Fireside Chat with Anurag Gupta and Paul Hsiao

Please welcome paul hushow, partner at canvas.

Ventures and anurag gupta, vp at Amazon web services. Hey guys. How's everyone doing today? Thanks for having us. I've always wanted to be a talk show host. I can't see you guys all, but thanks Anurag for giving us the opportunity. So my name is Paul Shao, general partner at Canvas. Joined the venture business in 2003. The first two investments I worked on were Salesforce and Tableau. So really come a long way. Sit on a few SaaS boards today. So privileged and excited to have Anurag here. For those of you who don't know him, he joined AWS in 2011 and leads to analytics and transactional database today. You know, from a short span of six years, these guys have built a multi billion ARR business. You know, we'll get into sort of how they did it and it just really is exciting to have you here.

Thanks.

How did you get to aws?

So I was at a startup which we sold to another company. You're on the board of course. And so I spent a couple of years there, you know, to get the handcuffs off. And then at that point I couldn't do another startup for personal reasons. And so it was the question of where to go next. And so the question I asked myself at that time was so every, you know, it's easy to look back and figure out 10 years ago, what company would you have loved to have been joined at that time? So the question for me was 10 years from now, what's the company I wanted to join now, you know, and you know, would have wanted to join now. And you know, Amazon and AWS really were at the top of that list.

Yeah, Basil talks about the regret minimization. Sounds like you went through a very similar thought process there too. So what led you to AWS then?

So I'm systems guy and so if you look at systems, there are these points where things transition like mainframe to mini computer, mini computer to PC, PC to intranet, Internet to cloud and mobile. And when you look at those transitions, basically the whole stack has an opportunity to change. And so I saw the opportunity to change the stack.

And so you know, if we think about it's been astonishing the depth of the innovation that you guys have introduced, the pace of the product so that you have also done. More importantly, I'm just surprised by sort of the business model. Right. You guys have turned a lot of non consumption clients into mainstream customers, right? I guess questions about like how did you guys approach this? Like what Sort of what was the insight there that found so such a short period of time you could actually come into the business and build such an offering.

So you're familiar with our working backwards process. Maybe I can fill you guys in a little bit. So what we do is before we write a line of code, we work on the press release and faq, frequently asked questions document. And basically there are two problems when you're building something, whether what people need and whether it's possible. And so whether it's possible takes a lot of time to figure out. But what people need is actually a simpler problem. And in some, particularly for engineers, it's very easy to get wrapped up in the technology. And that's very different from figuring out what people want. And so for these big services, we like to go into large spaces because you're not going to get 90% of some small space. You're going to if you, you want to do well enough. So even if you mess up, you're going to do pretty well. Right. And it's meaningful business. And then you know that you're going to go into a space where you're going to be feature poor compared to what people are at least when you launch. Right. I mean that's the general nature of minimum viable product. Right. And so you have to look for a space of non consumption. And so one of the things when we started redshift was this notion that hey, enterprise storage, the growth rate is about four times the pace of data warehousing. So why is that? And so you get into then with your customers figuring out why they're storing data that they're not analyzing. Now that's a strange thing, right? So what we figured out is that it just wasn't simple enough. It just was too expensive and was just too slow to put more data into these systems. So that sort of leads us to a somewhat different model than what they have today.

I mean, gosh, congrats on the success. Right. If I track you for the last six years, there's more than what, 12 products from Redshift to EMR to Elasticsearch and now most recently Neptune. I sort of think about how we sit on boards where we dream about building 100 million ARR, 150 million ARR in six years. It's incredible that you guys have built multibillion ARR in the same timeframe basically from scratch. It's a thousand x reach, sort of a growth waveform.

Yeah, I mean we don't break out anything below the AWS level. But you know we're pretty happy with where we are.

I worship these guys. If it's not, if it's not obvious to y', all, what surprised you?

So what surprises me is that when I first joined aws, it was a much smaller team than it is today, obviously, but the people in leadership roles are much the same. And so the ability to scale without going outside has been really impressive to see.

And how is that? Like, how do you sort of. I think about. I sit on board. So some of these SaaS enabled marketplaces, they are really powered by the services that you provide, right? I mean, like from Quicksight and others. I mean, I think about Houzz, I think about Transfix. It's incredible what you have done, but yet you held the headcount sort of the same. I'm just curious about what's the methodology, what about AWS that enable you to do so?

So I think we think about scaling all the time and the key thing there for us is really working through mechanisms because, you know, we have. Jeff has this famous statement that good intentions don't work. People don't come into the office expecting to mess up. Right. And, you know, if they do, you know, you've got bigger problems. Right. And so the question is really, how do you create mechanisms so that you can make things that are scalable? And so examples of that would be, so you set a goal, maybe it's a revenue target, and then you're going to have trackable output metrics that drive that, and then you might have input metrics that drive that output metrics, and then you maybe recurse a couple of times to say, okay, treat that input metric as an output metric, what drives that one. And that just lets you watch the metrics. And then you can just focus on the things that are doing really well, so you can double down on them, and the things that are doing really poorly. So you can focus your attention, which is a fixed resource, and apply into that. Because the things that are in the middle, that's fine, you don't have to pay that much attention.

