The “metaverse” discussion seems more about cultural “in-group” signaling then a thorough exploration of an idea. It’s frustrating. Rather than talking about “what the metaverse will look like” instead we should examine the forecast of a “metaverse” altogether.

Cyperpunk 2077 launched and it turns out the PS4 and Xbox One versions of the game were riddled with bugs. This has lead to an avalanche of omniscient pundits declaring “I told ya so!”. My personally favorite roast in this Miyamoto meme.

Rushing development feeds into narratives around greedy firms. “If only they didn’t want so much money!” Much of this banter is comprised of cheap shots devoid of making real claims about what CDPR should have done. Should the game have been delayed an additional 4 months? 6 months? And if so, why? If the board really didn’t understand the scope of the bugs then the question turns to the organizational design CDPR. What organizational breakdowns led the lack of information the board had about the bugs in game. Were QA leaders not empowered to speak up or not trusted?

These are much tougher questions to answer. After all, as Pixar is fond of saying, “[Games] don’t get finished, they just get released”. The key question is when to release. There will always be bugs and there will always be new features to add. Ultimately, release timing is a cost/benefit decision. Relative to the additional development cost what increase in sales would we expect from a delay? Do we have ever higher margins from a 4 or 6 month delay? To be clear, Cyberpunk is already outselling all other CDPR games, hardley “one of the most visible disasters in the history of video games“. What further increase would analysts expect with what additional delay?

Part I
Part II
Part III

A Quick Refresher

Pricing is tough to get right. Ideally, we’d like to select a price that maximizes profit, holding all else constant.

Let’s add consumer surplus to the story. In the example below, this is the shaded yellow area. Some readers value the book at $30 and thus are the most profitable in the transaction ($30 – $18 = $12 economic profit). The other readers made out, but perhaps not as well.

Imagine, however, that we could charge two different prices to two different segments of readers. Say one reader valued the book at $18 and another who valued the book at $30. The trick is to ensure that the good is non-tradeable; otherwise, the customer who faces a lower price could resell to the higher-priced customer and pocket the difference.

While we could raise the book’s price to $30, we’d lose out on the $18 reader. The problem of price discrimination, the one described above, is fundamental to understanding entertainment business models.

Our Case

The consumer surplus model struggles to capture time. After paying a fixed price for the book, the reader chooses to consume it (is anyone surprised?). The reader continues to accrue utility from this consumption – the enjoyment of reading the book outweighs other activities. At some point (but not always), the accumulated utility outweighs the initial cost.

Standard Utility Curve (Reading a Book)

It’s important to note: the reader is not “in the hole” when we see red in the above graph. The book is a sunk cost; the reader should only consume it better when it’s better than engaging in other activities. But if we extend this graph to include more Time, the curve kinks immensely.

Utility Curves: Books

There are incredibly steep diminishing returns to reading a book a second time. It’s boring. Deep diminishing returns explain why book rentals (libraries) and book reselling are so popular. Why pay a fixed price for what is usually a single-use item? Steep diminishing returns ring true for movies as well. Of all films you’ve watched, what percent have you seen a second or third time? 1%? 5%? Subscriptions and rentals make sense for this form of entertainment. But what if, instead of flattening, the utility curve grew? Enter gaming.

Utility Curves: Games & Books/Movies

Games have a unique resistance to diminishing returns. As described in Part III:

The genius of PvP (Player v Player) environments is how they necessitate the emergence of a meta-game. PvP environments resemble game theory models where it has been shown strategies evolve in an evolutionary process. In mathematics, Player vs. Environment (PvE) resembles optimization where strategies are static – one and done. Each balance change reshuffles Equilibrium in PvP environments; the search for dominant strategies in an ever-shifting equilibrium is the game itself.

The strategic evolutionary process is a near limitless piece of content to consume.

MB = MC

The efficiency of any monetization or pricing system is the degree to which it can correlate marginal cost (MC) to marginal benefit (MB). In the above examples, we fixed the price. Fixing the price makes sense, given that the utility curves flattened out. But the more the curve refuses to flatten, the most decorrelated MC to MB becomes as Time continues.

The above gets us to the emergence of DLC and MTX. Players were playing PvP titles for hundreds, if not thousands of hours. MC failed to catch up. DLC map packs like those in Call of Duty and Battlefield helped MC catch up (and grow MB!) in fixed intervals, but the correlation was still weak as Time persisted.

MTX solved for the explosion in the marginal benefit multiplayer games were providing. Unlimited or greatly exaggerated spend caps allowed players to spend closer to their MB curves than they were previously able to do so.

Utility/Price Curve: MTX & F2P Games

Software as a service (Saas) can generate similar growing utility, but they only charged a fixed price in recurring intervals. Again, this suggests that subscriptions might make sense for games. Subscriptions are better than fixed prices in correlating MB and MC, given that SaaS generates recurring homogenous LTU returns. Games, however, generate heterogeneous LTU (lifetime utility) than do many SaaS products.

We can model the heterogeneity as such:

As we consider the total Lifetime Utility generated by a standard good or game, we add up an individual’s LTU from lowest to highest LTU. If everyone valued a standard good the same, LTU would be linear. If a few players valued a game at a relativity extreme LTU, you would see a bowed curve – the high LTUs skyrocket total LTU upon addition. Look familiar? A bowed LTU curve correlates to observed LTVs in F2P games.

Does Tech leave too much LTU on the Table?

But that’s not to say all non-video games have a linear Total LTU curve. Some users value, say, Zoom more than others. As Zoom usage increases, LTU does as well, but the price does not. Zoom, therefore, fails to capture a great deal of LTU from high usage customers. MTX theory could offer a hand.

Zoom does offer tiered pricing for organizations with a higher price charged for additional features, but this doesn’t capture the high LTU users within an organization. Perhaps Zoom should get in the cosmetics business – backgrounds were a fantastic opportunity that never capitalized on.

Even at the organizational offering level, Zoom could create an additional tier that offered other features to high usage customers in the tier. Multi-track cloud recording, for instance, generates incredible value for Podcasters but costs nothing further.

The dramatically different value propositions of standard goods and games necessitate other monetization schemes. Different content economics make applying the monetization of tech companies less applicable to gaming. Instead, SaaS firms have something to learn from games.

Gaming Companies Are Not Tech Companies!