Software Programs the World

“All of a sudden you can program the world” — it’s the continuation of the software eating the world thesis we put out over five years ago, and of the trajectory of past and current technology shifts. So what are those shifts? What tech trends and platforms do we find most interesting on the heels of raising our fifth fund? Are we just building on and extending existing platforms though, or will there be new platforms; and if so, what will they be? Well, distributed systems for one…

This episode of the a16z Podcast covers all things distributed systems — encompassing cloud and SaaS; A.I., machine learning, deep learning; and quantum computing — to the role of hardware; future interfaces; and data, big and small. Podcast guests Marc Andreessen and Ben Horowitz (in conversation with Scott Kupor and Sonal Chokshi) also share the one piece of advice from a management and go-to-market perspective that all founders should know. And finally, why simulations matter… and what do we make of our current reality if we are all really living in a simulation as Elon Musk believes?

Show Notes

How advances in hardware and reduced prices are pushing A.I. and other technological advancements [0:27]

The current state of A.I. and where it’s headed [8:39]

Real-world applications for technology (life sciences, SaaS, and company creation) [20:21]

The firm’s philosophy around team-building [32:59] and advice for founders [37:50]

Transcript

Sonal: Hi, everyone. Welcome to the “a16z Podcast.” I am Sonal. And I’m here today with a special podcast we have on the heels of announcing our fifth fund for Andreessen Horowitz. And we thought we’d talk more broadly about what’s changed between the first fund and now and, more importantly, some of the technology trends and trends we’re seeing with founders. And to have that conversation with us, we have our co-founders, Marc Andreessen and Ben Horowitz, and our managing partner, Scott Kupor. Welcome, guys.

Scott: Hey.

Advances in hardware

Sonal: Okay. So let’s just kick things off. One of the things that I want to understand is that it’s been — since fund one, which is, what, six, seven years ago?

Marc: Seven.

Ben: Seven years.

Sonal: Yeah, seven years ago. A lot’s changed in seven years, and I’ve actually heard you argue, Marc, that things have accelerated in that time period, more so than previous decades before. So what do you guys think are the biggest shifts now that are important to us in this newest fund, and what changed in that period, like, the biggest things?

Ben: So, in fund one, when we started, we thought that our timing was really good, despite the fact that I think the world thought our timing was really bad in starting a new venture capital fund. And the reason why we thought that was that there were three gigantic new platforms hitting all at the same time, which was kind of unprecedented in the history of technology. One was mobile, the second was social, and the third was cloud. And that really proved out, through the course of the early history, that the applications on top of those — particularly mobile and cloud — were just spectacular. And I think we’re coming a little bit to the end of the first phase of, you know, some of the obvious applications that could be built on those things, and we’re moving into some new areas.

Marc: Yeah. So, let me go kinda to the foundations. So, there’s different ways of looking at it. The foundational levels — one is Moore’s law has really flipped, and this actually has happened. I think this actually has happened over the last seven or eight years, actually, almost exactly over the life of the fund. Which is, you know, for many, many years, Moore’s law was a process of the chip industry, bringing out a new chip every year and a half, that was twice as fast as the last one at the same price. And that continued for 40, 50 years, and that’s, by the way, what resulted in everything from mainframes, mini-computers, PCs, and then smartphones. About, you know, 7, 8, 9, 10 years ago, that process actually started to come to an end the way that it had worked up until then. So, chips have kind of topped out at a speed of about three gigahertz, and a lot of people have said, therefore, like, progress in the tech industry is gonna stall out, because the chips aren’t getting faster. I think what’s actually happened is, Moore’s law has now flipped.

The dynamic now, instead of increased performance, is reduced cost. You now have this dynamic where, every year, a year and a half, chip companies come out with a chip that’s just as fast but half the price. And so, this is the, sort of, just this massive deflationary force, I think, in the technology world, and I actually also suspect in the economy more broadly, where, basically, computing is just becoming free. Basically, what we do in this business is we just kind of chart out the graphs and then just kind of assume, at some point, you’re gonna get to the end state, and the end state is gonna be the chips are gonna be free. Which means chips will be embedded in everything. You’ll be able to use chips for, literally, everything. And we’ve never lived in a world before where you can do that. So, that’s the first one.

Second one is just the obvious implication from that, which is, all those chips will be on the network, right? So, all those chips will be connected to the internet. They’ll all be on Wi-Fi, or mobile carrier networks, or wired networks, or whatever, but they’ll all fundamentally be on the internet, you know. That’s something that’s now happening at a very rapid pace. And then the third is the continuation of the piece that I wrote, actually, five years ago, which was called “Software Eats the World,” which basically just [says], if you’re gonna live in a world in which there’s gonna be a chip in every physical object, and if you live in a world in which every physical object, therefore, is going to be networked — it’s gonna be smart because it has a chip, and it’s gonna be connected to the network — then basically, you can then program the world. You can basically write software that applies to the entire world. So, you can write software that, all of a sudden, applies to all cars, or you can write software that applies to all, you know, everything flying in the sky, or you can write software that applies to all buildings, or you can write software that applies to, you know, all homes, or all businesses, or whatever, all factories.

