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Jan 2, 2019 · 19m · a16z

a16z Podcast | The Topic That's Lasted the Entire History of Computing -- Bundling and Unbundling

Benedict Evans · 9m spoken Steven Sinofsky · 8m spoken
0:00 / 0:00
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In this a16z podcast, Benedict Evans and Steven Sinofsky explore the fundamental dynamics of software bundling and unbundling across tech history, comparing Western single-purpose apps with Chinese super-apps while analyzing user attention limits and app store discovery challenges.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. How this is scored →

The host as informed peer 6.3 Guest teaching 3.5 Guest disagreement 1.5 The host pushing back 1.5
05100:0010:000:00–3:05 · The host as informed peer 6/10 The Mechanics of Software Bundling and Unbundling Benedict frame-sets the conversation on bundling with historical tech quotes and current smartphone dynamics. Steven adds perspective on software bloat cycles based on his product background.3:05–6:14 · The host as informed peer 6/10 Chinese Super-Apps and Contextual Feature Integration Benedict highlights Chinese super-apps like Baidu Maps using contextual feature integration rather than unbundling. Steven agrees and discusses how focus on context drives user utility.6:14–10:20 · The host as informed peer 6/10 Engagement Metrics, Product Design, and Aggregation Models Steven explains how engagement metrics impact product design versus download vanity metrics. Benedict counters with an inverted aggregation model comparison between US and Chinese app ecosystems.10:20–13:58 · The host as informed peer 7/10 The Seven-App Limit and Long-Tail Distribution Steven invokes Miller's Law regarding home screen app limits while Benedict cites Comscore data and applies Lenin's distribution theory to modern traffic aggregation.0:00–3:05 · Guest teaching 3/10 The Mechanics of Software Bundling and Unbundling Benedict frame-sets the conversation on bundling with historical tech quotes and current smartphone dynamics. Steven adds perspective on software bloat cycles based on his product background.3:05–6:14 · Guest teaching 3/10 Chinese Super-Apps and Contextual Feature Integration Benedict highlights Chinese super-apps like Baidu Maps using contextual feature integration rather than unbundling. Steven agrees and discusses how focus on context drives user utility.6:14–10:20 · Guest teaching 4/10 Engagement Metrics, Product Design, and Aggregation Models Steven explains how engagement metrics impact product design versus download vanity metrics. Benedict counters with an inverted aggregation model comparison between US and Chinese app ecosystems.10:20–13:58 · Guest teaching 4/10 The Seven-App Limit and Long-Tail Distribution Steven invokes Miller's Law regarding home screen app limits while Benedict cites Comscore data and applies Lenin's distribution theory to modern traffic aggregation.0:00–3:05 · Guest disagreement 1/10 The Mechanics of Software Bundling and Unbundling Benedict frame-sets the conversation on bundling with historical tech quotes and current smartphone dynamics. Steven adds perspective on software bloat cycles based on his product background.3:05–6:14 · Guest disagreement 1/10 Chinese Super-Apps and Contextual Feature Integration Benedict highlights Chinese super-apps like Baidu Maps using contextual feature integration rather than unbundling. Steven agrees and discusses how focus on context drives user utility.6:14–10:20 · Guest disagreement 1/10 Engagement Metrics, Product Design, and Aggregation Models Steven explains how engagement metrics impact product design versus download vanity metrics. Benedict counters with an inverted aggregation model comparison between US and Chinese app ecosystems.10:20–13:58 · Guest disagreement 3/10 The Seven-App Limit and Long-Tail Distribution Steven invokes Miller's Law regarding home screen app limits while Benedict cites Comscore data and applies Lenin's distribution theory to modern traffic aggregation.0:00–3:05 · The host pushing back 1/10 The Mechanics of Software Bundling and Unbundling Benedict frame-sets the conversation on bundling with historical tech quotes and current smartphone dynamics. Steven adds perspective on software bloat cycles based on his product background.3:05–6:14 · The host pushing back 1/10 Chinese Super-Apps and Contextual Feature Integration Benedict highlights Chinese super-apps like Baidu Maps using contextual feature integration rather than unbundling. Steven agrees and discusses how focus on context drives user utility.6:14–10:20 · The host pushing back 1/10 Engagement Metrics, Product Design, and Aggregation Models Steven explains how engagement metrics impact product design versus download vanity metrics. Benedict counters with an inverted aggregation model comparison between US and Chinese app ecosystems.10:20–13:58 · The host pushing back 3/10 The Seven-App Limit and Long-Tail Distribution Steven invokes Miller's Law regarding home screen app limits while Benedict cites Comscore data and applies Lenin's distribution theory to modern traffic aggregation.

speaking balance: gold is the host, purple is the guest (3 minute bins)

0:00 · the host 0% · guest 100%0:00 · the host 0% · guest 100%3:00 · the host 0% · guest 100%3:00 · the host 0% · guest 100%6:00 · the host 0% · guest 100%6:00 · the host 0% · guest 100%9:00 · the host 0% · guest 100%9:00 · the host 0% · guest 100%12:00 · the host 0% · guest 100%12:00 · the host 0% · guest 100%15:00 · the host 0% · guest 100%15:00 · the host 0% · guest 100%18:00 · the host 0% · guest 100%18:00 · the host 0% · guest 100%
Sharpest disagreement ▶ 10:20 Sinofsky re-frames philosophical framing

Steven gently dismisses Benedict's abstract philosophical turtle analogy to push a more practical psychological limit perspective.

