Transcript: James Cham on Venture Capital, Risk Taking, and the Future Impacts of AI – Episode #12

Steve: Hi, this is Steve Shu and this is Manifold. Our guest today is James Cham, a venture capitalist at Bloomberg Beta. Cory couldn’t make it today, so it’s just me and James. We got to know each other I think, starting many years ago through a kind of unstructured Silicon Valley meeting that has become popular.

Steve: These meetings have often they’re broken into sub-meetings through a kind of self-organization process. The sub-meetings are often on all kinds of wacky topics ranging from how to raise your kids in the digital age, to what are the five best books you’ve read in your life, to how do you fix your failing startup.

Steve: In all of those various kinds of discussions, I’ve always found James to be a very insightful guy who asks really good questions, and also has really unique perspectives and insights. I actually think of him, no offense to other VCs, as one of the most thoughtful VC friends that I have. I always like to get Jame’s perspective and input on-

James: That might be a low bar, might be a low bar.

Steve: Not just things related to tech, but things related to life or the future or almost anything, culture. Let me start James by asking just a little bit about your life history, which actually I don’t really know that well. Where did you grow up?

James: I grew up in the San Gabriel Valley, which is just east of downtown Los Angeles. My folks lived initially in Montebello and now they live actually fairly close to Caltech.

Steve: Wow.

James: They moved after I graduated from high school. While I was in high school, but I do remember sneaking into Caltech and playing with various nexus machines because of course one, Caltech had notoriously bad security probably by design and all the best computers.

Steve: What high school were you at?

James: I went to a great public high school called Montebello High School.

Steve: Wow, the area of L.A. you’re describing is where about half of my cousins live ranging from Pasadena to Hacienda Heights to Pamona.

James: Sure.

Steve: It’s like a Chinese, not really ghetto, but Chinese area of Los Angeles.

James: Yeah, the part that I grew up with was just south of that. Montebello at the time was probably 60-70% Latino. That was post-migration in the 70s and 80s.

Steve: When you were a kid in high school, what did you want to do? What were your life aspirations?

James: My mom found this a few years ago. When I was in the fifth grade, which would have been I guess whatever, 1984 or ’85 I wrote a report about what I wanted to do when I grew up. In that report, I lived in Silicon Valley and I worked, this is not a lie. I worked as a developer evangelist. I wanted to convince other developers about what frameworks they should use. This was an oddly specific thing. I think that was probably because I must have read something by Guy Kawasaki or something.

James: Yes, that was my initial dream and then when I was in high school, I was very interested in journalism and was quite involved in the school newspaper. Then when I went to college, I sort of fell back into programming, because honestly there’s nothing quite as satisfying as software development. Nothing quite as satisfying and maybe nothing quite as maddening, but there’s that joy in actually getting something to work. I was a CS major in college.

Steve: I’ve heard it said many times by my former students and colleagues who are really active software developers that there’s nothing cooler than code because you’re creating your own little universe and you get to control what happens in that little universe and only you created it at the end of the day. I think I definitely understand where you’re coming from.

James: Yeah there was definitely a way in which I was quite pretentious in high school and the early part of my college career in part because I felt like computer science was the only honest science because it admitted that everything was entirely human made and everything was pragmatic as opposed to pretending that something came from some grand design as opposed to something that people put together.

Steve: Right. Now as a physicist we could have debates about-

James: That’s why I said that-

Steve: Primacy of computer science versus physics. Though the physics attitude is like, “What are these guys doing? They’re just writing little execution lists? Algorithm is just a little set of instructions. They’re not really getting at the deep reality of nature and things like this.”

Steve: Let me leave that there for a moment. You went to Harvard, which many people think of, maybe me too as perhaps the greatest university in the world, but it isn’t really the greatest computer science department in the world. Given your already existing interest in computer science, why did you choose to go there?

James: I probably would have made it, to be honest I would have made a different decision if I thought I was going to major in computer science when I actually attended. I think this was a case where I thought I was going to do something either I was going to be premed or I was going to do journalism. I spent a bunch of time in the school newspaper. I was one of those kids where I sort of fell into it sophomore year.

Steve: After Harvard, you worked for a while and then you went to the Sloan School to get your MBA? Right, you’re an MIT Sloan MBA?

James: That’s right. For those, I actually don’t know your audience, the demographics of your audience, but there was this time in the late ’90s when there was a little boomlet that’s nothing like the boom that we’re in now. I was a software developer in Boston at that point. Then everything fell apart. Everything fell apart in 1990-91, I mean 2000-2001. Then of course 9/11 happened. I was part of a start-up that was failing and got saved by being acquired by a large unnamed corporation.

James: That was a terrible experience, but I will admit that the most maddening thing about it was a felt there was a secret language that the people who weren’t building stuff would speak in and a secret language around economics and accounting. The best way, at least for me at the time was to escape to business school. I really enjoyed Sloan. I probably took it a little bit too seriously because [inaudible 00:06:52] I actually took no economics courses or accounting. Harvard doesn’t offer accounting. I took none of those courses as an undergrad. I fell in love.

Steve: It’s funny because at the meetings where I often hear you talk and ask questions I can tell you have an economics world view because you’re often analyzing things in terms of markets and market response. I would have guessed if someone had just asked me, “Oh yeah, James took some economics courses at some point in his life because he knows the lingo and all the specialized terminology.”

James: Yeah, that’s all from business school and world economist afterwards, but I do probably come at with the enthusiasm of a convert rather than someone who was forced to take act ten.

Steve: So I think you went to business school with a friend of mine named Michael Ibrahim? Am I correct?

James: I did indeed. I did indeed.

Steve: So just a shoutout to Mike Ibrahim. Mike did a PhD in theoretical physics as Yale. That’s how I got to know him. I was on the faculty when he was a graduate student. He joined my first startup called Safe Web where he was the CTO for a while. Mike is one of these guys whose just got an incredible amount of ability and he can work like a horse. So he played a big role in that startup before he went to Sloan. Just by coincidence I guess we both know Mike.

James: That’s right.

Steve: Okay so after business school, how did you get into Venture?

James: During the summers and my intention was to do something software related and at the time I thought I’d want to be a product manager, but I actually ended up returning to L.A. for family reasons and I worked at Boston Consulting Group which I enjoyed. Then as it is in life, a friend of mine actually poked me out of the blue while I was just on paternity leave actually saying that I should talk to this VC firm in San Francisco called Bessemer because they were looking for someone to join and he thought I’d be a good fit. He was right. I really enjoyed it. There’s a way in which investing matches a lot of the things that I liked and that I’m good at. There’s this sense in which you get the constantly look for the novelty of the new and see what’s different and so that’s a lot of fun. There’s a way in which the problems are varied.

