by Y Combinator11/15/2017
Qi Lu is the COO of Baidu.
Daniel Gross is a Partner at YC.
Craig Cannon [00:00:00] – Hey, this is Craig Cannon and you’re listening to Y Combinator’s podcast. Today’s episode is with Qi Lu and Daniel Gross. Qi is the COO of Baidu, and Daniel is a partner here at YC. Just before we get going, if you haven’t yet subscribed or reviewed the podcast, it’d be awesome if you did. Alright, here we go.
Daniel Gross [00:00:18] – Alright, hello. My name is Daniel. I’m a partner at Y Combinator. And I’m here today with Qi Lu, who’s the COO of Baidu and is in particular focused on a lot of their AI strategy. Qi, thank you so much for coming today.
Qi Lu [00:00:32] – You bet, thanks for having me.
Daniel Gross [00:00:34] – Cool. I guess, first question that is on my mind is, and I think many others, help us understand why you left. Previous to Baidu, you were very senior at Microsoft and a lot of us are wondering why you decided to leave to Baidu.
Qi Lu [00:00:52] – Two things. One is, I left Microsoft purely for personal reasons because I had an injury. I broke my left hip, so I needed a second surgery and needed to take some time off. Because my job at Microsoft was very critical to the company, I felt it was in the best interest of the company for me to move on. A great thing is that I had a good successor who is extremely capable. I’m super happy that he is taking over and leading the company’s productivity business moving forward. In particular, also, I was able to have a very good relationship with Microsoft. I continued to serve as the personal adviser to the CEO, Satya Nadella, and also to Bill Gates. When I go back to Seattle, I often go see them.
Daniel Gross [00:01:50] – Then, how did you decide to go to Baidu as opposed to any other place?
Qi Lu [00:01:55] – That’s for a simple reason, which is AI… Most people in our field would agree, AI is the next big wave. I think AI is particularly meaningful, because in my views, China has a structural advantage in terms of AI technological development and the commercializations. In that context, Baidu offers a very unique opportunity for me. First of all, Baidu, in many ways, is the Google of China. Its heritage was a search engine, and as a result, from an engineering capability perspective and a cultural perspective, it’s uniquely positioned to seize the AI opportunity. Also, I happened to be, a friend known Robin Lee, the founder and CEO for almost 20 years, so there’s a lot of long term relationship and trust, so that was just a good opportunity for me to take.
Daniel Gross [00:03:06] – In what ways is China’s approach to AI different from America’s?
Qi Lu [00:03:16] – First of all, I think it’s environmentally different. Approach wise, I’ll come back to the approach aspect from my vantage point. From the environmental perspective, I think China has a unique structural advantage for AI technological development and commercialization of AI technologies for a simple reason, if I may just explain my thinking on why this is so. In this wave of technology development, there’s one aspect that is fundamentally different from the previous generation of the big technology wave, which is data plays an essential role. I’ll offer you this simple example. You can have 10,000 engineers, great engineers, or you can have a million great engineers. You will not be able to build a system that understands human conversations. You will not be able to build a system that will recognize objects or scenes of images because you need to have data. A simple analogy is very much like humans. When you and I grew up, it’s not like our parents or God is writing coding to our brains. Our builtin neuro-engines have the ability to learn, so our sensory systems, essentially, our perceptive systems, whether it’s visual systems or whether it’s auditory systems that we are able to observe the world. Our observations, those sensors, these are data. This data carries knowledge, and we are able to learn from our interaction with the world. As we grew up, we acquired knowledge. The same thing happens for AI technology. It’s not about writing code this time. It’s about writing code that implements algorithms
Qi Lu [00:05:14] – with both soft and hard wares that are able to learn, and learn knowledge from the data. If you take that perspective, data in my view is for the AI era. It will become a primary means of production. By definition, means of production is a form of capital. We look at, historically, in our human history, let’s say, in the agricultural era, land is the primary means of production. You can see everything is organized around the land. All the wars are competing for land. In the industrial eras, the means of productions are primary labor equipment, different type of equipment. And certainly, financial capitals, human talent. But in the AI era, my view is that data will become a primary means of production. Harnessing data becomes key. And that comes back to China, because China has a different socio-economical policy around it. For certain segments, not on everything. For certain segments, it’s much easier to acquire and harness it. With that, it creates an environment for developing AI technologies, and then commercializing those technologies towards market oriented applications or social applications. It is in that context that China has a structural advantage. In terms of approach, there would be cultural differences, even in the entrepreneur world. The startups in the China environment, they tend to work in their ways. That, I will say, Silicon Valley and China, there’s common attitudes, there’s some different approaches, but that’s not the bigger factor. In my view, it’s the environment that’s the more
Qi Lu [00:07:03] – determinant factor making China to be, relatively compared to other marketplaces or other regions, a better place for AI development, because of data.
Daniel Gross [00:07:15] – Interesting. I guess, one question I’m wondering in particular is, in the US, there’s this belief that one of the ways China is somehow doing better when it comes to technology is that the government is much more integrated with companies and their initiatives. Is that something that you see at Baidu? As you guys focus on your different AI initiatives, are you able to work very closely with the government?
