The a16z Show - a16z Podcast: From Research to Startup, There and Back Again

Episode Date: October 8, 2018

The period from 2000-2016 was one of the best of times and worst of times for tech and the Valley (dotcom, financial crisis, Google IPO, Facebook founded, unprecedented growth, and so on), and John Hennessy -- current chairman of Alphabet, also on the boards of Cisco and other organizations -- was the president of Stanford University during that entire time. Given this vantage point, what are his views on Silicon Valley (will there ever be another one, and if so where?); the "Stanford model" (for transferring IP, and talent, into the world); and of course, on education (and especially access)? Hennessy also co-founded startups, including one based on pioneering microprocessor architecture used in 99% of devices today (for which he and his collaborator won the prestigious Turing Award)... so what did it take to go from research/idea to industry/implementation? General partners Marc Andreessen and Martin Casado, who also founded startups while inside universities (Netscape, Nicira) and led them to successful exits (IPO, acquisition by VMWare), also join this episode of the a16z podcast with Sonal Chokshi to share their perspectives. But beyond those instances, how has the overall relationship and "divide" between academia and industry shifted, especially as the tech industry itself has changed... and perhaps talent has, too? Finally, in his new book, Leading Matters, Hennessy shares some of the leadership principles he's learned -- and instilling through the Knight-Hennessy Scholars Program -- offering nuanced takes on topics like humility (needs ambition), empathy (without contravening fairness and reason), and others. What does it take to build not just tech, but a successful organization? image credit: Jitze Couperus / Flickr Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hi, everyone. Welcome to the A6 and Z podcast. I'm Sonal. I'm here today with A6 and Z general partners Mark Andreessen and Martine Casado, and we're interviewing John Hennessy, who is the current chairman of Alphabet and was president of Stanford University from 2000 to 2016, which also happened to be one of the most interesting times for tech and the valley. So in this episode, we cover everything from the Silicon Valley and Stanford models to if it's possible to create other Silicon Valley's and if so, where and how. And of course, we cover everything. We cover everything from the Silicon Valley and Stanford models to if it's possible to create other Silicon Valley and if so, where and how. And of course, we also cover education as well as the tech and economics of education to what it takes the lead companies. John has a new book out, Leading Matters on Principles for Leadership, and he also recently launched the Knight-Hennessee Scholars Program for graduate students focusing on both knowledge and leadership. Finally, we discuss the evolving shift between academia and industry, including the role of universities, big company R&D and the heyday of famous labs, and entrepreneurship then and now, which, by the way, is why I asked Mark, and Martin to join this episode, given their experiences going from university research to industry,

Mark with Netscape and Martine with Nicera, which came out of Stanford before being acquired by VMware. But first we began with John's own history as a startup founder based on pioneering the microprocessor architecture used in 99% of devices today. So welcome, guys. Thanks. I'd like to point out that Hennessy's Ocelot-Turning Award winner. This is unbelievably awesome. That's like the Nobel Prize of computing.

So you and Dave Patterson won that. Why did you guys win it? Well, I think we want it because the work we did has reshaped the entire industry. Many times when you find a fundamental breakthrough, its importance may take a really long time to emerge, particularly in the hardware sector. It moves so much slower than software. And in this case, with the explosion of the mobile world and Internet of things, efficient process architectures became really crucial.

And that really changed the world. and that's why our work has had such great impact over time. Well, actually, break down risk for us. Like, that's reduced? Instruction set computing. The way to think about it is building a machine with a simpler vocabulary, which can be executed more quickly.

If you think about it in English language terms, imagine reading sentences that have giant $5 words in and are really hard to parse and understand. You're constantly pulling out the dictionary. Now imagine a sentence that's written in clear, precise English. maybe it has a few more words, but you read it much faster. And we use that same key insight to try to build faster computers. So you reduce the instruction set in order for the computers to process information faster,

and therefore operate faster? Cheaper, faster. And why was that so cutting edge at the time? I mean, weren't the dominant players like IBM and deck? IBM and deck, and this is a time in the 80s when if you wanted to go talk to the leaders in the computer industry, and you were in Silicon Valley, the first thing you did was get on a plane and fly back east. It was a very different environment. They were building machines which were getting increasingly complicated rather than simpler.

And they missed the whole importance of the microprocessor and VLSI and how we completely changed the industry. So risk was invented roughly when? Early 1980s. In 1980s. And when do you think it really tipped to become as mainstream? It's so mainstream today. Risk processes run almost everything. Yes.

Except the desktop. Except the desktop on the server. Some of the servers. Right. But like every smartphone. Every IOT. Every IOT device. Every camera.

You probably own 100 of them that you don't even know about it. So it's like by far the dominant architecture today. Exactly. So how long did it take from inception to kind of win the market tip to when we knew it was going to be absolutely dominant?

You know, there was an early run at the mainstream market in the late 1980s, and it almost flipped then. But what happened is, rather than the industry converging on one risk architecture, they converged on three or four.

Oh, interesting. That gave Intel a real lead-up because they didn't have to beat anyone. They had to kind of beat these little three or four. IBM, DAC, Silicon Graphics. And they were all kind of beating up on each other, right? And so rather than getting behind one architecture, which would have made it much easier to build a software stack for it,

that didn't happen. So there was a period where they were a lot faster, and then Intel really came back, and it probably took until the emergence of the cell phone. What? That long? Yeah, probably until mid-90s. So there was a period where it really wasn't. It was working in the scientific computing space,

but scientific markets relatively small. compared to the general purpose market. But even the cell phone, right, the cell phone pre, the smartphone was not bad. Yeah, it was a phone. It was great. It was a phone with a wrist chip. It was a computer in the sense that we understand, right? So really, the iPhone probably is the...

Yeah, the iPhone was really the taking off point. Some of the earlier Nokia phones began to use the technology, but then when the iPhone came along, boom. So 1980-something, early 80s through to 2007 to really have it... Yeah, to really have that big effect. Yeah, so I just think it's a great example of, like, these things are generational. Like, the really, really big things do take a very long time.

But then when they tip, right, How many risk chips do you think are globally today? Well, 99% of the market space. So, you know, it's much larger than the number of... Now, that's counting processor chips, right? But including embedded systems, 10 billion chips worldwide? Oh, more than that. Probably 50 billion.