James: That’s a lot of fun. There’s also a way in which you get to talk to lots of people. I didn’t really have an extrovert focused job for most of the first half of my career, but it turned out I like people and I’m curious.

Steve: I want to delve into Venture and Venture Capital in some detail because here we have a very special beast [inaudible 00:09:43] Venture capitalist to talk to. I’m recording this from Michigan which is you could say an underserved area when it comes to Venture Capital. The whole Midwest is in some sense, even Chicago I would say. So what I often tell people around here and even sometimes when I’m talking to people internationally like if I’m in Hong Kong or in Berlin or places like that, I’ll tell people that this is a new aspect of the economy which is extremely central to having economic dynamism. If you want a pathway from the laboratory or from the individual inventor to the marketplace, you really need a mechanism which pulls capital and that capital is willing to take risk.

Steve: Risk on new technology, new business models, new attempts at organizing society. So that’s sort of what Venture capitalist do, but I think it’s a new enough phenomenon that almost nobody, like if you go to Modal University campus even Modal Business School and talk to a business school professor, they don’t really have a deep understanding of what all these people are doing and I think these people are playing a super central role in our economy.

James: How old is modern Venture Capital, 60 years old?

Steve: It really depends. So for example when I got to Silicon Valley in the late ’90s, we had that little bubble. The one that blew up around 2000, 2001 as you said, but just the old school where the VC would invest a quarter of a million dollars and take a huge chunk of your company and it was a hardware startup. That kind of old school Venture Capital there was still a lot of evidence for that around and so that had persisted I think for 20 odd years, but there were only a few funds and they all knew each other and it was from there that this huge thing exploded and kept exploding to become what we see today. When you ask how old it is, it existed as a very unique narrow thing for a while, but I think it really only got big in the late ’90s I would say. That’s my informal history of it.

James: Others have sort of deeper histories. How much of this is actually Venture Capital and how much of this is the reflection of the way that network software is able to grow and create new business models and expand extraordinarily quickly? It’s not actually clear to me right? I mean as a VC most of your listeners would have noticed that VC spent a lot more time talking about themselves these days and part of that is just because of the nature of marketing information. I think it’s all really good and the things we talk about in VC are really important, et cetera, et cetera. The only critical thing is that it’s great as long as the VCs don’t actually believe what they’re saying.

James: The Venture plays an important role in the ecosystem, but the core of what happens or why the economy grows a certain way is because of some series of unique characteristics about software which I think just can’t be underestimated and then also the fact that in Silicon Valley and a few other places you’ve got customers who are willing to try new things much more quickly than they would have before and I think that results in a bunch of benefits to the entire ecosystem.

Steve: I think you’re right in taking softwares maybe the sort of best model for the type of technology that’s involved here, but let’s not forget there’s also hardware. I think they were hardware startups really before they were even big software startups and there’s also biotech for example which is a whole different beast and absorbs quite a lot of venture investment right now. If we were more careful historians, maybe we would’ve prepared a graph that showed the percentage of free investment capital that was actually devoted to Venture as a function of time and then maybe we’d see some big uptick in the late ’90s that just continued to grow.

Steve: One thing I wanted to mention is that I think both Bessemer and Bloomberg the two funds that you’ve worked for, am I correct in saying they have only one LP? No they have multiple LPs.

James: No Bessemer’s roots go back to the Phipps family which of course sort of steal money, but by the time I joined I believe they started taking outside investors and by now they take a bunch of outside investors who are all clamoring to try to get inside Bessemer because it’s just an extraordinary fund. Then in the case of Bloomberg that’s correct. Our fund has only one investor Bloomberg LP.

Steve: Okay. I want to come back to the issues of LP. So for our listeners who are not really experts in this, LP means limited partner and those are the people who put the capital, put the millions of dollars into the Venture fund which the Venture capitalists then allocates to startups. So the Venture capitalist is in a sense a money manager and instead of buying stocks with the money that the investors give him, the VC is investing in shares of small startups.

Steve: Getting back to what you were saying, I think you were making some modest noises about the role of VC. One of the reasons why I can take the other side, of course as an entrepreneur I agree with you completely. It’s the entrepreneurs who do all the work and get screwed sometimes. As an entrepreneur I tend to be a little bit more anti-VC, but as somebody in the Midwest who works at a big university and tries to get technology from the university to the marketplace or tries to foster economic growth here in Michigan, I can see how important VC is because when you don’t have it, it becomes incredibly hard for the entrepreneur to make progress. Of course Silicon Valley’s become a magnet for really talented entrepreneurs so there’s just a plethora of people that are well suited and have the skillset and the risk tolerance to start companies, but there still are people like that in the Midwest and it’s just much, much harder for them even if they have a great idea and they’ve developed some great technology maybe with their PhD advisor.

Steve: It’s much, much tougher for them to get going because we lack a kind of critical infrastructure which VC actually is. So that aspect of it is large in my mind.

James: My partner Roy Bahat has led a bunch of trips, a mix of VCs and politicians through places that are under invested in Venture and technology. He just did one a couple weeks ago and I joined him on a couple. I think that that is true, but there’s a little bit of a causation ordering of things question where I think the VCs come when there are great businesses and the great businesses come where there are great entrepreneurs and people with lots of good ideas, but I have to be honest the thing that no one thinks about enough, at least I don’t think people think about this enough. People don’t talk about the [inaudible 00:17:00] side enough. I think that part of what makes Silicon Valley great was this massive over investment by the defense department in Silicon Valley that was driven partly by some very clever things that people at Stanford did and that meant that you could have this phenomena where you could have companies grow really quickly.

James: Companies grow really quickly because people buy things from them. That turns out to be I think underappreciated because when we visit a bunch of great towns all of them would have the signals of entrepreneurship and they have the talent. They have the cool coffee shop and the accelerator, but the ones that look like they’re doing really well had local customers who were saying you know what I’m willing to take a risk and get invested. I think we’re seeing that in some cities. We talk about China occasionally, but you look at China and you look at the adoption of new IT software frameworks in China and their willingness to basically move your entire workload to some new [inaudible 00:18:10] project that’s been around for six months because you just need it to work. That actually is what drives innovation, right? I feel like that goes under discussed and that’s part of the reason why, I don’t know you and I talk about politics occasionally.

James: I would vote for a president who made project management and procurement the center of their policy because I think that that’s the thing that slows down the economy and that slows down innovation. If that actually changed, we’d all be better off.

Steve: Did you mean specifically the way federal government, procurement, and project management work or meaning just emphasizing that skillset in the economy?

James: Emphasizing that skillset and making that the primary thing that people should fight about.

Steve: Yeah.

James: We should be fighting about, the New York Times should be covering the details or intricacies of procurement rather than the intricacies of some movie star that I probably don’t know about. I think if we did that, we’d all be better off.

