Eric Schmidt on Structuring Teams and Scaling Google | Blitzscaling 08

This is session 8 of technology enabled Blitzscaling, a Stanford University class taught by Reid Hoffman, John Lilly, Alan Blue and Chris Yeh. This class features Reid Hoffman interviewing Eric Schmidt, the Executive Chairman of Alphabet Inc, formerly Google. This podcast has been produced by Greylock Partners. For more podcasts, class notes, slides and videos, please visit greylock.com it is my.

Great privilege that we have Eric Schmidt here with us today, actually on the topic of Blitzscaling. There is nobody higher in the world that I would like to talk to about this subject than Eric. He has literally done it in the most serious ways possible. He thinks about these things in theoretical and deep ways and actually teaches his own class on entrepreneurship and everything else. So it is with great pleasure. Why don't we welcome Eric and then kick it off? So I don't know if everyone knows that you had a substantive career before Google, since they all know about your career at Google. First at sun, doing very hardcore tech stuff, and then Novella. What did you learn from those two gigs that you took with you to Google that was really helpful and essential for Google?

So first, thank you for having me and obviously I support what you're trying to do at Stanford and in fact all of your activities. And there's nobody I work with who's more clever and insightful on most of these issues than Reid Hoffman. It's interesting that yesterday I went to visit my old boss when I worked at Xerox parc, whose name is Bob Taylor, and to talk to somebody who is the person who funded the arpanet and ask him how did he do that and the person who conceived the personal computer and who did he work with, gives you a reminder of both the extraordinary and the awesome things that have happened in our industry. And also frankly, how old I am. That's my conclusion. So my history is I'm a computer scientist, PhD from Berkeley, but I've been around here for almost 40 years working at Xerox PARC. I went to sun and one of my rules as a sort of first year young manager is that as a young manager you absorb everything. So my manager at Xerox, Bob Taylor, had a particular management style which I now realize 40 years later is kind of like mine. The way I approach things and my first impressions of being a Sun manager and the way sun worked, I think colored the next 30 years of development. And so one of the things to remember is that the next five or ten years of your careers are in fact crucial because that's when you learn all this shit, right? And that all sort of where you are and the people you. The sort of, the little subtle things that make leadership happen are learned during that period, which is a surprise to me. So at sun, sun was both very tumultuous, very political, and very complicated. There were many, many good things that I learned. And I also learned some real negatives. One of the things that I learned negatively is that companies can reorganize prematurely. Companies can become religious. Companies can not react to actual facts. The low point in my time at sun was we had a meeting where we were looking at sun computers versus PCs and we concluded we could not get our manufacturing costs equal to that of PCs. Now, by the way, that's a signal that you should quit, because I failed to answer the question that I advocate now, which is, what does the next five years look like? So one of the sort of things we talk about in my other class in the MBA school is ask the five year question. And I learned that sort of the hard way, because for most of my life, the answers were given to me or they were highly narrow. So now I ask, what's going to happen in the next five years? What's going to happen in the next. And I learned that. So, anyway, after 14 years of sun, lots of politics and so forth, I did the Java maneuver, which you can talk about if you're interested in. I went to Novell under the mistaken idea that I wanted to be a CEO and I failed to do any due diligence. So had I done the due diligence, I wouldn't have taken the job and therefore I would not have gone to Google. So it was probably good I didn't do the due diligence. But at Novell, there was a week which we remember, my colleague and I who joined me with me, said was the worst week ever, where our basic goal was to get out of this with our professional reputations intact and not in jail. And that's after you join a company, you discover the books are cooked and the people are frauds and the customers aren't paying and so forth and so on. But in hindsight, what I learned something, which is that you can actually overcome that. And the skills that I developed in Novell, in fact, allowed us to do things at Google. And so what happened when I showed up at Google? Make a long story short, there was that company's very interesting, very innovative, but because I had such a tough time at Novell, I understood the role of cash. And so we did everything we could to run the company for revenue, and the rest is history.

And Is there anything other than maybe telling yourself to not one time not do due diligence, but other times do it for the obvious thing of getting to Google.

If you go back to your younger.

Self, is there anything you would have told yourself to do differently pre Google?

I think all answers of that category in our industry are to do things that you did sooner and to make fewer mistakes. So in hindsight I would always have made the right decision faster and I would have made future mistakes. And I wonder what is it that causes me to not make the decisions quickly? And by the way, I continue to have this problem. So it may just be that some people are quicker decision makers than others. And it's not obvious that you have to make all the decisions as quickly, but in hindsight you would judge them all that way. And every executive in every business class and every management class will say the same thing, which is, I should have fixed this earlier, I should have fired this person sooner, I should have cleaned that up earlier and so forth. And that's why businesses are not like the government, where none of that ever happens or very little. Yeah.

So let's go to Google. So you what was the employee size.

When you joined Google? About 150. Right. I should tell you the story with Google is that Larry and Sergey had taken funding $25 million from two venture capitalists who. Thank you very much, Peter. This by the way, delivering the Diet Coke is Peter Wendell, my co professor. Thank you very much, Peter. In my business school class who's joining us for a minute. And we hope to steal all of your best ideas for our class. Reed. So we're clear, that's why we're here. Just so we're clear.

With pleasure and honor.

So what happened was the venture guys had said these two young men are brilliant, crazy and unreliable and they need a proper CEO. So the two young men agreed to hire a CEO and as long as they could vet them. And so their vetting algorithm was they had to spend a weekend with the two of them so each candidate would do something for the weekend. So they took some of them skiing, some of them boating, some of them had to go to the various farms. They went to New York once with somebody and so forth. This guaranteed no one was going to get hired. So I was eventually called by John Doar and I said, well, I'm really not going to do that. But it turns out we met and it was pretty obvious we agreed and I made a list of things they had to fix. They had to fix them and the rest happened. What's interesting about Larry and Sergey is that they had both been here and they'd had the same professor 18 years later. So we're far more similar than we would like to admit. Got it. Similar backgrounds. So joining at 100, we have a question. I was wondering if you can remember some of the things that were on the list. I have the list because I save everything. Remember, this is Google. When you think about a small company, almost all of them have the property that they're full of energy and no process. And they get to the point that Google was at by just sheer force of energy, hard work and figuring stuff out. So my list was pretty straightforward. International expansion plans, sales plans, hiring plans. Is the money real proper accounting? Do you understand where your inventory is? In this case, Google didn't have any development plans, 18 month product plans, none of which existed. The first meeting I was in at Google, it was like being in graduate school in a university full of very interesting conversations without deadlines or deliverables, which is sort of the style. And much of the Google culture was developed out of the graduate school of Stanford. In other words, the offices had four people in it because that was the office that Larry and Sergey had in. It felt very much like that. And it turns out when that kind of raw material, what I figured out is all you have to do is ask questions like what's the plan? Oh, then an answer comes out. It's not that they haven't thought it well, it is that if you push them, they can develop a plan. They didn't think it was important. It was more interesting to talk about this. And that's where the raw talent matters.

Actually, just as an elaboration on that, what advice would you give? So how does the partnership between young, ambitious, highly talented, super smart founders and new people coming in, what were the key things to make that work really well?

