YouSaid · the spoken record

Mark Leslie

lines on the record: 84
first: 2022-07-21
most recent: 2022-07-21
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“I think my favorite thing is seeing the intersection of art and humanities and code. And people used to keep them as separate in their heads. And there's a whole new way of talent that's native in both. And that's really exciting to me because, you know, art is code, code is art. So to me, that's like the biggest or most exciting talent shift. Well, John, just want to say thank you for joining the A6NZ podcast.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“The biggest shift that Navy has impacted me is like, I just remember the transition where pretty much everybody was in computer science for the love of it because it wasn't really clear where the industry was going. Often they were doing it to get something else done to basically the professionalization of an industry, meaning it is a real discipline. People are in it to make money. People are in it for a future, which is not a bad thing. This is required. And I think it's actually quite good because it requires to really think about what it is, what people do. And so kind of on the negative spectrum, people are a lot more mercenary about it than they were before. And on the positive end, I do think we have a lot of framing around it. What does it mean to have a workforce in computer science that will come and go and to handle that in a way? But for me, it's been a very, very stark difference. The people that I used to work with 20 years ago when we were literally all there, you know, for the love of solving these great problems to now, it's like, you know, this is your job.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“I think the big one I see that I think is probably under remarked on is engineers are so much more productive today, especially in software than they were 20, 30 years ago. The tools are so much more sophisticated and powerful than all the infrastructure technologies. And then all the ability to learn, kind of to your point on the undergrads, but like the ability to go online and learn. It's like, I'm an engineer and I don't know how to do something. Stack overflow it. Boom, boom, boom, boom. I know it in 10 seconds. Yeah.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“There were a lot of women in it, and then it got wiped out with the growth of the field and the number of males grew. And now we've seen a resurgence. I think begun by a group of very energetic women that started to build support groups and things like that. The other thing that's been remarkable for me is I thought 10 years ago that computer science was going to become second to the biological sciences in terms of getting the best students and that everybody, the really best students were going to go do the biological biotech, things like this. Well, that's changed. And now computer science gets the very best students in many of these fields. I mean, I've seen freshmen that know more mathematics than I knew when I was a senior getting my college degree now. That's remarkable. And they're going to build great things, I believe.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“So, one of the changes I've seen recently that really has me delighted is to see the number of young women going into computer science. What's funny about it is computer science in the 80s was one of the...”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“And it's injected new technology and new products into the space and things. Right. Last question. What do you think has changed with talent, like the whole talent landscape over the last 30 years? Because we've talked a lot about tech trends changing, the availability of capital, the ecosystem industry, collaboration, academia, et cetera. But the people themselves in this ecosystem, what is the biggest change that you've seen, or are they the same”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's actually a really interesting point. The thing I've been most impressed with Cisco over the years is they've really, I think, are probably the top companies in making those acquisitions successful and doing spin-ins. I mean, there are very, very few companies you can put in that have been so successful in acquisitions. It's basically a core competence unit.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, the only downside is that once that company gets far enough along that little startup that it's got some great technology, there are often more than one company is bidding for. Then you could actually lose out”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, why wouldn't you? Because then you're essentially betting on a thousand experiments and figuring out which one's a winner instead of trying to internally, captively figure it out yourself. Like, I just can't see any alternative to that.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“And they've had a model for many years of we buy interesting companies and we bring technology in that way and then we grow it and use the rest of our ability to really make it successful. So it's a different innovation model as opposed to one that's more organic.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Another interesting example is a company that you said on the board of, which is Cisco Systems, which is Cisco's long had this stated goal of no internal research. However, they really made modern networking in no small sense of the word, right? If you had a PhD in networking, you do great research in the universities. But when you actually go in Cisco and see what they're actually doing, you're like, wow, they understand the real problems.