The a16z Show - a16z Podcast: The Role of Academia in the Startup World

Episode Date: October 13, 2015

Getting denied another round of NSF funding in the early days of Mosaic turned out to be a huge catalyst to start a company around the fledgling web browser, says Marc Andreessen. That company was Netscape. Andreessen was still at the University of Illinois at the time, and he wanted the NSF money to help build what amounted to a customer support team. That wasn’t the NSF’s business. Since Andreessen’s Mosaic days, calibrating the interplay between academia, government, and the private sector has gotten, if not easier, less exotic -- with schools like UC Berkeley and Stanford setting the standard for providing students and faculty with a clear path forward. From picking the right classes, to picking the right institution from which to turn research into a company, Andreessen and Chris Dixon discuss the role academia plays in the startup world in this segment of the a16z Podcast. This discussion was part of the firm’s 2015 Academic Roundtable. 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.

Welcome to the A16Z podcast. I'm Michael Copeland. Getting denied another round of NSF funding in the early days of Mosaic turned out to be a huge catalyst to start a company around the fledgling web browser, says Mark Andresen. That company was Netscape. At the time, Andresen was still at the University of Illinois, and he wanted the NSF money to help build what amounted to a customer support team. That wasn't the NSF's business. Since Andresen's Mosaic Days, calibrating the interplay between academia, government, and the private sector has gotten, if not easier, less exotic, with schools like UC Berkeley and Stanford setting the standard for providing students and faculty with a clear path forward.

In this segment of the A16Z podcast, Mark Andreessen and Chris Dixon discussed the role academia plays in the startup world, from picking the right classes to picking the right institution from which to turn research into a company. The conversation you were about to hear was recorded at our 2015 academic roundtable. Chris Dixon starts things off. So, Mark, let's talk about, so you way back were a recipient of, government grants, right? Netscape came out of that. How, can you talk about sort of how in your, I guess, how you see the role of academia as it relates to startups of venture capital and how that's changed

over the last, over your career? Yeah. So first of all, it's great, great to see everybody back this year for those of you who are returning. We're thrilled that you're all here. So, so my work at University of Illinois, around Mosaic, which later became Netscape, was was NSF funded. And so I owe the NSF a huge debt of gratitude for that. I also owe them a huge debt of gratitude for something else, which was turning down the additional funding that we requested.

And one of my fondest momentos is the cover sheet of the NSF proposal with the decline on it. And it was literally, by the way, they were completely right. We literally had so many people early on using Mosaic that we were dying under the customer support load. I mean, customer support is an overstatement because they were paying for it. So it was user support. And so we applied for, you know, being in university, it's like, well, go get more funding.

So we applied for NSF funding to basically build a customer support team. And they informed us very kindly that that wasn't part of what NSF funds. So that was a good catalyst to go start a company, which was very helpful. So, you know, there's been obviously just, you know, a lot of things have changed in the last, you know, this is 23. years ago now, 22 years ago. So many, many things have changed. Probably my single biggest thing that I think has changed that we see with academic computer science and venture capital, probably the biggest change, and I think this holds generally. I know this holds for my alma mater, and I think this holds more generally. When I was getting my computer science

degree at Illinois in the late 80s, early 90s, the department sent just an overpowering message to the undergrads that the purpose of the department was to MIT PhDs and future professors. And that industry was a very kind of lower class, side show, dead end kind of thing. And they were very, very clear on that. And that, I think, had a lot to do with obviously the selection of the material with a much greater focus on theory than practice.

And then I believe it even had a selection on things like programming languages for the coursework. I think the faculty actually went out of their way to pick languages that would never ever be useful in a production environment. And so I think I'm. I knew Pascal in scheme really well. It turned out to me not that helpful.

So the biggest change, and I guess people could probably argue both sides of this, but the biggest change is just we have a sense for a lot of universities we deal with that the computer science departments have a much, I mean they still want to breed PhDs and professors, but a much bigger focus on practical impact in industry. And the students that we see coming out generally have a large amount of practical skill in addition to theoretical skill, which I think has been a dramatic change. What do you think about the idea that people say that Silicon Valley isn't working on big problems? That maybe, you know, I don't know, that only, you know, we used to have the space program, we used to have, we were promised self-driving cars, and we got Twitter.

We went to the moon. Yeah, we did go to the moon? So, you know, what do you think about that? Do you feel like the computer, I guess both the computer science academic community and the industry is tax? big problems? Yeah. So there's kind of two critiques right now in the media and then kind of popular discussions around this topic that apply to tech.

Critique number one is tech's not working on big problems. You know, it's basically all, you know, everything in Silicon Valley is these silly little apps and like, why don't we take on the hard problems? And of course, the other critique is where like tech is having way too big an impact on our culture and society and like throwing everything at upheaval and destroying all the jobs and reordering all the industries and changing the culture and just having this disastrous impact and, you know, the impact needs to slow down. Nobody attempts to ever reconcile those two critiques.