Steve: Well I definitely won’t agree with you that as somebody who’s been involved in startups, I realize that there’s this whole skillset and time spent reflecting on that skillset which is how to go from zero to one. How to actually effectively build the team, how to decide on a strategy and what technologies to use to build the product, how to take the product to market. All of those things are very specialized skills which honestly I don’t even think you can really learn them in an MBA program. You have to learn them in the environment and so the places that have that and have lots of people floating around that have done it before and not just done it, but actually spent a lot of time thinking deeply about it.

Steve: I can remember this guy Mike Ibrahim that I mentioned before he went off to Sloan where you met him, we had spent years working together trying to figure out what is the right development process to get this done. What’s the right way to handle the hardware procurement for our clients and all those problems we had spent a lot of, and he’s a smart guy, a lot of smart guy hours just trying to figure out what’s the right thing to do. In Silicon Valley you can go to any café and you’ll just bump into somebody whose been through that and other parts of the world not so much, maybe Beijing. Certainly not here in Michigan.

James: That’s right. It’s a huge missed opportunity. There’s also a way in which one of the things that we haven’t figured out how to do well over social media’s so good at propagating information in so many ways. It’s not really good at propagating that class of things that are almost semi-secrets. They are things that are known, but not widely known and sort of figuring out how to do that. That feels like a missed opportunity because it’s certainly true that right down your street, Duo, why did Duo do so well? It’s in part because Doug and team had a bunch of roots all over the country that meant that they knew how to scale a software company differently than let’s say the brand new undergraduate.

Steve: Yeah you know I have a feeling that if you and I were sitting at one of these Silicon Valley meetings like we’re sitting around the GooglePlex and some young guy comes up to talk to us and inevitably just starts pitching the startup that he’s working on, that you and I would have a lot of, we wouldn’t necessarily agree on all evaluations of the things he said, but there’d be a lot of commonality. Whereas if it were a person from my team here in the research office at Michigan State or even a business school professor who hadn’t done a Venture, that person might have completely orthogonal or very, very different takes on what the guy said than I do. There’s that common knowledge and experience in what works, what doesn’t work, what’s likely to happen in the future and I think that that is the intangible thing that is very hard to instantiate. Do they have enough people in Pittsburgh who get that? Maybe they do now, maybe they don’t, but it’s still a long ways from Silicon Valley.

James: Yeah I mean you’re right. It’s a case where there’s a lot of unsaid assumed knowledge that is very hard to articulate clearly.

Steve: Yep. Now let me go through for the benefit of my audience a brief intro into Venture and I’ll give my own cartoon version of it and you just correct any stupid stuff that I say or just make it more nuanced okay? So my friend Joe wants to start his own Venture fund. Okay, Joe has been a successful entrepreneur. He’s got some of his own money. First thing he has to do though is attract some limited partners, some LPs to invest in his fund. So he goes out and meets with these guys and some of these guys are super high net worth individuals or maybe they run institutional money like they manage the endowment of a university of something. He says to them, this is my fund. It’s going to focus on some hot thing. Let’s say it’s going to focus on AI startups, maybe this guys says oh I’m going to focus on AI startups which really use narrow AI to automate factories or something. Something like this. He’s got some theme for what his portfolio’s going to invest in.

Steve: He goes out and tries to raise the money. Now when he gets some investors that are willing to invest, he has to basically create a legal entity which is a fund. The fund has a certain life cycle, right, and some schedule of dispersing the profits which presumably they’ll make by selling the shares in the company that they invest in or get from an IPO from those companies back to the investors. Say a little bit about how that looks to the guy who’s starting the fund? I’ve heard things like you’re not really ready to go unless you’ve raised 50 million, 100 million. The minimum amount it takes to operate a decent Venture fund. Can you just say a few things about that?

James: It’s interesting because Venture Capital is so opaque and hard to understand in many ways. The thing that’s even more opaque and harder to understand is the folks who invest in Venture Capital. The limited partners, the mix of family offices, and wealthy individuals and institutions. Each one of them have slightly different incentives. The person who actually has, you should do a call out to Viser Clarkson. She’s done a very good job of trying to increase transparency and allowing VCs to understand how LPs think. I think that the first thing to realize is that for most of the these types of investors, Venture is just a small part of their overall asset allocation. They’ve got a bunch of real estate. They’ve got a bunch of public market investing and there’s a way in which Venture is basically a small chunk of their portfolio in order to add a little bit of randomness and hopefully the randomness is in the positive side rather than the negative side.

James: Then for those investors, the Venture exposure is important, but not the critical thing. That’s the first part. Then I think as far as strategies for VC starting new funds. It entirely depends on the strategy. There are a bunch of folks who will want to deploy hundreds of millions of dollars or there’ll be a bunch of folks who will start these quote on quote smaller funds that will be about 50 million dollars and they’ll start off by investing in the single digit million dollars. So I don’t know that there’s a strong rule. So much of it depends on your strategy and the other thing that’s true about Venture these days is the range of investment now goes everything from investing hundreds of thousands of dollars to investing hundreds of millions of dollars. So we live in this very confusing and exciting world in which the private market. Innovation around the private markets, what to do with the companies who have yet to go public, that innovation and all the new ways of being exposed to that, that’s changed a lot. So that’s constantly shifting.

Steve: I’d love to get into soft bank and some of these other things a little bit with you maybe in a few minutes after we just finish this brief intro to Venture because it’s exactly what you said like what’s happening to pre-IPO unicorns and what is soft bank strategy? These things are all extremely interesting, but the floor for this sort of minimum fund size that I had in mind was that well you’re going to have to pay your team and you’re going to have to get your team from the, in the old days, I’m not even sure if this is still true. The old structure was two and 20. So you would get a management fee of two percent of assets under management roughly speaking. It could be lower. It could be higher.

James: Or higher for smaller funds depending. Just getting started you might make money doing other things as well. There’s a number of some of the interesting funds that we’ve seen that have been thematically based will sometimes make money by doing recruiting or by doing other services, or by taking a larger ownership stake in a company by helping to start it or something like that and I think that that dynamic you’re right is something. I’m a little reluctant to do a hard [inaudible 00:27:41], but you were right that the core economics of a VC firm. So how does a VC firm actually make money and earn its keep? There’s some portion that comes out of management fees and that’s a percentage of the assets under management that they get put into the pocket of the partners over the course of either five years or ten years and there’s what’s called carry, some percentage of the money that you make.

James: For the guys who made hundreds of millions of dollars investing in Zoom or something like that, some portion of that goes back to the partnership and the rest of it gets returned to the investors.