Qi Lu [00:07:40] – In general, the Chinese government, at this stage, has a lot more willingness to invest in infrastructures, in talent, and they in particular see AI as an opportunity for China, in many ways, to ride that big wave, to elevate its innovation capacity. There was, about somewhere between one to two months ago, there was a white paper published by the Chinese government that actually spills details about, by 2030, how the Chinese government plan to systematically invest in infrastructure, talent and technologies to enable China to lead in AI technologies in many different dimensions. In general, the government indeed has a lot more willingness and commitment to invest. With regard to private company, a particular company like Baidu, which is more a view that’s culture and practice-wise closer to an American company. Listed in NASDAQ. In turn, the working culture is very entrepreneurially closer to Silicon Valley style. We do, in many ways, operate independently. We view, essentially, market opportunities as the primary objective to pursue those opportunities. And we enter a win-win environment with the government initiative. We welcome that. For example, Baidu is the host of national labs and Baidu is also working with various different government entities when they have expressed willingness to support certain areas of AI technology. For example, let’s say for self driving cars. We will work with those government entities to discuss opportunities that are mutually beneficial. But as a company, our primary means is market success. We don’t have any other agendas,
Qi Lu [00:09:51] – because we are an independent company. We want to build products that service our users. When there’s synergetic opportunities with government support, we will collaborate with the government when there is mutual benefit, mutual win-wins.
Daniel Gross [00:10:06] – Do you think that China will beat the United States to having mass adoption of self-driving cars?
Qi Lu [00:10:15] – My belief is, the opportunity to commercialize and deploy autonomous driving technologies in various forms, China will have opportunities to get ahead of the United States over the next three to five years. Primary, I will say, a few areas. One is, different regions, whether it’s municipality or provincial government or central government, they see this as an opportunity for China’s auto industry. Right now, the China auto industry, there’s no real strong technology. Heavy fragmentation with over 250 OEMs. The Chinese government would very much like to take the autonomous driving dimension of innovation to enable the Chinese auto industry to leapfrog, to be the world’s best and lead the world. The government is a factor. For example, there’s five municipal governments right now. Members or partners of Baidu’s open autonomous driving ecosystem, an open platform called Apollo, they work with us on a variety of initiatives. For example, a new kind of driving schools that will certify autonomous vehicles for different levels of maneuverability. Just like a driving school today, they will certify human drivers if you pass a certain test. We’re working on that. We’re working with a new city that’s being kind of build ground up. By the Chinese government’s plan, it will be bigger than Shenzhen. It will be bigger than Dubai in five to 10 years. It’s called Xiongan. It’s a massive new city that’s being built from pretty much zero. So, we’re working with them, designing new infrastructures,
Qi Lu [00:12:15] – a new segment of the city that makes it much easier for autonomous vehicles to be deployed. As an example, let’s say, today’s cities, you have street lights, and the street lights, in many ways, are a sensor device. It enables the sensors of a vehicle to be able to better see the road. It just happens to be the one area that does the sensing are humans, and humans use eyeballs. When it’s dark, you won’t be able to see the road, see the separation of the roads, and you have street lights. But imagine in the future when the sensor is not done by the human eyeballs, but a different sensor, whether it’s lidar, radar, or cameras, whatever the sensor technology used. The future city infrastructures, those street lights will be intended for non-human sensor capabilities to see the road and to be able to navigate the road. We’re actively designing those new type of infrastructures and having ongoing discussions with these municipal governments to lay out plans to build those infrastructures, with the intent to have commercial deployment of autonomous driving in various forms. If you combine all those efforts together, I very much believe in the next three to five years we’ll see autonomous driving in China to get deployed in more variety, in larger scales than other markets.
Daniel Gross [00:13:50] – Fascinating. Going back a little bit to, kind of, more broadly, China and the United States. You were managing very large software engineering teams here in the United States, and now, you’re doing the equivalent in China. What are some cultural differences you’ve noticed in terms of how people work, how you have to manage, in between those two countries?
Qi Lu [00:14:15] – First of all, Baidu’s engineering cultures, product cultures, it’s very similar to Microsoft. Very similar to what I know of Google, even though I haven’t worked at Google, but I have enough interaction with friends who’ve worked at Google. Essentially, very heavy in technology. Very heavy in algorithms. Very heavy in large scale computing. Very weak in product design. Very weak in understanding user needs, human needs. As a result, the technology is good. The product, generally, isn’t great. I’m not critiquing or criticizing my former colleagues, but Microsoft as a company, in many ways, lacked behind companies such as Apple and Facebook in building truly mobile, particularly mobile consumer products that stressed the emotional connections with users. Whether it’s applications or services or devices, the fit and the finish, the experience design is very much more than appealed to a young demographic, the young generation. Microsoft, as a company, struggled on that. I see similar things, from what I can see. Google as a company, the products that I use. Baidu is at the same way. That’s one aspect. I always tried to change the engineering culture at Microsoft. Actually, that was the reason why I broke my leg. It’s a different story. You need to unlearn and learn a new way of doing things.
Daniel Gross [00:15:49] – Can you just tell us bout the bicycle you rode, which is ow you got that injury?