So I worked for years under Pat Gelsinger at VMware, who was the GM of the 486 at Intel and a longtime proponent of Cisco. And he still maintains that Cisco's the right architecture and, you know, dollar value. It's still the predominant market or whatever. Are these different problem statements or do you think it's still just dying a slow death

and we just haven't gotten there yet? And we should know, Cisco stands for complex instruction. So it's the opposite of risk. And the classic Intel might. Yeah, I think you have to separate out the technical argument from does it have a large established base

and hence a large software stack. I think on the latter point, Pat's exactly right, it's a large software base with large established software. But in terms of things like energy efficiency, which now it becomes the primary concern. And as we get to the,

the end of Moore's law and energy efficiency becomes more important, which you carry around a lot of devices in your pocket. They're battery powered. The fascinating thing people don't realize is that after the cost of the physical servers themselves, the second biggest cost in a large data center is power. So you care about energy efficiency, even in these large data centers. And when it comes to that measure, the CISC architectures are far behind. One of the things that surprised me is that the chips that were using early gaming systems. Yes, that was one of the earliest breakthroughs for the risk people in the embedded space were games, high-end network switches, places where there was really high-end color printers, where there was really a fair amount

of performance demand, but also considerable sensitivity to price. So why was the games like the breakthrough then? And the reason I think about this is because I think about what happened with GPUs and Nvidia and how it then became the enabling for like artificial intelligence, more parallelized computing. So I was just trying to figure out what the parallel was with the risk story. So one of the reasons was the risk architectures, MIPS was the first architecture along with the alpha architecture deck to get to a 64-bit implementation. And in the games, as in graphics, how quickly you can move data around makes a really big difference. And so 64-bit architectures were much better at doing that. And that accelerated their, and first with Sony Play Station being the first big breakthrough

in terms of creating a much more realistic graphics framework for games. By the way, is that way Nintendo is called Nintendo 64? Because of the 64-bit? from that. Never connected those two dots. Back to Mark's question, though, what do you think made risk tip? Yes, it took a long time. But how do you think,

especially because in that time, you founded a startup, MIPs technologies, to bring it to market. You could have just left it as a paper and expected the industry to adopt it. I was a bit of the reluctant entrepreneur. I mean, when we wrote our papers,

we thought the evidence was so convincing that industry would just pick it up. Yeah. I mean, you said that about Vipfriended. Yeah, I remember that. That's what we thought. And in fact, Digital Equipment Corporation actually had a research layout here that took some of our ideas, some of the people who worked with us and worked on technology. But they couldn't sell it back east. And that's where the headquarters of the company was. You know, IBM canceled their project several times. So eventually what happened was a famous early computer entrepreneur, Gordon Bell had been one of the people that built Digital Equipment Corporation came to me and said, you know what? If you want to get this technology out, you're going to have to go start a company. And eventually he convinced me, although I have to say I was the technical entrepreneur that didn't know the first thing about running a business, not the first thing. We have so many founders to do that. What was like the biggest thing when you went to start a company that was like, holy crap, I don't know what I'm doing?

I thought engineering should get roughly half the revenue. I didn't realize how important salespeople really were. I thought if you have a great product, people just buy it. So there were a lot of things like that I didn't realize. So not only did people not go ahead and build the products until you did, you had to start the company with the products. Once you had built the products, they didn't even just buy them. What we needed to do was find people who were, you know,

companies are always a little reluctant to take a risk on a startup, particularly with something like a new architecture, which really is a long commitment. So what you had to do is find companies who felt like they needed a leg up over the other players in order to advance themselves. And that helped, we found a few players like that early on. It's kind of shocking that you found your company. company in 1981, and we're talking to founders in 2018, and it's the exact same conversation.

You just described the exact same dynamic. Somebody said to me once, I mean, what's the difference between you and somebody else who's read about technology? I said, well, the people who've worked on it, they see the glass as half full, not half empty. People said to us, well, that's a nice academic experiment, but you'll never be able to make a real product out of it. It'll lose all its advantages when you try to engineer the rest. Because we built a university prototype. It wasn't a commercial product. There's an old line, forget who said if it's an old line in the industry, which is everybody worries about protecting their idea. But if your idea is actually any good, you're

going to have to bludgeon people to adopt it. Exactly. This was a great example of that. I think it's interesting that you said that it was like a prototype, like research. Do you think that's changed today where because of all the systems that we have available to us, you know, AWS, all these different things where you can essentially prototype in the cloud, do you think that people now have more, when they're in a university or research lab, is there stuff more immediately and more easily transferable because it's more pre-industry scale or production ready. Well, I think it's probably a whole lot easier to transfer a software product than it is to transfer a hardware product. Software now, students are incredible programmers. I mean,

graduate students and you can really build something. That's pretty good shape. I mean, when both Yahoo and Google left the Stanford Labs, they were pretty good pieces of software. They weren't yet scaled up to deal with millions of users at once, but they were pretty impressive. Yeah. Was that true for you guys actually? I mean, when I think about Netscape, did you have to do a lot more work based on what you... Well, there were two things that happened. One is when we were at Illinois, we started actually getting like people actually using our software, and then we ended up getting lots of customer support calls. And so we applied for an NSF grant to staff a customer support operation. That's hilarious. And the very nice people in

the National Science Foundation explained to us that that was not actually the purpose of taxpayer fund and research, which was a gift in retrospect and that catalyst us in part to start a company. But then the other thing was we actually You rewrote everything, Mark. Yeah, I remember. And actually, I think Nassir, you guys did something very similar. Yeah, yeah, yeah, yeah. And so you do end up in the cold end up re-engineering.

When you have paying customers, you do end up having to do a set of things that are not. What I always thought was really interesting. And so my experience was very similar to yours, which I had these academic papers, the academic community liked it, industry hated it. And I found out it was actually much easier to sell somebody something than to give it away. And I don't know what the psychology is about it. This actually happened to me twice, where I'm like, oh, like the paper's done, the research is done, I'm going to do the next thing.

Now I want someone to adopt it, and I have the conversation, and then they won't put the effort or whatever. And in both cases, I ended up just selling it them, and in the cases of companies. And I think it does two things. One of the things it does is it actually just qualifies. Because if you ask somebody for money, like, if they're actually not interested, they'll say no. And the second one, if you get a transaction to happen, you actually have some skin in the game, you actually have something behind it. And so I actually tell this to a lot of academics coming out of industry now. Like, listen, it's hard to give something away.