Steve: Right, so the number that I most commonly hear which is also the number for hedge funds, the numbers are typically two and 20. So two percent manage, and this is all just rough justice, but two percent management fee and maybe 20% of maybe profits or profits relative to some hurdle which is some benchmark. It may vary, but something like two and 20. The 20’s going to come years later maybe. The two is what you use to pay your team right now, and if you raise 50 million then you have a buck. You guys don’t say buck, but the hedge fund guys say a buck for a million.

James: The hedge fund guys live in such a different world.

Steve: Yeah. I’m unusual because I know tons of hedge fund guys from my physics background because most of the physics guys who left basically work at hedge funds, but I know a lot of VC guys from the startups that I’ve done. So I kind of know both. I can speak to both populations of people. There’s actually some crossover nowadays.

James: Oh yeah there’s a lot of crossover. Some of then most innovative Venture practices come out of guys who come out of public market where they’re saying wow, VCs you guys are such small fries and you’re unimaginative about the structures that you’re using to invest in things.

Steve: That’s exactly right. For the current startups that I’m involved in, I think there’s an even mix of traditional VC type guys and hedge funds that are actually investing as well. So it’s interesting because as you point out, their world views are quite different.

James: You’re right, the right way to think about it is there’s some base amount of money that Venture capitalists make every year in order to pay their bills, but the real hope is that at some point the companies that they invest in or some percentage of the companies they invest in will return so much money that it’ll make more money than the initial fund size and they’ll be able to return a portion of that to their investors and keep a portion of that themselves.

Steve: Right. Now to finish this intro I’ll describe what it looks like from the entrepreneur side since I’ve never worked as a VC. From the entrepreneur side it looks like this. The hedge fund is a team. There’s maybe a few partners and then maybe a slightly larger number of people called associates and then there are some lower level non- descrip people who are just I think getting coffee and doing other stuff. The thing the entrepreneur learns right away is that even if the associate likes your idea and has spent a lot of time with you, it doesn’t mean anything unless one of the partners is a champion for your idea to actually get the fund to invest in it. Is that a distorted perception that we entrepreneurs have or is that a realistic impression?

James: Yeah you just think about incentives right? The way that incentives work ends up being that the partner or the set of people who own some of then fund or a big chunk of the fund, a lot of their compensation reputation depends on the companies that they invest in and thus funds will have different ways of allowing investments to get approved and typically most funds will have some sort of mix of consensus or some vote taking method in order to get investment. Typically someone has to put their neck on the line to say I’m sponsoring this investment and my career will be made or broken based on whether this works or it doesn’t work. That will typically be someone who’s called a partner. It all gets confusing because of course many VC firms realize that startup entrepreneurs realize this and so they call everyone a partner.

James: My only observation about that is that is meant to make sure that good decisions made and that we are good stewards of our LP money. Sometimes that means that VCs overthink things or they spend too much time doing irrelevant diligence and that’s always a balance. The other observation is that VC partnerships are highly delicate structures that are designed to fall apart. You look at the history of Venture. There are very few institutions, very few outside Bessemers or Sequoia. Most of these funds fall apart because they’re made up of individual high ego partners who think that they know better and when they’re wrong they suddenly use a lot of political power and when they’re right their heads get big and fight. So I think that’s sort of the nature of investing and I won’t claim that the decision making is the soundest.

James: I’ve seen a couple of funds hire outside consultants or business school professors to try to improve that decision making process, but still not ideal.

Steve: It’s still got to be kind of an art and situations that I’m feeling are with you can have one partner that has maybe the best relationships with the LPs and so he can claim, hey I brought all this money in so I’m entitled to the biggest chunk of the two percent. Another guy could say, yeah but I’m the guy who is responsible for the most successful investments that we’ve made and so I deserve this. So obviously everybody’s got their own distorted sense of their contribution.

James: It’s worse than that because in public markets you can always know your score because for better or worse there’s some objective thing out there. In private markets, the only way to keep score is to try to keep track of some business metrics, but those business metrics may or may not represent future behavior and then you get the keep score by saying that someone else invested a slightly higher price, but that may or may not reflect the actual value of the company either. So the politics of representing how well you’re doing is confusing and opaque.

Steve: For sure you better be able to talk a good game. Otherwise, I don’t know any VCs who can’t at least talk a good game because then how is anybody going to sign credit to you unless you actually finally get liquid, like your company actually goes IPO or gets acquired and you literally turn it into cash.

James: That’s right.

Steve: Now in hedge fund world a very important and common term is alpha. So what alpha typically means is that for given level of risk that you’re taking in the firm with your investments, are you beating a portfolio benchmark with similar risk characteristics? So in other words, suppose you invest in just standard, just plain stocks that are in the SMP500. One could ask, hey are you getting return which is greater than the SMP 500 and if you do, then you have positive alpha. If you underperform, then you have negative alpha. So big issue in the hedge fund world is does my PM, my portfolio manage that we just hired, does this guy actually have alpha or is he just lucky? It’s the most fundamental question that you can ask in that area of finances whether someone really has alpha, can we actually measure alpha, are we just rewarding luck or people who talk a good game? Does that kind of discussion materialize as much in Venture?

Steve: I mean I feel like Venture is even more volatile and subject to very small events. I think Peter Teal once admitted that he made more from his single Facebook investment than anything else he’s done in his life and so how do you guys talk about the equivalent of alpha in the VC world?

James: Yeah I think you’re right. I think it’s very, very hard to figure out. I think that part of it is also remember Peter Teal’s point it’s worth unpacking what he just said there right because on the one hand it’s a small investment. On the other hand, it also represents why Venture is different. There’s a way in which you can not just make multiples on your money, but you can make orders of magnitude multiples on your initial investments. There’s a way in which the outcomes from Venture on the upside are sort of unbounded depending on the market that you get in and I think that informs a lot of the decision making and a lot of the ways that LPs treat Venture and that realization.

Steve: To give you a funny example, I had a good friend, someone I’ve known since undergrad who went the whole physics career but then ended up leaving and becoming hedge fund manager and he invested in text stocks for quite a long time. At one point, he left the multi-billion dollar hedge fund that he was actually at to start his own fund under his own name and to raise money, to get LPs to invest in his own fund, he once said to me. He said, you know Steve all I have to do is show them my tax returns because if I show them my tax returns for the last ten years they’ll see that I was making excuse me, a shit ton of money for my previous firm because otherwise I wouldn’t have gotten that compensation over the last ten years. So in their world, it’s very easy to demonstrate alpha in a sense.

Steve: It’s like I got compensated this much for running this fund for this bigger hedge fund entity. Now if for instance you were leaving your firm and trying to get a job at another firm or raising money from LPs what would you point to?

