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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

Music for Productivity

As part of my ongoing work on a personal operating manual, I’ve been thinking a lot about what Tyler Cowen would call the “Julian Production Function”. Namely, a list of all the habits, routines and things that make me more productive.

One element of my personal production function I find particularly interesting is the impact of music (or audio more general) on my productivity.

Based on almost 15 years of Last.fm data, I know that music consumption overall is a pretty good proxy to measure my productivity. The graph above shows the number of songs I listened to on a monthly basis (the gray bars) as well as the monthly average per year (the black line).

My music consumption peaked in years where I did lots of deep work such as studying, writing or coding (2008, 2010, 2013, 2017-2018). Years with less screen time correlate with a lower number of songs I listened to (2014-2016, 2019).

Of course not every type of music is necessarily great for productivity. When I dug a little deeper into the data I found another interesting trend: Over the last ~10 years, I have increasingly listened to ambient music and soundtracks. These two graphs essentially represent my efforts to optimize my music consumption for further productivity improvement.

The problem is that I haven’t found a good way to measure if (or which of) those attempts to improve my productivity have actually paid off. More music consumption tells me that I worked more – but it doesn’t tell me if I worked better or more efficiently.

It’s hard to quantify actual productivity or focus work: I’ve tried using productivity proxies such as RescueTime, number of emails sent and GitHub contributions – but all of those are pretty vague. Rating each day with a “perceived productivity”-score also hasn’t really produced any meaningful data.

Nevertheless, I will continue to experiment with different music to improve my focus. Here’s a list of music apps and playlists I’m currently using:

AMBIENT MUSIC
Brian Eno’s Music for Airports is great for productivity – I especially like this 6 hour time-stretched version. Someone put together a fantastic Spotify playlist of similar airport music. Max Richter’s Sleepis meant to be listened to at night” but I find it perfect for focus work. (I also tried listening to it at night to see if it would improve my sleep. It didn’t.) If you like Richter’s work, you might also enjoy Nils Frahm and Jóhann Jóhannsson.

SOUNDTRACKS
I can recommend pretty much everything by Trent Reznor and Atticus Ross (the new Watchmen OST is particularly great). The Black Mirror soundtracks have a similar dystopian feel – Jason Kottke compiled all of the songs in a playlist here. Hans Zimmer’s Interstellar and Dunkirk soundtracks are also great for productive work.

NATURE SOUNDS
You can find endless nature sound playlists on Spotify and YouTube. I prefer thunderstorm and rainforest sounds. Noisli is great to mix and match different sounds.

LO-FI ANIME BEATS / CHILLHOP
I’m subscribed to ChilledCow and Chillhop Music on YouTube, but to be honest this type of music has never really worked for me.

FOCUS MUSIC APPS
My go-to productivity app these days is a service called Endel which “creates personalized, sound-based, adaptive environments that help people focus and relax” based on a variety of inputs (including time of day, weather, heart rate and location). I have also heard good things about Brain.fm and focus@will.

Last but not least, Flow State is an excellent newsletter that sends you a daily focus music recommendation.

Do you have any other recommendations?
Please let me know on Twitter!

Mar 16, 2020  ×  Berlin, DE

Media Consumption (Feb 2020)

>_ Summary
  • Read 2 books (315 min, -73% MoM) and 19 long-form articles (-44%)
  • Listened to 656 songs (+34%) and 9 podcast episodes (472 min, +6%)
  • Watched 0 movies (-100%), 11 soccer games (1125 min, +389%) and 22 TV episodes (1298 min, +127%)
  • Played 4 video games (55 min, +∞%)
>_ Books

The Design of Everyday Things (Donald A. Norman)
░░░░░▓▓▓▓▓▓▓▓░░ Progress: 31-85%

Ra (Sam Hughes)
░░░░▓▓░░░░░░░░░ Progress: 25-35%

>_ Recommended Articles

Spotify: The Ambient Media Company (Brett Bivens)

Reddit’s Profane, Greedy Traders Are Shaking Up the Stock Market (Bloomberg)

Yuval Noah Harari’s History of Everyone, Ever (The New Yorker)

>_ Recommended Podcasts

Sriram Krishnan on Building Consumer Social Platforms (Venture Stories)

World After Capital, with Albert Wenger (Invest Like the Best)

>_ TV Shows

Chernobyl (Season 1)
░░░▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 20-100%

