Game Companies Are Not Tech Companies Part I: Every Game has Networking Effects and They Don’t Count for Shit

It’s a billionaire bonanza, but from single to multi-digit billionaires.

Ran Mo and Joseph Kim (@jokim1) argue for looking at games from a Silicon Valley perspective. The usual three make an appearance: moats, networking effects and platforms. And while I thought it had died out after the launch of Halo 3, there continues to exist an inferiority complex amongst game makers. Games never seem to get the mainstream or broader tech circle legitimacy many think they deserve. Despite operating in the Valley and major tech hubs, game companies don’t reach the crazy evaluations of FAANG (ATVI has a market cap of $63B, while Facebook sits at $215B). Furthermore, public intellectuals like Tyler Cowen or Ben Thompson rarely discuss games as a share of their commentary (not the case with Matthew Ball – but maybe he drinks too much kool-aid). Even internally, game firms seem to be chasing subscriptions off the heels of Netflix and Spotify.

The fundamental problem comes from not respecting or understanding games as a distinct and unique medium separate from linear content. In many ways, Kim’s piece wants to make this point, but doesn’t go for the kill by the end of the article. How to Build the Amazon of Game Companies mainly describes why game companies won’t be Amazon.

I’m writing a two-sided series to address and expound on this. In two parts (networking effects & platform power), I’ll examine why game companies fall short of traditional tech companies. Then, with another two parts, I’ll address what tech companies have to learn from games (the content problem & MC = MB).

Networking Effects

Networking effects describe the positive externalities from the n+1 user to a service. Every time someone joins Facebook, the service increases in value as others can interact with that person. Gyms, for instance, work in the opposite direction: every member who joins a gym occupies a fixed amount of capital and decreases the value to each other member.

Fawning over networking effects comes from the path dependency inherent in the model: more users leads to more users. What VC doesn’t want self-perpetuating growth? But as Margolis and Liebowitz argued during the Microsoft case:

The logic underlying path dependence is seductive but incomplete. Although these simple numerical and algebraic examples appear both logically sound and structurally uncontroversial, these examples actually entail severe restrictions. […] Given that the theoretical claim that can be made for path dependency should be understood as only a demonstration of possibility, the case for path dependence becomes an empirical one.

It’s not that networking effects aren’t real, but they’re not as powerful as first made out to be. After all, networking effects couldn’t save Friendster or Myspace and as we’ll see they mean little for particular games.

At a certain scale, diminishing returns decay positive network effects to zero. The 2nd user who joined Facebook was far more beneficial to the 1st user then the 100th million user who joined. While not directly comparable, Google’s Chief Economist, Hal Varian, makes this point in regards to the predictive accuracy of models with additional data.

And we can create a similar arbitrary model for a particular game: diminishing network effect value for the marginal user that inevitably results in an asymptotic total network effect value.

Not all games face the same curve. A game like Hearthstone, with 1v1 play, has far less to gain from an additional user then say, League of Legends, which has 5v5 and many ranked segments. More users reduce matchmaking times, potential latency, and thicken skill distribution (higher P you’ll be matched against similar skill). The less segments (modes, ranks etc), the less powerful networking effects are and the quicker the marginal value curve depresses. Cross-play doubled down on this by removing platform segmentation. But even for League the networking effect power is infinitesimally small at scale, the big gains are eaten with a relatively low user count.

Games don’t have the “sticky” elements of networking effects. Synchronous consumption is another example. In any real-time game, a network effect is delivered when you play with friends. Whereas something like Instagram Stories are consumed whenever the user pleases. It’s not clear that having a friend play the same game I do is beneficial unless we play at the same time.

Schelling’s Nobel Prize winning work on tipping is a more apt model for describing many games today. Consider a group of players all playing the same game. These players are partially driven to play this particular game to be “in the know” or a part of the pop-culture conversation. Each of these players has a given threshold for defecting to a new game based on the share of public conversation consumed by the game. For instance, some might defect once the game goes from 100% of the public conversion to 99%, while more might leave if the share declines from 90% to 80%. Of course, the inverse is true as well. Some might join a game if it’s share of public conversation goes from 0% to 1% and even more would join if it went from say, 20% to 30%. A new game release can set off this “tipping” chain-reaction in players. Look no further than the migration of Fortnite players into Warzone. The power of tipping is as strong as “public conversation” is as a player motivation. Unfortunately for developers this means instability in long-run capitalization of viral game hits.

I’ve avoided addressing two-sided marketplaces as they’ll more neatly fit into the next part: platform power (or lack thereof).

Part II

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