And the same commentators will literally write both critiques in like different columns, you know, two weeks apart without ever attempting to reconcile them. And so, and when I call them on it, they basically say, yeah, yeah, but it's all consistent because the tech industry is having a huge industry. It's just all negative, which I think is maybe just slightly too cynical of a view. So I would say I'm kind of a little bit of a split mind on this myself, which is I do think it's unfair and inaccurate. to say the tech industry and computer science as a field. It's not tackling big problems. And I think that it's just, it's obvious, you know, when you look around, you know, the nature of a lot of the things that people are working on are, you know, really go after, you know, really kind of foundational things. Like, I think communication is actually a really foundational

thing in terms of how our civilization works. You know, obviously money, financial services is that's being reordered with technology. Now logistics, how the real world works, transportation, real estate are being reordered, you know, culture. society, you know, as being deeply affected by technology. And so I think that there are, there are a lot of big problems actually being tackled. At the same time, I think it's a fair critique, and I think this is the part of Peter Thiel's critique that I agree with, it's a fair critique that there are many fields that are not moving as fast as they could. And in particular fields, you know, applications of computer science or other fields of engineering that intersect

more in the world of atoms as opposed to the world of bits. And so, you know, drug discovery is an obvious one, you know, advances, you know, in mechanical engineering, advances in, you know, space travel, you know, lots of different areas where you would say, you know, cars, where you would say, boy, you know, you would think that we could make more rapid progress. I'm a glass half-fold kind of guy, so I look at that and I kind of say we now have the opportunity to make progress in many of those fields. And in fact, the best way to make progress in many of those fields is to apply more computer science. And so I count myself as an optimist. But, you know, nevertheless, I do think there, you know, there is something to the critique. Like, people are quite capable of looking at the huge advance that has been made in their smartphone over time.

and then looking at the advance that's been made in their kitchen or in their car. And I think you can kind of see that there's been a difference in the rate of improvement. And it's an exciting prospect to be able to go tackle some of these other fields. So going back to the academic question, so obviously Stanford and Berkeley and some other schools have been especially successful at seating startups that go on to be very successful. Why do you think that is? I mean, obviously some of its proximity to industry and being here, but there seems to be other factors. And I think specifically also for people here who are thinking about how they can make their own universities more entrepreneurial. Like, what do you think the key lessons are? Yeah, so I'm a case study of this in a lot of ways.

You know, in theory, I could have started my company out of Illinois in practice. I think that was impractical at the time. May or may not still be impractical, but it was definitely impractical at the time. So I'm an import, you know, I'm a classic import to Silicon Valley. I speak with the zealotry of the converted. You know, I've adopted Stanford for a lot of the work that, that I do out here. And so I sort of think that I have a reasonably good understanding

of the delta between the approaches. I think a lot of it has become obvious over the last couple years. I would highlight a couple things that I think Stanford and Berkeley do particularly well. I think one is there is, and again, for better for worse, but I think for better, there's very deep connectivity between Stanford and Berkeley and then the valley at a ground level.

And so there's just a very porous barrier for the professors and for the students, students and for the administration. And administration is an example. Like we all know the leadership at Stanford. And they spend a lot of time. In fact, it's not an accident that Stanford picked as their president sometime back, John Hennessy, who in addition to

being a legendary theoretical computer scientist, it was also himself a former company founder. And maybe the best university president anybody's ever had. That's worked very well. The other thing that I think Stanford in particular, has done really well is Stanford has been what I would consider to be the most enlightened in the sense of understanding the full kind of cycle of life of ideas being incubated in your university and then companies being formed and then in the

fullness of time the wealth creation that can happen through company formation and success and then the philanthropy that can flow back into the university and if you walk around the Stanford campus you know it's not an accident that there's the Jim Clark building right and there's the Jerry Yang building and there's the you know there's the Bill Gates building And it's just building after building after building the Hewlett, you know, the Hewlett Packard guys did a lot. My father-in-law, actually, not in computer science in real estate, but is another example of that, went to Stanford on a scholarship. My father-in-law went to Stanford on a geography scholarship.

The last year, Stanford taught geography. They canceled the major after he graduated. But it was a good idea to keep the major until he graduated because he went on to become a very successful real estate developer and has donated what has to be at this point more than a billion dollars back. to Stanford personally. And there's 150 buildings in the Stanford campus that he's paid for. And so Stanford has a very kind of deep understanding that's evolved over time

of the fact. And I would say in contrast universities where the IP licensing office is dominant. I think it's really a different, I would describe it as a different business model and now people really understand that, which is give it away in the beginning and then make the money back on philanthropy as opposed

to, I don't know, I won't say which universities, but like some other ones that I have friends who've interacted with. They have a very, they think the value lives in patents and transactional and licensing. I mean, we, like, we, you know, for example, when we make investments, we ask about patents, but it's like a side item. Like, it's never actually core to any investment. Because anyone who's built a company knows it's a dynamic process.

You're constantly building. And any tech you start with, it's probably completely different in four years, five years. It's all about who you recruited, and the people you recruit tend to not like patents. They like open source software. They like cool, open innovation or something like this. And I don't know. So it feels to me like a very, I don't know, like a lot of people just really misunderstand how it works. Yeah.