And so, all of a sudden, you can program the world. That’s really just starting, and I think a lot of the — there’s a number of things that make the entrepreneurs we’re seeing these days, in many ways, more interesting and more aggressive than entrepreneurs we’ve seen in the past. And part of it is, they just assume — if there’s something to be done in the world, there must be a way to write software to be able to do it. That’s at a new level of power, sophistication. It’s a new scope of what the tech industry can do. The consequence of that for us, as a fund, is that we find ourselves evaluating business plans and funding companies that are in markets where, I think, seven or eight years ago, we would have never anticipated operating.

Scott: So, Marc, does that mean that there’s no new innovation in platforms themselves, and everything — all the innovation will be applications that ride on that existing infrastructure, or do you think there’s also the opportunity to build a new platform, even given some of those trends?

Marc: I think there are new platforms, and I think there will be new platforms. I just think they’ll be different kinds of platforms than we’ve had in the past. The idea of a platform in the tech industry, as you know, up until, you know, 5 or 10 years ago, was there is a new chip that has new capabilities, is faster, and then, therefore, you build a new operating system for it. And that might be Windows, or it might be, you know, might be iOS, or whatever it is. The platforms that we’re seeing getting built these days are distributed systems. So, scale-out systems, sort of, being built on a chip necessarily with new unique capabilities. They are platforms that are getting built across lots of chips. And so, in computer science terms, they’re distributed systems. Cloud is one of the first examples, right? So anybody who uses AWS can now go on and can program an application on AWS that will run across 20,000 computers. And they can run it for an hour, and it’ll cost, you know, 50 bucks. And that’s a kind of platform that did not exist before.

And, by the way, there are many specific elements to that. So, for example, we’ve seen the rise of, in that category, we’ve seen the rise of Hadoop, and now the rise of Spark for distributed data processing. We’ve seen — in financial technology, we’ve seen the rise of Bitcoin and cryptocurrency, which is, literally, a distributed platform, you know, for currency and for exchanging value. And now, we’re seeing the emergence of a major new platform, which is A.I. — machine learning and deep learning, which is inherently — the great thing about machine learning and deep learning is they’re inherently parallelizable. They can run across many chips, and they get very powerful as you do that. And you can do things in A.I. today as a consequence of being able to run across many chips that you just couldn’t even envision doing 5 or 10 years ago.

Sonal: So, let’s talk about the rise of the GPU as part of this next platform chip. I mean, I think the biggest surprise people have had is that this is the graphical processor unit, which is something that was developed in the gaming industry for really high-resolution graphics processing, and is now finding, I guess, unexpected — is it a surprise to us that it’s finding uses in these new platforms, like VR, AR, deep learning?

Marc: It’s actually, interestingly — it’s a new application of an old idea. Back when I was getting started 30 years ago, working in physics labs, if you wanted to run just a normal program, you just buy a normal computer and run the program. But if you wanted to run a program — many physics simulations had this property where you would want to run a very large number of calculations in parallel, right. So, you could basically divide up a problem, simulating anything from a black hole or to different kinds of biological simulations. You could basically write these algorithms in a way that you could basically parcel the problem into many different pieces and then run them all in parallel. There was actually, in the old days, there was actually a whole industry of what were called vector processors, which were, literally, these kind of sidecar computers that you would buy and you would hook up to your main computer, and they would let you run these parallel problems much faster.

And so, literally, 30 years later, the GPU is — it’s basically a vector processor. It’s basically a sidecar processor that sits along a CPU and runs these parallel problems much faster. And graphics are a natural application of that, but as it turns out, graphics aren’t the only application.

Ben: Yeah. Actually, interestingly, and I was at a company making one of these called Silicon Graphics — and the applications then were, as Marc was saying, a lot of physics applications, computational fluid dynamics, and simulating, you know, flight simulation, and all these kinds of things that are hard physics to calculate. When you go into the virtual world, and you’re simulating the physics of the real world, guess what? You need the exact same processor to do it. So, it’s a super logical conclusion to what’s been going on, but I think we’re also in the world of big data, seeing kind of more reasons to do just lots of math in parallel. And so, it’s an exciting application.

Marc: Yeah. You talk about platforms — one of the really interesting hardware platforms that’s emerging right now is Nvidia, which is a very well established public chip company, but very successful, to your point, doing graphics chips for a very long time — has become seemingly overnight — it’s really, of course, the result of years of work — but seemingly overnight has become the market leader in both not just GPUs but also in chips being used for A.I.. And it’s basically extensions of the GPU technology. And we see this overriding theme, which is kind of an amazing thing, which is, basically, every sharp A.I. software entrepreneur that comes in here is now building on top of Nvidia’s chips. Which is, of course, a very different outcome than entrepreneurs of previous years, who would have built other kinds of programs primarily on top of Intel chips.

Advances in A.I.