Narcos: Mexico (Season 2)
▓▓▓▓▓▓░░░░░░░░░ Progress: 0-40%

Succession (Season 2)
░░░▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 20-100%

Watchmen (Season 1)
▓▓▓▓▓▓▓▓░░░░░░░ Progress: 0-56%

>_ Music

Top Artists: Endel (110 plays), Zoot Woman (80), Trent Reznor and Atticus Ross (68), Abel Korzeniowski (35), Tame Impala (25)

Mar 01, 2020  ×  Berlin, DE

My Quantified Self Setup

The number one question I get asked on Twitter these days is how I get the data for my media consumption posts and quantified self reports. So I thought I’d dedicate this week’s post to explain my tracking setup.

I got into self tracking in the early 2000s when I first discovered HistoryStats – a Miranda plugin that gave me interesting statistics about my messaging behavior (who I talked most often to, what words I used the most, etc). A few years later, Audioscrobbler (which is now last.fm) allowed me to get similar statistics about my music habits. I also started to keep logs of books and movies that I had read and seen.

Inspired by the work of Nicholas Felton, I later began to quantify all sorts of other activities. My 2013 Quantified Self report – my most comprehensive tracking project so far – included data such as: How much I walked, drank, ate and slept. How many people I talked to. How many buildings I entered. I even tracked how much time I spent in the shower and brushing my teeth.

At the beginning, I always carried a little notebook around with me to write down things as they happened, but over time I got into the habit of simply being more present and remembering the core activities I wanted to track, so I could digitize them later. Even activities such as reading books I now consciously quantify and memorize while I’m doing them.

I currently use an Airtable spreadsheet for the vast majority of my QS activities, which I update about once or twice a day.

My 2020 tracking sheet looks like this: I have 10 spreadsheet tabs – one for each tracking category. These include Sleep, Wellbeing, Fitness, Work, Media, Drink, Food, Social, Travel and Screen Time.

Each tab then has a variety of metrics that I update on a daily basis. This is my current structure:

Sleep 🌙
  ↳ Bed Time (hh:mm)
  ↳ Wake Up Time (hh:mm)
  ↳ Perceived Sleep Quality (1-5 rating)
  ↳ Sleep Location
     ↳ City (single select)
     ↳ Location type (single select: home/hotel/…)
  ↳ Nightmares (y/n)
  ↳ ██████ ██

Wellbeing 🙏
  ↳ Perceived Happiness (1-5 rating)
  ↳ Perceived Stress (1-5 rating)
  ↳ Perceived Overall Healthiness (1-5 rating)
  ↳ How Worried am I About the Future? (1-5 rating)
  ↳ Perceived Backpain (1-10 rating)
  ↳ Illnesses (multi-select)
  ↳ ██████ ██
  ↳ ███ ███

Fitness 💪
  ↳ Meditation (hh:mm)
  ↳ Back Exercise (y/n)
  ↳ Stretching (y/n)
  ↳ Swimming
     ↳ Swim distance (meters)
     ↳ Swim time (hh:mm)
  ↳ Push-ups (number)
  ↳ Plank (duration in minutes)
  ↳ Other Sports
     ↳ Sport (single select)
     ↳ Duration (hh:mm)

Work 💼
  ↳ Start Time (hh:mm)
  ↳ End Time (hh:mm)
  ↳ Work Type (single select: work/writing/…)
  ↳ Work Location (single select: home/office/coworking/…)
  ↳ Perceived Productivity (1-5 rating)
  ↳ Perceived Job Happiness (1-5 rating)

Media Consumption 📚
  ↳ Kindle Books
     ↳ Book Title (single select)
     ↳ Reading Time (hh:mm)
  ↳ Physical Books
     ↳ Book Title (single select)
     ↳ Reading Time (hh:mm)
  ↳ Audio Books
     ↳ Book Title (single select)
     ↳ Reading Time (hh:mm)
  ↳ Podcasts
     ↳ Podcast Show (single select)
     ↳ Podcast Episode (single select)
     ↳ Listening Time (hh:mm)
  ↳ TV
     ↳ TV Show (single select)
     ↳ TV Episode (single select)
     ↳ Watch Time (hh:mm)
  ↳ Soccer
     ↳ Teams (multi-select)
     ↳ Goals (number)
     ↳ Watch Time (hh:mm)
  ↳ Movies
     ↳ Movie (single select)
     ↳ Watch Time (hh:mm)
  ↳ Games
     ↳ Game (single select)
     ↳ Play Time (hh:mm)