Well, I was well aware of this John Scully Steves job story and you all are too young to have lived through it. But the fact of the matter is that Jobs needed somebody to be the CEO. The board picked Scully. The two had a fight. Sculley went to the board. The board had to support the CEO because if they don't, the CEO quits and the rest happened. Eventually Steve left, formed a company called Next, and then as you know, he very cleverly ultimately got himself acquired and then within a month or two got the CEO acquired him, fired and replaced the entire board. By the way, that's a Steve Jobs moment. That's very hard to do. Certainly for you and I, that's like an amazing achievement in and of itself. And it produced the most valuable company in the world, so it's clearly the right choice in hindsight. I was very, very aware of this issue of founders and the outsider, and I understood I was the outsider. So from the moment I started, I knew it was their company and I didn't get confused. It's their company, it's their company, it's their company. So what does this mean? I refuse to do any press. And so as young executives and all sorts of press, they really enjoyed it. They had all these pictures they were going to cover on time and so forth and so on. And then they. Right before the ipo, they did an interview with Playboy. No pictures, so we're clear. And the interview with Playboy was on roughly February 20th or April 20th of that year. And it turns out that the interview was at the wrong time in the cycle of being quiet period. And it put our IPO in jeopardy. So I still remember in August of that year sitting with them and saying, these are my closest friends, my colleagues. I really care deeply for them. And they have those long faces that people get on them. And they say uncharacteristically, they said, well, did we screw up? And the correct answer is, yes, you really screwed up. But the even more correct answer is, no problem, easy mistake to make. I would have made this mistake. And we papered it over in a legally correct way. From that moment on, they're given no interviews. Really?

From that moment on.

From that moment on. Now, that was 12 years ago. And so what's interesting about that psychology is it's somehow, as young executives, they wanted it, but once they didn't want was fine for me to do it, but intuitively, it is their company. It's not my company. I am the professional that was hired. They're the owners, they're the founders. It's their company. And if you get confused on this point, you're not going to win. Nowadays, the founders are much less likely to offer control the way, say, Pierre Omidar offered it to Meg Whitman. There's an increasing view of their own ab, and that's fine too. If you look at Facebook, it's worked really quite well with Mark, who's brilliant and young, by bringing in someone who is much more experienced in many, many things. Sheryl Sandberg. That partnership worked extremely well. So there's not one answer.

So joined 100. It's now over 50,000 people.

60 plus thousand.

60 plus thousand. A classic example of. Of Blitzscaling. Matter of fact, there was one year, I think it was 2004 to 2005. You over tripled the number of employees in that year.

I do have a saying about this. It's easy to double. It's very hard to quadruple every year. Right. And it's a bit of advice. You can double. You can kind of see it. You can kind of, okay, I can add another person here and I can add another person and then, you know, I can expand in this country and so forth and so on. But to quadruple is almost impossible.

So what were the kind of. Because you had to invent a lot of these techniques, right? These hacks. I mean, everything from the three of you reading every resume to keeping innovation going with 20% time, other kinds of things. What were the key hacks that worked and the key hacks that didn't work.

So I want to offer a cautionary tale. And again, having both read your book and also the notes that you kindly sent me before this, if you don't understand the subtlety here, you would conclude that the correct thing to do is to grow everything as fast as you possibly can everywhere. Let's just make as many engineers, as many salespeople, as many products and so on and so on. That completely does not work. And it doesn't work because no product shall ship before it actually works. The way you build great products is you have small teams with strong leaders who obsess over trade offs and they push things off and they say we've got to get it done. And they put a lot of pressure on the team. They work all night and they produce a product that just barely works. Use non Google examples. The original ipod just barely worked. Look at what it became. People remember the original iPhone, no apps. Everyone's forgotten that just barely worked. But it just was just the right combination to create an enormous franchise that's now 70% of the revenue of the world's most valuable company. I was talking to Travis Kalanick. We are major investors inside of Uber, so we have good relationships with Uber. And his description of Uber was that he understood scaling, but the product, the app, wasn't ready. So you have to sort of have judgment about when the product is ready to scale against it. We would debate this over and over again. And so Larry and Segur would play tricks on me. So a typical example would be, I would say, we're not strong enough to take on Microsoft and they wanted to do an operating system in a browser. And I said, there's no Scenario where we'll ever do an operating system in a browser because Microsoft will kill us and I don't want to get killed. We're a small company. And yes, yes, you're very smart. And yes, yes, we're full of smart people. And yes, yes, we have lots of revenue. These guys can kill us. Because I'd been previously killed in the previous jobs. And so they hired somebody to improve the performance of Firefox. Six months later, I'm called down to one office and this person who is supposed to improve the performance of Firefox has managed to invent Chrome. Shocking. And I say, well, how long have you been doing this? Well, I've been doing it on the side. No, you haven't. You've done it full time. Well, yeah, you know, I had three other people. Okay, well how many more people do you have? Oh, ten more people. Well, did Larry. Okay, did Larry and Sergey know about this? Larry and Zurgy encouraged it. I said, those assholes. Right? I knew it. I knew they were going around me. Right, ok. And then I said, well, we can't do an operating system. So they bought this company called Android and they said, yeah, yeah, it's just for smartphones. It's software for smartphones. Don't worry, Eric. Today Android is 1.4 billion plus operating systems use largest number of operating systems in the world. Chrome is the largest and most successful browser. So maybe the lesson to learn in management is I'm just wrong all the time. But the secret was you have to have judgment as to when these things can scale. And I can give you plenty of negative examples. An obvious one is Wave. It was a complicated email product that we launched to great fanfare and we watched its adoption rate. Marissa had this rule, which is a good rule, that you cannot, at Google, you cannot tell how successful a product is until the first six months. Because what happens is you get this adoption cycle and everyone loves the product because everyone tests it. And then you watch what happens. And the great products, they're bumpy, but they go up. Well, this is what it looked like, like this. So it took Eric, your friendly CEO, 18 months of going down, straight down, before that project was canceled. So again, lack of due diligence last. So the reason I'm answering your question in the blitz growth argument by saying you've got to have the products that you can scale. If you have the products that can scale, the thing that's new is you can scale very quickly. The Uber example, once you have an app and a business model that more or less works. There's no reason why you shouldn't go global and wide. When I started at Google, I sat down. My first concern with Google was I thought the whole thing was a sham. The company was using QuickBooks for its accounting, and I was convinced that there were errors. So I asked to be shown the. The actual cash balance in the bank. There was in fact, cash in the bank, which was shocking to me. So then the sales guy, his name was Omid, who's now just gone off to Twitter, I said, okay, well, where's this money coming from? He said, well, we have Salesforce. We sell these ads. Who clicks on these ads? Well, nobody. Well, okay, whatever. This must be some fake thing. So we investigated. We decided people really were clicking on these ads. So I said, well, how many clicks do you get outside of America? And he says, well, none. He said, well, no, you have lots of traffic. So I said, leave and go to Europe and don't come back until you have sales operations in Britain, the UK and France. Which is a strange thing to say to somebody. So this is a month after I'd started. And so he left, and he came back in three weeks having set up those operations. Today, the operations he set up contribute 60% of the profits of a $60 billion corporation. So in hindsight, I should have done it a month earlier. Think about the compound, the compound value of that decision. So there's a point when you keep it small and there's a point where you expand. When you expand, you grow very, very quickly, and it has to be replicable. So let's using Uber as an example, always the favorite example, because everybody understands Uber, as I said. Do you believe that the Uber model scales well outside the United States? Well, ask the French. The French love it, but the government hates it. So they need two problems. They need a scalable, and they need a scalable negotiation strategy with the governments. Now you can guess how far they'll be. Google had the same problem with China, and we lost.

So any heuristics for when the product is right for scaling?

