When we say 'mobile' we don't mean mobile just as when we said 'PCs' we didn't mean PC. This isn't about the screen size or keyboard or location or use. Rather, the ecosystem of ARM, iOS and Android, with 10x the scale of Wintel, will become the new centre of gravity throughout computing.Read More
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As I wrote here at the end of last year, tracking precisely how well Apple and Google's mobile platforms are going has ceased to be very interesting. They both won, and both got most of what they wanted, more or less, and at this stage iPhone or Android phone sales announcements are really just victory laps. That's reflected in the fact that Google hasn't given a new Android sales statistic since last summer.
Meanwhile, it's too early to tell how the Apple Watch is going to do. Apple implies 2-3m unit sales and something over $1bn in revenue in less than a quarter, which clearly kills the 'flop' stories, but as this chart shows, that's not a strong indication of what will happen next.
Hence, rather than analyzing the results in detail (what as a sell-side analyst we used to call 'maintenance research'), here are a few notes I scribbled in the margins of Twitter, together with an essential emoji summary.
Google Now looks like Google's most magical, artificially intelligent product. And in some senses it is, but it is also in some senses Google's most manual product. Each category that Now knows about and makes magical suggestions around is the result of a human making an editorial choice to create that category. Google Now gained cricket scores not because it 'learned' that there was cricket but because someone at Google wrote a recipe for cricket.
Obviously, you can't do this for everything - Google Now cannot scale to the whole internet. It can do a lot, and certainly enough to be a great product, but it can't do everything, not until we have 'true' HAL 9000-style AI, which is a long way off. The underlying domain is not actually infinite, but like Borges's library it might as well be for any manually-edited project - Yahoo's manually-edited directory peaked at 3.2m sites but had become absurd long before that. So there are gaps - you're making something that looks like AI but isn't. Google Now covers the gaps by keeping quiet, where Siri covers the same kinds of gaps by making jokes (that was probably a better product management decision - if you baffle Siri you get a blank stare or a laugh but if you baffle Now you never know about it).
Contrast this with Google Maps, which is also partly a manual product. Hundreds or thousands of people drive Streetview cars down roads and hundreds or thousands of people check the images and edit the maps. There's lots of machine vision too, as well (and crowd-sourcing), but the back-stop is lots of paid employees. The algorithms alone aren't good enough yet. Editing the internet by hand was just too big, but editing maps isn't - it's just very expensive. Google is willing to spend that (and Apple is going to try too).
So, mapping the internet by hand is impossible - you need algorithms. But mapping the world with algorithms alone is also impossible, for now - you need people to look at the data too.
I see Apple Music as sitting somewhere in this second category - manual curation at scale. Here the problem to be solved is that the commodity streaming service's 'search box plus 30m tracks' offers no way to discover anything you might like but haven't already heard of. You need personalised suggestions, from somewhere. But how do you suggest things from 30m tracks? It is in principle pretty easy to get some data about what people like, especially once they start listening (and so stop lying). But how do you know what aligns to that preference data?
Pandora applies an algorithm. Apple Music is trying the Google Maps approach: "doing this at least partly by hand is not an impossibly large problem, just a very big one". Just as Google manually adds metadata to the raw maps (street names, one-way streets, business names etc) Apple manually places tracks into playlists with its own metadata. Then it takes the interest data it has about a user and suggest 10 or 20 or 30 playlists, and, hopefully, there's a good chance that enough of them will be right. (It covers the gap that Siri covers with jokes and Now covers by keeping quiet by overshooting - it's probably OK if 15 are not quite right if 10 are on target.)
How many playlists you need? 5,000? 10,000? With a few hundred editors that's not actually an impossible challenge, just a big one. And a cynic might suggest that really, you only need a few hundred for the vast majority of music listeners, if you do them right.
As a company moves from insurgent to incumbent, and gets big and complex and involved in lots of different things, it tends to end up with lots of different objectives, tactics and strategies. At that point, trying to understand it from outside, it can be useful to think not about what it's trying to do but what it's afraid of. This company want to do lots of things, but what's the existential threat? What does it want not to happen? What scares it, late at night?
For Google, the fear is around reach. Google is a data company, and a machine learning company, and everything it does is about reach - reach to get data in so that it can understand everything better, and then reach so that it can serve that understanding out to the users. And so Android exists partly to enable the expansion of the mobile internet, but also, and more fundamentally, to ensure that no-one (meaning first Microsoft and later Apple) would be able to block Google from reaching those users, both to give them each results and to see what they are doing. Google is afraid of going blind.
For Apple, I'd suggest the fear is that the developers leave. This is what happened in the 90s and it was a key part of the company's near-death experience (and arguably Apple only survived because the web made the lack of Mac apps matter less as a reason to buy a computer). Once developers start leaving you're in a vicious circle that's very hard to reverse (this is where Windows Phone is now). Today the iOS ecosystem is smaller than Android in absolute users and downloads, but has 7-800m live device, which is three times the size of the PC install base in 1995, and twice as much app store revenue per user as Google Play. More importantly, perhaps, the users are highly concentrated in key locations - Chase isn't going to abandon its iPhone app because there are 500m Android users in China. So right now the ecosystem looks sustainable, but that could change. Developers can leave. That's Apple's existential fear.
This is a useful lens to apply to the announcements at WWDC and IO. Google, this year in particular, always seems happier and more comfortable talking about the great stuff it can do with its own unmatched cloud intelligence - Now on Tap, for example. Losing that intelligence is what Google's afraid of. Apple is happiest talking about the new platforms and technologies that it wants developers to use - Apple Pay, iBeacons, Extensions or App Search. And losing that developer adoption is what Apple's afraid of.
Apple and Google both give headline statistics of how well their respective app stores are doing, generally at their summer developer conferences. These are rounded numbers at scheduled events and they're not always comparable, but they do give us a sense of what's going on.
Last summer, at their developer events, both Apple and Google gave numbers for the money they had paid to developers in their respective app stores: $5bn in the previous 12 months for Google Play and $10bn for the iOS App Store. Given Android has double the user base of iOS, this meant that the average iOS user was worth around 4x the average Android user in app store revenue.
This year Apple gave the same number - $10bn (more precisely, it gave a cumulative figure of $30bn this WWDC versus $20bn last WWDC). The lack of growth may be partly due to rounding but still implies that people are spending less on average, since the user base is still growing. Google gave no number at Google IO but it gave one earlier in the year of $7bn. It looks as through Play is growing faster than iOS and might overtake it this year (unless Apple is rounding down very aggressively - certainly the uneven shape of the graph in 2013 is due to rounding).
Since Google Android has close to double the number of users, this implies that the average user is spending perhaps half as much as the average iOS user - a change from 1/4 a year ago but still a big gap.
Meanwhile, this was the first year since 2013 that we could compare downloads.
Google Play had 50bn app downloads in the last 12 months and iOS had 25bn, with Play appearing to be growing faster. Since, again, Play has more users, this implies roughly the same downloads per user on Android and iOS.
Incidentally, these numbers show annualized consumer spending on apps of around $25bn, and 75bn apps downloaded in the last 12 months.
Unpicking WWDC: maintenance releases, proactive analysis of user data and two aggregation and curation platforms, News and Music.