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay, we can drive in this desert road in a fairly constrained environment till I can drive in a city environment with lots of people who do wrong things, including look at their cell phone while they're driving is a much harder environment to do it in.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, one could argue in that example that DARPA was a VC because they were putting up the prize money and everyone was competing in the startups, i.e. the individual people trying to meet the challenge, et cetera.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, if you look at the self-driving car on a tipping point was when the DARPA Grand Challenge was won. And that really was a key tipping point because it demonstrated the technology was considerably. Considering that the previous contest before that, the car had not driven very far at all, and all of a sudden boom. So there's a tipping point in that. When you see those tipping points, that probably is a time when you say, let's move it from an academic setting that's kind of more freewheeling and operates more incrementally to”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“With all that, but I would also say, even with what you just said, even that Cialto, but also look, Apple made the iPhone, right? Like, that was what $150 million project. Over the course of they were able to do that. Google, you're well aware, has basically invented the self-driving car. Those are on Pala with Alto.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“So there's a lot more happening today to the extent to which I can't imagine a startup kind of thinking about the length and the amount of money that was invested to build the Alto. I mean, that's a major, major undertaking by any measure. On the other hand, I think you're right. There are now a much larger number of players doing interesting things. And in the software-driven world that we live in, The cost of experimentation anymore is not the same amount in terms of capital.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“About, and arguably, from a corporate investment capital standpoint, they were worth it just for the marketing value, right? Of being able to demonstrate that they weren't just sitting on their rear ends in the corporate office. And then the other precondition was they were all pre-1975, 1980. They were all pre-venture capital. Right. And so when the monopolies cracked and then venture capital pulled the talent out, like that was basically it. And the downside case would be that removed this kind of long-term commercial research, but the upside case would be that led to what I would argue is just an explosion of R&D at far greater scale, right, across the corporate landscape than ever existed in the 1960s, 1970s. And so we've kind of mythologized these things, but they were tiny. They were tiny relative to what's happening today.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Kind of a skunk on this topic. So I think the reason at the garden party. So I think the reason why, I mean, they did great work. Serious Park, Bell Labs, IBM Research. But here's the thing it's always those three examples. They're basically like they were running errors on everything. There weren't 10, there weren't 20, there weren't 100, there were three or four. And there were two preconditions for them. One is they all were offshoots of monopolies.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“I think you're right. I think there is a bit of a shift occurring here. It's driven by not only the amount of resources that are available at Google, Facebook, Microsoft. It's driven by data, and it's driven by computational resources that are available in those companies that are much larger than is available to a typical university setting. So I think we're seeing a growth of kind of new research environment in industry that's quite a bit different than the old environment and may be a harbinger of how things get invented in the future.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Mark and I are both dying to jump in. I think historically that's certainly been the case. One could make an argument that this is shifting in some of the most fundamental research contributions are actually happening in industry today. And not only that, that the academic system has actually moved towards short-termism, especially in incremental publishing. I even felt like I've seen that dynamic shift in the last 15 years and just my kind of professional career where I would say Google and Microsoft are doing some of the more intimated fundamental contributions. And then I still sit on program committees. That's interesting. They publish a paper. I'm in the PC committee and then all of the professors are basically trying to do incremental work on top of Google's work, right? So are we seeing like an imbalance lately or is this a moment?”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“It's harder to find that in an industry nowadays. It's harder to find that patience, partly because of the observation that if you discover something really big, lots of people have to eventually benefit from it, right? Bell Labs and AT&T were not the major beneficiaries of the discovery of the transistor. Xerox was not the major beneficiary of the discovery of modern personal computing, right? That's why universities are the ideal place to do this kind of work because society benefits. Universities do technology transfer in a very natural way. It's called graduation.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“So I think there was a time when IBM research, Xerox PARC, and Bellams were the great giants. What they had, they were not devoid of application and things. I mean, the work on the transistor was really begun to solve a fundamental problem that a telephone switch built out of tubes. What they did have was they had the advantage of a long investment.