Well, I have another opinion about this. And if you think about the greatest products, they've almost always been designed for the benefit of the people who actually are building them. And so the Uber story, they built this to share apps. In fact, the original Uber was a private sharing group. Larry and Sergey built Google for Stanford and particular for themselves. The server was in Larry's dorm room, and they needed a second server. They didn't have enough power, so they ran the cord over the room into the gentleman next door's dorm room. They opened up the server for the entire campus here and. And the usage was phenomenal. Andy Bechtolsheim heard about it through David Chariton. He wrote a $100,000 check. They didn't have a name for a company. He wrote the check, he gave it to Sergei. Sergei put it in his wallet without a name on it for $100,000 and left it in his wallet for a month until they had the name Google invented. They moved into Sergei's girlfriend's sister at the time, then wife, ex wife, the long history of life there house which we now own, where they set up these servers in the garage and they slowly took over each of the bedrooms. And this poor woman who I think was pregnant at the time kept saying, the baby's coming, the baby's coming. They put Google world headquarters in the baby's room. Right. So the rest happened.

But then is the heuristic. You already see such impressive demand, you know, you have to keep keep up with it. Or sometimes the heuristics simpler or different than that.

In order. It's easier if I give you a latch. It is tempting to believe that you have a product that works before it works. And the error that's made especially by non technical people is they believe what the engineers tell them and then they pre scale for a product that doesn't work very well. So think of it in a different way. Think of it as I'm going to raise money from you because you're a very famous venture capitalist. I'm going to take your money and I'm going to do everything I can to build a great product and you're going to be all over me. Where's your international strategy? Where's your sales strategy? And I'm going to say, Reid, just stop. Product doesn't work yet. I'm saving your money because I know in today's market, the moment I have that product, I know I can hire a sales and marketing and evangelism team in a week and I can do it globally. That's the difference. So think of it not as the measure that I described here, but rather as a funnel which is very tight. And then there's a global expansion period. And I think that's the essence of what you're trying to say in your book, which is there's a point where the sum of all of this, it's like this huge push, right? But you have to time it right now. How do you know? Well, one is people use the product Itself. Let me give you lots of failures in my case. Let's use Wave. Nobody at Google used Wave. Okay? Other people did. They were. And by the way, they were really nice and really energetic. They're not using it anymore. They were just like, caught up in the hype. Right. How many people do you see walking around Stanford using Google Glass? Anyone here wearing Google Glass? I don't see it. Okay, let's keep going through the list. Right. Again, maybe it's just we're at version one and we need version 1.1. Maybe we need just a small change and then boom. Those are two examples. You can pick hundreds of examples in Google. Thank goodness you've all forgotten them.

So once you have the product market set, that works sufficiently. Part of the thing that I think we do here in the Valley, China does, but it's actually relatively rare, is figure out how to really build fast into that global opportunity. And some of those techniques were things that you guys invented. So what was this stuff in order to be able to.

Well, I want to talk a little bit about hiring. Before your excellent book, Jonathan and I wrote a book called How Google Works, and a third of the book is on recruiting. And I want to give Sheryl Sandberg credit because when she was with us, she set up a lot of this. There is a way to systematically hire better people than anybody else. What? Bob Taylor, who I interviewed yesterday for a new book I'm working on, the Future of Everything, which involves looking at the past of everything said was sell the dream. So here's how he funded the arpanet, right? He called up people and he described what he wanted. And they either got it and got incredibly excited or they didn't. And if they didn't, he just went to the next person. How hard is that? Well, if your idea is pretty good, if the person is sufficiently dull that they don't get it, then are they going to get it after some persuasion? Probably not. You want somebody who's quick. One of the things you say is hire generalists. We, in the form of Larry and Sergey to start with, had very strict rules. Here's a typical example. I would go in and I would say, we need some experience in this layer in this area, and we need some people who can program in Java and SQL and various other sort of XML protocols, which I was very familiar with my previous jobs, and they would say, that's the stupidest thing we've ever heard. No one would ever want to use Java or any of those kinds of things. They did this partly Just to annoy me. And I said, well, who do you want to hire? I said, we want to hire incredibly intelligent people. So do I. Okay, you're implying that people working on the stuff I care about are not very intelligent. And they would say, no, no, no, no, no, we want to hire incredibly intelligent people. And incredibly intelligent people would figure out that the stuff you're talking about is stupid and therefore you should work on the right thing. Okay, so we would have these debates, right? And it's all in great fun, of course. And by the way, today we have thousands and thousands of Java programmers, just in case you're worried about it. But they were actually making a different point. They were making a point that you hire people who can get the job done, that the industry overvalues experience and undervalues strategic and intellectual flexibility, which is the point you make in your book. And I feel very strongly about that. And there's an easy way to understand this. I'll tell you. In all the issues we had at Google, I realized that I had no idea what to do. I am, after all, the CEO. I'm supposed to know what to do. I have no idea what to do. But I knew I had by far the best team ever assembled to address them. Examples would be dealing with the then competitors. At the time, the shift that we did in our business from one way of selling to now what is called a Vickrey auction or a second price auction. I'll give you an example. There was this fellow named Salar who was a biology undergraduate here who looked to me to be about 21. He's actually older than that, but he looked very young. And so one day he announces, we're switching our entire revenue system from an AS sold model by the Salesforce to an auction based system. And I was convinced, remember that these things were being oversold, that they weren't worth very much. And our sales guys were so incredibly good that they were maximizing the value to the point where the price was too high. Seller said, well, I'm not sure, but we won't get there with salespeople, we have to have an auction. And I said, okay, whatever. And Larry and Sergey were on his side anyway. So I got so worried about this, I put in place a program in the first year, which I called the Cash Restriction Program. You can imagine the acronym. And we would only spend money at 10am on Fridays because I figured that way I could control the amount of money because I was worried we were going to go bankrupt while this very, very young man Implemented this auction. We turned on the auction. I'm terrified. And the revenue triples in day one. Of course, we completely forget the whole program. And I'm saying, you're telling me that our incredibly talented sales force is underpricing our product by a factor of three. So then I immediately call the sales guys and say, what are you doing? I don't know whether that's luck or scale, but it showed you in solar, of course, a brilliant man and a person who really is responsible for the architecture that drove the $60 billion. You need ideas like that, that scale so fast. Let me put in a pitch for my current best idea, which is machine learning. In the careers that you all will have and in the companies you'll found and so forth, machine learning will be the way in which you get that multiplier. And for the non programmers in the room, the way to understand this is that programmers like me were taught how to write algorithms that precisely specified a methodology. And we got really good at it, and we're very proud of it. And we're very arrogant. Okay? A new set of programmers understand how to have a computer learn something. And then the learning model is applied to this problem. And that's a very scalable model and that's going to produce immensely larger companies than the kind of companies we're talking. And the speed will be immense because once you've learned something, the scaling can occur globally in a matter of hours.

Or even necessarily quicker. So hiring, is there stuff that you want to go into how you did hiring?