Qi Lu [00:15:54] – Yeah, there’s something called a backwards brain bike. If you search on YouTube how to ride a bicycle, there’s plenty of videos. Essentially, the bike goes the other way. If you turn the handle this way, the wheel actually goes the other way. There’s some profoundly important reasons, because first of all, we humans learn, there’s three primary ways that we learn. This is called experiential learning, and the bicycle riding, often said, is the best example because you cannot learn how to ride a bicycle by watching other people riding a bicycle, by reading about it, by people telling about it. You have to ride a bicycle yourself, and often, bumping, bruising, hurting, but guess what? There’s one thing. Once you’ve learnt, you never forget. It’s in the muscle memory, you don’t think about it. And that’s the problem for large organizations, for cultures, because the reason those big companies, they couldn’t survive when we’ve come, that’s based on Professor Rebecca Henderson’s study at the Harvard Business School. Those mature organizations, their muscle memory, the way they talk to customers, the way they do research, the way their design experiences was built, like, 30 years ago. They try to think, but their muscle memory doesn’t think. They will just do things that way. If you ask me why Microsoft couldn’t get mobile at all, it isn’t that we’re not working hard. We’re working super hard. It isn’t that people are not smart. We tried everything, we bought Nokia, we built Cortana. You name it, we tried everything.
Qi Lu [00:17:25] – But the product, honestly, sucks. It’s just because of the muscle memory. I was searching for an answer. Rebecca Henderson was the one who convinced me that this is the real problem. A Microsoft colleague of mine, his name is Bill Buxton. He’s one of a kind of people. He said, “Hey, Qi, you should try this bicycle thing.” It was really interesting. We built the bike, Bill Buxton and another one. The three of us tried to practice, because this bike, for a normal adult who knows how to bike, takes you about eight months turning every day. And once you learn how to ride that bike, you won’t be able to ride the normal bike anymore because you need to rewire your brain. I think for large organizations, culture change is that difficult because it’s your muscle memory. The way you do things, it just becomes habit. You don’t even think about it. Even though the CEO says, “You guys have to figure out mobile.” They tried, tried, tried. The mobile product just looked like a PC product in smaller form, right? Because that’s how they do it. Coming back on culture, I see Baidu has very similar traits of Microsoft that I work with. What I’m working on today at Baidu is really to change that engineering culture to be a lot more product centric, to be a lot more understanding of user needs, particularly for mobile products, for AI products. Then, brief answer, the engineering culture between companies that are in China versus companies in the United States, there are very various different aspects of it. The biggest thing, and I need to perhaps think more
Qi Lu [00:19:09] – about summarizing in my head what I observed so far, some of the key differences in terms of product engineering culture, the one thing I will say that stands out for me, I learned a lot in my eight months plus living and working in China. The product people in China are a lot more philosophical. They are a lot more reflective. They think a lot deeper than what you would typically observe from product people when they describe their product. Also, the Chinese R&D product leaders emphasize a lot more self reflection. They use the word cognition, but it means a person’s ability to understand, to make judgment, make decisions. Essentially, they emphasize a lot more self improvement for product people in particular.
Daniel Gross [00:20:06] – Interesting.
Qi Lu [00:20:07] – How you elevate your cognitive capacity. If you ask me, the one thing that stood out for me is, I used to believe the product people in the United States companies were better. Now, I kind of have it the other way around. I see better product people more often in Baidu and other Chinese companies that I interact with than perhaps, I will say, on average, the percentage.
Daniel Gross [00:20:32] – On that point, there’s another belief, in the West that California and Silicon Valley are very creative environments and they really allow ideas to come up and bubble up from any person in an organization, versus China where the image, I guess, that we think to ourselves is a very structured society that is very good at implementing something, but maybe not as good at creative, free thought. Would you agree with this sentiment at all, and if so, how do you think that plays out for, say, doing core research that involves a lot of creativity?
Qi Lu [00:21:14] – Yeah, great question, that’s a good one. I will say there are different degrees of truths towards the top-down nature for Chinese companies. Baidu, even though, among the Chinese tech companies, Baidu is the closest in terms of culture to Silicon Valley. A lot of people, their pedigrees are Google, worked at Google, worked at Microsoft. Mainly English, it’s also kind of a common working language. You won’t have any problem if you just speak English or write an email in English. Even that, the top-down phenomena happens. My hypothesis was, this is perhaps due to 2,000 plus years of Confucianism, you know? Confucius is essentially harmony through hierarchy, right? That’s the central idea of Confucianism. Having said that, the companies that I work with, including Baidu, all realize driver innovations is a lot more about empowering teams, empowering capable leaders to experiment, to try new ideas at a fast velocity. Baidu does a lot of those, and in the startups that I interact with, they emphasize that aspect a lot. There’s no difference in terms of belief and practices in Silicon Valley startups that I see. The large company, one company I’ll probably point to I believe, overall, does a good job is Tencent. Tencent, they have this challenging culture. Any ideas, they encourage and challenge the more authoritative or senior people. And also, for any major initiatives or any areas of new innovation, they tend to have two, three teams working on the same thing. There’s a lot more internal competitive dynamics that are going on. One last thing.