 Hardest push from the host ▶ 12:20 Benedict challenges home screen simplicity

Benedict pivots Steven's home screen focus by introducing Flurry and Comscore data to highlight long-tail web traffic.

 Biggest teaching moment ▶ 6:14 Sinofsky details vanity vs engagement metrics

Steven educates on product design traps, explaining how focusing on download counts creates misleading download shrapnel.

 The host holds their own ▶ 13:29 Benedict applies Lenin's theory to tech traffic

Benedict demonstrates deep analytical expertise by using political power dynamics to explain mobile distribution control.
the scores for every segment, with the reasoning behind each
ChapterTopicThe host as informed peerGuest teachingGuest disagreementThe host pushing backWhy
The Mechanics of Software Bundling and Unbundling 6311 Benedict frame-sets the conversation on bundling with historical tech quotes and current smartphone dynamics. Steven adds perspective on software bloat cycles based on his product background.
Chinese Super-Apps and Contextual Feature Integration 6311 Benedict highlights Chinese super-apps like Baidu Maps using contextual feature integration rather than unbundling. Steven agrees and discusses how focus on context drives user utility.
Engagement Metrics, Product Design, and Aggregation Models 6411 Steven explains how engagement metrics impact product design versus download vanity metrics. Benedict counters with an inverted aggregation model comparison between US and Chinese app ecosystems.
The Seven-App Limit and Long-Tail Distribution 7433 Steven invokes Miller's Law regarding home screen app limits while Benedict cites Comscore data and applies Lenin's distribution theory to modern traffic aggregation.

Statements from this episode (8)

Insight
Sinofsky: Bundled features rarely match standalone apps but prevent churn
“And then one day you have, like, someone else's whole vertical integrated into your product, but it's never as good as the separate one. But it might be good enough to keep your customers from jumping and using another product.”
Steven Sinofsky Jan 2, 2019 ▶ 1:30
Assertion Supported
Evans: Baidu Maps bundles restaurant booking, cinema seating, and food delivery
“What you have is these very strong portal models, and particularly on mobile, you have, for example, Baidu building a Maps app in which you can book restaurants, and you can book cinema tickets, and you can not just book cinema tickets, but choose the seat in …”
Benedict Evans Jan 2, 2019 ▶ 3:42
Assertion Contradicted
Evans: Baidu Maps bids across five taxi companies inside its app
“If you go into Baidu and you want a taxi, it will show you all the cabs in your city from the five different taxi companies that are in that city, and then it will bid for all of them, and, you know, whoever, whoever gives the best response gets the thing firs…”
Benedict Evans Jan 2, 2019 ▶ 9:07
Assertion Supported
Sinofsky: Most consumers regularly use only seven apps on phones and PCs
“Most everybody is going to just use about seven apps on their phone. And the same holds for PCs.”
Steven Sinofsky Jan 2, 2019 ▶ 10:53
Disclosure
Sinofsky: Over 800,000 apps were used during the Windows 7 beta
“We did what the Windows seven launch, we talked about that there were over 800,000 apps that got used just during the beta process.”
Steven Sinofsky Jan 2, 2019 ▶ 11:00
Insight
Evans: Desktop users navigate to daily sites by searching Google directly
“Google picks up that vast long tail of, and it probably, in fact, on the desktop internet, Google also picks up the 20 things that you use every day, because that's how you get to them. You just go into Google and you search for Facebook, or you go into Google…”
Benedict Evans Jan 2, 2019 ▶ 13:15
Assertion Not checkable as stated
Evans: Software creation costs dropped two orders of magnitude in 15 years
“The cost of creating anything has collapsed, Probably two orders of magnitude in the last 15 years.”
Benedict Evans Jan 2, 2019 ▶ 17:06
Insight
Evans: Bundling versus unbundling is a trade-off between app and feature discovery
“There's kind of a trade-off between application discovery and feature discovery. That if you've got a simple app, if you've got an application with all of your stuff in it, it's probably easier for you to get people to install that app. But then they're never …”
Benedict Evans Jan 2, 2019 ▶ 18:23
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