Scott: We’ve mentioned A.I. and machine learning a couple of times here. And one of the interesting things, at least, that I think we see in the industry is, at the same time we’ve got startups doing it, we also see some of the very largest established players investing significantly in A.I. and machine learning. So, certainly Facebook, and Google, Apple, and others are obviously building big operations. How do you think about the universe from an investment perspective? What are the kinds of things that actually lend themselves well to startup opportunities in the A.I. space, versus things that actually might make sense kind of living inside of one of the larger companies, like a Facebook or a Google?

Ben: Yeah. So, you know, A.I. is extremely broad, and I think one of the challenges that people have with it is they try to paint it as a narrower thing than it is, but one can think of it as an entirely new way to write a computer program. And so, then, it’s applicable to, you know, the universe of problems. So, there are things that advantage a big company. You know, if you’re building A.I. to analyze consumer internet data, like, that’s hard to take Google on at that. They do have an awful lot of data. And you know, Facebook, you know, with A.I., computing power matters and the dataset matters. Having said that, there are a lot of areas where nobody has any data yet, in the areas of healthcare and the areas of autonomy. So, you know, there’s lots and lots of opportunities, and you know, there’s also interesting ideas about, “Well, is there a better user interface than the smartphone using A.I. techniques? And then, what is the form of that?”

Sonal: What do you mean by that, when you say there’s a better user interface?

Ben: Well, yeah, if you think about a smartphone, it was kind of an advance over what we used to call the WIMP interface. Windows, icons, what was it?

Marc: Menus.

Ben: Menus.

Sonal: Oh. What was the P?

Marc: Pointer.

Ben: Pointer, yeah.

Sonal: Oh, pointer, right.

Ben: Which, you know, was, like, a big advance over the text-based interface of DOS. And then, you know, the smartphone with the touch interface, it was more of a direct manipulation — was an advance over that. And so, you go, “Okay, well, but that’s not actually what people do in life,” right? It’s, anthropologically — it’s a backward step, in terms of the natural interface that we’ve become accustomed to, like, for example, natural language. With A.I., you get into a world where things like natural language, and natural gestures, and so forth, become much more plausible. So, there’s, you know, potentially an opportunity to build interfaces for things that you couldn’t before. I mean, I think there’s one, like, really interesting thing, which I’m sure — and I know that Google, and Apple, and all the giant companies are very focused on — which is, how do you replace the current set of user interfaces with it? But there’s another dimension, which is, what are all the applications that you just couldn’t have before, because you couldn’t build a workable user interface for it. And A.I. seems very promising in those areas.

Sonal: You didn’t mention Amazon, which is sort of the stealth player here, with Echo and Alexa. I mean, really, Trojan Horse of the home.

Ben: Well, you know, in a way, they’ve got an interesting advantage in that they’re not tied to the last generation of user interfaces, so that they don’t have to pay the strategy tax for shoehorning in their A.I. into, say, the iPhone, and that’s something.

Marc: Yeah, that’s worth pointing out. There’s sort of two, kind of, classic rules of thumb in this industry. One is for major new advances, especially in things like interfaces, if you don’t own a platform, you can’t do them. And so, the assumption, I think, had been up, until recently, you know, that it would have to be Google or Apple that does these kinds of natural language or interface advances, because they own iOS and Android. The other rule, of course, is the exact opposite rule, which is the one that Ben mentioned, which is the problem that big established companies get into — is what he referred to as the strategy tax, which is, basically, big companies with existing agendas have to, sort of, fit their next thing into their existing agenda, and they often compromise it in the process.

And so, it’s sort of this ironic twist of fate that Amazon has, all of a sudden, taken the lead from Google and Apple, even though Amazon, you know, famously flopped with their phone, right, which is sort of the obvious place where you have a voice interface. It didn’t matter because they came out with this new product, which was, basically, the speaker, the smart speaker called Echo, and the fact that, all of a sudden, Amazon didn’t have a phone, all of a sudden, became an advantage because they could just do the clean actual breakthrough product without worrying about tying it into the existing strategy.

Sonal: Right. And those are all still big companies, though. I’m not really hearing where startups can really play in this space, especially when you are describing this huge data network effect that all these big companies have.

Marc: A year ago, we would have probably been sitting here and say that A.I. was going to be likely would be a domain of big companies, because of this sort of thing of, like, “Okay, only big companies can afford the very large number of engineers that are required to do A.I., only big companies can afford the amount of hardware required to do A.I., and then only big companies can get the giant datasets required to do A.I..” In the last 12 months, what we’ve seen, basically, is all three of those changing very fast, and to the advantage of startups. We’ve seen a lot of A.I. technologies, actually — now, interestingly standardizing — so going to open source. And then the next step is going to be, they’re gonna go to cloud, and that we’re right — because we think we’re right on the verge of that. We think all the major cloud providers are going to be providing A.I. as a service, and they’re gonna really radically reduce the amount of technical knowledge you need to apply A.I.. And so that plays very well to the startups.

Sonal: So, there will be, like, an AWS for A.I..

Marc: Yeah, exactly. And that may be literally AWS, or it may be Google, or Microsoft, or all three of them, and you know, in some combination. Or, it may be other, you know, other companies yet to emerge.