Is it an example of a service that you guys have applied that methodology to?

Really, all of them? Yeah, it's across the board from Aurora.

To Athena, you guys have done similar things. Yep, got it. So switching topic a little bit, I mean, you have an amazing vantage point on the transformation that's really happening in the stack that many of companies here build on. Just curious about what you see, you know, in the transformation right now.

So I guess I'd look at the growth of data. I think that's been a really big deal. I mean I often, you know, my estimate is as GROW data grows 10 times tenfold every five years.

Yeah.

And so that's basically three orders of magnitude every 15 years. So if you have a petabyte now you're going to have a exabyte in 15 years. If you have a terabyte now, you're going to have a petabyte in 15 years. And that's a lot. Right. And that actually changes how you approach things because it used to be that you built these monolithic systems and then just because of the growth of data alone, we've had to go to distributed systems, things like Hadoop, et cetera. And I think that's also in the future going to drive a move towards things like serverless and going to multi tenant systems and pay for what you use in very small request bursts. And we can talk a little bit about that.

Yeah, totally. I mean I think the notion that data that 10x every 5 years. I encourage you to name it Gupta's Data Law because on the Venture side we've sort of followed the Moore's law for many years. That's been the defining technology curve. What's interesting is that with the data increasing 10x, there's just a lot one.

Could do and Moore's law is basically out of vent.

It is. Right. And so then how do you drive the innovation based on the data that we are collecting?

So I think, I mean data is really like potential energy. So it doesn't do you any good if you just store it away somewhere. But if you look at, you know, it used to be that voice recognition systems were just garbage. Right. And you know, I mean if you ever had like a wrist that was hurt and you tried to use one of those systems, you know, it would just, every word would have a mistake in it. Yeah. And now suddenly they all work. And why is that? Well, it's really two factors. One is that we have enough data to provide a meaningful corpus as a training set and we have inexpensive compute power that we can use to sort of run the training models on and just make them better and better and better. And I think that's generally true across a lot of AI. And AI has been a pretty big change in the industry. I think we're still in the hype cycle on it, but you know, maybe today, but you know, it's going to be I think transformative. And you know, the other thing that I've seen is, is that people's expectations Change, right? Like you, you know, you start off and you're saying to yourself, wow, it's, Isn't it amazing? I can talk to my phone and have it do something and then like two weeks later you're like, how ridiculous is it that I'm looking on this voice conference enough to tap things in while I'm sitting trying to drive my car? And rather than just being able to tell it who I'm trying to talk to.

And that intelligence, how you build into the software stack makes a difference.

That's right.

As we talk about the data increasing so much, what are these data? Are these business data? Are they shining? What are you seeing? What are we collecting here?

Yeah, so, I mean, at the highest level, I guess the Internet itself is a video serving platform, right? With a small amount of data going on it, but just focusing on the data, you know, there's this, you know, we used to think about data in terms of things like business transactions. So that's a tiny fraction of it. Now it's all really machine to machine now, or it's Internet scale, consumer or, you know, and so forth. So the real question still is, you know, what do you do with it? I can give you an example, maybe. So let's think about if I were building an expense reporting app five years ago, what would I do? So I obviously make it mobile first. I do gestures, maybe nowadays I do voice. I'd focus on productivity and so forth. So that's all the basic meat and potatoes, if you will, of building something. And it's not particularly defensive because anybody else can do the same thing, particularly when they have my app to go and copy, if you will. And so the question Jeff would ask.

The software development stack is so easy now. Everyone could have these mobile sort of issues.

That's right. And so what Jeff would say is like, okay, you've built a castle, now the barbarians are coming to attack your castle. You know what you need? You need a moat around your castle. And so the question is, what's your moat? And so I think data presents a moat because there's a flywheel for it. The more data you have, the more use you can provide into it and then the more data you have. Right. And those are positive things. And an example of that, going back to expense reporting, is that if you think about it, expense reporting could be viewed as a productivity tool or something like that, but it's something more than that. It's actually a measurement of transactions or interactions between producers and consumers, much like Amazon.com is. And so If I'm a consumer, wouldn't it be great if I go to Kansas City to find out what are the restaurants that people like me use? Very similar to finding out a book. Or if I'm a producer, where do I stack rank relative to other people and what could I be doing to make it better? Or if I'm a company trying to provide this app, you know, how do I do automated acceptance of trivial transactions to prevent people from dealing with them or understand fraud where people are doing something where I can collect that. And what's great about that sort of domain in SaaS is that you don't have data for one company. You have data for a lot of companies and there's a positive cycle there. The more data I have, the more useful it is, the more data I have. Right, right.

And so that's, you know, I'm glad.

You let you know it's a real shift away from.