Drinks ☕️
  ↳ Filter-based Coffee (number)
  ↳ Espresso-based Coffee (number)
  ↳ Beer (ml)
  ↳ Wine (number of glasses)
  ↳ Non-alcoholic Drinks
     ↳ Drink (single select)
     ↳ Amount (ml)
  ↳ Alcoholic Drinks
     ↳ Drink (single select)
     ↳ Amount (ml)

Food 🍕
  ↳ Breakfast
     ↳ Dish (multi-select)
     ↳ Location (single select)
  ↳ Lunch
     ↳ Dish (multi-select)
     ↳ Location (single select)
  ↳ Dinner
     ↳ Dish (multi-select)
     ↳ Location (single select)
  ↳ Pizza (number of slices)
  ↳ Meat Consumption (multi-select: beef/pork/…)
  ↳ Fish Consumption (multi-select: salmon/tuna/…)
  ↳ Perceived Healthiness (1-5 rating)

Social 👫
  ↳ In-person Conversations
     ↳ Partner (y/n)
     ↳ Close Friends (y/n)
     ↳ Friends / Acquaintances (y/n)
     ↳ Co-worker (y/n)
     ↳ Other (y/n)

Travel 🚗
  ↳ Did I use this mode of transport today?
     ↳ Plane (y/n)
     ↳ Car (y/n)
     ↳ Subway (y/n)
     ↳ Taxi (y/n)
     ↳ Tram (y/n)
     ↳ Train (y/n)
     ↳ Bus (y/n)
     ↳ Scooter (y/n)
     ↳ Boat (y/n)
     ↳ Other (y/n)

Screen Time 📱
  ↳ Total Phone Screen Time (hh:mm)
  ↳ Time Spent in Social Apps (hh:mm)
  ↳ Phone Pickups (number)
  ↳ Notifications Received (number)

While this might sound like a lot of work, it doesn’t actually take a lot of time to fill out the sheet each day. I probably spend less than 5 minutes per day in my Airtable spreadsheet.

In addition to my tracking sheet, there are a few metrics that are calculated (semi) automatically:

Number of Articles Read
Whenever I finish reading an article I add it to Pocket with a specific tag. Every article (title, URL) then automatically gets added to another Airtable spreadsheet via IFTTT.

Music Behavior
Last.fm tracks every song I listen to.

Fitness and Sleep Data
I have a Fitness Alta that tracks the number of steps I take and how active I am. It also tells me how long and well I slept.

Location Data
I check into every single place I visit with Swarm. I also have constant location tracking turned on in Google Maps, Gyroscope and Zenly. None of these always-on services is particularly great – I really miss Moves App.

Screen Time
I use Screen Time on my iPhone and RescueTime on my laptop. I wish Apple would release a Screen Time API so that I didn’t have to manually copy data from the app to Airtable.

Air Quality
The Plume Flow 2 is the latest addition to my quantified self setup. This is a mobile air pollution sensor that measures Particulate Matter (PM1, PM2.5, PM10), Volatile Organic Compounds (VOCs) and nitrous oxides (NO2).

I’m thinking about upgrading to a newer fitness tracker with a heart rate monitor or a more sophisticated sleep tracker like the Oura Ring. It would also be interesting to move to a sort of tracking bot that randomly asks me questions through-out the day (see Felix Krause’s LifeSheet or Reporter app) to avoid peak-end bias.

Eventually, I’d also like to build a dashboard that combines my Airtable spreadsheet with my yearly goals and gives me real-time reminders and statistics about my routines and habits – but this is for another article.

If you have thoughts or questions about my setup, I’d love hear them.

Feb 23, 2020  ×  Berlin, DE

Media Consumption (Jan 2020)

>_ Summary
  • Read 5 books (1160 min, -19% MoM) and 34 long-form articles (+36%)
  • Listened to 488 songs (-21%) and 8 podcast episodes (444 min, +26%)
  • Watched 3 movies (-40%), 2 soccer games (230 min, +5%) and 10 TV episodes (571 min, -43%)
  • Played 0 video games (0 min, -100%)
>_ Books

A Little Life (Hanya Yanagihara)
░░░░░░░░░░▓▓▓▓▓ Progress: 66-100%

The Design of Everyday Things (Donald A. Norman)
░▓▓▓▓░░░░░░░░░░ Progress: 3-31%

Designing Design (Kenya Hara)
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 0-100%