It's much, much easier to sell it, especially if you want to have impact afterwards. Well, I propose the third rule from that, which is the more you charge. the more successful the implementation. 100%. Because the more painful it's going to be for them to write it off. It doesn't work. Right.

So they have to commit. That's your two-word mantra. It's like raise prices. It's another great example. Well, you know, Nysera and before you guys were acquired by VMware, I remember you wrote about how you guys actually had some early adopters,

but then you had like sort of a hump. And you talked about too, how you had the initial fast and then you kind of stall. And so one question I have is like when you get to that moment, coming out of academia and then into industry, what sort of tipped you over

to sticking it out and then figuring out how to get over that hump. Well, we had a situation where we had probably expanded a little bit fast, and the first CEO, remember, this is a bunch of three technical founders. We didn't know anything about really running a company. He had expanded too fast on the evidence of the first customer, and, you know, we had too many people, and we were about to run out of cash. So we had to kind to kind of do a reset on that. We had to go through a layoff, which was a really tough situation, 20 people, you've got to lay off 40 of them, you know everybody. And then the CEO asked me to get up at the Friday TGIF and give the rally call for the company,

how we were still going to be a great company, and this was a small hiccup on it. But I had to learn from that process and re-energize the company. I mean, your whole book is about leadership lessons. What was like the biggest leadership lesson in that moment? Well, for me, it was if you have a crisis and you've got to take a tough step, do it quickly, get it over with, and move through, reset the clock so you can then charge ahead. And that turned out when the financial crisis hit,

you know, Stanford lost billions of dollars of its endowment. About 28% of the endowment vaporized in a six-month period. So there was no way we could continue to spend money the way we were. We were going to have to go through that process again. I realized, you know, that's going to lead to five or ten years worth of small budget cuts that are going to not be very efficient, and we're going to not be able to do anything new. So we sat down and said, we need to do this quick.

So instead of death by a thousand cuts, you're going to do like one hard stab. We'll be generous, we'll be humane, we'll give nice severance packages, and then we'll restart and begin to rebuild the financial core of the university. We had one year that was sort of a down year, and then we're back. Yeah, that's great. You've started a company in the 80s, and you started a couple of companies, in fact, and you IPOed only five years, I think, after starting your company. and today a lot of companies don't IPO so quickly.

So that's one big trend shift. What are some other shifts that you've seen, especially since you counsel and meet a lot of entrepreneurs, between then and now? I think probably one of the biggest shifts, the space of startups has changed dramatically. You know, when we were starting, our goal was to build a product that was more efficient

that solved some particular problem. Now, with so many software companies, the whole big question is, you know, will the dogs eat the dog food? I mean, is it really going to get traction? is it going to go viral? I think that's a very hard thing to predict ahead of time. I mean, look, I was sitting at Google when Facebook came along.

Nobody foresaw how big social media. Some did. Mark, clearly, a few other people, but most of us didn't see how big it was going to be. And that happens all the time. Yeah, it's interesting. Even enterprise companies now are having this type of characteristic. So it used to be the case.

You're like, oh, consumer company, it's kind of a popularity contest. You'll have three companies that all look the same. One will get adopted. Two won't. But the enterprise. was kind of core tech, and then you could actually talk to the buyer, and then you could predict somewhat whether it's going to do well or not, or at least whether a category is going to do well or not.

But what's happening now is especially because developers are so influential in the enterprise, and developers are also kind of fickle, and they have their own philosophies and so forth, whether or not a company is going to do well is somewhat independent of technology often and somewhat independent of the approach they take. It's more like, you know, do they become the popular one that they use? So I think this is something we see across the industry. Yeah, people in the enterprise, it's not just developers. You guys talk a lot about like departmental level.

Yeah, exactly. It's coming from the bottom up. I think even in complex organizations, universities like to have a very slow deliberative process. But in a complex organization, all decisions are gray when they get to the top. And so you've got to get comfortable making decisions, making calls in that situation. And I learned that in the startup environment. And I wouldn't have learned. It would have taken a long time to learn in university. Well, what do you think about, we have this view that, professors that are part-time co-founders.

I mean, we don't believe that when a professor is listed as a co-founder and a company, that if they're part-time, that they're actually fully committed, we need to see more skin in the game. Having lived through this. Oh, did they talk you the same thing? Are you trying to do this part-time thing? No, no, I had two part-time professor. You were part-time professor.

Yeah, you had two part-time professors. Yeah, two part-time professors. I mean, here's the reality. Startups require a tremendous amount of work and effort and time, and you make real commitments to customers and teams and investors. And early on, while you may have a great idea, the investment is in you. And so there's really a mismatchen expectations between someone giving you money, a team coming to join you if you're not going to be there long term.

So we like to know if we're investing in someone, whether they come from academia or not, that they're going to stay with the company for the duration of kind of the team in the investment. Now that doesn't mean that a part-time professor doesn't come in and help out, right? I had two, and they helped out a tremendous amount, but what we like to see is someone that is fully committed. What advice would you give to universities

who are trying to do something like the quote, Stanford model, which I don't even know if we define what the Stanford model is, but it's pretty cutting edge, and we take it for granted in the valley that Stanford and Berkeley, for that matter, will give away more IP than they hold on to. And I used to see when we were at Xerox Park, a lot of university tech transfer offices, and

it's so extractive and kind of nightmarish in fact. Right. Mark has the great experience in doing this, but my view of people think of their technology licensing office as extracting blood as opposed to being partners with their entrepreneurs. And the purpose of technology licensing from a federal government's viewpoint is the university should get their technology out there. If they focused more on that, that would be great. And be more flexible with respect to faculty. My experience is the faculty, the faculty I know at Stanford, they've gone out and started companies, are better researchers, they're better teachers. They're all around better because they have a wider range of experience. And most of the students we educate,

they're not going to become future academics, they're going to go work in industry. So a faculty member that has experience from that is actually a better teacher. So let me play devil's advocate, which is, okay, that's all fine and good for you to say, but we only have so many professors. If they go leave and start companies, like they may or may not come back, they're distracted, they're not teaching, they're not doing research, aren't we depleting the core mission of the university of doing research and education by enabling that? It's a good question. I think we're in a tricky position right now, especially around the machine learning AI area, where there are lots of faculty who are leaving. And that will hurt the industry in the long term because that means we're eating the sea corn.

