The 3P Theorem for Optimizing Distributed User-centric Services

There has been a lot of talk on Distributed, User-centric Web Services enabled by Artificial Intelligence lately. The fierce rivalry between Google, Facebook, Microsoft and Apple has shifted to this new playground, and Apple is lagging behind the competition. Facebook, Google and Microsoft (to some extent) each have a treasure trove of data gathered from users of their purported "free" services. They use this data to build deeply integrated, contextually rich services for users that enable use cases that blow Apple's current crop of services out of the water. While this lack of richness and specificity of context has been Apple's Achilles heel, I like that Apple doesn't mine my personal data to further its corporate goals. How can Apple make progress to even compete with its rivals?

After having looked at the myriad reasons as to why Apple hasn't been able to compete where upstarts like Snapchat and WhatsApp have excelled, I synthesized my findings into a theorem that I call the 3P theorem.

 

Introducing the 3P Theorem

When designing and optimizing massively distributed services, companies have to balance three key considerations:

1. Performance and contextual richness

2. Price

3. Privacy

The 3P theorem states that only any 2 of these 3 considerations can be maximized/optimized when building distributed, user-centric services. This theorem for governing user-centric services optimization is akin to the CAP storage optimization theorem. 

Using the theorem, let's score Apple's rivals, shall we. 

A. In the Red Corner: Facebook and Google.

1. Performance and contextual richness: A 

2. Price: Free: A 

3. Privacy: D

B. In the Blue Corner: Apple 

1. Performance and contextual richness: C 

2. Price: Free tier (higher usage incurs costs): B

3. Privacy: A

As you can see, Apple has taken the alternative approach of sacrificing contextual richness for the sake of upholding user privacy while maintaining an almost free price for all its services. Users like me like me prefer that stance, but I am, as many would say, a dying breed. Millennials, for example, have grown up with Facebook, Snapchat and Google, and couldn't care less what is done with their data as long as they continue to get free access to services. 

For Apple to get a leg up on its rivals then, it needs to increase the contextual richness and purported performance of its services. Something has got to give then, and neither scenario bodes well for Apple. 

1. Charge for services: This proposal is DOA. Few will pay for services that rivals are offering for free. Besides, overcoming technology inertia is one of the most difficult things to do these days.

2. Soften stance on user privacy: While this seems like the most obvious solution, and yesterday's announcement on Differential Privacy indicates that Apple recognizes the need to gather data to tune their AI algorithms, it is a slippery slope. All along, Apple has differentiated itself by claiming that they truly don't mine user data; everything is unequivocally done on the device.

This is a very interesting space that will develop significantly over the next 6-9 months. To compete, especially in the nascent wearables space, Apple has to deliver smarter services to its users, enable sharing and social features, and create recurring revenue streams that offset sagging device sales. As of this writing, I am long AAPL, and recognize the headwinds that can slow them down. But, headwinds are simply decelerators; they aren't show-stoppers. The AAPL show will go on...