And it feels like a lot of, and you know, there's legislation. There's the, I forget the name of the, there's a law on this that people are grappling with. And so it's a complicated topic. But, you know, my University of Illinois is an example. My alma mater, I think in the last 20 years, has made significant progress. And they now have the results to show for it. So the first thing was the Beckman Center, which appeared when I was there, which is kind of this just like absolutely amazing complex up on the north side of campus

from Arnold Beckman, who was a legendary company founder who came out of Illinois decades ago. And then Tom Siebel, you know, has basically rebuilt the Illinois Engineering Campus in a very similar model. And so I think, you know, this is an area in which success should lead to success, as you have more and more examples of how this happens, you should be able to, you know, to be able to see that. I think also the model that works in the medical sphere and in pharma licensing is more transactional patent oriented

and does seem to work and is very different than computer science and that throws people off. But computer science education. So New York City just announced that they're going to, in the next 10 years, start teaching computer science in all, I think, elementary in high schools. For the most part, it's very, I think it's computer science is rarely taught at the pre-college level.

I think, and it's, as I understand, you know, the, we're constantly complaining about there being a shortage of computer science expertise. What do you know, what do you think is going on there and what can we do to fix it? Yeah, so, I mean, there is a cynical view, by the way. I mean, by default, you would say, having every kid taught computer science as a plus, There is a cynical view that says that if they teach computer science to school, they'll beat curiosity on the topic out of the students, just like they do every other topic they teach. And so there is a potential dark side. I hope that's not what happens.

The positive side seems overwhelmingly positive, which is, you know, talk about like a foundational technology of our time, you know, to learn about how software works and how to build software is really fundamental. So, you know, it's incredibly exciting. Obviously, it's a big dependency. It's good for kids to have a familiarity with it. It's, you know, you then get an interesting question about how many, you know, how many computer science students can universities take, right? And then what's the dropout rate along the way? And, you know, you have all these problems around underrepresented groups, you know,

with computer science degrees. And so if you still have the drop-off take place in the university, you know, then you may not fix that problem. But it's certainly progress. And then I think there's a deeper idea that I think is worth thinking about, which is the impact that computer science is going to have in many other fields. both in the academy and also in industry over time. And something I think a lot about is a famous essay

by an Englishman named C.P. Snow from the 1960s, I think. It's a famous essay. You can Google it if you haven't seen it. It's about what he calls the two cultures. And C.P. Snow was this very interesting character. Brief digression. C.P. Snow was an interesting character because he was both a chemist and a novelist. And so he kind of was right brain and left brain,

and he understood both worlds. And he wrote this essay kind of at the height of the Cold War, and specifically at the height of physics being kind of the top scientific field of its era. And so, you know, nuclear energy and space travel and the atomic bomb and the hydrogen bomb and all these, you know, incredibly central topics around physics. And he wrote this essay, and if you read the essay, it's like, you know, 60 years later if you read the essay, and if you just substitute physics for physics, you just take physics out and you put computer science in, it reads exact, right?

And he talks about the two cultures. He basically says there's the, I'll adapt it, he says, there's the, well, I'll adapt that. There's the computer science culture or the engineering culture, which is kind of ascendant culturally because this science and this engineering is reordering the world and having a huge impact. And all the physics people then, computer science people, now are all cocky and aggressive and confident and say all these bold things. And then there's the other culture, which is the artistic culture, the liberal arts culture.

And the liberal arts culture is art and literature and music and philosophy and political science and all these things, sociology. And they're very much on the defensive. and they feel very attacked and victimized and, you know, are the days of liberal arts over because engineering is just going to take everything over. And so he comes out from a sociological standpoint, and it's a fun diagnosis to read. But then he proposes what he calls the third culture, which are the people who can bring the two cultures together and the people who can bring physics or now computer science into liberal arts,

and the people in liberal arts who can learn and understand, even if they're not engineers, can learn and understand how engineering works and how computer science works. And he basically proposes that the third culture will be able to do things that each of the cultures by themselves will be unable to do. And I really think we collectively have an opportunity to do that with computer science. We have an opportunity for computer science to have a hugely positive impact on many other fields of human activity. And we have the opportunity to have computer science be something that is open and accessible to people who aren't going to be full-time programmers, but who are going to be able to learn about this, understand the mentality, and then be able

to really understand what's happening and be able to contribute. And so my hope would be that that's what will flow from these investments in earlier CS education. And if you had to pick areas, just some examples of what computer science might, you know, computer science plus X, what would X be? Yeah, I mean, so the obvious giant one right now is biology and life sciences. And it just seems to us like there's a revolution of foot in a fundamentally new way. that's just extraordinarily exciting. Now, you know, that's not really a two-cultural thing

because biology is also, you know, biology is close to engineering than a lot closer engineering to liberal arts to start with. You know, but for sure that. And then I think in liberal arts, I think more on mind, you see a lot of this on, you know, Stanford's doing a lot of this, a lot of other universities are as well. But, you know, it's everything, it's, you know, literature, there's new ways of thinking about literature, the written word,