I'm a great fan of faculty members who go out, commit themselves to a company for some period of time, but say clearly that their long-term goal is to go back to the university. That works well. I think if all the faculty leave, then we will have a problem long term. But there's also some presumably benefit to being the place where people feel like they have a lot of flexibility, where the place that encourages creativity, the place that encourages ventures that presumably will play a role in attracting. Right. So you're a young person, you've got multiple faculty offers. You might be interested someday in taking your technology out. Where's the place to come? Well, it's pretty obvious where the place to come is, and that's a big benefit to the university in terms of recruiting people.

So we all the time get the delegations from, you know, various countries, various cities in the U.S., various countries outside the U.S. And sort of the question is, you know, how do we create Silicon Valley of X? It could be Silicon Valley of Chicago or it could be Silicon Valley of France or Kazakhstan. Right, anywhere. And I'm sure they come and see you as well. And so what is your answer to that question? First of all, build some great universities because they are a center of innovation.

And many of the ideas, which build not just a single niche company, but help transform an entire industry and create an entire industry come out of universities. Build the rest of the ecosystem out. I mean, the fact that ventures, was out here and people were comfortable with it. The fact that you had legal firms who knew how to work with startups and make that work, but risk tolerance is a big part of it. You can fail in the valley, provided you had a reasonable strategy and a reasonable set of goals and reboot, and it works okay. That's not true in many parts of the world. So maybe let me polarize the question

a step further. So the cynical view would be you can't. You can't create Silicon Valley anywhere else because there's only a couple areas of technology where it's even feasible to create a Silicon Valley and Silicon Valley already has information technology. And then further, the things that you just described, like they're just too difficult to do. It's very hard to create a new research university from scratch. It's very hard to change the culture of the country that you're in. That's why there's only going to be a handful of these places. The optimistic view would be, no, no, no, no, no, all these ideas are now spreading. The world of globalizing technology is globalizing. The knowledge of how to do all these things is globalizing. And then there's many new areas of technology that are becoming kind of

more amenable to this kind of flexible innovation and many countries that, you know, want lots of entrepreneurship and many kids worldwide who are growing up watching YouTube videos of, you know, Stanford classes on how to build a startup and then, you know, getting out their compiler and getting into work and writing code and starting their companies. And so in that positive vision of the world, there's, you know, 80 or 100 Silicon Valley's in 10 or 20 years. Where do you come out on that? I don't know that there are 80 or 100, so it is going to happen in China. I have no doubt about it. The government is pouring a normal. amounts of money into building their top half dozen research universities. The people are very

entrepreneurial. There's a lot of risk capital available. There may be some issues around liquidity and exits that are a little difficult, but they'll work that out over time. It surprised me that nobody in the U.S. has built a real competitor. In fact, just the opposite has happened over time. If you had asked me 15, 20 years ago, will there be another Silicon Valley in the U.S.? I would say, yes, for sure. In fact, just the opposite has happened. The Valley is it has gotten bigger. Now, we may be the victims of our own success, given land and traffic and cost of housing. We may be laying the foundation for some other Silicon Valley area, but it's got to be a place where people want to live. And that helped bootstrap it. And so we should be looking and

thinking, where is that going to happen next? Where is that a kind of opportunity? Do you think we're at risk of strangling our own success by all of the fundamental issues around housing, transportation, I think we are. Taxes. I think we are. A state government that seems to hate us. I think we are. Or it hates us and loves us at the same time, right? You know, our cities and the state have such dramatic issues. And yet you pull out the high-tech sector. I mean, the state and the city of San Francisco will collapse. So we've got to think about it. And it really, you know, the younger generation moved to this area, but without that kind of suburban dream of, oh, I need the large house. I mean, they'd rather have something maybe a little smaller, not have the big yard to,

have some nice parks, have some open space, and by the way, be able to walk to three restaurants and a movie theater, and that's a different view than the Valley grew up doing. Then you've got to figure out how to make the transportation network. It may be that rather than rely on government, we've got to get the companies to play a much bigger, forceful lead in pushing governments to do the right thing.

I mean, one could argue that's what's already happened with the shuttle system. Yeah, the shuttle system is that. It's sort of this private tunnel. Right, it's like a patch, exactly, into this public infrastructure. The newest trend that I've seen, because I'm friends with a lot of 20-year-olds,

they are doing a lot of co-housing arrangements where they're all renting big houses with like 20, 15, 10, 8 people. And our friends would never have thought of doing that when I was in grad school and undergrad. It would have been like two roommates at most. I think when I see a lot of the startups coming, I mean, that's what they're doing.

They go rent a house and squeeze more people into it than you ever thought were possible, right? But it doesn't matter because they're working 60, 70, 80 hours a week. so. One question on the note that Mark was asking about the next Silicon Valley. So the network effect of it becoming more valuable, the more people that are there. The other part of the ecosystem is obviously people who are, you know, like yourselves, ex-founders, ex-salespeople, ex-marketing heads, etc., who can then help these companies as they grow and get to the next level. That's the biggest

argument I've heard for why there might not ever be another Silicon Valley. That's a great argument. I remember startup founded at Mark's alma mater, University of Illinois. And great group of people, they could hire great young engineers because it's one of the best engineering schools in the country, but they couldn't get the kind of middle and upper level management there. Right, exactly. And so they ended up moving the company to the Valley because there was lots of depth there.

You look over history. I mean, Euler Packard was there, then talent from Euler Packard helped build Sun, talent from Intel, helped build the first generation of fabulous semi-conductor companies, and that spread out over time. And that's one of the great things that happens in the Valley. I agree.

And I know this sounds so hokey, but I'm going to say it, because I don't think people really appreciate how unique it is, the generosity of mentorship. And, you know, a big theme of your book is about mentoring and molding the next generation of leaders. So let's transition to talking about what some of that mentoring and molding principles are. So each chapter is devoted to a specific principle. Humility, empathy, you know, honesty, transparency. There's different levels of that.