Well, we had a whole bunch of reasons. One of the rules is that we didn't want to hire your friends, which always was upsetting. We also didn't want to hire anybody from what were considered to be lesser universities. And so we also wanted to hire people who had very high GPAs. So the constant problem was that somebody was a good employee, had someone they had worked with, who was very loyal, very hard working, who was not from a great university, did not have a very good GPA, and we would not hire them. So that was controversial. We've relaxed that a little bit now. But the fact of the matter is it got us to where we were today because it produced these high IQ generalists from prestigious universities. That's the best way to understand this. The second thing is one day we were looking and when I was at Novell, I had learned that there were people who I called glue people. And the glue people are incredibly nice people who sit at interstitial boundaries between groups and they assist in Activity. They are glue, and they're very, very loyal, and people love them, and you don't need them at all. And at Novell, I kept trying to get rid of these glue people because they were getting in the way, because they slowed everything down. And every time I'd get rid of in one group, they'd show up in another group and they'd transfer and get rehired and all that. So I was telling Larry and Sergey this one day, and Larry said, I don't understand what your problem is. I go, why don't we just review all the hiring? And I said, what? And Larry said, yeah, let's just review all the hiring. I said, really? He said, yes. So guess what? From that moment on, we reviewed every offer packet. Literally everyone. And anybody who smelled or looked like a glue person, plus the people that Larry and Sergey thought had backgrounds that I liked, that they didn't, would all be flacked. And so anybody who sort of met the original profile, they'd go right through and we'd have this huge argument. And every once in a while, we'd have arguments that were sort of specious. I remember one argument about a fellow who, one of the founders did not like his last name because it was too humorous. So you add a little. And by the way, we hired him. So you can imagine you can have some fun with it. But the fact of the matter is, because we never fire anybody, the hiring decision is crucial, and the people you hire define the culture, whether you like it or not. So Larry and Sergey, when they started the company, said, we need to have really smart people. So they hired a rocket scientist, and they hired a medical doctor, and they hired a Stanford graduate who had been a professional football player because they figured that they wanted normal people who had done something exceptional. So in my case, I was a pilot, and I had done other things. And they said, okay, you're good enough. The CEO has to have something besides being a CEO that makes them interesting. And everyone had something. And I would go around and I'd say, well, what's special about you? It's like Sesame Street. What's special about you? And what's special about you? But there was always an interesting story. So another lesson I learned is that you don't hire generic people. You hire people who've had some kind of stress or achievement or whatever. The best people hire, by the way, are CFOs who've gone bankrupt, because, boy, have they been through the war. And on the CFO side and the finance side, you want Somebody who's not going to happen again. And you only go bankrupt once, by the way.

Exactly. Now the interview process, as I recall from early Google, was fairly involved.

Oh yes, yes. So the interviews. Well, so anyway, finish that.

Well, I mean it's precise to that is was that a good thing, bad thing, mixed thing? It was definitely a deliberate thing.

Okay, so once we decide we're going to review all the packets, then we have to figure out how to score everybody. So we put in a scoring system and asked people to score and they were scored between 1 and 5. So Sergey said, well, the problem here is that these scores will bias. So we put in a statistical measure where we would look at the average weight of the scorers and then correlate that with the future performance of their, you know, their scoring. So a year later we would look back and say how we had graded that employee versus the way you had rated them. And oh my God, guess what we discovered that if it's a woman, the female is scored with inverse correlation to her performance. It's the most extreme evidence of male bias against female candidates were sitting right in front of us. How could this be? I mean, this is as liberal a company as we can. Full of guys. Well, it's called unconscious bias. Now we didn't have the term hidden bias. Hidden bias, excuse me. So we in fact changed the entire way in which we run the recruiting for female candidates and so forth and managed to correct that. So there's an example where something that even as as liberal and analytical a company as we had, we didn't realize we were doing, and this was now a decade ago. So we did. The good news is we corrected it. But it's an interesting story that even the best meaning people can have these kinds of biases. So anyway, so we would do this scoring and then we had the problem of people being interviewed. And so I still remember this packet, this gentleman who we'd interviewed 16 times and the person who was running this process had come in and said, look, you guys have got to make a decision. And the recommendation was to not hire him. And I said, look, you just can't do this anymore to people. So I said, you cannot interview people more than eight times. Okay. They have since revised the statistical metaphor that for engineers they have five interviews and for non engineers they have four. And that turns out to be statistically significant. So very interesting you can systematize even that.

Yeah, well, because as I actually recall, the way you did that is you actually looked at when was the score drop off no longer predictive because you were taking all that and you said, okay at this interview, that's it. Yeah. And that was.

But those are simple techniques that wouldn't have occurred to the normal company. But because we'd hired all these smart generalist types with obnoxious degrees from famous universities that were obnoxious, you know, whatever stereotype you want, they applied an analytical bias. So we did the same thing. We did an analytical bias to facilities and to cash management and so forth and so on. So during this heyday, we made very sure that our marketing was disorder. So I used to call this the lava lamp pitch. So what we would do is we would show you around all these incredible things we were doing. We show you all the lava lamps, say it's complete chaos. People can do whatever they want, which of course is completely not true. And so if it was somebody important, like a shareholder, I would say, oh, by the way, there's another half of the company that's run incredibly rigorously with goals and so forth and so on that produces the results. So you want to distinguish between the marketing and the reality of how you manage them.

Speaking of management, so growing an organization this fast is a nightmare to figure.

Out how to manage.

Like, who do you promote in management? How do you run it? So what were the things that worked and what were the things that didn't work?

Well, I had had negative experience at sun in divisionalization and operating companies and things like this. My position was pretty simple. Larry and Serge and I are running things. It's chaos anyway. We're just going to run it as a group and run it as fast as we can. So we would have a meeting on Monday, which was called 60 minutes, which always took two hours. And it was called 60 minutes because you had to be offline and off your computer for 60 minutes, and if you were found at the time typing on your BlackBerry, there was a 25 cent fine. And we used to call this the Prayer 25 cents. Well, it's a cheap place. And so people would be on their blackberries underneath this and we would do BlackBerry detection. This strategy eventually failed, by the way, and it turns out it was impossible to get people to get off of their computers for one hour per week. Another example of a strategy that didn't work for me. So on Tuesdays we had something called the Google Product Strategy meeting where we would review, we would use that as a checkpoint with so much going on that we realized we were launching all these products and no one knew what they were launching. So we put in a launch review process and you had to have. You could not announce something without going through a launch review a week before. And that was sort of the way in which we caught the problems. And inevitably the problem was the engineers were doing something that they hadn't told the support people or the lawyers about. My best example here was the fellow who walked in and very young, probably early twenties, and had taken one of our products and added the ability to geolocate where your friends are and predict where they were going to be and where you were going to be. And he puts this demo up and my face goes ashen, right? And the lawyers are going, oh my God. And the marketing people are going, oh my God. And Larry and Sergey loved it. And they went. And I still remember this went on and we had a schedule and of course it was blown. And we're in there for two hours and they're just screaming about how incredibly brilliant this product idea is and how wonderful this is and what was wrong with me and so forth and so on. And I pointed out that there's this minor problem, that such predictive behavior is almost certainly illegal. And in any case, even if it were, I don't want to deal with all the real time subpoenas they're going to come in because now we know not only where you were, but where your friends are and we have it all in one database. And I said, look, we can't even afford the lawyers to deal with the current subpoenas. So the great thing about Google is after you have one of these. And by the way, just so we're clear, Larry and Serger were doing this for effect, they were just trying to be disruptive and have a good time. And this is part of the culture, is to draw this out, to be provocative in a good way. It's healthy. I'm saying this in a good way and I think it was a very healthy process. So guess what the solution was. This particular product, you could actually pin it and lie as to your location, which makes the legal records unreliable. We launched it. You see another example that we had Latitude. Yeah, remember Latitude. And then another example is that we were having. This is back after I discovered we were doing Chrome and we launched Chrome and it was beginning to be successful. We had a debate over HTML5 and the exclusivity of browsers and the rate at which we put stuff into the browsers and so forth. And at the time, Chrome was not the number one. It was well behind Safari and Internet Explorer. And So we have 20 people in the room. And we're having this argument. And all of a sudden it's pretty clear that Sergey is not going to give up. He is absolutely convinced, and I disagree with him and Larry disagrees with him and me. And we had a rule that the three of us, that we would not have huge arguments in public. So which, of course, we did anyway. So I realized at that point I kicked everyone else out of the room, and the three of us had it out. We clearly didn't agree. And I said, look, you guys go back to your office. They shared an office. You guys argue it through, and if you haven't decided by noon tomorrow, I'll make the decision. It's clearly the worst decision of the three from their perspective.

A forcing function.