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“When I think of examples like Xerox Park, which honestly, despite the mythology, they actually did put a lot repeat successes out into the world. It wasn't that they had like a carte blanche to just invent whatever they wanted. They had a very specific mission and they invented towards that mission. When you talk about the differences between academia and industry, academia is about ideas and industry is about implementation, and you believe that there's an interface that VCs and others carry across those two. Do you think, though, that that sort of a false divide in some ways? And it wasn't so, it was actually not just ideas versus implementation. It was ideas in practice, in industry settings, because it was for a corporate research lab. So I just wonder how you're thinking about this was then and now and how it's evolved.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Fascinating. Yeah, no, I think you're absolutely right. I think in the academic world, we like things that really look elegant. And we often actually delay publishing a paper or getting a result out there until we get it all geled just right, right? That doesn't work in a startup company. I think the one thing that is common is focus really does help in both cases, right? I mean, you relentless in a startup company, you've got to focus, you've got to drive, you've got to decide what's peripheral and you're not going to do now. And the same thing is true in academia. If you want to do really great work, you need to focus. You need to kind of, somebody once told me, they give me some good advice. They said, you know, you ought to be working on three or four things, but you ought to have one or two of them that are really important, where you're really putting your energy. And these others are your backup in case those really great things don't work and you don't get tenure for those. And that was good advice about how to think about a research career, but doesn't work in a company. You've got to get rid of those.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“But where I did find this synergy very useful is a lot of leadership is thinking simply. And so if you start a company, you can extract that elegance. You can use that to really lead a company. And you can convince a customer and you can talk to an investor because you've really distilled what's important about it. But you can't let that constrain you because ultimately you have to build something that solves a real problem and the universe is a messy, messy place. And so if you can get beyond that kind of ability to have everything be incredibly elegant, I think you can have both the leadership and kind of like the actual complexity.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Real world applications where there is actually a truth and a way to test the truth, right? Exactly. So for me, the most difficult thing about moving from academia to industry was that in academia, you look at a problem domain and kind of that your job is to think very, very clearly and pull out like these kind of global truths and they have to be very elegant. And very rarely do you write a paper where you're like, here's this problem domain and here's my litany of 50 fixes and read through every one of my heuristics and oh look how elegant it is, right? It's almost the exact opposite. What you learn about starting a company is it's actually the opposite, which is almost every solution is dealing with the heavy tail of complexity and it's a bunch of patches and the real world and everything else. And so mentally you've got to go from, I'm going to look at a problem space and extract elegance to I'm going to deal with all of this complexity and master it.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“And the space where that works, sort of unsupervised learning, is such a small part of the giant ML space. It's a relatively small. And most of its interesting applications are in the natural science world, not in real-world applications.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“So, this is something that we have to deal with a lot in venture capitalism, which is a number of constituencies and entrepreneurs actually view AI or ML as almost like the end of theory. So it's like almost like, I don't have to know what I'm doing. The AI and ML will figure it out for me. So they'll come in and they'll say, listen, there's all of this data in Enterprise X or whatever. We're going to apply AIMNL, and then the net result is going to be value. Like, well, what's that value? Well, I don't know. The AIML is going to tell you it's going to be valuable because we've applied this. And so it's a very important tool set, but I think you have to understand the domain to your point, garbage in, garbage out. You have to have some way of getting the expertise or whatever in the prior to get the answer. It's not like this has become the end of theory and we don't have to know what we're doing anymore and we're going to get valuable results.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“So you've got to look at that. You've also got to look at how and who establishes ground truth in these. I may have an AI program that can recognize some medical condition, but who decides whether or not it's right on the basis of that? ML is the ultimate garbage in, garbage out technology, because if the data isn't good and properly validated and the learning process isn't, you're going to get assumptions and outputs that are ridiculous.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“And more and more, it's almost serendipitous understanding of the data prior to manipulating it, right? It's almost impossible to remove the context and the domain understanding from data. From programs maybe, from data almost certainly not, which is why we're seeing such kind of a confluence of CS statistics and data understanding and domain expertise.