Redefining Capitalism (McKinsey)

It brings me great joy, seeing some of the world’s largest corporations contributing a progressive, thought-provoking piece to our collective knowledge trove. This piece by is Eric Beinhocker and Nick Hanauer of McKinsey is a good example:

Redefining Capitalism

The core argument is that while capitalism has indeed been the source of historical growth and prosperity, we have misidentified how and why it works so well in driving these outcomes, leading us to develop incorrect economic theories around it.

It is well worth the read, but here’s a simple table I’ve put together to capture the key ideas:



Redefining Capitalism (McKinsey)

Jeff Bezos on decision-making

Jeff Bezos’ 2015 annual letter to shareholders is well worth the read in its entirety.

But there’s one short segment concerning decision making that stands out in its broad applicability:

“Some decisions are consequential and irreversible or nearly irreversible – one-way doors – and these decisions must be made methodically, carefully, slowly, with great deliberation and consultation. If you walk through and don’t like what you see on the other side, you can’t get back to where you were before. We can call these Type 1 decisions. But most decisions aren’t like that – they are changeable, reversible – they’re two-way doors. If you’ve made a suboptimal Type 2 decision, you don’t have to live with the consequences for that long. You can reopen the door and go back through. Type 2 decisions can and should be made quickly by high judgment individuals or small groups. As organizations get larger, there seems to be a tendency to use the heavy-weight Type 1 decision-making process on most decisions, including many Type 2 decisions. The end result of this is slowness, unthoughtful risk aversion, failure to experiment sufficiently, and consequently diminished invention*. We’ll have to figure out how to fight that tendency. [* The opposite situation is less interesting and there is undoubtedly some survivorship bias. Any companies that habitually use the light-weight Type 2 decision-making process to make Type 1 decisions go extinct before they get large.]”

This piece is even more interesting when we connect it with Kent Beck’s “taming complexity with reversibility” piece that I covered here a few months ago.

Bezos presents a more static view on decision types: some are Type 1, some are Type 2 – use the appropriate process for each.

Beck presents a more dynamic view on decision types: in some (but not all cases), by thoughtfully designing the system, you can transition some decisions from Type 1 to Type 2 and then use a more lightweight, distributed process for making them. This enables you to keep the inherent complexity in a large system under control.

Jeff Bezos on decision-making

Network Effects 101

As mentioned last week, Network Effects deserve a deep dive of their own. There’s a lot of misunderstanding out there with regards to what network effects are and are not. Which is particularly disconcerting given the critical role that they play in modern business models.

Fortunately, there also some good resources up there. The write-up below is a mash-up of three of the best resources that are out there. It contains very little to original content, other than attempting to restructure information in more coherent fashion. In my opinion at least:


A product displays positive network effects when more usage of the product by any user increases the product’s value for other users (and sometimes all users).

Types of network effects

  • Direct –  increases in usage lead to direct increases in value
    • Example: telephone
  • Indirect – increased in usage of the product spawns the production of increasingly valuable complementary goods, and this results in an increase in the value of the original product
    • Example: “standards” – Windows, VHS, USB Type C
  • Two-sided –  increases in usage by one set of users increases the value of a complementary product to another distinct set of users, and vice versa
    • Example: “marketplaces” – more Uber riders → attracts more Uber drivers → service for other riders improves (note that more riders in and of itself does not improve the service)

Where does value come from?

There are four sources of value created on networks: connection, content, clout, and data.

  • Connection: Networks allow users to discover and/or connect with other users. As more users join the network, there is greater value for every individual user. Skype and WhatsApp become more useful as a user’s connections increase. and LinkedIn become more useful as more users come on board.
  • Content: Users discover and consume content created by other users on the network. As more users come on board, the corpus of content scales, leading to greater value for the user base. Content platforms like YouTube, Flickr, and Quora, as well as marketplaces like Airbnb and Etsy becomes more useful as the number of creators and the volume of content increase.
  • Clout: Some networks have power users, who enjoy influence and clout on the network. Follower counts (Twitter), leaderboards (Foursquare) and reputation platforms (Yahoo Answers) are used to separate power users from the rest. On networks like Twitter, the larger the network, the larger is the following that a power user can develop
  • Data: Data network effects occur when the product, generally powered by machine learning, becomes smarter as it get more data from the users. The smarter the product is, the better it serves the users and the more likely they are to keep using it and contribute more data

What network effects are not

  • Supply-side economies of scale – see more in the “application” section, but in general: being able to deliver a service more efficiently / cheaper when you have more users/customers is NOT a network effect.
  • Virality – A viral product is one whose rate of adoption increases with each additional user. The more people join, the faster it grows. There are products that exhibit virality without network effects and products that exhibit network effects without virality. The two attributes are decoupled. For example, in a two-sided marketplace, you can drive virality in each side of the market using promotions without any connection to a network effect.  