A forcing function. So that afternoon I go back over to visit their office. Hi, guys, how are you? And they're just kind of chatting around. Did you come up with a new solution? They come up with a completely better solution that was far better than any of their previous ideas. So sometimes the cauldron of ideas works. Give you another example, we had an engineer who had invented free WI fi and they decided to. They had launched this thing in San Francisco. And so we're doing Nikesh, who has since left the company, had just come from Deutsche Telekom and knew a lot about this. We knew nothing, nothing about free WI fi, telecoms or whatever. So he starts asking this guy very reasonable business questions, none of which he could answer. And the review is going south and it's getting worse and worse and worse. And at some point, it was pretty clear that this was like torture and we had to stop. So I said, look, let's stop. And so at that point, you have a choice. You can say to the person, you're an idiot for having brought such a terrible idea. You've clearly embarrassed yourself and the company and so forth. That would be the wrong thing at Google. The right thing to Google in a situation like that is to say, which is what I did is I said, I think this is the most incredible idea I've ever heard. You need to come up with a better approach. The objective of Free WI Fi is incredibly strategic to the company. So all of a sudden you can see this guy. Oh. And then he runs out the room. Six months later, we reintroduced it and got huge benefit from it. So we ran with this product strategy thing, and then we would do deals on Wednesdays. So we did. That was sort of the structure. Thursdays we had a no meeting day, which meant that we were Always in meetings. And it was organized so that the salespeople could travel Wednesday and Thursday and come back Friday and the cycle would begin again.

So, but in terms of, I mean, Google had a bunch of different things where like, sometimes, like everyone. It was like a day that everyone worked for Larry. Like, it was like, we're going to be completely flat.

Like, I have to tell you about the disorg. So one day, again, this is early in the first year, I'm a normal engineering manager and we have normal engineering plans and I think they're fine. And we have five engineering directors and we just. Wayne Rosing was the engineering vice president. And so Wayne came in and said, you got to come talk to me. I said, what? Larry and Sergey are on the rampage. I said, over what? I said, well, we had something called snippets and the engineers were supposed to write down what they did per week. So Larry has been reading the snippets and he's been correlating it with what the managers have been saying. Not good. And Larry's a very precise and very analytical person. And so he says, look, we got a problem. I mean, I can't deny it, Larry, what are you going to do? I said, well, we're going to get rid of all the managers. You can't do that, Larry. So nevertheless, and that was, of course, an even greater challenge. So they took the five managers and made them individual contributors. All of them are still in the company, I might add, 10 years later. And we had 120 people working for one engineering vice president directly. And we reasoned that this would guarantee he couldn't interfere with their work. So we ran that way for two years. His name was Bill Coren. He's now a very successful venture capitalist. And he jokes about what it was like, but you can, in these situations do extraordinary things.

And so actually that's probably a good tee up too. So in this kind of blitzscaling circumstance, what's the role of the CEO?

Well, I can tell you my role was to manage the chaos. You have different kinds of CEOs and there's no single answer. In any successful company, you've got to have somebody who can run very fast and has very, very good product sense and has the sort of intellectual, emotional leadership of the key stakeholders. In that sense, it's like a faculty, the key engineering talent, they sort of put up with the management. They know what they're doing, they're incredibly professional, they're very, very driven. They know what great products. Look, that's what success Looks like. So my job was to sort of organize the world around them. So ultimately, if you ask Larry and Sergey, when they hired me, Larry said to me, well, we don't need you now, but we'll need you in the future. And I think that was roughly right. Right. As a small company, they were doing fine, but in order to scale, you needed somebody who understood replication and you could basically put the systems in place. So I set out to hire the non essential executives, which was everyone else, all the other functions that you need, and deal with all the other kinds of things. While Larry and Sergey as founders were working on the technical things. There are other models where you have a very technical founder who won't give up control, who cedes partial business control to a business associate. I think Mark is a good example of that. Mark's not going to give up control over Facebook. And by the way, Larry and Sergey didn't either. It doesn't matter whether you're called CE or not. But Mark allows the key stakeholders, in his case the CFO and Cheryl, to run large parts of the company. And of course they do a great job. Exactly.

One of our theses in Blitzscaling is that part of what you do is you make decisions about also what not to do in order to move fast. Were there kind of key decisions about what not to do in order to be able to move fast at Google?

Surprisingly, no. Because the ambition was so broad, the only lever I had was to slow some things down and emphasize other things. I don't agree that you should narrow your focus. What I believe is that you get the best outcome when you make the broadest appeal in terms of leadership and excitement and so forth. And we worked very hard to make that happen. But there were other things. For example, we would refuse to do exclusive deals with people. But I would explain to people, well, we don't really do exclusive deals, but we only have the capacity to work with one and it's going to be you. Right. So there are other ways of achieving those kinds of things. I'd be careful to conclude that you should do a small thing. All success starts from doing one thing really, really well. But you'll recruit better with a broader vision that's credible and that you can articulate. But go back to that. Selling the dream, right? What you do is selling a dream. If you can't sell the dream, then you're not going to be successful.

What were the key things that kept Google innovating intelligently as it scaled 20%? Time was one of the things that was publicized.

We talk a lot about that. 20% time was a rule invented Again, when I started there, there were two actually interesting. One was don't be evil. And the Second one was 20% time. Don't be evil, I thought was a joke because, I mean, nobody here is evil. I don't want to be evil. You don't want to be evil. And so I was sitting in the original conference room in the small building when we started, and there's this conversation about ad targeting in a particular ad click. And one of the engineers, whose name is Ron, pounds the table and said, that would be evil. And I'm like, oh, okay, what just happened? And the whole mood of the room changes. And there's this huge debate over whether that's evil or not. And ultimately they didn't do the change. And so I thought, what's the analogy there? And the analogy was it was, it's the Kanban system, right? In Japan, the notion that any employee can stop the line if they see a poor quality thing, and it works because of shared values, strong shared values, a strong buy in to principles takes you far. And I think that helped us. And that. I think it may or may not be true today that the company has that feeling, but there are certainly its history is clearly that. The Second one was 20% time. And the idea was that we could ask you as an employee to do everything you can in 80% of your time. And the other 20% time, you could do whatever you wanted. Don't be too worried here. These are engineers. They're not exactly going to run off and do something wild. But if they're passionate about something, they could do a 20% project that began. Many of the features that we offer today began as 20% explorations. So part of the management job was to sort of listen for those 20% time things and then sort of aggregate them. And Marissa, again, I'm just mentioning people that you all would know now running Yahoo, of course, and a superb executive would watch for these things. So Marissa maintained a top 100 list which had 300 things on it. And she was constantly, constantly culling that list and trying to identify these little ideas and trying to get them to talk to each other.

Well, Marissa's a good example. Her, the APM program, et cetera. How did you end up growing managers?

I would argue immodestly that the program that's called the APM program is the largest source of entrepreneurs in the Bay Area today. So Marissa had this idea. Marissa was, I think, an undergraduate here Or a ta something.

Undergraduate. Undergraduate in symbolic systems.

Ah yes, incredible talent, Very, very technical. And she became a product manager. And when I started there were she was one of the three product manager. The company now has thousands of. And she had the idea that we should grow these associate product managers as she called them. And the idea was that you would hire people who were right out of college with a technical degree, typically computer science, who did not themselves want to become programmers, they wanted to work with programmers. So she would identify them and they're all again very young, right out of college. And then she would train them and so they would for example, go on these trips for weeks and sleep four to a hotel room just like in college or graduate school, high school. And it forged these incredibly tight bonds of people who were highly technical and who could specify products and then she would run them through this sort of training program. And Sundar was an example of this slightly older version of the same thing. All of the executives running the key parts of Google came in through that program, it turns out. And the program, although she was too young to understand it, was originally invented by Microsoft. Microsoft had these people called technical leaders who were non programming liaisons who then. And they were incredibly technical and they were the ones that did product specs. So I think we give Microsoft the credit and we give Marissa the idea for inventing the model at Google. And that model is scalable today.