Qi Lu [00:23:17] – In Baidu, we have this quarterly meeting. We have all our company directors. We have about 200 directors. Once a quarter, we invite speakers, and the past few speakers, they all emphasized the aspects of building a learning organization so that a truly thriving organization, each cell, each team, they are able to be nimble, adapt, quickly learn. Even though there are these couple thousand years of Confucianism, I think it’s still somewhat there. It reflects to different degrees in different companies, but by and large, driving innovation, empowering teams, empowering leaders are the common understanding. Everyone in the organization is striving to do more and do better in that regard. There’s no fundamental difference than in Silicon Valley, I would say.
Daniel Gross [00:24:09] – Interesting. Do you think that Baidu and Tencent then are kind of the exception to the rule? Do you guys feel somewhat alien compared to other Chinese companies which may be more structured?
Qi Lu [00:24:23] – Yeah, I will say, among the internet or technology, IT technology related companies, even though I haven’t talked to a whole lot of them yet, they’re based on, what I have seen so far, largely in the mode that I just described. But when you go out of that range, you go to much more traditional companies. Let’s say, steel industries, or traditional retailers, then you will see more of the Confucianism, hierarchical styles in management. Again, I haven’t done studies. It’s just my perception, I will say. This is how I perceive it.
Daniel Gross [00:25:06] – Today, it feels like, in particular when it comes to AI research, most of the great research is still being done here in the United States. Do you think that will change over time? Will we start to see, 20 to 30 percent of the papers suddenly be published from China? Or will America kind of always be the hub of AI innovation?
Qi Lu [00:25:29] – This is one topic I have a somewhat ongoing discussion with many of my colleagues in China. Our current view is that the very top end of research that’s fundamentally paving new ground, I will say, the example being, let’s say, DeepMind and OpenAI, I think that won’t happen in the next few years, and that won’t happen in China. I wouldn’t say won’t. It’s unlikely to happen. The odds of that type of research happening in China, perhaps will take quite a few years. Right now, we see the research community, particularly the upper echelon, the gap is closing. The leading Chinese universities, the way the gap is being closed is, a lot of those researchers, their pedigree, they’re studying at top tier universities in the United States, whether it’s Stanford, Princeton, and they go back. The gap is closing, but the overall environment, the culture, context, it isn’t quite there yet, meaning that it’s completely driven by your imagination. The social economic surroundings is still not quite the same as the United States whereby you have truly world class people driven by purely the desire to seek knowledge, the desire to unleash imagination. Often these researchers are doing it in the context of personal fames, economic payback. Once you have those, you constrain yourself. You don’t see very far, you don’t pursue the bigger dreams. But our collective belief, it is myself, a bunch of colleagues, friends, we all believe, given enough time, let’s say, in the next five to 10 years, you will see top echelon research work happening in Chinese institutions,
Qi Lu [00:27:35] – and it’s certainly my hope that in the next, somewhere between five year to 10 year windows, we will have equivalent research organizations, let’s say, OpenAI, DeepMind type, that would be truly doing groundbreaking research towards AGI or different types of initiatives that would be at the very forefrontiers of extending the scope of humanness. It will take time, we believe it takes time. Not in the short future yet, but it will happen.
Daniel Gross [00:28:11] – How are you going to nurture that? Are you going to try to create a Baidu research lab that somehow has a different culture around it than what traditional Chinese academia has?
Qi Lu [00:28:24] – There are several organizations, but one is corporate research labs. Baidu is doing quite a bit and our peers, whether it’s Alibaba or Tencent, they are also investing quite heavily in corporate research labs. At the same time, the national labs or the top-tier universities, they are doing more and more. And in the private sectors, there is always ongoing discussion, a new type of research entities can be envisioned and they can be created. There is an ongoing set of ideas being explored. I think it likely will be a combination of corporate research lab, university and some new generation. Let’s say OpenAI of research of the issues will be established over time that will be capable of carrying top-tier research work that’s based in China.
Daniel Gross [00:29:18] – Interesting. Shifting gears to a completely different topic, another thing that is a hotly debated topic out here in Silicon Valley is crypto currency. No one really knows how to understand China’s approach to crypto currency. What’s your take on it?
Qi Lu [00:29:39] – Yeah, so I will speak from Baidu’s perspective, not necessarily my personal view because I haven’t spend enough time on this particular subject to develop views that I thought would be educated views. It’s more from Baidu’s company perspective. One is we view blockchain the underlying technology as a fundamental foundation or capabilities the company needs to have because in the financial services business, we have a unit we do of financial services and we try to turn that into a pathos to enable traditional financial institutions to be able to modernize the avenues and we have a team in total, they build up a set of core infrastructures that enable us to build a future generation of financial services using that. And at the same time, we’re also using blockchain based technologies to build a new generation of data platforms because when I said earlier data will be a primary means of production and ownership, provenance, value attribution of data will become increasing important, so we want to make sure we, Baidu, as a company, builds the right infrastructure to anticipate for that future of the world. We as a company, at this stage, do not have active participation in the crypto currency aspects of the equation. With regard, I want to add one more thing about the research you mentioned, which is in some ways important. I forgot to mention. Essentially, we now have a view, but this is more of Baidu’s view. We thought that China as a nation has been a talent exporter. We essentially send our best people to the United States. Some of them come back, most of them don’t.