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“All kinds of problems, all kinds I would argue that it needs to be more applied. We have an executive briefing center with a lot of big companies coming in. And the number one challenge they have when it comes to ML and AI is production ready, industry applicable machine learning. It's actually like what's happening in academia is not at all connected to what they need to actually do.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I think there's about to be a great test to this because the wide applicability of machine learning to all kinds of problems. I mean, you know, you just see breakthroughs in biology and chemistry, in astrophysics coming out of various forms of machine learning. So all of a sudden, it becomes this tool that is applicable to a whole range of things and is changing those fields. What do the scientists, the people who think themselves as astrophysicists or as organic chemist, how much do they need to understand? How do they deploy this technology? And this is a big gap right now because the senior people in the field, it's highly unlikely that most of them are going to take a year or two out and go back and learn a bunch of things about computer science and statistics and machine learning ideas. We're really going to have to build a new breed of people who kind of fill up this interstitial space and become the key innovators in this.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“It is unique and it is this metadiscipline. I mean, I think it's become the new metadiscipline that everybody needs to learn because algorithmic thinking is such a fundamental thing about how the world.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Think computer science is a little bit unique in this, in that, you know, listen, we call it a science, but I mean, ultimately it's an engineering discipline. And while there is pure computer science, almost all of it is applied. And so when I did my PhD at Stanford, we had people that would work in graphics, and they work very, very closely with computational physics, for example, solving very real problems. Same thing with biology, right? One of my best friends, I mean, he did some really core work in DNA sequencing. And if you squinted him one way, he looked like a biologist who squinted another way, looked like a computer scientist. The thing that I love about computer science, and I've always loved, if we wrote a program that solved grand unified field theory, physics would go away as a discipline, and we'd be like, okay, that was one more application. Let's go on to biology, right? So in some ways, it doesn't exist without the other disciplines. In another way, it really is kind of this metadiscipline. And so I do think it's pretty unique in that way.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“They're not going to thrive very well in that kind of online setting where they don't see how that directly translates to getting a job at Facebook, for example. They've got a long way to go before they're there. So they need a rather different educational system than somebody who's already got their degree. They see if I take this course, I'll get this new opportunity.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Happens a lot more at the graduate level and the research level, partly because I don't believe that multidisciplinary or interdisciplinary things are a substitute for some deep domain knowledge. I'm a firm believer that you start with deep domain knowledge and then you build on top of that. One of the challenges with these small courses that certify you in an area those work well for a professional. They've already got an undergraduate degree. There's a clear connection between the value of the education program and how they'll be rewarded. Take an undergraduate coming in without some of the advantages that you'd have if you went to an elite high school.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Actually, on this very note, I would love your take on the interdisciplinary side of things because to me, the one unique thing that universities can do that a lot of these other institutions cannot do is break down barriers between disciplines. And you guys have tried experiments or legitimate degrees like symbolic systems, et cetera, that cross multiple disciplines. But I've yet to see examples of true success as a multidisciplinary degrees or entities like maybe Xerox Park would be the best example, but I really can't think of any others.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“I think there's some truth to this observation. And I think one way of interpreting it is that the drive and the determination to finish that degree is actually the key signal that employers are looking for, not just what courses you took. Now, I should say post bachelor's degree, this is changing dramatically. But if you think about other kinds of post-bachelor degree, we're moving very quickly towards a certification type model where you take a course or a sequence of courses, right? So you go and take the sequence of courses on cryptography and blockchain, and you become an expert on that. And by demonstrating that you've mastered three, four, five courses in that, that all of a sudden becomes the key to getting a new job opportunity. I think we're going to see more and more of that as we go along.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, correct. But it's also look at the default rates. Now, part of this is the for-profit industry, unfortunately, in the higher education space doesn't deliver a lot of value. So you end up with lots of students who are not able to use their education to get ahead. We've got to figure out how to deliver a high quality education, not decrease the quality in order to just get the cost down, but hold the quality up while reducing the cost. And the only way Aina had to do that is by using technology.