Ra (Sam Hughes)
▓▓▓▓░░░░░░░░░░░ Progress: 0-25%

Hackers & Painters (Paul Graham)
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 0-100%

>_ Recommended Articles

2019 Letter (Dan Wang)

Products I Wish Existed (2020 Edition) (Elad Gil)

Products I’d Pay For (2020 Edition) (Dominic Monn)

Underutilized Fixed Assets (Kevin Kwok)

Two Hands Are A Lot (Dominic Cummings)

Shopify: A Starcraft Inspired Business Strategy (Non-GAAP Thoughts)

>_ Recommended Podcasts

Matt Clifford on Investing Pre-Company (Invest Like The Best)

Reid Hoffman on Systems, Levers, and Quixotic Quests (Conversations with Tyler)

>_ Music

Top Artists: Trent Reznor & Atticus Ross (201), Woodkid (36), Auf Der Maur (35), 坂本龍一 (35), Abel Korzeniowski (33)

Feb 01, 2020  ×  Berlin, DE

Superhuman & the Productivity Meta-Layer

01 The Arc of Collaboration

Kevin Kwok posted an excellent article a while back about The Arc of Collaboration. I highly recommend reading the whole thing, but the main argument is that productivity and collaboration have always been handled as two separate workflows:

  1. We started with individual files that we sent back and forth via email
  2. Then Dropbox came along and enabled collaboration within documents, but communication about these docs remained a separate channel
  3. Slack wants to become the central communication channel for all productivity apps

The problem, Kevin argues, is that productivity and collaboration shouldn’t be treated separately. Instead, they should go hand in hand and that’s exactly what a lot of the latest productivity tools do: Figma, Notion, Airtable, etc all have messaging natively built in to their apps.

While these functional workflows work great on their own, they are still separate silos between which you have to switch back and forth. The solution might be a meta layer on top of the productivity stack that works horizontally across all function workflows.

It’s not clear yet what exactly this meta layer would look like but it might be something similar to what Discord is to gaming.

I mostly agree with the points Kevin makes, but I see the role of Slack slightly different. While I don’t think that Slack will become the meta layer, I do think it came closer to that idea than people give it credit for.

Instead of Slack, I believe that email – and more importantly Superhuman – will return to become the center of gravity for productivity.

02 Notifications & the Multiple Inbox Problem

With more and more productivity apps creating their own messaging systems, users suddenly face a new problem: Multiple inboxes. You now have to check notifications in Github, Trello, Google Docs and half a dozen (if not more) other tools in your productivity stack.

Slack basically wants to be the unified notification center that captures all those incoming alerts from your productivity tools – a high frequency communications layer that ties everything together.

The way I see it notifications serve three important functions:

  • Being notified about (relevant) new developments
  • Taking actions on these developments (if necessary)
  • Building a (personalized) history of company records

As Kevin points out in his article, Slack only really handles the “being notified” part. Whenever you want to take action on notifications you have to switch to whatever app you’ve received the notification from. Productivity and collaboration remain separate.

There are a few exceptions though: Slack does integrate pretty well with a couple of tools and allows you to manage certain tasks straight from within the app. You can close and reopen issues and pull requests from GitHub, for example. Or create Asana tasks. Or instantly reply to Intercom messages.

But Slack isn’t the perfect tool to manage notifications. Incoming alerts aren’t really bundled in one place but appear across different channels and between different messages. This makes it really hard to keep track of which notifications you have seen and which you have taken action on.

You need a single notification stream that allows you to treat notifications like tasks. Slack isn’t that. But you know what is? Email!

03 Emails as To-Do’s

Back in 2013, the Mailbox team built an email client that looked more like a to-do list than an inbox. With a simple swipe users could simply mark an email as done, add to it to a list or snooze it to deal with it later. Emails became tasks.

While Mailbox eventually got deprecated (after Dropbox acquired it), the emails-as-tasks concept lives on. Snoozing emails and Inbox Zero are now standard features in most email apps.

I’ve always wondered why no one ever developed the idea further: Why stop at snoozing emails? Why not add other actions to your email inbox? Inspired by these questions, I briefly worked on an idea a while back that can be summarized as an inbox that only lets you reply with pre-defined actions.

Sounds confusing?
Let’s look at a few examples.