Sonal: An example of the open source, like TensorFlow, Google releasing TensorFlow.

Marc: Yeah. And this is a big deal, of course. Yeah, that’s right. So, Google open-sourced a pretty significant part of how they do deep learning, and that, actually, now, is something other companies can pick up and use directly. And we see, actually, not only a lot of companies but, like, a lot of university — a lot of student projects now just kind of pick that up and run with it. So, this technology is kind of trickling down very fast.

Sonal: Just this past weekend, we had a Hackathon. And I think most of the teams had some machine learning, A.I. component into their hacks. And these are college kids.

Marc: Yeah, yeah. You know, if you’re a 21-year-old junior in college and you’re doing some project, just, kind of — it’s rapidly becoming very obvious that you would have A.I. be part of it, which was very much not the case even 12 months ago. And that’s a direct, to your point, that’s a direct consequence of the open sourcing and kind of this knowledge spreading out. The second thing was the hardware cost, and there, again, the cloud, A.I. in the cloud — just the existence of the cloud is bringing down hardware costs across the board, but A.I. in the cloud is gonna bring that down even further. And by the way, these trends all slam together. So, you get what I think, in a year, is gonna be very common to these sort of A.I. supercomputing chips, with A.I. algorithms in the cloud available to anybody for a dollar, right? And so, there’s gonna be this massive deflation of hardware cost on that side. These big datasets are interesting.

Ben made the case that the startups can assemble big datasets, and I think that there are, certainly, examples of that. We also see another thing happening, which is the newest generation of experts in deep learning, or many of them are specializing in the idea of deep learning applied against small datasets. If you talk to those folks, what they’ll tell you is — [what] they’ll basically say is — primitive and crude deep learning require big datasets, but the really good stuff doesn’t. Small datasets are fine. And so, that’s still very early, but it’s extremely enticing. It’s an extremely enticing idea, because it really brings a lot of these problems, to your point, further into being tractable for small companies.

But actually, one of the things you can do with these — especially with these GPUs, is you can literally use the same tools that are used to make video games, and you can create simulated versions of the real world, and then you can actually let the A.I. train inside the simulation. And so, if you’re building a new self-driving car, or a drone, or something like that, you can actually create simulated worlds in which there are everything from earthquakes, to floods, to, you know, thunderstorms, hailstorms. You can create birds, swarms of birds. You can literally simulate the real-world environment, and then you can let the A.I. actually train inside that world. And actually, it’s funny. The A.I. actually has no idea it’s training in the virtual world. It’s learning just the same as if it were learning in the physical world. And so, again, for startups with access to cloud-based A.I., you could potentially run, basically, millions of hours of simulated training at very low costs, and all of a sudden catch up to big companies.

Ben: Interestingly, you know, the very famous A.I. project that Google did with DeepMind, that whole dataset came from the game playing itself. So, you know, it wasn’t some dataset that Google had collected over 20 years. It was the game playing itself.

Sonal: So, you guys have both mentioned simulations a few times. Why are they so important? Because I feel like there was this period, like, you know, maybe even a decade ago, where simulations were almost frowned upon as this promised thing that didn’t really actually deliver in what you needed to be able to navigate complex environments in real life.

Ben: Yeah. Well, it’s interesting, so was A.I. — was frowned upon 10 years ago, saying it was all — it didn’t work. I mean, particularly, neural nets and deep learning were the most frowned upon area. And there’s been similar, kind of, breakthroughs for simulation, first of all. So, if you think about the field of data science and what you do with data, you have a giant set of data, which is always historical in nature, and you can analyze that. And maybe it’s predictive of the future but oftentimes, it’s not. We see this, in particular, in things like really dynamic things, where the past affects the future, like, say, stock picking or the weather, or other kinds of things where data analysis doesn’t get you an accurate answer. Simulation is the flip side of that, where you can say, “Okay, here are all the entities in the world, and let’s generate their behavior over time,” and then their actual behavior feeds back into the simulation, which is critical — you know, a critical component.

Historically, that’s been difficult at scale, but there have been some really important breakthroughs lately, particularly from a company that we’re invested in called Improbable, which is able to do very large scale scale-out simulation, you know, using cloud computing techniques and some very important new technology that they’ve developed. And so, you can get a really complete picture of the world. And as Marc was saying, you can actually generate your own dataset, rather than collecting it for certain kinds of situations.

Marc: Yeah. Let me add one thing to that. So, one way to think about it is it’s expensive to make things happen in the real world. Like, it’s expensive to change things in the real world, because the real world is physical, and causing physical changes to happen — I mean, everything from building roads to flying planes, all these things are very expensive. And then things in the real world — changes have serious consequences, right? And so, you know, depending on where you put the dam, or where you put the airport, or what your evacuation plan you have for the city if something bad happens — like, you know, these decisions have huge consequences.

Ben: Which banks you bail out.