But there are things that everyone say about leadership. So I'm going to challenge you to convince me what is the nuanced take on why humility matters. And by the way, on that one, especially, I don't know that many humble leaders, quite frankly, that are really successful. I think you can succeed while being humble if you're also ambitious at the same time. Classical person who's humble and ambitious is Abraham Lincoln. He's just got to maneuver things over an extended period of time. He has to go to war. But he was a very humble person. I mean, and I think that combination, what humility does for you is it removes the barrier to asking for help, to admitting that you've made a mistake, which for many people,

that's a fundamental thing. Look how many of our leaders won't admit that they made a mistake, right, and won't ask for the advice of others. I think the challenges of leaders confront on that is if I show weakness, my people will start to lose faith in me. And so what do you advise a leader who's worried about that? I think there's a difference between being humble and being indecisive, and I think it's a question of making that decision. You know, when Abraham Lincoln and finally drafted the Emancipation Proclamation, the majority of the cabinet didn't want him to publish it, didn't want him to release it.

And yet he knew that that was the moment, that that was the time he had to do it, that he had to make that decision and move forward. And I think that kind of decisiveness is crucial. So you've got to take responsibility for making the decision and moving forward, but that doesn't mean you shouldn't gather all the inputs and be open.

If you're humble, then your staff, your team can come up and say, you know what, Hennessy, that's a really stupid idea. And if you do that, it's going to come out bad. Then you say, okay, well, you're probably right. I need to rethink this. That's fine.

It's kind of like our strong opinions weekly held, which feels like a very A6 and Z value. It really seems to define the place. I love this phrase that you use in your book. It's not enough to understand how many people are depending on you. It's just as important to realize how you are depending on them. And I thought that was a very neat thing to think about mentally inverting the org chart. Yeah, I like to think of my org chart upside down.

I'm the person supporting the rest of that team and serving them. I always think about how this plays out when it comes to things like equity, though, because you have to share the success. But, you know, quite frankly, some people do more, some people do less. Some people are less fungible. Others are more. And you have to take that into account. And I think that's sort of an interesting calculus.

People tend to sort of balance. Well, you have to think about the value of the individuals. Everybody's work has value, but obviously some of it is more crucial to the success of the organization than others. work. So everybody should be rewarded, but that doesn't mean all the rewards should be equal. Let's talk about empathy, because you're one of the pioneers and your tenure as president of the largest increase in financial aid ever, which allows more lower income families to experience Stanford, and this is incredible. But you talk about how it was hard for you to actually

make this happen because empathy needs to be balanced with fairness. And that really resonated. So tell us about how you sort of navigated that horny issue. So we decided that one of the challenges that people who came from disadvantaged backgrounds faced is just getting through the whole process of applying to a highly selective school. You know, the federal financial aid form is 23 pages long. Often you get people, they may not even speak English

because they're an immigrant family. And so that's a major barrier. We decided we need a very simple message, right? Your family makes less than $100,000 a year. Your tuition at Stanford is zero. The next thing that happened, though, was somebody came in and said, well, I make $110,000 a year,

and my tuition is $30,000 a year. This doesn't make any sense. So we concluded you had to balance this with fairness. You had to ask the students to have some skin in the game. Right. So we said even though your tuition is zero, you have to work for the university 10 hours a week during the year and 20 hours a week during the summer

and contribute that to your education. And then everybody said, well, that's fair. That's reasonable. So balancing that was really key. So can I ask you the obvious follow-up question? So how many 18-year-olds a year, How many kids come of age to be 18 in the world each year right now? Oh, gigantic number.

I don't know, Mark. 100, I don't know, 100 million, 200 million, some large number like that. How many undergraduate freshmen slots does Stanford have each year? About 1750 this year? Yeah, and how many total university slots are there globally in Stanford scale institutions, or Stanford quality institutions for the freshman class? Well, let's say, I mean, then you'd have to put all the elite publics in. I mean, I'd say probably there are maybe 200,000 slots in the entire United States.

take 100 million 18-year-olds to 200,000 slots. You know, like, the obvious question, right? Which is like, it's fantastic, obviously, what Stanford is doing for the kids who then end up in Stanford, but most kids don't. And most kids don't end up in anything resembling at Stanford quality education. I came to the view that the university had a moral imperative to increase the size of the student body.

Now, there's a limit how far you can increase it before you change the quality of the experience, right? We house all our students on campus, things like that. But we could certainly do more. And the provost and I made an argument. So in the end, what happened, the financial crisis came along. We had to put that on the back burner. But then it came back later.

And we've engaged in the gigantic expansion of undergraduate housing so we can house students on campus. This does sound a little bit like the director of the Globe Theater in 1550 or whatever, kind of saying more people should get exposed to Shakespeare's plays. And so therefore, we should build a balcony, right? And we should, you know, double the number of people who can come to London and see the play. But, like, most people in the world are never going to be able to get to London to see the play. Like, at some point, isn't the right answer to invent television? No, the right answer is to change the way we educate people.

I mean, I think if you were to make an accusation against higher education, it's that they haven't really done very much to bend the cost curve. And part of this is understanding what it means to bend the cost curve. Think about Vivaldi writing four seasons and having four musicians play the four seasons, right? It takes 23 minutes. It took 23 minutes in whatever it was 1790s. It takes 23 minutes today. What's the big difference? Those musicians get paid a lot more today than they got paid.

then. So actually, there has been no productivity gain in the presentation of the Four Seasons piece, right? I mean, universities are somewhat in that. It's still a craft to some extent. Now, that has to change. That has to change. We've got to figure out how to leverage technology in an appropriate fashion to get the cost of education down. Otherwise, it's simply going to become more and more expensive for American families. We're going to load up student debts going through the roof. And part of the reason is going through Ruth is families are less able to save than they used to be. And so we see student debt going out. The one form of debt that is not discharged through bankruptcy. Yeah, correct. But it's also, look at the default rates. Now, part of this is the for-profit industry, unfortunately, in the higher

education space, doesn't deliver a lot of value. So you end up with lots of students who are not able to use their education to get ahead. We've got to figure out how to deliver a high-quality education, Not decrease the quality in order to just get the cost down, but hold the quality up while reducing the cost. And the only way I know how to do that is by using technology. Have you read Brian Kaplan's book? No, I have read it. It's probably not a common book on the Stairford campus.