James: Your existing investments and the outcomes from those investments. You’re right, the nice thing about Venture is that these are family traded stocks. So in the case of a PM they might say they have a fairly complicated transaction log. In my case, my transaction log is pretty straightforward. I invested at some point. I invested a little bit more some other point and at some point something become valued at some point. That ends up much less complicated process. Then there’s also a way in which for better or worse, a starter hedge fund performer may do very well inside a system, but may not do well in other systems. While in the case of Venture, often times a lot of the best guys are better or worse solo practitioners and so I don’t know Bill Gurley’s ability to find a new investment is less dependent on benchmark than the other way around.

Steve: Yeah I sort of feel like in your world, this friend of mine wouldn’t have said anything like everybody knows me, they love me because he’s investing in public markets. So he would just say this is how much money I made for the firm last 10 years. Whereas, I think in Silicon Valley you might easily get hired because people say well the entrepreneurs really love this guy. They think he’s really smart. They value his opinion. They all want to run their ideas by him. He could get good deal flow. Even if the guy, that particular VC, didn’t have huge exits maybe they were still in the pipeline, he could still be a desirable quantity because he presents well and he just has a great reputation in the community. Does that sound realistic to you?

James: That’s partly just a reflection of the fact that in the public markets your ability to get access to investment is based on your ability to use a Bloomberg terminal and talk to your various intermediaries.

Steve: Yeah there’s no deal flow.

James: Right.

Steve: That’s not the issue right.

James: That’s right, that’s right. So the game is slightly different.

Steve: I think maybe I’ve given a brief intro, so these Venture guys they accumulate the capital that they’re going to invest. They need to talk to lots of entrepreneurs. They need to decide which ones they want to invest in. There’s typically a lengthy due diligence process, sometimes not so lengthy if there’s a bidding war. The startup is really hot and people just want to get their money in, but typically it’s a fairly rigorous due diligence process where they meet the investors, sorry they meet the entrepreneurs and they actually look at the technology and maybe they even talk to customers that are using the product. There’s a whole process there. I find in my experience it’s usually all the difficult lengthy work is done by the associates and the partner swoops in at the end and makes the decision about what to do. What does it look like from your side of things? What’s the allocation of time you spend on diligence, trying to get access to deal flow, talking to LPs, all those different activities?

James: Yeah I mean I think that so much of this depends on the stage you’re investing in right? When you’re investing in a company that has seven million dollars in revenue that’s been around for four years, it’s a very different thing than when I invest right now which I will invest when it’s three people there’s a promise of a market and it may or may not work out right. So then so much of my time in my world is spent assessing the team. Some is spent assessing the technology and the market, but a lot of it is spent just trying to figure out the team and their dynamics and whether they’ll work well together and then figuring out whether their belief about some new market or some new opportunity is differentiated or special enough that I think they’ll be able to break through the noise.

Steve: I think you and i maybe talk a lot about decision making under uncertainty and rationality, et cetera. Do you ever feel like you could be fooling yourself on your ability to evaluate people and teams and that in fact your “alpha” in that respect is a bit illusory?

James: Oh my god all the time. All the time right? I mean there’s also a little bit of a rooftop [inaudible 00:41:53] like George Soros reflexivity thing here where to what extent is my belief going to mirror the belief of either potential buyers or employees or future investors? There’s a way in which I’m constantly trying to measure up. I guess this is a weird and subtle point. I’m trying to measure whether my biases match up with the biases of others rather than some objective truth, right?

Steve: Yeah.

James: You know what I mean? Entrepreneurs basically are creating something from nothing and there’s a way in which how they do that is a mixture of creating belief and creating the sub-system or something to believe in that both customers and employees and investors will follow.

Steve: I totally agree with what you’re saying and it’s related a little bit to Keynse’s old beauty contest remark. When you look at the team you’re deciding A, can they impress future investors? Can they impress future employees who will want to join their mission and their vision? Then a totally different thing is can they actually execute objectively to build the product because that’s actually a little bit different from can they impress investors. It is more related to whether they can invest impress the employees.

James: Then currently some of my worst investments have been cases where the individuals or the founders are very, very good at executing themselves, but they are unable to get other people to join and buy the mission. Steve Jobs was a terrible chip designer and Steve Jobs actually had relatively bad taste initially, but because he surrounded himself with a set of people who had good taste and were great chip designers and UI people and he was a good editor and he constantly made them feel bad enough about themselves that they wanted to work for him. He was able to create something amazing and I think that assessment it’s true. That’s a weird assessment to be making of people.

Steve: Right so we agree that there are these different aspects of the entrepreneurial team that you want to evaluate. I’m curious if you detect cultural differences. So for example if you go to east Asia, the standard level of aggressiveness and extroversion that Americans want to see from a CEO, is it any different in these other cultures because it seems to me like in some of these other cultures, a sort of standard Silicon Valley CEO would be regarded as a weird pathological person in these other cultures and that person would be evaluated negatively by people in that culture. Do you ever see anything like that?

James: I think that’s really hard for me to figure out. I come from a very specific context and time and I’ve had historically a difficult time than assessing different enough cultures. I don’t know it took me long enough to figure out whatever early 21st century American culture and [inaudible 00:45:13] and so I do hear you that expectations are very different and also practices are different, but I don’t know that it’s an aggressiveness, not aggressiveness. Don’t you find that some of these Chinese CEOs are incredibly aggressive and ambitious and quite extroverted in ways that I find uncomfortable.

Steve: They are. They can be way out on the tail just like a lot of the standard Silicon Valley guys that you meet. Let me give you an example of somebody that I think you and I both know. My former post doc who now is an AI, not just researcher but he leads a whole team for a Chinese company. I think here his English is actually pretty good. His Mandarin is actually not that great either because he grew up in Hong Kong, so his main language is Cantonese. In the U.S. people would not listen to this guy at all basically. He would come in the room and he’s this quiet guy and although he was very smart and well spoken, people just ignore him, but it just happened that the companies that he went to after he left physics to do AI related stuff were Chinese companies and there it seemed like people listened to him and then quickly realized this guy knows his shit. Basically he got quite a lot of responsibility right away and now he’s running a big team. So that did seem to be somewhat culturally determined, but I’m curious what you think about it.

James: Well I don’t know I think cultures are not static. One of the good things about the U.S.’s willingness to try new things and one of the best things about Silicon Valley is that when something’s working, then everyone talks and everyone says you know actually this thing is working. You’re an idiot if you’re not trying this. That extends to both technology practices, but also business practices and cultural expectations. You saw the way that I don’t know if this hit your radar yet, but Amazon requires people to write these six page memos. You know this practice?

Steve: Yes. Before a meeting?

James: Yeah it’s an incredibly elaborate process by which you get the six page memo with the appendix written down and presented and what’s funny about that is Amazon’s been doing that for a long time. At some point, enough people talked about it. The word got out and a bunch of my companies are doing it now and it totally changes the balance of power because now it is that quiet really good writer who suddenly has a lot of influence and this is something that’s changed in the last six to twelve months and it’ll be interesting to see whether that business practice will continue. To me, I’m reluctant to think of culture as super fixed or overdetermined.