there's no ways to think about music, there's new ways of thinking about art. You know, one of the really interesting things happening right now are the attempts to digitize, you know, things like ancient ruins and artworks in regions of the world that have lots of war and conflict. So even in the worst case, if they get destroyed, we're able to have like a complete 3D recreation of like an ancient city. And so there's a potential to kind of really advanced cultural knowledge and understanding. Entertainment, you know, is obviously a straightforward one. Education itself, you know, software-driven

education, you know, with all the tools and techniques we have in computer science applied education, you know, it seems like a huge opportunity. Let's talk more about bio, because it's something that we spend a lot of time on lately. Like what, you know, specifically, you know, is happening there that makes it an exciting time? Yeah, so I think the core foundational thing that's happening is something very subtle and very important. And this is happening in other fields as well, but we're definitely seeing it in bio, which

is, you know, biologists like physicists or chemists or a lot of other, you know, sort of highly advanced specialists in different areas of science and engineering. You know, up until 10 years ago, if you would meet, you know, kind of a state-of-the-art, you know, world-class research biologist, odds are they weren't very comfortable with computers. And odds are they had never really programmed. And in fact, in physics, in example, my job in college, my first job in college was actually to write computer code for physicists who hated computers. And it's been the same thing in biology, you know, for a long time.

And you meet a lot of senior biologists who are just still sort of fundamentally uncomfortable with a lot of the stuff, or they have a grad student who writes, you know, who writes the code for the lab. You know, the big thing has happened is there's just so many now young, incredibly smart, you know, biology PhDs, doctors, chemistry PhDs who are coming out. And it turns out, in addition to being fully qualified in those fields, they've also been programming, in many cases since age 10. And so they've got that same foundation in software and computers. You know, they had a PC in the house growing up. You know, they've had a smartphone. You know, the real young ones have had a smartphone for most of their life. And they've got the same kind of foundational knowledge about computer science and software

that a lot of the kids in computer science have because they started programming at age 10. And so I think it's actually, first and foremost, is a change in the field, which is as a consequence of generational change. The field as well as other fields is just going to change as a consequence of that. And then, you know, you'd have to add to that the more formal efforts. interdisciplinary research at the university level. And then there's the big macro trends actually happening in the science. And so, you know, the realization of genomics is a mature field.

You know, the enormous advantages of cloud computing and big data, you know, now being able to be applied to biology, you know, the computational biomedicine, all these sort of fundamental areas of things that can be done now, you know, quantified self as an example we think is going to evolve into, you know, a cornerstone of biology in the future. You know, one of our theories is we think we're living in the Stone Age today in the sense of like we really don't know what's happening in our bodies. Like we don't know what's happening in our bodies until something goes wrong and then we get whatever tests we get. And then if something goes right again, we kind of go back to our state of ignorance. Whereas in the future, we just think it's going to be standard that you're going to know everything is happening in your body and you're going to know your blood work and you're going to know your genome and you're going to know your MRI and you're going to know all these things all the time.

And so there's, you know, an enormous turn that's going to happen as a consequence of that. So just to get specific, if you're a professor in computer science and you want to encourage, you want to get more involved in entrepreneurship or encourage your students to or your school to, you know, what can we, I mean, it's part of what we're doing with this conference is to try to kind of increase the, I don't know, communication, I guess. But what can, what, I guess, advice would you have or kind of suggestions or, et cetera, for people in the audience? Yeah, so, you know, in any given, I would say a couple things, sort of, in any given, error in the valley, there are a set of venture capital firms that are kind of front and center,

and we've kind of tried to make ourselves one of those, but they're grudgently concede that there are others. And there's, you know, there's, you know, three, four, five, six, ten, whatever the number is, but firms that are kind of on the leading edge funding the next generation of interesting companies. And so, and those firms, as we do, tend to have a very panoramic view of what's happening in industry. And so I think we're, you know, I think, it's why we're thrilled to have you guys here, but I think we're, you know, we're able to be a resource on that. to a certain extent the other firms, maybe. And then there's a set of companies.

You know, there's a, like to say, like, there's a lot of tech companies. In any given generation of companies, there's three or four or five that are kind of clearly top of the heap. You know, kind of have, you know, they're kind of at scale and are doing very interesting and important things, but have not become kind of classic big companies that just kind of drift along, you know, sort of companies that are still alert and alive, you know, often still run by their founders. and are, you know, hiring voraciously, right?

And so they're just, you know, they're just a sync for talent, you know, coming out of all of your programs. And so I think between, you know, the top handful of venture capital firms, the top handful of tech companies, I think, you know, these days, it's, you know, those are the key things that you want to really be, you know, even if there's no formal connectivity, just, you know, associated, you know, understand, you know people at those firms and at those companies. And then, you know, the other, I'm sure this is obvious,

but just, when it works, it works really, really well. is just the natural flow of students coming out into industry and then retaining connectivity back into their programs and being brought back to campus, bringing the learnings back, you know, telling the next generation of students, you know, what the opportunities are, you know, being guided by, you know, the professors who are up to speed

on what's happening out here about where to go and what to do. And, you know, I would say we see lots of success stories of how this works and works really well. And then, you know, we do see cases where, you know, there are programs where there's just, they're just completely, you know, still isolated. after all this time and the students really have very little idea of what's happening out here