Although he is a tenured professor of economics. And so he has an instance of what he is talking about. And so I'll just focus on one aspect of the book that he talks about, the sheepskin effect, if I recall correctly, is basically if you take somebody, if you think an undergrad who's completed seven out of eight of their semesters, right? So they're three and a half years into their program and they drop out. You might think that they would get seven-eighths of the income in their first job as somebody who does all four years.

And it turns out that's not the case at all. Right. Which then basically means that the value of that four-year education program is primarily in the signal of the diploma as compared to the actual education. I think statistically, I think this is in the numbers. So anyway, you might interpret that different ways. I'd be curious how you would interpret that. I think there's some truth to this observation. and I think one way of interpreting it is that the drive and the determination to finish that degree

is actually the key signal that employers are looking for, not just what courses you took. Now, I should say post-bachelors degree, this is changing dramatically. But if you think about other kinds of post-batchel degree, we're moving very quickly towards a certification-type model where you take a course or a sequence of courses, right? So you go and take the sequence of courses on cryptography and blockchain, and you become an expert on that. And by demonstrating that you've mastered three, four, five courses in that, that all of a sudden becomes the key to getting a new job opportunity.

I think we're going to see more and more of that as we go along. So that's like an alternative to a master's? Yeah, it's an alternative to a master's degree. You actually have to demonstrate mastery of the material, I think. That's the key thing, and that's what an employer wants to know, right? It's like audacity with the nanodegrades to some extent. Yeah, it is like. actually on this very note, like, I would love your take on the interdisciplinary side of things,

because to me, the one unique thing that universities can do that a lot of these other institutions cannot do is break down barriers between disciplines. And you guys have tried experiments or legitimate degrees like symbolic systems, et cetera, that cross across, you know, multiple disciplines. But I've yet to see examples of true successes of multidisciplinary degrees or entities. Like maybe Xerox Park would be the best example, but I really can't think of any others. Happens a lot more at the graduate level and the research level, partly because I don't believe that multidisciplinary or interdisciplinary things are a substitute for some deep domain knowledge. I'm a firm believer that you start with deep domain knowledge and then

you build on top of that. You know, one of the challenge with these small courses that certify you in an area, those work well for a professional. They've already got an undergraduate degree. there's a clear connection between the value of the education program and how they'll be rewarded. Take an undergraduate coming in without some of the advantages that you'd have if you went to in elite high school. They're not going to thrive very well in that kind of online setting where they don't see how that directly translates

to getting a job at Facebook, for example. They've got a long way to go before they're there. So they need a rather different educational system than somebody who's already got their degree, they see if I take this course, I'll get this new opportunity. I also think computer science is a little bit unique in this. And that, you know, listen, we call it a science,

but, I mean, ultimately it's an engineering discipline. And while there is, like, pure computer science, almost all of it is applied. And so when I did my PhD at Stanford, we had people that would work in graphics, and they were very, very closely with, you know, computational physics, for example, solving very real problems.

Same thing with biology, right? One of my best friends, I mean, he did some really core work in DNA sequencing. And if you squinted in one way, he looked like a biologist. He squinted another way, looked like a computer scientist. The thing that I love about computer science, and I've always loved it is. If we wrote a program that solved grand unified field theory, physics would go away as a discipline, and we'd be like, okay, that was one more application. Let's go on to biology, right?

So in some ways, it doesn't exist without, like, the other disciplines. In another way, it really is kind of this meta-discipline. And so I do think it's pretty unique in that way. It is unique, and it is this meta discipline. I mean, I think, and it's become the new meta discipline that everybody needs to learn. Exactly. Because algorithmic thinking is such a fundamental thing about how the world operates these days. Like math, reading. Like math, right?

You know, computational literacy should be just one other form of that. I was thinking there was this debate with Vitalik Buteran, who's like the inventor of Ethereum, and this professor who's a former editee of mine. And the debate they were having was whether there should be a dedicated degree for blockchain. So the professor was saying, we don't need this. You should have fundamental basic science, and that's good enough. And Vatollick's point was, well, actually, this is a really interdisciplinary, multidisciplinary unique case where you're layering economics and computer science

and lots of other finance and lots of other things in a very intersected way. So I thought that was fascinating, that there was a sort of tug of war. And this, to me, is the wave of the future. Like, I can even see the blockchain as a laboratory for people learning on their own in the future, especially if you think about what Mark mentioned earlier about all these kids coming online around the world who don't have access to these universities locally and are learning from YouTube.

I could see programmers in my parents' village in India becoming people who become such experts in this world. I mean, you've been the president of a university for 16 years that I greatly respect, but I wonder if it means that maybe the university model might have to really evolve in a different direction. Well, I think there's about to be a great test to this because the wide applicability of machine learning

to all kinds of problems. All kinds of problems. I mean, you know, you just see breakthroughs in biology and chemistry in astrophysics coming out of various forms of machine learning. So all of a sudden, it becomes this tool that is applicable to a whole range of things and is changing those fields. What do the scientists, the people who think of themselves as astrophysicist or as organic chemists, how much do they need to understand?

How do they deploy this technology? And this is a big gap right now because the senior people in the field, it's highly unlikely that most of them are going to take a year or two out and go back and learn a bunch of things about computer science and statistics and machine learning ideas. We're really going to have to build a new breed of people who kind of fill up this interstitial space and become the key innovators in the disciplines. Well, I would argue that it needs to be more applied. We have an executive briefing center with a lot of big companies coming in. and the number one challenge they have when it comes to ML and AI is production-ready industry applicable machine learning. It's actually like what's happening in academia

is not at all connected to what they need to actually do. Yeah, it's not only that, which is as you move to AI and ML, more and more the value is the data. Absolutely. And more and more, it's almost serendipitous understanding of the data prior to manipulating it, right? It's almost impossible to remove the context of the domain understanding from data. from programs maybe, from data almost certainly not,

which is why we're seeing such kind of a confluence of CS, statistics and data understanding, and domain expertise. It also goes to your views about the end of theory. Or not. Or not. So you've got to look at that. You've also got to look at how and who establishes ground truths in these. I may have an AI program that can recognize some medical condition. But who decides whether or not it's right on the basis of that?