Network Effects as a business model

Using network effects as a business model means creating a dynamic in which with more usage value increases super-linearly while cost increases only linearly. This is particularly compelling approach in businesses with a substantial “brick and mortar” footprint, where the opportunities for significant cost reduction is limited.


Conversely, when economies of scale are used as a business mode, the intent is to create a dynamic in which with more usage, value keeps increasing linearly, while costs only increase sub-linearly. This is a particularly compelling approach in businesses with a substantial “digital” footprint, where opportunities for significant cost reduction exist.


It’s worth calling out that the two approaches are complementary rather than contradictory, and have a more profound impact when applied in unison.

Network Effects as a competitive advantage

Network effects, and in particular strong, direct network effects can act as a powerful competitive advantage. Since the value to the individual user is driven more by the participation of the other users in the network, rather than by the direct service provided by the company, the switching cost for the individual user remain relatively high, even when a competing company offers a better service.

Furthermore, creating a “better” network effect by a competitor is not an easy challenge, especially given the critical mass threshold (see “requirements” section below).   


Network effects need to be big enough to matter

Critical Mass

A network effect typically requires a critical mass of usage in order to be meaningful enough to matter in the user’s “value equation”. Critical mass can be reached using different strategies:

  • Come for the tool, stay for the network – attract users first with a “single-player tool” value proposition – assume the network effect doesn’t exist and generate enough value without it.
    • Example: instagram (photo filters), Warcraft (pre-WoW)
  • Dominate extremely tiny markets – fully lever the “locality”/”density” of the network, to reach a “local critical mass” sooner:
    • Example: Facebook (one college campus at a time), Uber (one city at a time), Twitter (focus on celebrities and VIPs)

“Locality”/”Density” – leveraging irregular network topologies

The microstructure of an underlying network of connections often influences how much network effects matter. Each user is influenced directly by the decisions of only a small subset of other users — those they are “connected” to via an underlying social or business network.

  • Examples: AirBnB/Uber – geo, LinkedIn – professional affiliation, Facebook – social relationships


When Network Effects backfire

One would expect that the bigger the network, the more value users derive from it.

However, as networks scale, the value for users may drop for several reasons:

  1. Connection: New users joining the online community may lower the quality of interactions and increase noise/spam through unsolicited connection requests.
  2. Content: The network may fail to manage the abundance of content created, on it and may fail to scale the curation of content created and the personalization of the content served to users.
  3. Clout: The network may get inadvertently biased towards early users and promote them over users who join later.

Just as network effects create a rich-becomes-richer cycle leading to rapid growth of the network, reverse network effects can work in the opposite direction, leading to users quitting the network in droves.

  • ExampleS: Friendster, Myspace, and Orkut

Important questions

  • What type of network effect is your product trying to generate? (direct/indirect/two-sided)
  • What user base are you trying to generate a network effect for? (customer segment/side of market/3rd party users/3rd party developers)
  • What is the source of value? (connections, content, clout, data)
  • What are the critical drivers of irregularities in your network topology?
  • What is your strategy for reaching critical mass?
  • Extra-credit: how will you know if our network effect backfires?
Network Effects 101

Coase Theorem and Network Effects

Both of these topics merit way more than a single blog post, so I’m fairly confident that I’ll come back to them sometime in the future. I’ve been obsessed with Network Effects in particular for more than a year now. This post will start explaining why, through the work of Esko Kilpi:

The Future of Firms. Is there an App for That?

One Theory to Rule them All

I’m going to try to keep my own summary short as both of these posts are well worth a deeper read.

In the first Esko introduces Coase’s Theorem, defined in 1932 by 22 year old Ronald Coase (22!, and did I mention that he was 103 when he passed away 3 years ago?). Coase challenged the common wisdom at the time that prices and competition are the perfect mechanism for coordination. He argued that there are non-trivial “transaction costs” involved in market transactions that lead to inefficient outcomes. Therefore, the driving force behind the formation of firms is the assumption that their planning, coordination and management functions can be done with lower transaction costs than the ones in the outside market. At some size, as the complexity of the company increases, the transactions costs inside the company increase as well, until it’s being outperformed by the market.