Marc: Which banks you bail out, which banks you don’t bail out. And so, you always have these consequences, and people who have to make these decisions are often flying blind, because they don’t have any real sense of what’s gonna happen as a consequence of their decisions. In contrast, if you can simulate a world, and if you can run an experiment — if you can simulate the real world or some portion of it, like the highway system, or the banking system, or whatever, and then you can basically introduce change into that simulation, and you can see what the consequences are — it’s very cheap to do that because Moore’s law, the collapse of chips, and the rise of cloud computing, all these other things we’ve been talking about, all of a sudden, make it very cheap to run these simulations. It’s much cheaper to do it in a simulated world, and then there are no consequences. You run a simulation and everything goes, you know, wrong, and everybody dies, or the entire financial system collapses, or whatever. It doesn’t matter. You just erase it and you run it again.

Sonal: Yeah. You have infinite testability.

Marc: Great. Yeah.

Ben: I wanna challenge that. There is Elon Musk’s simulation, in which case, the consequences are quite dire.

Marc: There is a scenario that we’re all living in a simulation…

Ben: Right, we’re living in one.

Marc: …in which case, I would argue it’s gone badly awry, as evidenced by the current political situation.

Ben: There’s no do-over button in this simulation.

Marc: Yes. And then you, basically, again, you look at the progress of Moore’s Law and the rise of these new technologies, and you say, “Okay, how about instead of running one simulation, let’s run a million simulations, or let’s run a billion simulations? And let’s try every conceivable thing we can possibly think of, and let’s imagine — let’s literally model all potential future states of the world, and then let’s decide which one of those — which path is the one that leads to the best consequences.” And so we can then make these very big real-world decisions with a lot more foreknowledge of what will unfold afterwards.

Real-world applications for technology

Scott: Maybe just to get concrete on some opportunities, what are the other areas in — maybe it’s life sciences, or what are some of the other kind of more tangible areas that you think near-term, as you think about kind of deploying this fund or beyond over the next, you know, 5 or 10 years that might be interesting for, you know, people to think about in the context of real-world applications of this technology?

Ben: Yeah. So, as Marc was saying, we’re coming into this era of new platforms, and with the intersection of health and computer science, what we’re seeing is really exciting new platforms around data and around, basically, you being able to get much more information about someone’s health from a variety of techniques that had been developed, you know, based on the, kind of, historic breakthroughs and sequencing the genome. But beyond that as well, where we can get really, really powerful data about people and understand them better. And once you have that data about people, wherein you can be predictive of diseases that they might get or things that are wrong, and you aggregate that into a platform, then you can actually make new scientific discovery off it as well. So, that’s one interesting area.

If you think about the A.I. platform itself, one of the things about it is the hardware that’s been built for it, or that’s been built historically, is for a completely different kind of computer programming. And we’ve seen Google already announce a chip to power their deep learning cloud. And you know, similarly, there’s new breakthroughs in quantum computing, which, at least on the surface, look like they may be very promising for much more powerful deep learning systems, and so forth. So, there’s a lot of things that are coming out of these platforms. And then, you know, as we get to chip and everything, the platforms to run and manage and understand those chips are equally as exciting.

Sonal: So, you know, one of the themes that’s come up through here is that tech is reaching into places it never did before. I mean, every company is becoming a tech company, or they have tech inside. Or, as Benedict likes to say, “Tech’s outgrowing the tech industry.” The reality is it’s permeating everywhere. And the question I have for us is that we are founded on this thesis that software is eating the world, that’s our premise. And yet we seem to have been making a lot of hard investments, you know, if you count things like Soylent, Oculus, Nutribox. So, are we changing our thesis about hardware as a result of this software eating in the world?

Ben: No, I don’t think so. I mean, I think that what we see with the companies that you’ve named are interesting. So, Oculus, I think we would all agree that the software component of Oculus is both more complex, has many more people working on it, and is kind of the core of the investment. Sometimes, if you have a breakthrough technology, then you require new hardware to actually support it. And that’s the case there. And I think that Soylent and Nutribox, both of them apply computer science techniques and information technology to get people to optimal health, and that’s what we’re doing there. So, I think we’re big, big believers that, you know, in the last 100 years, the great breakthroughs in knowledge have been the breakthroughs of people like Alan Turing and Claude Shannon, who gave us a new model of the world and how to understand it. And companies that build on that fundamental knowledge breakthrough are what we’re about, and we’ll continue to be about that.

Marc: Even if some of them may ship their products in a box.

Ben: Yes, a package is not a technology.

Scott: Let’s talk a little bit about SaaS. As you’ve probably seen, there’s been actually a bunch of acquisitions in the space recently, but what’s left to do there? So, is the new platform the salesforce.coms and others of the world, or are there actually both, kind of, vertical applications and/or are there other platforms that actually might exist over time in that market?

Ben: So, there’s SaaS as the metaphorical in-the-cloud version of all the stuff that we had built over the previous, you know, 30, 40 years. So, that’s, like, Workday, Salesforce, SuccessFactors, you know, the kind of big categories. The thing that we believe that’s changed as you go from on-premise to the cloud is, the technology is so much easier to adopt that we’re now seeing software applications for things that you just would never do as a software application, because the cost of — as we used to say in the old days, screwing it in, and paying the army of eccentric consultants to get it going — just wasn’t worth it for, say, expense reporting, which, you know, Concur, of course, built a really powerful product in that.