Steve: Yeah I’m not saying that cultures don’t change, it’s just that at a given moment a particular culture like the one built by Bezos might be focused on rigorous analysis and writing plan. Whereas some other one might be totally cowboy it and get in the room and dominate the conversation and that’s how you get your idea accepted by just force of personality and the ability within a 90 minute period with nothing written down, just dominate the discussion. I’ve been in cultures like the second type as well.

James: Well I mean the second type is the dominant culture of most reality since human civilization’s been around.

Steve: If you go in a physics department or a team that’s building a big accelerator, there’s fa lot of detailed calculation and planning and stuff that will carry the day over.

James: I’m just joking on the fact that human beings.

Steve: Yes, it’s as old as people.

James: Historically and then we’re fortunate that we live in the civilization or the time and place where careful rational consideration sometimes occasionally might overrule the blunt [inaudible 00:49:08] guy right?

Steve: No I totally agree. I mean the big man culture is the oldest one probably right? The geeks analyzing things using abstract symbols to come to a non-obvious conclusion and then the team agrees that’s what we should do is very alien to the human species.

James: Incredibly useful.

Steve: I would actually argue that most of the really non-trivial things being done now require the second thing, the second kind of activity even though maybe the vast pools of money that power that second activity are decided in the old big man way.

James: Right at some point I think we talked about systems in which one bad thing destroys the whole system and systems with one single good thing ends up allowing the system to survive and civilization is a mix of both right? To the extent that we try to encourage a system in which the small good things accumulate rather than the small bad things kill all of us. We’re probably better off.

Steve: Yeah. So I wanted to come back to the Softbank discussion. So this is probably something that people who aren’t really kind of deeply into the industry, maybe don’t follow, but my perception is this guy Masayoshi Son who runs Softbank has developed a completely different kind of high stakes capitalism. He’s really a gambler in taking risks. He’s raised an order of 100 billion dollar type funds.

James: Then again, people may not have heard you say that. How much money again?

Steve: I think it’s at least 100 billion right?

James: That’s right which is I think that’s more money than will be deployed in Venture.

Steve: I’ve met people who are like I run the sovereign wealth fund for x and I control on the order of a 100 billion or maybe a trillion dollar type thing, but that’s very unusual and those guys are usually extremely careful and they parcel out the money to many, many different PMs. Whereas in this case, Softbank and this guy Masayoshi Son, 100 billion dollar, huge bets where he’ll go to some company like he might go to Lyft and say we’re going to give you five billion, you have to go big and you have to just destroy your competition with this five billion. If you say no, we’re going to go over to Uber and give it to them. Is that a caricature of the kind of thing that happens with those guys? Is that at all realistic? That’s what I’ve been told.

James: I mean I wouldn’t be surprised if they sharp elbows and a bunch of journalists who reported on ways that they created systems. So things like that. Although that sort of bet is only possible today and it’s possible to deploy that kind of capital today because everything is so connected and your ability to expand into different markets is really, really fast and the hope is that that continues to be a reality. That we continue to live in a world where things can grow very quickly and people can become important players in a short amount of time. Not clear, not clear to happen. It’s also not a clear what markets that’s true. If you wanted to deploy 50 billion dollars into real estate, not clearly you can do that. There’s something unique about the technology information tech centric companies where you can actually deploy that money and you can actually create an interesting dominant businesses in ways that you couldn’t have done before.

Steve: Right. So to unpack that a little, you can imagine if somebody gets ride sharing down perfectly or Air B&B platform perfectly they can unlock some huge market and it’s replicable from city to city and country to country to some extent. So it’s worth it making just a ginormous gamble that you’re going to dominate or be one of the main players in a space like that. On the other hand, then I go to some city and see millions of scooters clogging the sidewalks because somebody had the great idea that they were going to dominate the scooter transportation problem. So not all of the bets in this vein I think are going to pay off, maybe a few of them will pay off. I view this guy Son as almost like a performance artist with 100 billion capital.

Steve: He’s got these very strong beliefs and he’s like a gunslinger out here doing this stuff and nobody knows how it’s really going to turn out.

James: Yeah I mean the same way that 120 years ago you see JP Morgan making these claims and everyone relying on him and that mix of hoping he figured it out and also half the time thinking he’s full of it. I suspect that there’s a similar dynamic. You know how we’ll know, we’ll know 50 years from now.

Steve: Yeah I think Elon might be reaching a special moment soon right because he’s rolled SolarCity into Tesla and now Tesla, the investors seem to be souring on just giving him free money. So I can imagine a critical event happening with those guys at some point.

James: So I don’t know Elon Musk, but I think the thing about the business figures, I think we are better off when the business figures and the titles everyone talks about are more engineers and financiers. I also think that’s there’s a way in which I can’t predict whether someone will fail or succeed, but I much rather people be ambitious about sending someone to Mars or figuring out how to build certain types of machine learning tools than their ambition was to sell me more junk food.

Steve: I agree with you. I think we systematically as a society take too little risk. You can see if you just look at the incentives for most CEOs or university presidents, they’re not toward risk taking. They’re actually toward actually taking less risk than is optimal. Having people that are larger than life, few of them out there and some of them are going to crash, maybe most of them are going to crash and burn. I think net it’s good for society.

James: Yeah and I think taking that seriously is something, I don’t know how to do in a both popular culture and business practice because certainly there are lots of ways in which there’s lots of high profile failures, but I think the lesson, at least I want my kids to come out of it with is and therefore you should try great things. Rather than therefore you should settle for some wife of middling mediocrity.

Steve: Yeah I mean I’m trying to do that with my kids too is just get them used to the idea of taking more risk. They’re lucky because their dad is in a position to cushion them a little bit if the risk they take totally doesn’t pan out, they’re not going to starve. Other people are not so lucky. They’re not in that position right? They have to get a safe job and they have to hold that job otherwise they’re not going to survive.

James: It doesn’t have to be that way right? We can create systems to find talent all over the place and encourage that and for whatever reason we make it harder than it should be.

Steve: Yeah although I think America is certainly better than for example any European country in getting its younger people to take some risk, in China maybe because of their special history right now they have a generation of people that are really willing to take a lot of risk as well. There are plenty of countries where you go to France and talk to the average person and they don’t want to start a company. That’s the last thing they want to do. They want to get a nice job with the state in some elite capacity in the state and that’s their dream.

James: I mean I don’t want to over valorize startups although of course that is my business and I also don’t want to specifically make just the one thing that I do the best thing that everyone should do, but I think that that idea of starting new things in general is worth valorizing.