ML is the ultimate garbageing. garbage out technology, because if the data isn't good and properly validated and the learning process isn't, you're going to get assumptions and outputs that are ridiculous. So this is something that we have to deal with a lot in venture capital, which is a number of constituencies in entrepreneurs actually view AI or ML as almost like the end of theory. So it's almost like, I don't have to know what I'm doing. The AI and ML will figure it out for me. So they'll come in and they'll say, listen, there's all of this data in Enterprise

X or whatever. we're going to apply AAMNL, and then the net result is going to be value. Well, what's that value? Well, I don't know. The AIML is going to tell it. It's going to be valuable because we'll apply this. And so, like, it is a very important tool set. But I think you have to understand the domain, to your point, garbage in, garbage out.

You have to have some way of getting the expertise or whatever in the prior to get the answer. It's not like this has become the end of theory, and we don't have to know what we're doing anymore, and we're going to get valuable results. And the space where that works, sort of unsupervised learning, is such a small part. of the giant ML space. It's relatively small. And most of its interesting applications are in the natural science world,

not in real world applications. Where there is actually a truth in a way to test the truth. So for me, the most difficult thing about moving from academia to industry was that in academia, you look at a problem domain and kind of your job is to think very, very clearly and pull out these kind of global truths

and they have to be very elegant. And very rarely do you write a paper where you're like, here's this problem domain and here's my litany of 50 fixes. and read through every one of my heuristics and oh, look how elegant it is, right? It's almost the exact opposite. What you learn about starting a company is it's actually the opposite, which is almost every solution is dealing with the heavy tail of complexity,

and it's a bunch of patches and the real world and everything else. And so mentally you've got to go from, I'm going to look at a problem space and extract elegance to, you know, I'm going to deal with all of this complexity and master it. But where I did find this energy very useful is a lot of leaders, is thinking simply. And so if you start a company, you can extract that elegance,

you can use that to really lead a company, and you can convince a customer, and you can talk to an investor because you've really distilled what's important about it. But you can't let that constrain you because ultimately you have to build something that solves a real problem, and the universe is a messy, messy place.

And so if you can get beyond that kind of ability to have everything being incredibly elegant, I think you can have both the leadership and kind of like the actual complexity. That's fascinating. Yeah, no, I think you're absolutely right. I think in the academic world, we like things that really look elegant.

And we often actually delay publishing a paper or getting a result out there until we get it all gel just right, right? That doesn't work in a startup company. I think the one thing that is common is focus really does help in both cases, right? I mean, relentless in a startup company, you've got to focus, you've got to drive,

you've got to decide what's peripheral and you're not going to do now. And the same thing is true in academia. If you want to do really great work, you need to focus. You need to kind of, somebody once told me, they give me some good advice.

They said, you know, you ought to be working on three or four things, but you ought to have one or two of them that are really important where you're really putting your energy. And these others are your backup in case those really great things don't work

and you don't get tenure for those. And that was good advice about how to think of about a research career, but doesn't work in a company. You've got to get rid of those things that are not the home runs. When I think of examples like Xerox Park, which honestly,

despite the mythology, they actually did put a lot repeat successes out into the world. It wasn't that they had like a cart blanche to just invent whatever they wanted. They had a very specific mission, and they invented towards that mission. When you talk about the differences between academia and industry, academia is about ideas and industry is about implementation, and you believe that there is an interface that VCs and others carry across those two.

Do you think, though, that that's sort of a false divide in some ways? It wasn't so it was actually not just ideas versus implementation. It was ideas in practice, in industry. settings because it was for a corporate research lab. So I just wonder how you're thinking about this was then and now and how it's evolved. So I think there was a time when IBM research, Xerox Park, and Bell Labs, were the great giants. What they had, they were not devoid of application and things.

I mean, the work on the transistor was really begun to solve a fundamental problem that a telephone switch built out of tubes. What they did have was they had the advantage of a long, that's a long, that's a problem. horizon. It's harder to find that in industry nowadays. It's harder to find that patience, partly because of the observation that if you discover something really big, lots of people have to eventually benefit from it, right? Bell Labs and 18T were not the major beneficiaries of the discovery of the transistor. Zerox was not the major beneficiary of the discovery of modern personal computing, right? That's why universities are the ideal place to do this kind of work,

because society benefits. universities do technology transfer in a very natural way. It's called graduation. Mark and I are both dying to jump in. I think historically that's certainly been the case. One could make an argument that this is shifting in some of the most fundamental research contributions are actually happening in industry today.

And not only that, that the academic system has actually moved towards short-termism, especially in incremental publishing. I even felt like I've seen that dynamic shift. in the last 15 years in just my kind of professional career where I would say Google and Microsoft are doing some of the more innovative fundamental contributions and then I still sit on program committees

it's interesting they publish a paper I'm in the PC committee and then all of the professors are basically trying to do incremental work on top of Google's work right so are we seeing like an imbalance lately or is this a momentary thing? I think you're right I think there is a bit of a shift occurring here

It's driven by not only the amount of resources that are available at Google, Facebook, Microsoft. It's driven by data, and it's driven by computational resources that are available in those companies that are much larger than is available to a typical university setting. So I think we're seeing a growth of kind of new research environment in industry that's quite a bit different than the old environment. and may be a harbinger of how things get invented in the future. I'm kind of a skunk on this topic. So I think the reason...

The skunk at the garden party. So I think the reason... I mean, they did great work. Suras Park, Bell Labs, IBM Research. But here's the thing. It's always those three examples. They're basically like they were rounding errors on everything. Like, there weren't 10, there weren't 20, there weren't 100.

There were three or four. And there were two preconditions for them. One is they all were offshoots of monopolies. They were all offshoots and monopolies. You're exactly right. And the reason they had long-term thinking is because monopolies... They could afford it. By definition, all monopolies have us long-term thinking.

They all offshoot some monopolies. I never thought about that. And arguably from a corporate investment of capital standpoint, they were worth it just for the marketing value, right, of being able to demonstrate that they weren't just sitting on their rear ends in the corporate office. And then the other precondition was they were all pre-195-19-E. They were all pre-venture capital. Yep.

Right. And so when the monopolies cracked and then venture capital pulled the talent out, like that was basically it. And the downside case would be that removed this kind of long-term commercial research. But the upside case would be that led to what I would argue is just an explosion. of R&D at far greater scale, right, across the corporate landscape than ever existed in the 1960s, 1970s. And so we've kind of mythologized these things, but they were tiny.