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“American families. We're going to load up student debts going through the roof. And part of the reason going through the roof is families are less able to save than they used to be. And so we see student debt going up.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Number of people who can come to London The right answer is to change the way we educate people. I mean, I think if you were to make accusation against higher education, it's that they haven't really done very much to bend the cost curve. And part of this is understanding what it means to bend the cost curve. Think about Vivaldi writing four seasons and having four musicians play the four seasons, right? Takes 23 minutes, took 23 minutes in whatever it was, 1790s, it takes 23 minutes today. What's the big difference those musicians get paid a lot more today than they got paid then? So actually there has been no productivity gain in the presentation of the four seasons piece, right? I mean, universities are somewhat in that. It's still a craft to some extent. Now, that has to change. That has to change. We've got to figure out how to leverage technology in an appropriate fashion to get the cost of education down. Otherwise, it's simply going to become more and more expensive.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“This does sound a little bit like the director of the Globe Theater in 1550 or whatever, kind of saying more people should get exposed to Shakespeare's plays. And so therefore we should build a balcony, right? And we should, you know, double the number of people who can come to London and see the play. But most people in the world are never going to be able to get to London to see the play. Like at some point, it'sn't the right answer to invent television. No, the right answer is change.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“I came to the view that the university had a moral imperative to increase the size of the student body. Now, there's a limit how far you can increase it before you change the quality of the experience, right? We house all our students on campus, things like that. But we could certainly do more. And the provost and I made an argument. So in the end, what happened, the financial crisis came along. We had to put that on the back burner. But then it came back later and we have engaged in the gigantic expansion of undergraduate housing so we can house students on campus”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Take 100 million 18 year olds to 200,000 slots the obvious question, right? Which is fantastic obviously what Stanford is doing for the kids who then end up in Stanford, but most kids don't, and most kids don't end up in anything resembling Stanford Quality Education.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“You have to work for the university 10 hours a week during the year and 20 hours a week during the summer and contribute that to your education. And then everybody said, well, that's fair. That's reasonable. So balancing that was really key.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“So we decided that one of the challenges that people who came from disadvantaged backgrounds faced is just getting through the whole process of applying to a highly selective school. You know, the federal financial aid form is 23 pages long. Often you get people they may not even speak English because they're an immigrant family. And so that's a major barrier. We decided we need a very simple message, right? Your family makes less than $100,000 a year. Your tuition at Stanford is zero. The next thing that happened, though, was somebody came in and said, well, I make $110,000 a year and my tuition is $30,000 a year. This doesn't make any sense. So we concluded you had to balance this with fairness. You had to ask the students to have some skin in the game. Right. So we said even though your tuition is zero,”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Let's talk about empathy because you're one of the pioneers and you're tenure as president of the largest increase in financial aid ever, which allows more lower income families to experience Stanford. And this is incredible. But you talk about how it was hard for you to actually make this happen because empathy needs to be balanced with fairness. And that really resonated. So tell us about how you sort of navigated that horny issue.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, you have to think about the value of the individuals. Everybody's work has value, but obviously some of it is more crucial to the success of the organization than other work. So everybody should be rewarded, but that doesn't mean all the rewards should be equal.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“I always think about how this plays out when it comes to things like equity, though, because you have to share the success. But quite frankly, some people do more, some people do less. Some people are less fungible, others are more, and you have to take that into account. And I think that's sort of an interesting calculus that people tend to sort of balance.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source
“Kind of like our strong opinions weekly held, which feels like a very asex and z value. It really seems to define the place. I love this phrase that you use in your book. It's not enough to understand how many people are depending on you. It's just as important to realize how you are depending on them. And I thought that was a very neat thing to think about mentally inverting the org chart.”
2022-07-21 · a16z Podcast · From Research to Startup, There and Back Again · IDENTIFIED FROM THE TRANSCRIPT · source