Qi Lu [00:31:33] – We believe China as an economy, as a market, has the opportunity to become not necessarily net, but top-tier talent importers. In some ways, the Baidu research lab in Silicon Valley is intent to be the base station, if you will, to attract truly world-class researchers so they can work in an environment where they can have access to vast amount of computer resources, data access they may not otherwise have access to had they worked on their own volition, research in the United States. So, and also in terms of collaborations, we are actively working with top-tier university, whether it’s MIT, Stamford, CMUs, and the goal I set for my team is, one is we want to collaborate and fund some of the very best faculty members, graduate students. And when PhD students in the future from those top-tier university graduate, Baidu needs to be the top five names when they think about which company they want to work for. It’s not necessary Chinese companies’ research labs are done by Chinese. Research labs increasingly will be done by global talent and they are working on problems that is targeted towards the China market and then has the opportunity to globalize. Can I maybe spend one minute to give you that because I think it’s important lens on how we think about top-tier researchers. The context is the technology market. Up to this point, by and large, is something we at Microsoft always say it’s design for America, kick slightly, sell global industry because United States is the only country has all those ingredients, talent, capital, risk capital, technology, market. It’s the only place.
Qi Lu [00:33:37] – These conditions, this combination of conditions doesn’t exist in Europe, doesn’t exist in any other regions, but China now has all these. Not quite the top tier, not quite as good as the United States, but they have all those ingredients. It is my belief the technology industry would be a, for a while, would be a two pillar, essentially driven by the United States and portion of company come from China. So viewed from that perspective, the product that increasingly initially targeting for China market would have globalization opportunities increasingly because this is how we’re going to attract truly world-class people to work at the company like Baidu. Just as one example, let’s say for smart homes. We believe the product that’s landing in China market will, for smart home product, whether it’s speakers or new TVs use voice, dialogue-based interfaces, will have a better shot of globalizing than those products that will be designed in the United States for one positive reason. Homes in the United States only works in North America. Maybe a little bit of Europe. Outside those, you don’t have homes like this. You have very spacious, different rooms. The acoustic environment, far-field speech recognition. What has to be five meters around, really optimized for this. The home in China is a lot closer to a home in Japan, a home in India, a home in Brazil. We actually do have that view. In Baidu, we’re building our version of Alexa, our version of equal type of systems. The longterm aspiration of globalizing those products and because we believe we are targeting
Qi Lu [00:35:28] – a home environment, acoustic environment, usage environment that’s closer to our initial target market than United States, their initial target market. These cases, we will find more models cases as we move forward and that’s one important factor, those, really, Chinese companies, increasing my views. We have the opportunity or the ability to attract world-class researchers to be part of what we try to do.
Daniel Gross [00:36:00] – Interesting. I want to shift gears kind of one last time to a different collection of topics, which is around management. A lot of people listening to this podcast, maybe CEOs or managers, and they’re trying to figure out how to manage their first engineering team. So what are some learnings that I guess you have collected along your way managing very large teams at Yahoo, Microsoft, and now Baidu that you would give to someone who’s just starting out in engineering management?
Qi Lu [00:36:30] – First of all, I would say managing an engineering team, particularly for the first time you are managing an engineering team, you need to focus on making sure that the engineering foundations, particularly build processes, build tools are well designed, well engineered, well taken care of. My learning of managing engineering teams is if you take slacks in those regard, ultimately what you pay is like the boat anchors. Becomes bigger and bigger. Any time you, because you always have pressure to ship the product, build a business, get more customers. There’s always the temptations to cut corners. Don’t, because you will be far better served up front making sure that engineering foundations are sound. That’s more important, that’s for first time managers managing a new team. The second is engineers, I would say, is a means to an end. The leader himself or herself and the teams pay attention to product, pay attention to how the product gets used, understand that today’s usage, scenario usage patterns, anticipate the future usages, in my view, is extremely important because you won’t be able to truly build great engineering systems or engineering capabilities without granting those in the product context. In particular, immerse yourself in truly understanding what the users are using a product today, and how that usage, you will grow in the future so you can anticipate. That’s another aspect… Related to values, understand the business because a lot of times the engineer work will be driven by monetizations, driven by distributions, and these are
Qi Lu [00:38:30] – just as important as you grow the company’s business and understanding the business models, how that impacts your product, how that impacts your engineering capabilities early on and embrace those challenges up front. It may slow you down in certain aspects, but it pays off for you to take time to understand these and build those capabilities in and anticipate the future needs. I would say the last thing is just cultivate a learning, iterative experimentation culture in the mindset because it’s always been a journey. You may think at first point you figure out I know how to do this, this product should do it this way, but the market is always fluid, the competition is always dynamic. Setting yourself, you’re teeing up for rapid iterations for quick, different ideas and to be able to seize your opportunities is also very important part for a first time engineering managers to set the team up for.
Daniel Gross [00:39:37] – And I guess a related question is what does Qi Lu look for in people when you interview them?