I’m fascinated by the idea of thinking about management and hierarchy as a “coordination solution” meant to decrease transaction costs. It clearly illuminates a path towards less management and less hierarchy: reducing transaction costs in a more market based approach for coordination side the firm. Transparency is a vital tool in that game.

In the second piece Esko ties Coase’s Theorem with Network Effects. He argues that in a world in which transaction costs are lower, platforms through their network effects, are much more effective vehicles for enabling coordination than industrial firms and their asset base / economies of scale.

What assets were for the industrial firm, network effects are for the post-industrial firm.

Interestingly, while the focus of the second piece is on the way the firm interacts with its ecosystem/customers, the connect that Esko makes applies inside of the firm as well. What happens when you think about the company not just as a platform for its customers, but also as a platform for its employees?  How can one  go about creating network effects inside the company that offset the need for more management and bureaucracy as the company scales?


Coase Theorem and Network Effects

Unintuitive Things I’ve Learned about Management

Unintuitive Things I’ve Learned about Management – Julie Zhuo

Julie’s writings are almost always well worth the read, and this has been my favorite piece so far. In this piece, she offers four great lessons from her experience as a manager:

1. You must like dealing with people to be great at management

If the idea of talking to people for 8 hours straight sounds awful, then you will probably not enjoy the day-to-day of management … you can’t gloss over the fact that the pulsing lifeblood of management is people. If listening to and talking with people is not your cup of tea, then management will probably be an uphill slog.

I’ve seen many people choose to become managers for the wrong reasons. Perhaps the most sad of them all (as Julie acknowledges in her piece as well) is that their organizations did not offer them any other way to advance their careers other than becoming managers.

2. Having all the answers is not the goal. Motivating the team to find the answers is the goal

As a manager, you don’t need to know it all. You don’t even need to pretend to know it all. The best coaches aren’t the best athletes. The best teachers aren’t the best executors. Your job is to get better work out of the team then they could have gotten without you.

If you take Laszlo Bock’s advice and “only hire people who are smarter than you” then you’ll never be the smartest person in the room. Being a good managers is not about having all the right answers, it’s about knowing to ask the right questions.

3. To evaluate the strength of a manager, look at the strength of their team

At the most basic level, it means all the day-to-day things that you — yes YOU — personally accomplish don’t matter much in of themselves.

Your work as a manager should be focused less on efforts than have an intrinsic value and more on efforts that have an impact on theirs. Your ability to amplify their work matters more than your individual contributions.

4. The most significant advantage a senior manager has over a junior manager is an expanded perspective

These days, however, if I’m to be totally honest, I don’t think a lot of management is learnable without actually experiencing it. That is to say, I believe that it takes at least 3 years (and in most cases longer than that) to become a truly confident senior manager.

Out of the four lessons, this is the one worth spending the most time on. Julie’s making a strong argument that there’s very little you can do to proactively become a better manager, beyond the natural growth that happens over time with additional reps of dealing with tough managerial situations. While a rather accurate description of today’s reality, it’s more of a testament of the still untapped upside of turning modern management into a true craft. Consider a few meaningful caveats:

  • Theory – management is all about dealing with people, and there’s a growing body of real (rigorous) research on people. What motivates people? How would they react in a certain type of interaction? What enables groups (teams) of people to effectively collaborate together? These are just a few examples that you can maybe figure out on your own, through trial-and-error. But you start further up the learning curve by knowing  what all the managers before you have already figured out compared to starting from scratch.
  • Beyond “doing” – it is true you can learn the most by doing. But it’s not the only way to learn. Professional athletes can teach us a few other ways to learn:
    • Practice – The best athletes spend far more time practicing than actually competing. And yes, it’s much harder to practice/simulate managerial skills than physical skills. But it’s also far from impossible and it’s an area that we all under-invest in.
    • Observation – The best athletes, particularly in team sports (like management) spend a lot of time watching others compete. Ask good managers what are some of the things that helped them become good managers and one of the most common answers you’ll hear is that they were fortunate enough to have a good manager themselves. Just like other craftsmen, we can start the path to becoming good managers through apprenticeship. Shadowing and learning from people who’ve already mastered the craft.
  • Experience != Learn – while experience enables learning, it does not guarantee it. Learning requires feedback and reflection.  You can choose to just naturally absorb a certain experience, or proactively set up the situation in a way that will enable you to learn more from it. You can proactively seek feedback. You can choose to set time aside to reflect. Or you can choose not to.



Unintuitive Things I’ve Learned about Management