizing said distribution. This is why social media services are free to use. The added signaling value is solely captured by the physical products that are being shared.

The intangibility and fungibility of software also makes it difficult to create and share (and monetize) software products that contain hidden signal messages of their own. This explains why there are no software equivalents of luxury products such as Rolex watches or Louis Vuitton handbags.

The financially most lucrative strategy for software companies is to provide distribution for free and instead monetize users who want to stand out of the crowd with paid signal amplification.

A closing thought: I tell myself that I write these blog posts “just for fun”, but let’s be honest … all I really want is to signal how smart I am. So if you could head over to Twitter and give me a Like or a Follow, that’d be great. Thanks!

Thanks to Gonz Sanchez, Jan König, Max Cutler and Robin Dechant for reading drafts of this post.
Mar 28, 2020  ×  Berlin, DE

Inventory Update (Q1/20)

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

Spoonbill
I’ve been looking for a product like this for a while: Spoonbill connects with your Twitter (and GitHub) account and sends you diff-updates on the bios of the people you follow. You can receive updates via email and RSS. Someone should build this for LinkedIn.

Noto
Noto is an app to send email notes to yourself. The app opens directly to the input screen – a simple swipe then sends the note to a pre-defined email address. This is ideal for people like me who use their inbox as their primary productivity control center and to-do list. You can add up to six different email addresses which becomes pretty powerful in combination with Superhuman’s split inbox feature. I wish a note functionality like this was built directly into the iOS lock screen.

Zenly
This is one of the most interesting apps I’ve played around with lately. Zenly is essentially the Gen Z version of Foursquare: A location-first social network, but instead of manually checking into places, users constantly share their live location (as well as other data such as your current battery status). What I find most interesting though, is the app’s fog of war-like map that shows you exactly which areas you’ve already explored (plus the exact discovery percentage number per city). This is a great way to quantify my movement patterns and set monthly or yearly discovery goals (I currently do this with Swarm).

Feb 06, 2020  ×  Dublin, IE
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