But, like, there was no packaged software for expense reporting in the same way that there is now. And I think there’s a gigantic number of categories in everything that you do in business that can be automated in that way. In addition to that, you can scale down to very, very small companies. Companies below thousands of employees never bought Oracle Financials. It would have been insane to do so. But they’re absolutely buying, you know, NetSuite and things like that. And then beyond that, now it becomes economical and very interesting to build vertical applications for industries. So, to build an application that revolutionizes, say, the real estate industry, or something like that, or the construction industry, is becoming extremely viable. And not just as a niche business, but as a real venture capital-based kind of activity.

Marc: One of the consequences that will be interesting to watch play out is that, historically, enterprise software has been described as represented by companies like Oracle, SAP, IBM. Like, that stuff was really only accessible to the largest companies, the top 500, 1,000 companies in a country. And then, in particular, only in a handful of countries. Those businesses, their revenue and their customer base have always been dominated by, you know, 2,000 or 3,000 companies globally that are these, you know, these giant multinational companies that we’ve all heard of. So, big companies had this sort of inherent advantage versus a lot of midsize and small companies, and then companies in the U.S. and Western Europe had this big advantage versus companies in other parts of the world, where the large companies and the large companies in the U.S. and Western Europe could just afford to make technology investments that small and midsize companies all over the world couldn’t make.

The sort of changes in SaaS that Ben described, they lead to an interesting conclusion, which is it may actually be interesting for a smaller company, or a company not in the U.S. or Western Europe, to be able to adopt the next generation of SaaS and cloud technology. It’s almost like, the folks who’ve been able to skip landline telephones and just go straight to mobile phones. You can just leapfrog the old stuff because you never had it, and you can just start using the new stuff out of the box. And then the big established companies might have a harder time adapting, because they’ve made these giant investments in the old systems, and it’s hard to just jump to the new thing. And so, there may be a power shift happening from, on the one hand, large companies to small and medium companies that can now more aggressively adopt technology faster — and then from companies in the U.S. and Western Europe to companies all over the world that can also do the exact same thing. And so, at the very least, a leveling of the playing field and possibly even a national shift in balance for small and midsize companies all over the world may all of a sudden get a lot more competitive.

Scott: So you’ve got, kind of, democratization, on one point. And then, to your point, there’s one version of internationalization, which is adoption across international communities. So, how do you think about, then, the other aspect of internationalization, which is company formation? Should we, then, expect to see more new company formation outside the U.S., partly as a result of some of these trends? And why won’t we see or will we see 50 Silicon Valleys, you know, over the next, you know, 20, 30, 40 years? And how do you all think about what the strategy should be vis-à-vis those opportunities?

Ben: That would be probably the most amazing thing for the world that could happen in the realm of business and economics. So, we’re hoping for it, and certainly, building — kind of, help trying to build technologies that would facilitate it. And I think the world has never been kind of more ripe for that kind of thing. Having said that, look, there are real network effects, geographical network effects, and Silicon Valley, obviously, has the biggest one in technology. And you always have to keep in mind, and this is something that gets lost, is — there are no local technology companies, right? There’s nobody who sells, you know, internet search to Wyoming. That’s not, like, a viable thing. So, when you’re competing globally, it does matter, you know, “Do you have the best people? Do you have the best executives? Do you have the best engineers? Do you have access to money?” Like, all these things become real competitive things. So, we still are believers in Silicon Valley, and we’re very hopeful that the rest of the world grows and that we can, you know, participate in that as well, but that’s TBD.

Marc: There’s an interesting macro kind of thing that’s happening. You know, one of the really, kind of, negative stories is that there’s, basically, the world is starved for innovation and growth. One of the data points you point to on that is, there’s now $10 trillion of money being held in government bonds, governments all over the world, trading at what’s called negative yield. This is literally, like, the equivalent of a savings account where, instead of a bank paying you interest, you have to pay the bank interest to hold your money. And so, there’s literally $10 trillion of capital parked around the world that is actually losing money as it sits there, which means people cannot find enough productive places to deploy capital.

The conventional view, if you just pick up the newspaper and read the economics section, how horrible this is and how it means the world is just starved for growth — the optimistic side of it is there’s $10 trillion of money sitting on the sidelines waiting for something productive to be done with it. What could be productively done with it, right? New kinds of health care, new kinds of education, right, new kinds of consumer products, new kinds of media, new kinds of art, new kinds of science, you know, new kinds of, you know, self-driving cars, new kinds of housing, all these things that need to be done all over the world. And so, the world has never been more ripe for a, you know, very large wave of innovation that would actually be quite easy to finance.

A lot of the time, you just can’t get things done because you don’t have enough money, right? That’s just kind of the constant state of the world for a very long time. And now, ironically, we live in a world where the opposite is true. There’s actually “too much money.”

Ben: Yeah, more money than ideas…

Marc: More money than ideas.

Ben: …which really can’t be true.

