September 2012 Articles

From Steve Jobs’ biographer, more about the Steve’s sense of design.

Steve Jobs’ interest in design began with his love for his childhood home. It was in one of the many working-class subdivisions between San Francisco and San Jose that were developed by builders who churned out inexpensive modernist tract houses in the 1950s for the postwar suburban migration. Inspired by Frank Lloyd Wright’s vision of simple modern homes for the American “everyman,” developers such as Joseph Eichler and his imitators built houses that featured oor-to-ceiling glass walls, open oor plans, exposed post-and-beam construction, concrete slab oors and lots of sliding glass doors.

“Eichler did a great thing,” Jobs told me on one of our walks around his old neighborhood, which featured homes in the Eichler style. “His houses were smart and cheap and good. They brought clean design and simple taste to lower-income people.” His appreciation for Eichler-style homes, Jobs said, instilled his passion for making sharply designed products for the mass market. “I love it when you can bring really great design and simple capability to something that doesn’t cost much,” he said as he pointed out the clean elegance of the Eichlers. “It was the original vision for Apple. That’s what we tried to do with the first Mac. That’s what we did with the iPod.”

Imagine, for a moment, that you (yes, you) were the next Steve Jobs: what would your (real) challenges be? I’d bet they wouldn’t be scale (just call FoxConn), ef ciency (call FoxConn’s consultants), short- term pro tability (call FoxConn’s consultants’ bankers), or even “growth” (call FoxConn’s consultants’ bankers’ lobbyists). Those are the problems of yesterday – and today, here’s the thing: we largely know how to solve them.

Whether you’re an assiduous manager, a chin-stroking economist, a superstar footballer, or a rumpled artist, here’s the unshakeable fact: you don’t get to tomorrow by solving yesterday’s problems.

To solve today’s set of burning problems, you just might have to build new institutions, capable of handling stuff a little something like this…

Just as understanding the S-curve can keep discouragement at bay as we build new knowledge, it can also help us understand why ennui kicks in once we reach the plateau. As we approach mastery, our learning rate decelerates, and while the ability to do something automatically implies competence, it also means our brains are now producing less of the feel-good neurotransmitters – the thrill ride is over.

My rst boss at Bell Labs had a habit of yelling. While he was an equal-opportunity yeller, when he shouted at me in my rst department meeting, I got up, told him when he wanted to talk, not yell, I’d be in my of ce and walked out. I was 20 years old, just out of undergrad, and sitting among a group of aghast Ph.D.’s. Perhaps this was not the best initial career move. But about 30 minutes later, he walked into my of ce and apologized. He never yelled at me again (though he did keep yelling at the rest of the team), and became one of three manager-mentors that shaped my career at Bell Labs and AT&T – and taught me to manage others and myself. I’ll share one story from each boss and the lesson I learned from each.

I have always tried to plan my career in four- to ve-year stages, using an approach, as Whitney describes it, of letting my strategy emerge based on where trends seem to be going and where my interests lie. But happenstance also plays a role in how well this works out.

Behind every Google Map, there is a much more complex map that’s the key to your queries but hidden from your view. The deep map contains the logic of places: their no-left-turns and freeway on-ramps, speed limits and traf c conditions. This is the data that you’re drawing from when you ask Google to navigate you from point A to point B – and last week, Google showed me the internal map and demonstrated how it was built. It’s the rst time the company has let anyone watch how the project it calls GT, or “Ground Truth,” actually works.

By now, online shoppers are accustomed to getting these personalized suggestions. Netflix suggests videos to watch. TiVo records programs on its own, just in case we’re interested. And Pandora builds personalized music streams by predicting what we’ll want to listen to.

All of these suggestions come from recommender systems. Driven by computer algorithms, recommenders help consumers by selecting products they will probably like and might buy based on their browsing, searches, purchases, and preferences. Designed to help retailers boost sales, recommenders are a huge and growing business. Meanwhile, the field of recommender system development has grown from a couple of dozen researchers in the mid-1990s to hundreds of researchers today–working for universities, the large online retailers, and dozens of other companies whose sole focus is on these types of systems.

…Our experience, therefore, gives us a lot of insight into what’s going on behind the scenes at Amazon and other online retailers, even though those companies seldom speak publicly about exactly how their recommendations work. (In this article, our analysis is based on educated observation and deduction, not on any inside information.) Here’s what we know.