Qi Lu [00:39:43] – Depend on different jobs. Right now for my current job, the type of people I’m looking forward to are people who really understand the future mainstream users, usage patterns, particularly in depth understandings for human needs. And also, be able to see through the noise and understand that the fundamental undercurrent that driving those human needs because I think more and more engineering tools become more mature, product development methodology become more mature, those all become table stakes. What’s at the premier will be those individuals who really understand human and they can anticipate human needs and can envision experience in that context. That’s what I think will be at the premium for most companies that I can see and if I look for different type of jobs, even though I may not having a product manager, but I still look for that aspects. And my view on this is product sensitivities is the center of every line of work. Whether you’re sales person, or marketing person, or engineers, even HR person. If you understand the products, it helps you to do your job better.
Daniel Gross [00:41:09] – Understanding product and predicting future usage patterns.
Qi Lu [00:41:14] – Anticipating, seeing the future. To me this is an increasing a foundation of strength for any type of dealers.
Daniel Gross [00:41:23] – I spoke to some of your earlier colleagues in Microsoft and almost everyone said that you were an incredibly productive person. And so I’m curious to ask you if you were always that way and if not, what are some tips or tricks you’ve learned along the way that kind of made you what you are today?
Qi Lu [00:41:41] – I wouldn’t say I’m always productive. I try to be productive and I think that what helped me was a simple mindset, more of a personal belief, which is very simple view. I view myself as a piece of software. Today’s version must be better than yesterday’s version because there’s a cliche. Life is too short, why live the same day twice? Tomorrow’s version has to be better than today’s, so even though I make mistakes, the mistakes are a personal opportunity to learn so you can imagine the software have more if statement so when similar situation happen, you will avoid those. It’s that simple mindset. Keep the curiosity, keep learning. Again, I wouldn’t say I’m always productive, but I always try to be more productive.
Daniel Gross [00:42:34] – If someone is listening to this podcast now and is just thinking of somehow getting started either in the AI world or software engineering more broadly, what would you recommend they do? How should they go about figuring out what to do, where to apply, where to work?
Qi Lu [00:42:49] – I would say go to Hacker News, Slash Dot, Github, read a few articles, comments, and go to Github. To me, get your hands dirty. Grab some piece of code, run the model, and soon you will have inspirations, ideas coming to your mind. And as you keep doing this, I’m pretty confident you will find what you love to do.
Daniel Gross [00:43:19] – Was there a point in your career where you considered doing something else? Or was it always clear to you that this was going to be your craft?
Qi Lu [00:43:28] – Actually, yeah. Around the way, I thought about doing various different things. When I was in China in early childhood, I always wanted to be philosopher because I thought in order to truly solve the world’s lot of problems, we need to have philosophical underpinnings. At the time, it was a little bit influenced by studying Communism because it was required study. We were required to study Marxist and there’s the Communist Manifesto. In my view, is one of the, even though the theory in my view has issues, but it was one of the best written manifestos. I was always envisioning myself being a philosopher. But along the way, pragmatic constraints lead me to the current path because when I was young, I tried to be an engineer. Go to a shipbuilding factory. At the time, that was in the mid or late ’70s. Building big ships was kind of the most glamorous job. If you say I work for a shipbuilding company, you’re kind of wow. But I wasn’t strong enough. Because in my years, only 3% people can go to college. I wasn’t tall enough, I wasn’t heavy enough. You had to be like, for those type of schools, you need to be over 50 kilograms. And the way they do it is they weigh you before you go to the exam. I remember, keep eating, keep eating, every day weighing. Just couldn’t get to that 50 kilograms. Couldn’t get to that. I wasn’t qualified for a lot of those and then ask around. The people say. Oh, I have eyesight problem, I have near sight. I couldn’t really go to some of the discipline that I want to study. And then there are only two choices left for me in the field of study,
Qi Lu [00:45:33] – mathematics and the computer science. And ask my neighbors, people for feedback or a advice, what shall I pick? People say if you study mathematics, you can be a middle school teacher. If you study computer science, maybe you get to work at the radio factory. My parents thought radio factory was better, so I was like okay, let’s pick computer science.
Daniel Gross [00:45:57] – Wow.
Qi Lu [00:45:57] – I really had no idea why, once I start to work on this, I truly fell in love. I think very, very blessed, lucky that I get to work on the thing that I was able to do.
Daniel Gross [00:46:12] – Do you still code?
Qi Lu [00:46:14] – Not anymore. I read the code. I read because coding, I gave up quite a while ago actually. When I was at Yahoo. I as still doing coding when I was SVP. I thought that’s important for me to lay hands on. But when was reaching a point, my boss saw it at the time. He was yelling at me like, you are blocking your teams. He was right because if I don’t check in or if I have more bugs, it’s actually hurting them more. What I end up doing is, this was actually a brainstorm with Microsoft with Bill quite a bit. Essentially, you need to remain hands on the main shop. My approach is for the core algorithms, I must understand all the details, for the foundational of systems, the architectural design. I want to put myself, I can go toe-to-toe with the best architect, best other people to debate with them why you done that way. But coding, I thought, wasn’t the productive thing for me to do anymore, so I gave up when I was at Yahoo at that late stage.
Daniel Gross [00:47:23] – I actually want to dig into that a little bit because it’s a question I noticed as well at Apple, which is if the SVP is involved intimately in the algorithms, on the one hand you’re in control of the entire system. It’s quite good. On the other hand, it could be seen as micromanagement, you’re not giving an opportunity for the directors and the managers to grow. Did you ever get that feedback?