Marc: It can’t be true, right.

Ben: You have to unlock the ideas.

Marc: Human creativity is boundless. And so, if you can get more smart people around the world educated, and with the skills required to do these things, and if you can get them in environments, either create new environments to do that or figuring out how to get more of the people from other places in environments where they can do new things, we could do all kinds of new things, globally. And that’s something that we hope to contribute to, but I think is a very big opportunity for the world.

Scott: And so, do you think we’re getting to the point where it’s kind of geopolitical risk and rule of law issues that limit adoption or deployment of some of these new technologies in other countries outside the U.S.? It sounds like it’s less so technological advancement.

Marc: Well, I would say there’s bad news and good news. So, the bad news is, we frequently have delegations of folks coming into the valley from all over the U.S. and all over the world. And they basically come in, and its economic delegations, of different kinds of politicians, or whatever. And they come in, and they’re like, “Okay, what can we do to have our own Silicon Valley?” And then you kind of sit down with them, and you kind of go through, you know, ABCDEF, all these things. “Well, you want rule of law, you want ease of migration, you want ease of trade, you want deep investments in scientific research, you want no non-competes, you want fluid labor laws to let companies very easily both hire and fire, you want the ability for entrepreneurs to be able to start companies very quickly, you want bankruptcy laws that make it very easy to move on and start another company.” And at some point, the visitors give this stricken look on their face, and they’re like, “Whoa.” At the end of it, they’re like, “Okay, but, like, what if we want Silicon Valley but we can’t do any of those things?” And so, that’s the bad news.

Ben: And they can hire Donald Trump to run their country.

Marc: It’s ironic that we have this guy running for president who would seriously move us backwards on a number of those topics. So, even we struggle with these things, right? Like, I would argue, the formula is fairly well known. It’s just, people do not want to apply it for reasons that have a lot to do with politics and have a lot to do, you know, with other issues. The good news is it can be done, and then the other good news is it is happening, and there are very, very, very exciting things happening throughout much of the world. There are, you know, very active now startup scenes all through, you know, South America, Brazil, Argentina, Buenos Aires. Amazing things are happening in India. There’s all kinds of startup activity throughout the Middle East. There’s startup activity now throughout Africa. There’s, you know, obviously, China’s been a gigantic success story. Korea has all kinds of interesting things happening. So there are lots and lots of extremely positive early indications of what’s possible in many places all over the world. That said, there are very big political questions about whether or not those founders are gonna be able to operate in an environment that’s willing to let them succeed to the level that they should be capable of doing.

Ben: A big reason that we raised the fund and are excited about the fund is, it is a backing of our core belief system here, which is, we believe in the creativity, and ingeniousness, and intelligence of human beings and the entrepreneurs that we see and come to Silicon Valley and around the world. And we believe that these people absolutely have the ability to change things, and are changing things. And there’s plenty of room to improve the world, and there’s plenty of ideas to do so. And that’s really what we’re about with Fund V.

Team-building philosophy

Scott: So, let’s talk a little bit about, kind of, company-building and founders, in particular. So, you know, undoubtedly, you had a very distinct view of what types of founders you wanted to back when you started the firm, now, seven years ago. How has that evolved, if it all, over time? You know, what has changed either in terms of the types of founders you see, or the types of qualities you see that actually make founders successful, that’s caused you to either augment or rethink some of the initial, you know, foundations for the firm?

Ben: You know, I think a lot of the things — we had this great advantage when we started the firm that, you know, we, ourselves, were founders. I think that we’ve probably gotten, I would say, more risk-tolerant in our view of founders over time, even though sometimes…

Sonal: Wait, what do you mean by that? What do you mean by getting more risk-tolerant?

Ben: Well, we have this thing we say at the firm, which is we’re much more interested in the magnitude of the strength than the number of the weaknesses. We always believe that intellectually. I think that some of the number of weaknesses were fairly terrifying early on, just because, you know, you do have a lot of founders with a very small amount of experience these days, which is also, you know, part of their strength, in that it’s hard to rewrite the world if you’re too steeped in the world.

And so, I think, over time, we’ve kind of doubled down on that. And really, the founders who have figured out something really important, or who are true geniuses, or have will to power that we can’t even contain in the room — when they bring those things to the table, whatever is wrong with them, we tend to overlook and work with them on that. And if they’re strong enough in those areas, you know, the really interesting thing for us has been those weaknesses do go away pretty quickly. And that’s probably the biggest learning, is I’d say, we went in thinking that, but we’ve gotten even more extreme in our commitment to that kind of philosophy.

Scott: So almost in financial terms, you’re buying volatility to a certain extent.

Ben: Well, I think buying volatility, in the sense that we’re buying people who have world-class strengths where we care about them, and regardless of whatever else. There is volatility in that, but you can have a different kind of volatility. You know, you can have people who have gigantic weaknesses that are spectacular without having the strengths. And we’re not trying to buy that kind of volatility.