Example A: Your colleague Lisa invites you to a meeting

A right swipe accepts the meeting and adds the event to your calendar
A left swipe declines the meeting and lets you propose a different time

You never have to open the message or write a lengthy response – you can only react with a swipe.

Google Calendar notifications in Gmail are actually already pretty close to this, so let’s look at a more sophisticated example.

Example B: The New York Times notifies you about a new article they just published

○  A simple tap just opens the article
A short right swipe adds the article to Pocket
A long right swipe saves in Evernote
A right swipe sends the article to your Kindle

The problem with this idea is that you are limited to just a handful of actions (because you can’t fit more on the screen) and that it’s difficult to predict which actions are most relevant for each message you receive.

This is where Superhuman comes in.

04 Superhuman as the center of gravity for productivity

Superhuman, for those unfamiliar with it, is an email client that – among other features – lets you manage your inbox by just using your keyboard.

There are keyboard shortcuts for literally every single command you can think of: Compose a new email? Hit c. Discard a draft? Press , Shift and b. Reply to an introduction email with a Thank You note and move the original sender to bcc? Press , Shift and i (yes, this actually exists).

Most importantly though, users can trigger a command line interface so you can just write down the action you want to take without having to remember the exact keyboard shortcut. The NLP engine behind this thing works remarkably well and understands what you want to do no matter how you phrase it (this might be Superhuman’s most underrated feature).

At the moment, Superhuman commands are limited to typical email actions (snooze, send later, etc), but the obvious next step, in my opinion, is to add commands that work across different apps.

That meeting request your colleague Lisa sent you?
Instead of just sending a reply why that time she proposed doesn’t work for you, you should just be able to also send an updated calendar event without having to leave the Superhuman app.

But you should also be able to block 30 minutes in your calendar before the meeting so you can prepare – without having to switch over to your calendar app and add the events there. Hit ⌘K and type “Add 30 min buffer before event“. Done.

I suspect that Superhuman will build their own calendar features (as well as to-do list functionalities) and then start integrating third-party applications to become an actual platform.

With a strong enough NLP engine behind the command line interface, the possibilities become endless:

  • Add that New York Times article to your Pocket queue or send it directly to your Kindle to read it later
  • Re-assign Jira tickets directly from Superhuman or send them to your to-do list
  • Pay invoices or send money to a friend

You never have to leave the Superhuman app – the command line becomes your personal assistant that takes care of all your productivity tasks.

Side note: Opening up to 3rd-party developers and thus becoming a platform is also how you build a moat on top of an open standard like email and make the business more defensible.

05 Managing the information firehose

Once you can react to email notifications right from your inbox, you can forward all your 3rd-party notifications to your email inbox and manage them from one place. This is how you solve the multiple inbox problem we discussed earlier.

Having all notifications in one place sounds scary: People are already struggling to stay on top of their inbox today and risk missing important messages. This is where Superhuman’s Split Inbox feature comes in handy. Your main inbox is still reserved for only the most important emails you receive. For everything else you set up dedicated split inboxes.

You could set up an inbox for all your newsletters, a dedicated inbox for just your Github notifications (or any other tool) or group your emails by teams or projects.

Superhuman’s founder and CEO Rahul Vohra actually uses Superhuman in this way already (video starts at 21:46):

Collecting all notifications in one place has another benefit: Building a (personalized) history of company records. In the current world of multiple inboxes your information is dispersed across a dozen different services and whenever you try looking for something you never know where to find it.

This aspect feels like a very underrated benefit of a unified notifications inbox.

06 Closing Thoughts

I’m aware that this idea isn’t really the meta-layer that Kevin outlined in his article. Email and productivity would still remain separate workflows, but Superhuman would become the center of gravity from which all other tools are being managed.

An actual meta-layer might look closer to something like Tandem, but I could also imagine a Superhuman Command Line that lives outside of the Superhuman app – similar to what Command E are building.

If you have thoughts on this, please let me know.