They were tiny relative to what's happening today. So there's a lot more happening today, to the extent to which I can't imagine a startup kind of thinking about the length and the amount of money that was invested to build the alto. I mean, that's a major, major undertaking by any measure. on the other hand, I think you're right. They're now a much larger number of players doing interesting things. And in the software-driven world that we live in, the cost of experimentation and development

is not the same amount in terms of capital. Well, I agree with all that, but I would also say even with what you just said, even that, like, yes, the Alto, but also, like, look, Apple made the iPhone, right? Like, that was, what, $150 million project? Like, you know, over the course of it's, like, they were able to do that. Google, as you well aware, is, like, basically invented the self-driving car. Yeah. Those are on Powell with the Delta.

I mean, if you look at the self-driving car, the tipping point was when the DARPA Grand Challenge was won. And that really was a key tipping point because it demonstrated the technology was considerably. Considering that the previous contest before that, the car had not driven very far at all, and all of a sudden boom, so there's a tipping point in that. And when you see those tipping points,

that probably is a time when you say, let's move it from an academic setting that's kind of more freewheeling and operates more incrementally to a different environment. Well, one could argue in that example that DARPA was a VC because they were putting up the prize money and everyone was competing, the startups, i.e., the individual people trying to meet the challenge, etc.

But then Google has now put another, what, a dozen years. Oh, yeah. A lot more money behind it. And I think that, you know, the self-driving car, the Waymo project is as glorious as success as anything that ever came out of LACR. I mean, I think the gap between, okay, we can drive in this desert road

in a fairly constrained environment until I can drive in a city, environment with lots of people who do wrong things, including look at their cell phone while they're driving, is a much harder environment to do it. I think another interesting example is a company that you said on the border of, which is Cisco systems. Cisco's long had this stated goal of no internal research. However, they really made modern networking in like no small sense of the work, right? The PhD in networking, you do great research in the universities.

But when you actually go in Cisco and see what they're actually doing, you're like, wow, they understand the real problems, they understand the customer. So I think, like, actually, you know, they've taken a stance again. research there yet they've done a tremendous amount of innovation however they have done a good job collaborating so it's a little bit of a spectrum yeah and they've had a model for many years of we we buy interesting companies and we bring technology in that way and then we grow it and use the rest of our ability to really make it successful so it's a different innovation model as opposed to one that's more organic i mean why wouldn't you because then you're essentially betting on a thousand experiments and

figuring out which one's a winner instead of trying to internally captively figure it out yourself. Like, I just can't see any alternative to that. Well, the only downside is that once that company gets far enough along that little startup that it's got some great technology, they're often more than one company is bidding for it. Then you could actually lose out. Right, right. You don't want to lose that, right, right. God bless America.

It's actually good for the entrepreneurs. I mean, it's actually a really interesting point. The thing I've been most impressed with Cisco over the years is they've really, I think, are probably the top companies in making those acquisitions successful and doing spin-ins. I mean, there are very, very few companies you can point in that have been so successful in acquisitions. It's basically a core competency.

Yeah, it has been a core competency. So spin-in is they'll take an internal team. They will take them out of the company. They'll help fund them, and then they'll bring them back into the company. Fascinating. I don't realize that. It really is kept them relevant where many companies have actually not, you know, of the same vintage.

It has, and it's injected new technology and new products into the space and things. Last question. What do you think has changed with talent? like the whole talent landscape over the last 30 years because we've talked a lot about tech trends changing the availability of capital

the ecosystem, industry, collaboration, academia, etc. But the people themselves in this ecosystem, what is the biggest change that you've seen? Or are they the same? So one of the changes

I've seen recently that really has me delighted is to see the number of young women going into computer science. What's funny about it is computer science in the 80s was one of the...

It was. There were a lot of women in it. And then it got wiped out with the growth of the field and the number of males grew, and now we've seen a resurgence, I think, begun by a group of very energetic women that started to build support groups and things like that. And then we got over the critical mass. You got enough women in the discipline that they didn't feel isolated anymore. And that's really great to see. The number of opportunities in the software space are so large. We need to bring as much talent in. The other thing that's

been remarkable for me is I thought 10 years ago that computer science was going to become second to the biological sciences in terms of getting the best students and that everybody, the really best students were going to go to the biologicals, biotech, things like this. Well, that's changed. And now computer science gets the very best students in many of these fields. I mean, I've seen freshmen that know more mathematics than I knew when I was a senior getting my college degree now. That's remarkable. And they're going to build great things, I believe. And those are emerging, actually. Like a lot of the comm site folks are now starting bios startup. Yes, they are. They are in bringing computer science knowledge to the biospace.

Yeah. Do you guys have thoughts on any big talent shifts you've seen? I think the big one I see that I think is probably under remarked on is engineers are so much more productive today, especially in software than they were 20, 30 years ago. The tools are so much more sophisticated and powerful all the infrastructure technologies. And then all the ability to learn, kind of to your point on the undergrads, but the ability to go online and learn. It's like I'm an engineer

and I don't know how to do something. Stack overflow it. Boom, boom, boom, boom. I know it in 10 seconds. You may actually be able to fund the piece of code because code sharing has become such a big part of what we do, reuse. I mean, MIT was a pioneer there

with the MIT license and open source. What's your biggest shift? I think the biggest shift that maybe has impacted me is like I just remember the transition where pretty much everybody who's in computer science for the love it because it wasn't really clear where the industry was going. Often they were doing it to get something else done to basically the professionalization of an industry, meaning it is a real

discipline. People are in it to make money. People are in it for a future, which is not a bad thing. This is required. I think it's actually quite good because it requires to really think about what it is, what people do. And so, on the negative spectrum, there's people are a lot more mercenary about it than they were before. And on the positive end, I do think we have a lot of framing around it. What does it mean to have a workforce in computer science that will come and go and to handle that in a way. But for me, it's been a very, very stark difference to people that I used to work with 20 years ago

when we were literally all there, you know, for the love of solving these great problems to now. It's like, you know, this is your job. I think my favorite thing is seeing the intersection of art and humanities and code. And people used to keep them as separate in their heads. And there's a whole new wave of talent that's native and both. And that's really exciting to me because, you know, art is code,

code is art. To me, that's like the biggest or more exciting talent shift. Well, John, just want to say thank you for joining the A6 and Z podcast. Thank you. Delighted to be here. Thank you very much. Cool. Thank you, John.