Sonal: How do you know, though, that they’re going to be the ones to actually build the companies that scale? Because there seems to be this inflection point, where the very thing that makes you a founder that’s gonna punch through this tough industry, is also the thing that’s pretty much gonna hold you back from really building your company in a really meaningful way if you think you can do everything, you know, your way. And there seems to be an inherent contradiction in that.

Ben: I think that that would be right if founders did not evolve. So, I think what…

Marc: And some don’t.

Ben: And some don’t. And some don’t. Like, some don’t and may get stuck, and they can’t get past that point. But you know, it’s a real common characteristic in great founders that they want to know absolutely everything about the company and how it works, and, you know, every knob and every button. And they really would, like, have a strong desire to actually be able to do every job in that company themselves, if it came down to it. But those kinds of founders also have great ambition, and it’s very logical and easy to understand that there’s never actually been a gigantic long, you know — a really important long-lasting company that had, like, five employees. Those just don’t exist.

And so, if you’re gonna have to have a bigger company than that, you have to think about the company not only, you know, from the scale perspective, but from the perspective of the people working there. And how are you gonna get great people to work with you if you’re literally making every decision in the company? And I think that not every founder can let go of that, and sometimes it’s a psychological flaw rather than a desire for greatness. And if it’s a psychological flaw that they can’t overcome, then, you know, it’s just like any flaw that any of us have — you know, where we can’t stop eating ice cream or whatever. And you know, there’s nothing we can do at that point. Like, we can give them the logical explanation, but they’ve got to fix themselves.

Scott: One of the things that we’ve seen even in the short time that the firm has been in business is companies staying private longer, or taking a longer time to IPO. What are some of the implications of that on the company building process? How do you, kind of, balance that new reality, if it is a new reality around how companies stay private, with how you think about building management teams and other issues around the company?

Ben: I think this gets back to probably one of the more neglected parts of company building, which is, like, “What is the company culture? What does it believe? What’s our way of doing things, you know, when we come to work every day? What does quality mean? How do we prosecute an opportunity, and the kind of philosophy, onboarding, training into that culture, and so forth?” And so you kind of have to develop a philosophy. Like, what kind of employees do you want? How do you want them to behave when they get there? How do people contribute?

Scott: As we’re getting close to wrapping up here, what would be one piece of advice that you might give either from a management perspective, from a go-to-market perspective? What would be a takeaway for people listening to this podcast?

Ben: From a management perspective, I think the most common mistake that founders make is, they make decisions based on — management decisions and organizational design decisions — based on very kind of proximate perspective. So, what’s my perspective, what’s the person I’m talking to’s perspective, what’s my HR person’s perspective, without, like, taking the time to go, “Okay, like, how does everybody in the entire company see this decision, and how will they see it once it’s made? Is it motivating people in the way that I think it will? And let’s look past the person I’m talking to feeling good about what I’m saying, and really make this for the long-term health of the organization.”

Marc: Yep. The single biggest strategic piece of advice we just see across all of our companies is, literally, people just need to raise prices. People need to charge more for their products and services. The good news is you have all these new founders with many different backgrounds who have come in, many of them have never run companies before, run salesforces before. And so they have these extremely sophisticated views on things like products and design and engineering, and then I think, in some cases, relatively naive views on how to actually prosecute a campaign to be able to get the world to use your product. And so, the temptation we see from many founders is to have a one-dimensional view — what I call a one-dimensional view of the relationship between price and volume. Which is, if I price my product cheap, then I sell more of it, because the assumption is just that people just make purchase decisions based on cost. And so, you drive down prices, you drive up volume. And by the way, a lot of the history of the tech industry, like the chip industry, is “drive down prices, drive up volume.”

But a lot of startups really suffer from having that view. Instead, we encourage companies to adopt what I call, kind of, the two-dimensional view, which is the advantage of raising prices. Actually, there’s a couple of advantages. So, one big advantage — if you raise prices, you can afford a bigger sales and marketing effort. A lot of companies have prices that are actually too low to be able to mount the kind of sales and marketing campaign required to get people to ever actually buy the product. And I call this the “too hungry to eat problem,” right? I’m not selling enough, but I’m not selling enough because I don’t have the sales and marketing coverage required to actually get the product out there, and I don’t have that because I’m charging too little. As a consequence, I’m not selling any despite my low prices.

The other really interesting thing is that, for a very large number of products, it turns out, if you charge higher prices, the customers take the product more seriously. They impute more value into it when they’re making their purchase decision. And then once they’ve purchased, they’ve made a bigger commitment to it. And in particular, anybody selling anything to businesses, businesses will take something that they had to pay a lot of money for a lot more seriously than something that they didn’t have to pay very much money for. So, you can get a much higher level of engagement and stickiness, and actually use of your product, if you charge more. Going through this, this definitely has felt like swimming upstream for the last several years. We see some glimmers that more folks are starting to figure this out.

Sonal: Okay. Well, that’s all we have time for. I think this is the first time I’ve actually had all you guys together on the podcast since we did our fifth anniversary podcast a couple of years ago. Kind of amazing how much has changed even in that short amount of time. So, thank you. Thanks, everyone.

Marc: Thank you, Sonal.