or in industry broadly. And so for those of you who are doing that well, it's going great. I think that mentality could be applied more comprehensively in the field at a lot more schools. A lot of the best tech companies in the world today flow directly out of, I mean, my company's example of this, a lot of the best examples flow directly out of universities. And so we have a whole generation of these companies, you know, that have done this. And so there is a very good success kind of model that works incredibly well. there's a very clear failure case, which is the dominant failure case that I think that we see,

which is the professor has the idea. The professor starts the company, but with no intention of going full-time at the company. The professor runs the company or gets it started for a year or on an interim basis or on a part-time basis. Gets associated with some seat investor or some second-tier venture capital firm that goes out and finds a professional, a mediocre professional CEO. So the professor then is like, okay, my job is done. Professor goes back to teaching. And then the company just drifts and ultimately falls over and dies. And so what's missing there, what's missing there is basically, this is just a lesson we learn over and over again.

Like there is no substitute in these companies for having the core team, and in particular the CEO, the person who's going to run the company, the people who are really strong and really sharp and really clued in on one needs to happen and then are really full-time, like really able to be full-time and be able to be full-time on a sustained basis. It doesn't have to be the professor. Often it's like the key grad students, right? Exactly, exactly.

So that's going to say is that that's exactly right. So that's the model. The variation that works really well is when there's one or ideally a set of students, a set of grad students who really understand this and really want to do it. And the professor basically sponsors, you know, sort of informally sponsors the creation of the company or helps with the creation of the company. And then the students actually run the company the professor can be involved as an advisor

or can be involved in the board of directors or whatever the right thing is or can just go back and teach and go back and do more research but where the company is really formed around the students and you know Silicon Graphics was a back in that which was a huge success with this model was

an exact example of that you know Netscape was another there's been a whole bunch of examples like that so it's almost the professor as the Sherpa and as the advisor and as the guiding light And the professor might be very involved for the first year with the idea and the fundraising and catalyzing the entire thing, but where there's this core of people who are the students who are really able to pick this up and carry it forward. Natsira, for us, Nassir was a recent example of this, where, you know, Scott and Nick were very involved in the formation of the company.

But, you know, they found a good person to work with to be the CEO. And then the CTO was Marteen, who was the top grad student in the field, who really carried the company forward. And Martin now actually is a big executive at VMware and, runs, I don't know, a thousand people and has a $500 million business working for him. And so that's a particularly vivid kind of clear success story. And I think that's the kind of precedent to look at. Yeah, I mean, I would argue it's actually a special case of a broader misunderstanding of startups that's outside of academia as well.

And it comes from pop culture. You know, you see the Facebook movie and they like write an equation on the board and it's as if that equation were like the secret, right? But in fact, it's 10 years of tons of engineering and marketing and all sorts of other things, right? And network effects and a million other things. Or, you know, the idea that you, I didn't see the movie, but it's like the guy who invents the intermittent windshield wipers. So you invent this idea, you patent it, you hand the patent over to the business guy who then goes in the next scene, he's like living in a mansion, right? Like it's like, and it's sort of the pop culture view of it.

And when in fact what startups really are is a dynamic process that probably 90% of it is frankly recruiting a great product and engineering team. And what we find is only, just empirically, is it only great product engineering people are able to recruit those people. And the product ends up changing a lot over time. I almost never see, I don't know, like I don't know Nysera, for example, but I've been involved with many startups where it came from academia, but when you actually look at the product, product four years later, it was very, very different. I mean, some of the core ideas might have been there, but there were a lot of changes that were non-trivial, that you needed sort of the core people to do.

I mean, that's your experience. So we funded this here. So we funded this here at Netwee's Networking Lab, and it was the open flow. Openflow was the sort of protocol was the approach, and Martin had been one of the main people doing that. And so they started to Syria, and we're all excited because we're like, this is great, we've got open flow,

we've got the open flow technology, like, this is going to be the open flow company. And in the first board meeting, Martin sets us down, and he's like, okay, the first board meeting, the first thing you didn't understand is we're throwing away all that open flow stuff. And Ben and I went, what? And he said, you know, that was great for a research lab. But, like, for commercial applications, we need to do another, we need to do another version.

We need to take everything we learn from that research, and we now need to build the actual commercial product. And so he effectively... Even Google, like, page rank, people talk about page rank. I mean, it was very quickly copied by the idea of using inbound links as a part of the ranking algorithm. It's very quickly copied by the competitors. They actually did a lot of other things. It's one of, like, page rank is one of, like, 900 factors now. that go into a Google search result.