Jan 17, 2020  ×  Berlin, DE

Media Consumption (Dec 2019)

>_ Summary
  • Read 4 books (1435 min, +390% MoM) and 25 long-form articles (+79%)
  • Listened to 620 songs (+10%) and 6 podcast episodes (352 min, -38%)
  • Watched 5 movies (-38%), 2 soccer games (220 min, -46%) and 21 TV episodes (998 min, +372%)
  • Played 1 video game (60 min, +∞%)
>_ Books

The Great Successor. (Anna Fifield)
░▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 2-100%

A Little Life (Hanya Yanagihara)
▓▓▓▓▓▓▓▓▓▓░░░░░ Progress: 0-66%

The Design of Everyday Things (Donald A. Norman)
▓░░░░░░░░░░░░░░ Progress: 0-3%

Augmenting Human Intellect (Douglas C. Engelbart)
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Progress: 0-100%

>_ Recommended Articles

What will you do to stay weird? (Tyler Cowen)

The power of naming (Leo Widrich)

2020 Startup Themes (Part 1) + (Part 2) (Daniel Gross)

Whimsical ideas for 2020 (Sriram Krishnan)

>_ Recommended Podcasts

Tony Fadell on Building the iPod, iPhone, Nest, and a Life of Curiosity (The Tim Ferriss Show)

>_ Music

Top Artists: Max Richter (140 plays), Trent Reznor and Atticus Ross (59), Eminem (39), Frank Ocean (34), Abel Korzeniowski (31)

Jan 09, 2020  ×  Berlin, DE

List of Goals and Rules for 2020

  • Publish 52 blog posts
    This is my number one goal for 2020. I want to start publishing quality content on a regular basis and improve my writing skills. I’ll donate €100 to charity for every week I miss.
  • █████ ██ ███████ ██ ███
  • Read 20 books
  • Watch less TV
    Last year: 125 hours
  • Swim a total distance of 120km
  • Go for a swim at least once a week
  • Back exercise every second day
  • ██████ ████ ████
  • ████████ ████
  • ██ ████████████
  • Ship a redesign of this blog
  • Finish work on my daily uniform
  • Conduct a 2020 Quantified Self Project
    This will be my biggest QS project since 2013. I’m tracking more than 70 metrics across 10 categories this year.
  • Publish my 2019 Quantified Self Report before end of Jan
  • Build a Personal CRM system
  • Limit meat consumption to 24 days
  • No alcohol if I have to work the next day
  • Visit 1 country I haven’t been to before
  • Explore more new places
    25% of my Swarm check-ins should be places I’ve never visited before
  • Keep phone screen time below 1h per day
  • Meditate 1h per week
  • Do a 3 day silent retreat
  • █████████ ████ ████████
  • ██████████ █
Jan 03, 2020  ×  Berlin, DE

Review of my 2019 Goals

I set myself 15 goals for 2019 and completed 9 of them, which is a slightly higher success rate compared to my 2018 list (60% vs 56%).

  • ███████ ██████ ██████ ███████ ✅
  • Go for a swim at least once a week ✅
    Like in 2018, I didn’t miss a single week this year. My swim streak is now at 171 weeks.
  • Swim more than in 2018 ✅
    I barely managed to achieve this goal: I swam 132km this year, compared to 129km in 2018 (+2.3% YoY)
  • Back exercise daily ❌
    I did my back exercise training on just 29.6% of the last 365 days. My average perceived back pain increased to 2.45/5 (up from 1.45 in 2018). For 2020 I have set myself a 50% exercise goal.
  • ██████ ███████████████████ █████ ❌
  • Spend less time on social media ✅
    I definitely spent *at least* as much time on Twitter this year as in the year before.
  • Read 20 books ✅
    I started reading 34 books but finished only 19 of them.
  • Get a Switch and play Breath of the Wild ✅
  • Write 50 blog posts ❌
    I shipped just 25 blog posts and many of them weren’t what I would consider real articles. I’ll try to make writing a bigger priority in 2020.
  • Write 365 tweets ❌
  • Limit meat consumption to 24 meals ❌
    2019 was the first year I failed to achieve my meat consumption goal ever since I started tracking this in 2012.
  • Limit alcohol consumption to 2 days / week ❌
    I failed to hit this goal. I reduced my beer consumption by 47% YoY, but had more cocktails and glasses of wine.
  • Improve my cooking skills ✅
    Learned how to make sourdough bread, how to brew coffee with a Chemex and perfected my Old Fashioned skills, among others.
  • Explore more new places ✅
    I measure this in form of Swarm check-ins to locations I haven’t been to before. My 2019 goal was to make every 4th check-in a new place (25%). In the end 43% of this year’s check-ins were at new places (777 out of 1806).
  • Visit a country I haven’t been to before ✅
    Traveled to Japan and Finland.
Jan 02, 2020  ×  Berlin, DE

2020 Reading List

Jan 01, 2020  ×  Berlin, DE
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