Steve: It doesn’t have to be fancy technology. I mean if you just go to a good micro brewery or restaurant, I just see a lot of innovation in the way that people are handling the food. I see innovation all around me much more than I thought I saw 20 years ago in America, so maybe I’m overly optimistic.

James: It’s funny someone asked me what I believed, so I’m like I go to church and I’m a Christian so I believe in Jesus and I believe in software. Those are probably one of the things that I really believe in. There’s a way in which software is magic and it’s incredible that we’ve been able to do so much with it. These are long numbers that we’ve agreed to break up in systematic ways to do things for us. It’s just sort of shocking that we all agreed to do it.

Steve: So James, is it software that’s magic or is it some applied physicist who got you a factor of a million in compute?

James: I’m very grateful.

Steve: Has software gotten a million times better in your life or is it hardware? Is it the semi-conductor physics that got a million times better in your life?

James: I’m very, very grateful. I’m very grateful.

Steve: I often joke with my software friends that you got a 10x right? Your coding tools and such, they’re sort of 10x better than when I was a kid, or you could even say they’re 100 times better, but are they a million times better because you literally got a factor of a million in compute during our lifetimes.

James: That’s right. That’s right and that is one of the shocking things. Are we at the tail end of the S curve?

Steve: Maybe. Yeah.

James: Right and I think a lot of people who are smarter than I am and who actually think deeply worry a lot about the end of Moore’s Law and I think that that’s probably fair. I don’t know, but at the same time I think that there’s so much catch up for the rest of society to figure out ways to benefit from that million, actually more than a million x, increase in capability and human institutions and people we’re still slow. That ability catch up to what the hardware guys had done and to create institutions and practices that actually reflect that as the overwhelming way of thinking, that’s going to take time and maybe finally we’ve got some time to catch up now.

Steve: I totally agree with you. I think even though just at the pure number of transistor features per dollar kind of thing, Moore’s Law is actually effectively dead right now. Nevertheless there’s tons of low hanging fruit that is still available with the compute that we have or the sort of slowly improving compute that we’re going to have. That low hanging fruit is still going to transform society. I mean just look at Air B&B and Uber for example.

James: I think that I used to think a lot about what’s new and now I probably think more about tech diffusion. Diffusion of are there new technologies, but also new techniques and ideas and I think that those things just move at a pace that is different so it’s worth looking all over the place to see where tare the new ideas that should be practiced by others and connections that need to be made.

Steve: Now you mentioned software is magic and I think your fund is machine learning AI focused, is that correct?

James: When we pitched Mike Bloomberg and team, the pitch was that we were going to invest in the future of work. It just so happened that there were a bunch of people who talked about how long it took for economic gains from the steam engine and electricity et cetera, et cetera to show up in the economy and it typically is two generations. Two generations of managers and because it takes time to figure out what to do with it. So the pitch was that we’re now 20 some years into the ubiquitous network computer and now we’ll figure out finally how to take advantage of it. So that was the original, pitch and then because we started about six years ago, that was just as all the big data projects were failing and people were trying to figure out what to do with it and machine learning and especially deep learning was making a resurgence and so in that way we got lucky thanks to happenstance and the vision of a couple of my partners. We ended up focusing a lot of our attention there.

Steve: I want to get you to make some projections for me about where, so let me see a couple of categories. One, what’s the most exciting impact of narrow AI, maybe toward work if you want automation or whatever it is in the next say decade or two and then secondly, what’s your projection for AGI?

James: Okay, so I have no idea on AGI. I go to this blog, there’s this blog

Steve: I hear that’s pretty good.

James: I hear it’s pretty good. I typically look to it for some projection on AGI, repeat at some later times, but on the narrow AI piece I think we are so early and I think it’s one of those cases where we barely have figured out the outlines of how to build and deploy machine learning models and they’re so many opportunities right now around that and to me the biggest sign in how early we are is the fact that there’s no straightforward framework for building machine and learning models and deploying them and so there’s no equivalent of agile or extreme programming. So we’re still mostly in talky talk world where people will pontificate rather than actually build and suffer. So you only see the building and suffering in a few places out there where people are actually depending on machine learning. That’s one part. I think the other part that you see right now around machine learning models and the way that you’ll know we’re so early is that people don’t have good economic models for talking about how to think about it. Mostly it is in fantasy world or macroeconomic world rather than microeconomic world.

James: I think that’s one of the biggest opportunities out there where we just don’t know and so a way to think about is about 40 years ago when we figured out we should split up hardware and software [inaudible 01:03:41] how we sell it, out of that came Microsoft. It was a mixer of some technical innovation, but not really, but a lot of business and marketing innovation around both how to sell and talk about what they were doing. Then in ’95 and ’96 it was clear that computers were going to be ubiquitous and everyone sort of figured well someone also hosts my application I guess, but it took another seven years before sales force started and it took another 17 years for sales force to be taken seriously enough. So then we’re in this world now where everyone talks about SAS as if their subscription as a service software, software as a service, as if that was a fait de complete, but it took a bunch of people to make up a bunch of things and to be honest, not all of it was technical. Most of it was marketing and business model.

James: As a result of that, I’m in San Francisco. Mark Benioff has this really big tower that overlooks all of San Francisco and there’s going to be some man or woman who figures out how to think about machine learning models and AGI from a business point of view and a marketing point of view and they’ll actually have a deep understanding of the economics of it and that person is going to take advantage of some variation of a network effect that you and I have only thought through and then they’ll have a whole campus. They’ll rule the world. That search I think is still out there. We’re still, figuring it out.

Steve: Yeah I agree with you that the equivalent of what you described there which is that it wasn’t the tech that changed that much, it was sort of figuring out what the best application was or maybe convincing the customer to buy it et cetera. That still hasn’t happened with narrow AI and things like that, but we know of enough point applications where if they can just get over some threshold whether it’s autonomous vehicles or robotics in manufacturing, call centers, we can already point to a lot of places where a lot of people are employed right now where if the machines just reach another milestone they’re going to put a lot of people out of work. Do you see that happening soon or is that more past the horizon for you?

James: I think that that concern is real and I don’t want to poo poo it. I think that is an entirely fair concern. The rate at which that could happen, I don’t want to poo poo either. I think that is entirely legitimate. At the same time, I think the race is not to slow that down, but instead the race is to figure out what are new valuable things for people to do? I think that only comes out of having actually implemented machine learning models out there working in the world. There’s this paper I read the other day by two Stanford guys called Street Level Algorithms and their point is, when you actually deploy machine learning models out in the real world, there are lots of cases where it just doesn’t know what to do which will make bad decisions and you need an entire infrastructure of people to manage those models, find out the gaps, fix the models, pay for all the mistakes of the models, and all that sort of work is not being done right now by the biggest companies because in their fantasy land you’ll replace people with models and then everything will be great.