The other 899 happened after they left Stanford. Now, by the way, there are professors. Jim Clark was my partner when I started Netscape. He started selling graphics. He was actually a case of a professor who left, who left academia and went and started a company. I feel like the two exceptions are RSA and Qualcomm, maybe, arguably, where there actually is sort of a core, I don't know if you agree,

but there's a few exceptions where it actually does happen. It always annoyed, like, Instagram. You know, there's a couple of these really high-profile case where the guy just does have an epiphany, and it becomes this, and it takes, unfortunately it unwinds like five years of us trying to argue the other point because they're very high profile cases,

but it does occasionally happen, but for the most part. There is no substitute, there is no substitute, the big takeaway, and this applies to everything we fund and it's what we work on all the time. There is no substitute to the first five years of people in the office, 18 hours a day, six days a week, grinding away, and it's grinding away on making the product work for customers

and they're working with the customers. Like it's just, there's no way to, we have not found a way to shortcut that yet. And so, however the process of starting a company forms, it has to really focus in on that. Can you talk a bit about the leadership development piece? So on a day-to-day basis, as a professor, I am mentoring and helping students to develop technically, but also it's a core commitment of mine to help them to develop personally as well. So can you talk a bit about how we, in our mentoring rules, can help to develop people who

will eventually be strong leaders who are technically competent, but in addition to that, also like relationally competent in terms of being able to be that technical CEO founder person? Yeah. So I think there's two sides. I'm glad you asked that. So I think there's two sides. I think there's the soft side. There's sort of, I would say, is the informal side of leadership and then the formal side of

leadership. So the informal side is just being able to work with people and being able to lead people. And I think a lot of that has to do with what then happens in the lab or in the department. And so, you know, having the high potential people be able to be in some kind of leadership role, you know, on projects or on research programs. you know, relatively early, you know, is certainly going to be a good thing. Easier said than done, but, you know, giving people the opportunity to lead and then giving people the coaching along the way or being able to find mentors who can come in and work with

them on developing their sort of, the sort of, not the soft skills, but like the informal skills, the people skills, you know, that you're kind of getting at. There's another side of it that is also, I think, a big opportunity, which is the formal side, which is business skills training for engineers and for computer scientists. One of the things that is very exciting, actually, and this is, I know there are other schools doing this, but we see this most vividly at Stanford and at Berkeley. Stanford and Berkeley now both have organized programs to teach students in the engineering school business skills.

And this is the funniest, the comical version of this is what happens at Stanford, which is there's this sort of time-honored tradition at Stanford where the Stanford business school students, the MBAs, you know, the new MBAs kind of view themselves. as future CEOs. And so, you know, they'll come up with an idea, and then they'll go try to, basically, you know, they'll go to the computer lab at like midnight with a pizza and try to get, like, an engineer to help them on the idea.

And there was just a Dilbert cartoon that kind of immortalized this where the pointy-haired boss, you know, goes up to Dilbert and he says, I've got a great idea for a startup, and now all I need is an engineer and some funding. And Dilbert says, you know, the economic term for what you have is nothing. And so the Stanford version of that literally

is the NBA trying to go get the NB. engineer, the much better model is for the engineer to have the business skills, for the engineer to be a top-flight engineer, but also have the business skills to be able to start a company, and then the engineer hires the MBA as the head of marketing or the head of sales or the head of finance. And so we would always prefer that the engineers have the business skills so that they can be in the leadership position in these companies, even at the founding level. Some schools are very resistant to this idea that engineers should be trained in business,

because, again, it seems like a corruption. of the process of what of what of what they're trying to do in their field. Some schools have gotten very enlightened on this. What's so striking about the Stanford and Berkeley programs is those programs are completely separate from the business school. And we see this. You know, we'll go over and speak at Stanford and, you know, you'll get an invitation to go speak at the business school.

And you'll get the invitation to speak at the business class for the engineering school. And it's a completely different set of students. They're all engineers that want to learn about business. And so I think that's a really good idea. And I don't mean it's like, I'm not saying it's like half and half, but I'm saying it's like, you know, I don't know, four or five courses, you know, over the course of four years, to be able to learn, you know, the fundamentals of business, to be able to learn, you know, maybe a management course, maybe a startup course, maybe a finance course,

and maybe one or two other classes, like an econ course. Because then you can, with just a little bit of starting knowledge like that, you can set the engineers up to be able to think of themselves as business people out of the gate. I mean, I was, I guess I did it the hard way, which is I didn't take any business, it never even occurred to me to take a business course, they never offered. And so everything I learned about business happened after I graduated. And I think in retrospect, had I had any formal training in it, I think I would have been better. I mean, the other good model, I think, is to go to a startup, a relatively small startup for a couple of years.

Or intern. I mean, obviously. Yeah, intern. Internships are, so. Like Waterloo is a great example of that where they, I think they have, it's like they have a crazy number, like six or plus internships over their four years of undergrad. The whole sort of, it's deeply integrated sort of working in industry. and working in their coursework. And they come out, we see them coming out of college

and they're just like, they're just very sophisticated. Another good example, Penn's M&T program, so they combine, right? It's like some smaller, I think it's mostly engineering but some portion of Wharton. Yeah, no, no, that's, I agree with all that. On the internship side, this may be obvious, but it's very important for what's happening out here,

which is college recruiting for computer science students has gotten to be brutally difficult, because there's so many debt companies and there's just so few top flight can be. or science departments. And so, you know, it's World War III. The way the companies are looking at it is they're just desperate to be able to hire your students when they graduate. And what they've now learned is they can't wait until your students graduate. They have to get them, they have to get them, sooner. They'll never have a shot of getting them. And so they have to get them at the intern.