And is that the kind of key way that you populated a bunch of the key strategic leaders?

Yes. We did one other thing that we one day we'd hired a woman named Shona Brown who had been at McKinsey and she had been a partner and I guess she'd gotten tired of being a partner and she wanted to work in a proper company. So she came over and we made her chief operating poobah something or another and put her in charge of all the things that nobody knew what to do with. And so she comes in and says, I think we should have our own McKinsey operation. I said no, no, we're not going to pay McKinsey. She said no, no, no, we're just going to hire the people from McKinsey. And I said what? And she goes no, we're going to hire the following. We're going to get the people who would be associates, that is non tenured, non partner people, and we're going to have them come and do the McKinsey function. She didn't mean hiring from McKinsey, she meant replicating that function and they'll be in this group for one to two years and then they'll go with the business that they studied and that'll be their career path. I said, well, give that a try. So an example is that one of my students here at Stanford went into that program for a year and a half. Again, very, very good Stanford MBA analytical skills. And he's a pilot and just a great leader in all known ways. He runs half of YouTube today. How did he get from 10 years ago that to running half of YouTube? He earned it, right? He went from project to project. Give you another example. Dennis Woodside, who is now the chief operating officer of Dropbox, came in through a similar program. So he studied salesforce effectiveness and we put him in charge of the emerging world and he was doing so well. We eventually put him in charge of all of Europe and then eventually we put him in charge of all of Motorola and then he left to go become number two at Dropbox, which is a good move for him. So these people who have this sort of business analytical skills are incredibly useful. So whenever we've had problems involving business units and how do businesses interact, we take one of these people and inevitably they go with the problem and fix it. So again, think of that as an internal stable of talent in the same sense that the APM. Another story about the APMs. Marissa worked for Jonathan and Jonathan was very colorful. And Jonathan said, we need a chain gang. I said, what is a chain gang? He said, a chain gang is a whole bunch of people who are waiting for work. And what we're going to do is come up with projects which are good for the chain gang. So we wrote down a list of questions that we had about the business and our products and so forth that were sort of identifiable products and they had to be doable in a month. And all of a sudden we had answers to every question we had about competitors, positioning, evolution, costs and so forth through the chain gang. And by the way, these are 23 year olds, right? And it's part of their development learning. So you can use the talent. And those models that I just described are very, very scalable. Yes, sir. What are the most counterintuitive signs of talent and potential at a very young age? I would describe Gnome. So Noam went to Berkeley in the math department at 19, got bored. He applied to Google and didn't meet any of our criteria. So somebody knew him and said, you should talk to him.

Berkeley wasn't elite enough.

Berkeley was not good enough. And he applied to Google and he was rejected for obvious reasons. And so somebody knew him and said, you should talk to this guy. So he shows up and he's very unusual and he had figured out a way to do spelling correction. And so somebody said, well, we should just hire him to do spelling corrections. Okay, whatever. In other words, it's kind of loose. So he invents the spelling corrector, which is the first time this had ever been done, which is a huge achievement. Everybody said, well, we should have hired him. I said, well, he didn't meet any of our criteria. So we have another rule which is if the person is super smart, hire them anyway. But they've got to be able to do, like invent a new spelling corrector. So. So armed with that information, six months later, I'm in the cafeteria and it's six o' clock at night on a Friday night and I'm getting my dinner and Noam's next to me. And Noam says, I got to talk to you. I said, what? I said, I need 10,000 machines now. 10,000 machines at the time was a lot. It wouldn't be today. And I have a lot of credibility with gnome. So I said, what are you going to do? He says, I'm going to solve general knowledge by the weekend. Okay, good, okay. I said, okay. I put my tray down, I went over to the fellow in charge of machines and I said, get him as many machines as he does. So he turns on the software, we're chunking away and the algorithm stops. They can't figure it out. They don't need more than a couple thousand machines for the weekend. And I'm saved. Today, GNOME is still trying to solve general AI, but if there's anybody I can think of in the world who's likely to do it, it's going to be him. So what do you learn? Talk to the person and figure out what is it that they're going to do and if they've got a path, give them a shot. But they have to be able to answer it that way. That's how you handle these sorts of strange cases. There are always odd cases. I can give you lots of examples of hiring. We try to hire people who can coexist with other human beings because we tend to work in teams. But there are cases where we've said, okay, keep them over there.

Usually the measure on that is they have to be so valuable that you're willing to put all the infrastructure around.

And one of the things that, that Jonathan and I say in our book is that you should hire the divas. Now by the way, if you read any management text, it says, don't hire the divas because they're nothing but a pain in the ass, by the way. They are, but the people who are the divas, who believe are the ones that will drive the culture of excellence and they'll drive you to that excellence. Steve Jobs is a diva. I worked with Bill Joy, who was my colleague for many years. He's an example of a diva. And I mean this in the most flattering way. They expect a lot, they drive people hard, they're controversial and they care passionately. If you find those people, you're probably going to work for one. So be nice to them, pay attention or be that passionate.

Back to innovation. How important is it to have separable groups to accomplish something big with an existing company? The Android group, Google X.

This problem has never been solved. It's just a mess. So the correct answer is two people with a graduate student and a faculty member at Stanford, and they go off and change the world. Every project I've been associated in the 40 years I've been doing this has started with two people in roughly the equivalent of graduate student and roughly with an assistant professor who's working for tenure. So, for example, Windows, the platform, you know, was started by one person who I am to know. Unix was two people. Java was one person. I go on and on and on. Gmail was one person with his colleague Paul, and that's Paul Buchheit. Now today, how many people work on Gmail? Many, many, many, many hundreds. Android, a very small team. Linux done by Linus Dorvals. That's why it's called Linux again, go on and on and on. So one day I'll give you an example. One day I thought, I love this Google Docs product. And so I figured they've got a spreadsheet and a word processor and so forth, and I want to meet the team. And there was a conference room near me and I said, just bring them into the team. And they said, no, they won't fit. I said, how many people work on Google Docs? And they said, 150. I said, 150. What do they do? So we then get everybody in a larger room and I went, what do you do? Well, the answer is the projects today require a lot more resources because of internationalization, integration and so forth. So one of the point here I'm trying to make is actually slightly different. One of the complaints I have is that the teams that are doing the work of products you see are far larger than they should be and that's a failure of architecture. In other words, when you have that many programmers programming that means they don't have the right libraries to depend on. You've not generalized the the phenomena. It is my hope that the machine learning revolution will fix that problem.

And is there anything that you think is important about the separability so you don't have to interface with the cross coordination with all the rest of the company?