James: Then if you think that way, you’ll come up with bad models and you’ll come up with bad business practices.

Steve: I won’t agree with you that the world of narrow AI requires a lot of very smart humans to train the AI, figure out how to deploy it, know when to turn it off, all those things, but it is a fairly small sliver of the population that have the cognitive abilities in my view to really do that kind of work. So let me give you the famous horse versus car historical analogy. We went from a world where horses were doing a lot of the work and then automobiles came along. Two different fates we can discuss. The fate of the horse. So the horses were not able to adapt to the new world. Horse population went way down. I don’t see a lot of horses employed in my town anymore. They used to be all over the place, now they’re not employed at all.

Steve: The blacksmith was able to retool. The guy who was working as a blacksmith could go over to the Ford factory and start working on tires or this or that. So do we have a huge chunk of the population whose fate is more like the horse and perhaps the smaller chunk of the population whose fate is more like the blacksmith or is that a bad analogy?

James: I mean I think that is a fair question. You know what it’s like, this is going to sound too harsh. So there are a bunch of very, very smart AI academics and philosophers and ethicists all thinking through these issues and a lot of them are very well funded right now by very well meeting, very smart people. I think that it is mostly wrong headed. It’s mostly wrong headed in the way that if I were in France in 1880 and I was building a new engineering school and I wanted to figure out how to deal with bridges and I wanted to figure out the ethics of bridges. If I was really smart I would say you know what, we shouldn’t build new bridges because 50 years from now a country next to us will have these things that look like horses, they move faster than horses, but they’ll have armor and they’ll move very, very quickly and they will dust the other and bake carrots.

James: Thus, we should focus on the issues of security of bridges rather than just figuring out how the heck do we even get bridges to work. I think we’re still in a world where we’re just trying to figure out how the heck do we get models to work? How do we get labeling to work? How do we get the economics of replacing models to work? All those sorts of questions we need to figure out before we’re able to really speculate in smart ways about the actual profound implications of them and I think it’s dangerous. I also think it’s very dangerous to anthropomorphize these models and to treat them as if they have deep human agency. Right now, one of the ways that I talk about AI is if bureaucratized selective intelligence. You talk about that, less is much more boring, but I think it actually captures a lot of what’s actually happening with these models.

Steve: One of the most interesting things to me about AI is that often times the actual kind of “reasoning or inference” that it’s engaged in is really simple. It’s just adding up some numbers or something like that, but it’s able to track 1000 numbers at a time whereas no human can track 1000 numbers at a time.

James: That’s right. The economics, the fact that it’s now cheap and consistent and able to deal much higher dimensional space than we are I think that that is very interesting.

Steve: To me that’s the biggest factor right now. Eventually, you may get much more subtle models being learned and things like this and I think already for example in the case of alpha go, that’s true. A lot of what AI is doing like figuring out what ad to show you or optimizing something for Amazon or even in the case of genomics predicting some phenotype, the actual operation that it’s performing is very simple, but it is tracking 1000 different genetic variants at the same time.

James: Thus able to do unimaginable things. Things that we would never [inaudible 01:11:16] before.

Steve: Exactly. No human can really follow it very well, but it’s not that intricate. Whereas probably what alpha go is doing inside is pretty intricate.

James: Yeah, yes, yes that is right, but at the same time I feel like I would much rather lots of people spend their time trying to figure out how to make things work right now and figure out the implications of what we’re dealing with right now and solve the problems right in front of us. Unfortunately there are so many. You can tell when you go to some business and they’re really worried about how often to fix their models, versus they’ve got some very theoretical thing about the CEO watching too many episodes of Terminator or something like that. I think there’s a way in which people are afraid to test and try and come up with new solutions and there’s also a way in which by anthropomorphizing and treating these AIs as agents or other beings, we concede power to them in ways that we shouldn’t yet.

James: There’s a widespread misapplied or badly described version of AI right now where people say well the machines are just making predictions and we get to make the decisions. There’s something like that and I think that the economic insight there is right, it makes it too comfortable for people and it aligns the actually problems that we’re trying to figure out right now of which are many. That’s where all the excitement is.

Steve: When I first came to Michigan State seven years ago, we were still in the era where you wouldn’t use the word AI, you would just say machine learning because if you said AI people would still remember the previous AI winter when those guys made a bunch of aggressive predictions.

James: A friend of mine reminds me all the time that when he was raising money six years ago, I tried to get him not to use the word AI.

Steve: I once said this to a Chinese guy who was starting his company that’s now worth a billion dollars and I said don’t use AI, people will think you’re a bullshitter if you use AI. This was a while ago. He just looked at me like I was crazy and of course h used AI and now he’s a billionaire and I’m not. In the time that I’ve been in this job, we’ve gone from don’t use AI because you sound like a BS artist. Always be careful and say machine learning to now people who have really no idea of what is going on inside any of these systems will just routinely talk about AI. AI’s going to help us do this. Well we got to watch out for AI and so I feel like there are quite a lot of business leaders and educational leaders who don’t really have a good feel for even what we mean by AI now, but there’s using the term constantly.

James: Yeah okay, so I’ve got many, many thoughts on that. I think that there’s the economic lens, but my one pitch is for anyone who’s the senior executive and are using this term and secretly feels uncomfortable, send me an email. There’s a guy who teaches this course only for senior executives and for congressmen and which he basically spends three hours and he has the walkthrough build your own machine learning model. Then in that process, they see how fiddly and temperamental it is, but they also get that magic of but it actually works how amazing it is and at the same time, they experience how badly it screws up when you have data [inaudible 01:14:37] and you accidentally misattribute a bunch of factors. That sort of nuanced view of what actually happens, that is definitely the frontier for most business executives and political figures, but the ones who figured that out and understand it, they’re the ones who are going to be the next Mark Benioff or whatever.

Steve: Yeah I think that’s extremely valuable that you said it’s a three hour course or video or something.

James: Three hour course basically they modify, it’s sidekick learning. It’s not actually [inaudible 01:15:11]. They make some modifications. They classify a bunch of images and they see how well it could work and the other side is that part of its core is really complicated. Part of its core is basically a fairly straightforward economic change for something we thought people had to do or we thought was really expensive becomes really cheap and really, really fast.

Steve: That’s great. Reminds me a little bit of there’s a famous textbook and course at Berkeley called Physics for Presidents in which teach-

James: I’ve never heard of that. That’s a great idea.

Steve: Oh you should check it out. There’s a great textbook and it’s basically just simple physics concepts, but that actually have impact in the real world and we wished the president would understand all these things.

James: That’s great.

Steve: So I think we’re out of time. So going to have to end our conversation, but I hope we’ll have another one before too long.