They have to get them at the intern level. And so in the last five years alone in the valley, the tech companies have hugely increased their focus on their internship programs. And basically the goal is to get all of your best students as interns and then be able to basically get them locked in at least a year in advance to be able to come on full time. The good news of that for you guys is like, you know, and I don't even know if the tech companies are being fully transparent with you on this, but like they're desperate. They're really desperate. And so I think you guys have the opportunity to really set your students up in these best-of-class companies. And in fact, not just one, but like a whole series of them over the course of time. That was one of the things. And then on the water loop line, that was one of the, when I was in Illinois, it's one of the things I'd give them a lot of credit for is they also had, they did, they call it the co-op program.

but they would, you know, they would support you in going out for, I think I worked at, one of my stints was a full nine months summer and then the fall semester at IBM, which was just a, you know, for me as a kid was just a huge, you know, be able to spend nine months in a company, you know, it was just, I learned just an enormous amount. Back to the topic of patents and IP for a moment. Very specific question, but probably a lot of us in here are building companies based on work that we did at the university that is perhaps patented at the university. at the university and the university owns,

so we have to license it from them. I'm just curious on your thoughts about this. Is this sort of a red flag or problematic from an investor's point of view, or not so much? I'll give my answer to, Chris, think. So it depends on the university and it depends on the terms.

So there's a lot, I mean, we do fund companies all the time that have licenses to university patents. I would say in some cases, those are actually useful licenses. Like what I would argue is like the further down the stack the more useful the patents are. Like patents at the level of chips or at radios, you know, can be, or, you know, I don't know, memory or something like that can be

incredibly valuable. Patents at the level of software or applications, we generally don't view is very, very valuable. And I could go into, you know, more detail why, but generally not very valuable. You know, to be able to get the right team out of the university into a startup, if there's a licensing agreement that is only on the margin for the economics, you know, then it's just a cost you doing business. And we'll kind of put up with it. I have seen cases, and I've lived through cases where the universities take a very draconian

view on this, and the university licensing office has this just highly inflated view of the value of what they're going to be licensing, and they can kill companies in their cradle by taking that approach. So I think a lot of it depends on the specifics of the university. Yeah, I would just add, I think that some of the universities are doing themselves. We believe it is a disservice by doing this and are actually counterintuitively, I mean, to them it may be counterintuitive, but we think they're actually generating less revenue by asking for more. But it's such a counterintuitive argument

that it's very hard to make. Nick from Nick McEwen from Nysera, actually, he spoke, I thought it was really interesting. I think it was two years ago at our academic roundtable. And he's a professor of Stanford. He said that he, I thought it was an amazing talk. He said he literally, with every new thing that his lab would invent,

they would go to the incumbent company, like in his case Cisco, like it was a new networking invention, and literally just offer them for free all of the inventions. And he said, sometimes they would say, yes. In which case, we would say, OK, let's go do our next thing. Let's invent something else.

And when they say, no, he'd say, OK, well, that might be interesting because it might be like too futuristic for Cisco, and therefore, let's go do it. And apparently, he said Stanford just let him do it. And didn't, I don't think how it works Stanford. But it sounds from everything I hear, I guess, Dan, you might know. But you are.

OK, so yeah. So I think it's very liberal, is my understanding. And they then, in that case of Nicaire, went and did this company. And so it's just a very different kind of philosophy. And it's worked very well. It's just very hard. I know like I've tried to, I went to Columbia undergrad

and I've tried to argue this philosophy to the administrators there. And it's just very hard argument to make because it sounds very counterinduitive to most people. It's also outdated legislation. There's the, it's called the By Dole Act, which actually requires university, I don't know exactly what it is,

but it's like university research is paid for by the public. There has to be, the financial rights have to accrue back to the public. And so a lot of universities are still grappling with that. I think a really good reform would be to just eliminate that law and let universities make these decisions on their own. But that's not happening anytime soon. Yesterday. Peter Levine. About sort of a contrarian view in investing, he sort of pointed out the investment in

TACUNNNexis of, you know, many people saying, oh, that storage is not going to get as flat and when it does, then Taken's going to be in a good place. So having a contrarian view for investing, he was sort of espousing as an important sort of concept for the firm. How is it that the firm embodied that you guys are sort of talking about your worldviews and your lenses, and yet, you know, fairly openly, and yet you still have the ability to sort of find the things and identify the things that others aren't? So why don't you, yeah, talk about the, Your theory on this. Which theory?

The strong week? Good ideas that look like bad. Or you can talk about a strong week. Yeah, you're asking, like, this is a whole book here of material probably we could talk about. But I'd say a couple of things. One is venture capital works very differently than the public stock markets, for example, where it's not here we just sort of decide, okay, let's invest in this company to this company. A lot of it is, frankly, the entrepreneurs drive the process and decide who do they want to work with.

