So I wrote a case with my colleague Francesca Gino about Ducati motorcycles entering the MotoGP circuit, which are the biggest, fastest, nastiest motorcycles in the world. Ducati entered this MotoGP circuit in time for the 2003 race season. And they announced in May of 2001 that they were going to enter this race circuit. In May of 2002, they had a bike ready. Now, this is starting from scratch. And in twelve months, they designed the bike absolutely from scratch. Every component in it is custom made, including the engine; the first fourcylinder engine the company had ever made in its history. They simulated it, they designed it, they fabricated it, and they got it on a race track in twelve months. Now, I’m used to the world where derivative car projects take five and six years. This is what Kim Clark [former HBS dean] made a lot of his reputation studying. The fact that they could get this thing off the ground and onto a track in twelve months, and in the very first season that they competed, this team came in second place overall. This was a very, very successful effort on their part. And the question is, how on earth did they do it? And what role did technology play in it? There’s kind of a two-part answer to that. The first part is in how they designed the bike. And they used amazing amounts of technology to design that. This is a screenshot of the program, CATIA, which is a three-dimensional modeling program they used to design basically every part of that bike. They used incredibly powerful software to simulate the engine. So if you wanted to know how an engine was going to perform in the Old World, you went and reamed out a lot of metal, and put it in a testing platform, and tested an engine. Now you design it on a computer. You see exactly what you’re going to get out. And they told us that the engine simulation software that they used is so accurate that if they build the physical engine and they stick it on the test bed, and they get results back that are different from what the simulation software says, they're a lot more likely to question the test bed than to question the software. So they had access to fantastic software for designing this bike. We went to the track and actually watched them doing some test runs. We saw something interesting. Every time the bike came in from the track, they would immediately start messing with it; all the mechanics would go to work. And one guy would walk up and put this cable on the bike. And he wasn’t charging the battery. He was getting data from all the sensors that were on the motorcycle. These things have between thirty and fifty sensors on them all the time. After every run, they download the data from all of those, and they get information about tire pressure, and braking pressure, and engine RPMs, and suspension, and everything else that you might care about. And it gives them this amazing pool of data that they work with so that they know what to go tinker with on the bike. And they can do very intelligent, very tailored tinkering over time. So not only was this amazingly cool, but we actually learned a lot about one of the ways that information technology is actually making a pretty big performance difference these days. What they did on this team was simply infeasible—the speed with which they did it, simply infeasible certainly twenty years ago, probably ten. Maybe even five years ago they couldn’t have done this work as quickly. And what we learned from watching this was that there are some functions—engineering, tinkering on a test track—some jobs that are very much accelerated, revolutionized by information technology.