Computer scientists understand the concept of interface and API and well done. Platform APIs are the key to everything. One of the problems we have internally is that our system is incredibly powerful but now so commingled with itself because of its co dependence it's very difficult to partition. And I think that that's a fairly normal end state for these large software projects. And so again within the company there's a major initiative to put services into Google Compute, which is the externalization of this which requires APIs. So what I sorry for the technical jargon but I'll tell you what I tell people. I never want to do a platform product without an appropriate end user app that goes with it. And I want to look at the interface and I want a well designed interface between the two. If you can maintain that discipline, you can build great things. So an example would be today, the vast majority, if you guys go to any startup here in the Valley and most around the world, you're likely to use Amazon Web Services AWS and its associated interfaces. What's unique about those web services interfaces is that they're programmable by mere mortals, right? They're relatively straightforward, they're relatively intuitive. They did a pretty good job of it. There are limitations to what it can do, but it represents good design. The original Mac Toolkit is another example of something where you can get a lot work done. It's in a single book I worked on, something called the Alto, which dates me done. Before you guys were born, the Alto had a single book which showed all the APIs. So you know when you have that kind of proper interface, that's where you are. Can you ask about Alphabet? So scale this all the way up when you talk about organizational interfaces. Well, Alphabet. So the question was about Alphabet. So I was giving a technical answer to interfaces. There is a size at which companies begin to fall in on themselves. I think Google has done particularly well because the two founders are so strong minded and so technical and obviously so brilliant. And I would argue that Apple had done the same thing. If you look at the industry today, it's Apple, Amazon, Facebook and Google Driving sort of the first four, and then there's plenty of others right behind. Each of those companies has either a strong founder or a strong culture, very strong product focus and so forth. Steve told us, of course, I was on the board of Apple for many years. Steve told us that the problem with Google was that we had too many things going on. That his vision, which of course I think has been realized by the success of the company, is a very small number of incredibly well done products. And we can debate that, but the fact of the matter is the success speaks for itself. So in our case, I think we've hit some kind of a mission scale problem. We spent two years arguing about this and the argument was should we change the logo? Which I don't really care, but everyone else cared. And they said, well, the mission was all the world's information that we could index and use and obviously how do you match that with cars and biology and so forth. So Larry reviewed this at some length. We had visited with Warren Buffett on the 14th floor of a building in Omaha. It was the three of us and Warren Buffett, if you can imagine that. And he held forth. He was a fascinating and impressive man in every regard. And it had a big impression, I think. So you can understand Alphabet as an attempt to build a holding company that looks like Berkshire Hathaway out of an existing large company. This has never happened in the history of American business. It's a big bet, not a little one. It's typical of Google, More important to know that we announced this without actually even knowing what the companies were. So one of the fun things I do with employees is I'll say, let's do a show of hands. How many Alphabet companies do we have and what are they? Of course there isn't a good answer because we haven't announced it. No rational company would ever do that. They would have had months spending their time figuring out this market and so forth. And our attitude was just get started and work it out. So today there is one, which is the Google Life Sciences stuff, which looks like it's going to be hugely successful. We've also said publicly that Calico, which is an aging company, we've also said publicly that Access, which is about Google Fiber and a few others are coming. Characteristically, we're not doing this halfway. So having spent all week on this, I can tell you the instantaneous situation is that we're trying to push the Alphabet companies not to be divisions, but really separate companies. And that means severing the ties and really operating Autonomously. And that's inspired by Warren Buffett's rule that he loves to hire people who would do it. Whether he hired them or not, he never has to worry about them. He knows that they're going to wake up in the morning and show up at work and work very hard. That's just the nature of the people that they are. And those are the. Ultimately the recognition at our scale was we needed to have an organizational structure that would attract those leaders. We just hired this guy John, who's going to run the car stuff. I talked to him yesterday and he's fantastic. He came here to do this. This is what he wants to do. He's going to work 24 hours a day from the car industry. He wants some exciting driving house. There's all sorts of issues and all sorts of partnerships. I can't think of a better leader to take it forward. Normally companies would have given him a little bit of leeway. He is going to be the CEO, he's going to have his own operating and he's going to bear the risks and the downside as well as the upside. And that's very attractive to him.

What do you, if you're looking, if you're giving advice to entrepreneurs who are thinking about building game changing companies, what do you think the key things were that made Google win?

We. Well, first you have to have the right founders and you understand this, you are a founder and obviously the right founder in that context. You have to have the right founders for the problem. And the founders have to be good. And by good I mean they have to be impressive, they have to be smart, they have to be passionate, they have to be committed. This has to be their life's work. Which again was true of you and it's true of ours. That's point one. Point two is you need to have some luck. And in our case a search and the ads that we develop turn out to be a gold mine of revenue. And so once I figured out that it wasn't a sham and once I figured out people really were clicking on these ads, we set out to maximize the amount of revenue that we could get from that business. And if you look it just went like this and you maximize that by making the ads more relevant, making them more useful, all the things that you've heard. So the fact that we ended up in a very high gross margin business very early gave us the ability to take these risks. So when I talk to people I always say, well, how much gross margin do you have? How much flexibility do you have, and I've been so lucky, and I give this credit to luck first, that I forget that there are other businesses that are relatively low margin businesses like almost every other business by comparison. And so you want to be careful to understand how much flexibility you really have. The ideal business is the Microsoft business, a monopoly software business with hardware competitors who are competing for good treatment by you in a growing and global industry. Right. Again, let's use Uber as an example. Notice that Uber spends an awful lot of time talking about how the drivers don't work for them and that they don't own the cars. There's a reason, right? It's not the legal and liability reasons. It's because if you think of them as a software infrastructure company, in other words, they help assemble this thing, then they have very different economics than if you think of them as needing to open and own all those things. In that sense, it's a modern version of the franchisee. Why do people own McDonald's franchises rather than McDonald's? Well, because McDonald's couldn't get the capital to own them all themselves. So franchisees have to risk their business under a franchise model. Why do hotels like Four Seasons doesn't actually own the buildings. How could the Four Seasons hotel not own its own building? It's actually better to be the operator with a fixed revenue share in all the ways that you know and let somebody else ride the real estate up and down. Which is vicious. Right. If you haven't spent some time in it.

So I have a bunch of questions. I'm going to ask one more and then we're going to open up. If you guys don't have questions, I do have a stack more. More. Way too many for the amount of time.

Well, you and I are together tomorrow afternoon too. We're doing something together in San Francisco. Exactly.

And it will actually be on radio at one point.

Oh really? Yes.

You know, what do we do about the middle class? It's an important problem.

Okay, I'm in favor of them. They click on ads a lot.

Yes, exactly.

So it's clear what my position is, is I'm in favor of. Of more human beings because more human beings means more customers and more customers means more growth and revenue for the corporation that I represent. If you are confused on that, do the math around employment and people. We want more people, we want more immigration, we want more customers, we want more Internet, we want more revenue. That's called capitalism.

Exactly. We want more empowered people too. So Google obviously making massive amounts of progress in a lot of different areas. Everything from self driving cars, genetics.

Global.

Connectivity of Internet search, mobile phone systems, the list just goes on. Were there any kind of particular Valley of the shadow moments where you were like, oh gosh, is this going to work?