So that's probably a common mistake. outsiders make about this industry and they think you know Chris had this great idea of like I'm gonna go invest in X or Y and instead there's some of that like you have to be smart about what you want to invest it but but frankly a lot of what we do is we try to build an institution that's that just is the most attractive place that entrepreneurs want to come and so a lot of the businesses frankly about that and and a lot of the investments we've made that have been successful are investments other VCs wanted to make and we just the

entrepreneur chose to work with I mean that so that's it's kind of reversed from, it's not how the public markets work, you know, the, the pharma stock doesn't choose the hedge fund, right? Like, it's the opposite. So that's a big part of it, I would say. I think the other thing is that you can talk about your broad ideas, you know, mobile phones will change, you know, are changing the way that, you know, services are provided or something. It's also, it's very different to find the right set of entrepreneurs working on a specific idea. Often Peter Thiel has talked about how A lot of the, you know, like Airbnb is a great example where very early on it looked like a really kind of ridiculous idea. A lot of these things, you know, you can speak publicly broadly about you have to be contrary, but like actually in the moment figuring out kind of the puzzle of a particular startup and whether it makes sense and the team makes sense and the timing makes sense ends up being very applied.

It's probably like more like almost like an engineering problem. Like, you can talk about lots of broad principles and still going and actually building the program is a very different thing. And so, Chris likes to say that basically, like, so basically advantage big companies have. Advantage big companies have, they have all these resources. They've got, you know, Cisco and Intel and all these companies, those engineers, they got all this money, and they got all these people, and it's just all these customers and this brand and the Salesforce. So they have all these huge natural advantages. And so if there are any good ideas that are floating around, they're just going to go do them. And so, for example, Apple, you know, is doing great.

in smartphones. And so a really good idea would be a smartphone that's like, you know, half as thick and has a battery life twice as long. Like that's a really good idea. People really love that. Apple fully understands that. And they're spending billions of dollars trying to make that happen. So we can't fund a startup against that good idea. We basically have the disadvantage as a consequence that we can't do those. And so the ideas that we can fund are the ideas that look like bad ideas, right? And the ideas that just looks silly or stupid, specifically to the big companies. And then, of course, the twist to it is, out of the universe of ideas that look like bad ideas, most of them actually are bad ideas, which is something you can very easily learn the hard way.

And so, you know, and we see 2,000 startups a year, and, you know, many of them are good ideas that are just too obvious, and then many of them are bad ideas that are just bad ideas. And then every once in a while, there's the good idea that looks like a bad idea. And it's the thing that's just, like, counterintuitive, and it's radical. And, you know, and it's, you know, and by the way, even there we're going to have, we assume we basically have a 50% failure rate. So, you know, we assume that half the time, you know, we're going to back what we think is a good idea, it looks like a bad idea, and it's going to turn out to actually have been a bad idea. The good news with the good ideas that look like bad ideas is when they work, that's how you can build a major new company, right? Because then that's the psychology under which you can get a jump on all the other big incumbents.

Well, and the interesting thing to watch when you're actually involved in it is people continue to think, it's a bad idea for a really long time. Like, you know, like, Twitter got really big before people finally stopped saying it was about, like, tweeting what you're eating for lunch today. Like, it's amazing how long people will go on saying it. I mean, like, VR is a good example where I just, it's very hard to

go see a demo of the latest Oculus stuff and not think it's amazing. It's pretty much, in my experience, a one-to-one correlation between having tried the greatest demos and being excited about the field. And yet, it's highly controversial even within Silicon Valley. And so, and, you know, so that's the other thing.

It's kind of, it surprises you how long, in my opinion, how much headroom you get on these good ideas, look like bad ideas. Like, until, you know, companies almost going public or generating billions in revenue, like people still doubt it heavily. And you're kind of in this weird state as the startup as well as the venture firm. You're kind of in this weird state where you want the world to understand that it's a good idea because you want them to, like, buy it. You want them to be everybody to become a customer of it.

But you also kind of want them to, continue to think it's a bad idea so that you don't face direct competition. And so it's this really weird schizophrenic, you know, and we can't help ourselves. Like we're out evangelizing of half of our companies. But yeah, no, we have stuff that we're like, we have stuff that's going incredibly well where if I tweet about it, I just get this immediate backlash of, oh, that's really, you know, that's so stupid. Like you guys are so stupid.

How can you possibly think that that's really? I mean, we have companies that are like, you know, 100 million in revenue and like fanatic followings. And you get, you tweet about them and people are like, well, that'll never, no, we'll ever buy that. And you're just, okay. And you can just, it's amazing. amazing, actually. You can just like lay out the whole business plan. You can explain how everything

works. And most people just think it's so ridiculous that to this day, like take Twitter as an example where it's, it's, in anyone the tech world and in the journalism world, for example, it's a critical business tool, right? It's how we share links and how we share work information publicly in the tech world. And like to this day, I think that, you know. And it's a $2 billion dollar revenue business built in less than 10 years. And to this day, most of the mainstream press coverage still kind of treats it as, you know, like it's this silly thing for, you know, talking about what you had for lunch or something. Yeah, and that it could go away at any moment.

Yeah, all right. So we can't complain about it too much because it is our little secret, a little secret weapon. Yeah. Okay, so great. Well, thanks to the good. Good, good. Thanks, everybody.