Qi Lu [00:47:49] – Oh yeah, all of the time.
Daniel Gross [00:47:50] – And how did…?
Qi Lu [00:47:51] – This is, to me, it can be managed properly because I feel my role is not to make decisions. My role is to challenge it. I always say why you have to design this way? No, I want you to give me a perfect solution because I know algorithm can be this way, but also, always made a claim. It’s your decision to make, but I see you have holes in your thinking. I want to challenge you, I want to debate with you. I kind of ask Bill. How do you keep up, what’s your approach? He essentially had a similar approach. If he knows Excel code base, extremely, probably better than anybody else.
Daniel Gross [00:48:27] – Bill Gates?
Qi Lu [00:48:28] – Yeah, yeah. Well, sometimes we have argument. Trust me, I know the code better than you do! I know, I got that. And then you also surround yourself with a bunch of technical team and technical talent, that they all are superbly into domains. You have ongoing dialogs. This is how you essentially keep your, mentally very sharp. But decision making, to me, is managing…. I think it’s very, as a principle, you want to make sure that your chief scientist, gets to make algorithmic decisions. For me, like … He’s at Twitter. He and I have debate argument for I don’t know, God knows, on so many fundamental issues. But always, Yung you make the decisions. But I think you’re wrong here. I disagree with you, I want to debate with you. I participate on a lot of those to keep myself grounded in my thinkings, understand the low level detail of the algorithm, the key algorithms when it’s in the rankings or content quality, all those, and then on key systems, the underlying systems, low level fabric. I thought that it’s very important to understand those. And you pick a few, more of pillar type so that your high level understanding can only go to the physical layers. And that helps to calibrate other different type of systems where you have something to enter your thoughts. And in management, maybe I will say one more thing. This is a great way to not people bullshit you because bullshitting happen all the time. If you do a few times, they know you can’t bullshit this guy because he is going to challenge you.
Qi Lu [00:50:07] – Otherwise, people will cook up beautiful stories, try to bullshit lies because in large organizations everyone want to be promoted. Not necessary for anybody intent, but sugarcoating, exaggeration happens all the time. If you yourself are grounded in core technologies, it helps to set the tone right. When we talk about technology, let’s have honest debate. Sharp contrast debate, but make it clear. The leaders are in charge, making decisions.
Daniel Gross [00:50:40] – Pick a few key technologies that you actually have full stack knowledge of, but don’t try to have that knowledge for every single part of the organization.
Qi Lu [00:50:48] – I think it’s impossible. I used to….
Daniel Gross [00:50:51] – Then you break, yeah.
Qi Lu [00:50:51] – In terms of management, my material, my practice. At Yahoo, I was able to largely do this. Essentially, my requirement for myself is can do the jobs two level down. At the moment, positive reasons because I can’t tell whether my guy gets bullshited or not.
Daniel Gross [00:51:06] – Right.
Qi Lu [00:51:07] – Because I can often tell you, dude, you got bullshited by your guys because I talk to them, I know what they do. Because I do that few times, you set the tune. Do you work super hard? They make sure that I don’t spot them, they got bullshit.
Daniel Gross [00:51:22] – Right.
Qi Lu [00:51:22] – It really kind of anchors and you don’t want relations. Everybody is doing the best work, everybody’s honest in their communication. Nobody try to bullshit your boss. I insist I will be able to do the jobs two levels down, so I always do that. But at some point, it’s just too much. You just, physically impossible. Then you kick a few key… To me, I call it my mental model is left, right hand, left, particularly for products. It’s essentially algorithms and system architecture play equally important role. Get the core set of algorithms. I study everything, essentially. All the details, I study and debate, discuss it with the better people. And then core systems, whether it’s content systems, serving systems from all the low level all the way up, just to pick those. And then you can calibrate all other systems. You can just see all. That system’s similar to this. I don’t need to understand detail, but I can extrapolate on how that system works.
Daniel Gross [00:52:20] – That’s a very interesting role. So be able to do the job two levels down. Now, something that I think will be on the mind of any CEO listening to this, does that apply to Satya? That is to say, do you anticipate Sacha being able to do your job and people that report to you’s job?
Qi Lu [00:52:36] – Satya used to report to me for two years. We kind of talk about that. We all understand there is a spirit of that approach and there is also a limitation on what you can physically do, but if you can find ways that works for you, but achieve the same effect, meaning the effect is each leaders, you are grounded on technological and the real underpinning of technology. You don’t build your strategy based on shadow and support an understanding of technology. That, to me, is important. Our industry is driven by technology and there’s many different ways to do that. Different people may have different approach. As long as you achieve that, as long as you set the tune for any sort of discussions, nobody should bullshit on things. You don’t exaggerate, you don’t try to get extra credit you don’t deserve. You honestly talk about your technology, honestly talk about your pro and cons. As long as achieve that, I think, because it’s the outcome versus the approach. For me, I used to do that because when Yahoo is kind of easy for me to do, I was, when leaving Yahoo, I had organization, about 3,000 people. I thought that I pretty much know all of them. Hired a lot of them, work with a lot of them, so you kind of get used to them. Say okay, tell me. Show me your code, let’s look at the system. How you do that things? But in Microsoft, it’s a different setting. I don’t think you necessarily have to follow that approach, as long as the goal is achieved. Each senior executive, making your decisions based on grounded understanding
Qi Lu [00:54:25] – of the underpinning of technology and it’s trajectory. What’s driving those technology forward? And then organizationally, there is truthful, honest conversations.