Well, a lot I think. The one that drove me crazy was the conversion in the auction six months later we had a. For various reasons we had to merge our databases together. And I thought, well, this is a pretty big deal. So I talked to the engineers and we had three different databases to a single ad system. And I said, okay, well maybe I should spend the weekend in the data center watching them. Engineers said like, what planet are you from? We never visited the data centers. I said, okay. So I figured I'd take the weekend off, they would do the merger and I would go into work on Monday morning. And for various reasons I had to be in New York that day. So I flew to New York on Sunday and on Monday and I wait and I see what happened after the database merger. So we're now merging to a single auction based system which based on the data would produce a lot of revenue. So at five o' clock, I'm sitting next to a young woman, I have my little cube right next to me and the phone rings and this you can hear on the voice, it's a man and he is screaming at this woman, how could you do this to me? This is a disaster. So forth. And this goes on for 25 minutes and I'm listening and writing down to figure this is real feedback. They say in the book you're supposed to actually listen to the customers. I was happy to have her listen and I wrote down what this poor woman had to put up with. So she hangs up. Eventually she's as polite as she could be. And I said, well, what happened? And she said, well, first place, he calls me every day. I said, he treats you that way? Yep, absolutely, every day at five. And he said, well, why doesn't he drop you? I said, because everything in his business is dependent on us. I said, well, what was that all about? He said he couldn't get his report to show his boss that day of how much product they had sold. So sometimes you get the wrong impression if you build something that's that crucial, right? So what happened was I thought, oh my God. So I go to the airport, I fly back and I say, we've got a disaster and we're going to have meetings every day until we resolve this. So by Thursday it still wasn't resolved. So I said, okay, we're Going to have meetings twice a day at 10 in the morning and 4 in the afternoon. That started Friday. And I said, by the way, if next week this isn't resolved, we're going to meet every hour for seven days a week. And we fixed it. And that database is the benefit. So I think sometimes knowing where the problem is and this poor woman, right, and the abuse she had suffered at the hands of this unknown customer drove that. And I thought, well, okay, that's both a lesson but also a crisis. Yep. So at our company we use a model of Google, Google AdWords, Amlodipine, email, Google Docs, Android phones and the list goes on. And one of the things we noticed was that our experience with Google as a company and the products was differing in each product. So support was different, quality was different. Firstly, is it intentional or a result of those two things that we were talking about? And secondly, how is that going to change with Alphabet going to be more extreme? The products you're describing are all in the Google part, in the G of Alphabet. So there's no change from an Alphabet perspective. We've had various of these kinds of problems and I think a lot of these have to do with scale. There's just a level of how well you can integrate all your support. The products are at different levels of maturity, there are interconnection issues and so forth. Without knowing the specifics, I can't give you a better answer. Initially we didn't have any support at all and that was sort of a non starter. But we've tried. One of the things that Valley companies do is they try to minimize the human support that they provide because the model requires them not to have too many. You know, the old telecom support model, if you will.

Let's see a woman there.

Could you walk into your day yesterday? Well, let's see. In the morning I went to the doctor for my routine test, which was fine. I then drove to the office and we moved to. Larry and Sergio decided that they were bored with the building that we've been in. So we have moved ourselves to what we call the Rales building. And this building is the old Mayfield Mall, which was owned by HP. And Sergey 1 day decided that he didn't like all the money we were spending on interiors. So this building is nothing but a concrete shell. It's incredibly cold, with really uncomfortable chairs. And he views this as correct. And there are no walls and the desks and they have a single wire that comes down that's got power that you plug your computer into the Building is full of drones, self driving cars, kites and everything that is sort of, I excuse the stereotype, but a teenage boy's lab paradise. It's like popular science when I was a boy. So it's sort of dangerous. You have to duck occasionally. So we decided to have. So we had a meeting with the chairman of Wipro, who is an incredibly important person in India. He's spending a couple billion dollars on education in India and we're trying to do a partnership with his company to do deployment of Google services to address some of the issues you're describing. What's interesting in that meeting was they were talking about their biggest problem is they do business process outsourcing and bpo. They do it for travel. And they want to automate a lot of those sort of issues because that gives them a leg up in their competition. So that's an example of something that we could do together. Then we had our board meeting and our board meeting. Our rule about our board meeting is you dress down for the board meeting. So everyone shows up in jeans, there's always a shock. And Larry and Sergey typically arrive on rollerblades or bicycles with bicycle helmets, I might add. You sort of have to get used to it. So the board meeting consists of reports from the subcommittees, which is all fine. And then we've added, since we've added a company, we had a report from Sundar, who's the, the CEO of Google. And then at the end, and they have demos and stuff and then they have executive session and so forth. And then we had a meeting. One of the things we're arguing about in the Alphabet structure is how to allocate all of the software to the different teams. And there is a series of people who believe that the software should be partitioned among the companies. And my argument is the software should be a free good across all the companies. And I won that argument. But in order to do that, I had to get all the people in one room and get everyone to agree and send a congratulatory message of how brilliant they were. And then I went to dinner. How's that?

Chris, you had a question?

Yeah. So for Alphabet, you studied Berkshire Hathaway. What were the other organizations or things in the past that were most useful to study in terms of setting up? I don't think that we studied very much, except for Berkshire Hathaway, because all of the other solutions wouldn't get us to the kind of autonomy that we want. Now there are many, many technical issues of taking an existing company and splitting it in pieces. Technically Google sort of did what is called a reverse swap. And so the parent got the stock and then the contracts stay with the G. And there are many legal reasons for that. But one of the things you learn as a CEO is that in a well run company you can hire a very large number of very smart lawyers who can figure all these things out. So what you try to do is you try to say what is the goal? And then say please solve for this same thing with accounting and so forth. How do you think about capital management now that you have Alphabet? That was discussed yesterday at some length at the moment. So we had a joke, the previous CFO had an orange backpack and our joke was all the cash was kept in the backpack and it's on the order of 70 billion. So it's like a lot. And that's a joke by the way. So we're clear, it's not actually in the backpack, it's just an internal joke. So the cache sits at the moment at the. I believe it sits in Google, but it is controlled by the parent and at some point they'll move it around and there are legal reasons for that. But again this is the kind of stuff the shareholders own the cash and the shareholders are in the board. So the board effectively controls it. But more like how do you pick which ideas to fund which companies to put more capital? It's the same process as always. There's a competition for best ideas. Here's a typical example would be Google Fiber. Google Fiber is an incredibly good business once it's established. I argue that we should maximize the rollout of Google Fiber. We've announced I think 20 cities, I don't know the exact number and it's working very, very well. So now the question is from a shareholder perspective, how much cash do you invest in any particular year? Well, my attitude is as much as you can, but there's sort of a rate at which you can deploy that cash without wasting it. So it turns out that the deployment is a function of the rate at which you can get the permissions, hire the electricians and so forth. It's not a cash problem. So in the Valley companies that we're talking about, Microsoft, Cisco, Facebook and so forth, they have so much cash that the kind of normal cash arguments, cash debt, don't really apply. It's really much more. Can we use it effectively? Another example of a rule I should say is that every May we think our business is slowing down because that's the seasonal low. The strongest is in this period Before Christmas and every May I think, oh my God, we should stop hiring engineers. Never stop hiring engineers. Because engineers again, properly deployed can always generate enormous amounts of economics over their salary. You're going to need them.

In the very back.

Could you talk about some of the personal tolls that this has taken on you? Like with scaling, some of the emotional tolls? It hasn't been that hard. I mean, look, I went to Novell, I did a turnaround. These high growth companies are, they're a blast. I mean, I would strongly encourage you to join a high growth company early and stay in it. I used to tell people, this is a rocket ship, this is success. Stop complaining. Right? I mean, I've seen the other side. This is good. So the personal tolls have to do with time, with your family, exhaustion, burnout. We actually got so concerned last year that employees were burning out that we actually gave a series of speeches where we said, people, you have to take vacation. We put in place one of these vacation policies where you could take whatever you wanted and people started taking less. That's what we worry about.

Very last question right here. I thought you had a question.

Now this is one of the prime examples of a company that probably has to worry about scaling too large because of antitrust. And I'm just curious how you think about the limits of scaling and how that affects your business. What I've always told our product teams is I'll do the lowering, you do the producting. So just build me incredible products. Don't be a lawyer. You're probably not very good. Now, I'm not a good one either, but I'm in charge, so that kind of works. So what I always tell people is just build me incredible products. Now obviously there is a real issue because of the role and scale. It was much lonelier at Google, but now that the Europeans are very concerned about all of the American companies, it's much better. So in the US I think it's pretty clear that we're not in antitrust trouble. In other words, scale in and of itself in America is fine. The European law and the European biases tend to bias against large companies. And the tech companies are building large market share platforms very, very quickly. And there's a commissioner in Europe who just announced a proposal to study platform economics from the standpoint of antitrust and regulation issues. He identified 32 companies, of which 31 were American. So that gives you a sense of what's going on with that.

Let's thank Eric for joining us.

Thank you.