Daniel Gross [00:54:35] – If there’s a side to err on too much or too little rope, it seems like you’re there on the side of too little rope. That is to say, if you can micromanage or be too distant, would you err on the side of micromanaging, almost?
Qi Lu [00:54:54] – No. Let me say this. It’s an evolving journey for me. If you ask me today, I would err on the side of giving more for several important reasons. It’s my learning, particularly more recent. Increasingly, what I’ve come too realize is, each company, there’s overall operating output capacity. The capacity is really driven by the leaders’ understanding and their learning capabilities. And the structures they set up to enable unleash more independent points of views, independent learnings to pursue for the same objective, which is the company’s overarching vision and the mission. If you overly constrain, to say, there’s some degrees that are tight harmony along certain dimensions. You tend to overly constraint the collective imagination, creativity, capacities over the organizations. Therefore I’m more in the mystery of designing only structures and meta models that enable a organization’s have more senior leaders that is able to exercise different way of thinking. Different lens of looking at the same problem and be able to pursue and experiment when are trying to solve a larger problem or achieve larger missions. In the past, I was more on the operating more of so called a tight ship.
Qi Lu [00:56:44] – Ensure everything falls in line but out of my own learnings at this stage, I see a lot more good by having a organization that give more ropes to our leaders. Give them more autonomy, give them more independence but somehow orchestrate the in time delving away that the effort all add up towards a common mission, common goals. That to me is a important quest but I will lean towards giving my ropes.
Daniel Gross [00:57:16] – So I guess you just have to be very careful with those leaders to make sure that they themselves are not giving too much rope and so forth and so forth.
Qi Lu [00:57:25] – Yes, you need to design a meta structure. It is in that context motivation is very, very important. Understand the human motivation become a key part of designing that meta structure and I think what Reid Hoffman’s book The Allince is actually one of the simple but effective model. Essentially you have three different cup of two duties and particularly for senior ones, are you really foundation. Let’s say we’re going to go all the way into the end. We’re so aligned, our share goals or you’re much more of a transformational. You just want to get something under your belt so you can move on next phase. As long as you are very clear and then you know that your senior leaders or you are important position. Each people, they are motivated driven by what? Because motivation is very important. Capacity and motivation in a structure that ties these things. Loosely ties these things together to me is perhaps at the again we use the word at the of company design or initial designs that enables a original funding team. So when you managing the teams to unleash normal innovation capacities.
Daniel Gross [00:58:41] – As someone who is very philosophical, but has also has an engineering mindset. How do you marry both of those worlds to lay by a specific rules?
Qi Lu [00:58:54] – The answer I will give you is when I tell Microsoft Steve Balmer asked me to … my first speech is give a self introduction about myself. What I live for, how do I do my work and so essentially I wrote a simply set of slides. I think I mentioned five things. These are not necessarily rules but I think largely, I think will answer your questions. Essentially first learn every day. I view myself as a piece of a software. Don’t live the same day twice. The second is integrity. To me integrity has three subsets, one is always speak truthfully. You will not hear me say the same thing differently when I talk to different people. Always say it … I may be wrong but I will say the way I see it. That’s one aspect, integrity. The others, keep my word. If I give you my word. I will everything I can to keep my word to me it’s very important. A third part of integrity is acknowledge my mistake or weakness. To me, as a leader, this is important part of high integrity because we’re going to make mistakes. The leaders should publicly acknowledge the mistake he made or your weaknesses. So that’s integrity and being frugal. To me, a penny saved is same as a penny earned. There’s always rainy day when you can save financial resources. Always say because there’s always better way to use those resources. Being frugal to me is always important part of what I do. Let me see, there’s five things. Integrity, learn everyday, work ethics. I always say for me personally, I would do something. I will do a work only if this work I felt I love so much. I would be all in essentially.
Qi Lu [01:01:07] – Leave nothing behind. Every ounce of energy. It’s all in there so that’s work ethics. I also when I say work ethics is always making a… People in my own league. You don’t have to follow what I do, so because having a balance. A work life balance is always good thing but for me I always be… I forgot there’s one more thing. There’s one more things. I know there’s five things I said. Give me a little bit of time. Maybe able to remember but this is what I share with my teams when I initially joined Microsoft to say who I am, how I do my work. You can consider those are rules but these are the fundamental set of beliefs that guides what I do.
Daniel Gross [01:01:55] – Feel like that’s a very good note to close on. Qi, thank you so much for spending the time with us today.
Qi Lu [01:01:59] – You bet, thanks for having me.
Craig Cannon [01:02:00] – Alright, thanks for listening so as always the video and transcript are at blog.ycombinator.com and if you have a second, please subscribe and review the show. Alright see you next week.
Y Combinator created a new model for funding early stage startups. Twice a year we invest a small amount of money ($150k) in a large number of startups (recently 200). The startups move to Silicon