It is right. When we think about, you know, having watched this industry for gosh come out, you know, 20, 30 years, there was a semiconductor that sort of innovation drove a lot of. And then we put the ERP CRM on the, the last, what SaaS is now coming up 20 years old now.

Right.

We've gone to the cloud transformation, we've gone to the mobile transformation. It's harder to build that moat around SaaS. And we see the white spaces taking up the pitches that come into our shops are you target particular vertical industry like farming, like whatever the vertical industrial might be, or you target a market segments like a SMB or mid market and start we think about how do you make that SaaS more intelligent, how do you build something that self learns and that trained data is where something we think a lot about. Can you actually incorporate a proposal proprietary data into that SaaS stack?

Exactly, yeah.

Do you one thing, if you look at the capabilities that Athena and all these have provided, it feels like everyone needs a data lake strategy. What is a data lake?

So a data lake is basic. Basically once you have a lot of data, you know, you need to put it into a central location. So you know, in AWS that might be S3, which is our central repository. I think of S3 as sort of like Chicago O'. Hare. Everything can get into it, everything can get out of it. And then around you want that data to be in some standardized open format so that the tools that you might use externally or the tools that we provide, all of it can access it. You can't afford to have your data be trapped because it's way too expensive to move it around. Got it or change it or transform it or whatever. And so then you need a real portfolio of different data analytics capabilities around it. You know, so in our case, we have serverless SQL in Athena or we have a dupe in emr, we have, you know, data warehousing in Redshift and you know, so on, so on, so on. And the real goal for us is to be able to provide something. So you just throw your data in and the system will go and process it and understand it and so forth. You know, we're not 100% of the way there, but it matters, right? And so, you know, you need to secure that data lake, of course, you know, because, you know, and then around the basic core data analytics, you need some form of AI, right? So you can really gain incremental value off of it.

One of the things that we all struggle with is funding sort of, I mean, I think entrepreneurs here do as well is the scarcity of finding data scientists.

Right.

Including the data engineers. I'm just curious about what you've seen and suggest your sort of recommendation on that as well.

You know, I see a lot of people on LinkedIn who are suddenly data scientists. But you know, I think in some sense the question really is what you need. You know, I don't know that it's about data science and, you know, how many PhDs you can stack from CMU or someplace like that. It's really about figuring out the value you're trying to give to your customer. It's very much back to that working backwards process of going and figuring out what am I trying to do. If I'm trying to build a recommendation engine, then there are particular things I need. I need someone who knows recommendation engines, right. And the degree to which you can get, you know, the great thing is that most of the algorithms are well known, published, have open source formats. A lot of us in the infrastructure industry are providing our technology stacks in open source. And so I think that. But the pace of innovation is really significant. So I think what people really need right now is advice on what to do. And then once they have a measure of advice, you know, the building part of it isn't dramatically different. And you know, advice is, I don't know that you need in house advice necessarily, depending on the size of your company.

Right, of course, you know, we see one thing that we see is because that stack is the capabilities coming from you is so sort of, you know, even though you're humble to say not full stack but it's really a very complete. So someone wants to actually have the data lake, they can actually come to you. They want to integrate the data into this app. They can do that as well. One thing I think what's interesting is then the moat, going back to the moat conversation is what's the proprietary data set that the individual customers have that can make it, that could feed into their machine learning model that will give them an edge on whatever they can do for their customers.

That's actually a really important point because I think a lot of people, when they set up their initial contracts with, with their customers, they don't include what rights that they have to the customer's data. And that's an important question. And I think we all sort of end up on these systems, whether they're consumer or business, where every so often the EULA changes and you just have to hit accept after paging through eight pages before you can do anything. But and I think a large part of that is because people need better access to the data, not because they're interested in invading your privacy, just because they want to share it, aggregate it, figure out how to provide a better experience. And so I think the degree to which we can, a company can set those things up easily, quickly and so forth, removes those internal barriers. Because the value really is in aggregating a multitude of people together.

I mean, I think we used to think about what's the IP rise, right. The software, you know, what is the IP stack? Nowadays we ask a lot of question to the companies that we fund. What's your data rights? Right? Do you have the rise to when your customers give you data or when you're collecting data from different sources, do you have rights to that? We've been surprised by some how some of the large SaaS companies, as much as the talent based machine learning talent that they have, apply to it. They actually can look into their own customers data to actually apply intelligence.

I mean you have to be very careful about it. Right?

Because the privacy things here.

Well, and you know, it's a trust thing, right. You can't afford to break customer trust ever. Right? I mean, and if you're a customer centric company, you know, like we aspire to be at Amazon, you know, I think you have to be very thoughtful about what you're doing and why you're doing it and that you're doing it in such a way that it really benefits the person who's providing you that information. Not doing it in any sort of negative way.

Yeah, totally shifting a Little bit where we're seeing so much of the things is global in nature now. Right. Just curious about what are you seeing from that perspective. A lot of customers want to have these multi master data sets everywhere. Historically that's been a really difficult thing to do. I'm just curious about what you guys doing on that front.

You know, we're working on it. So you know, projects are underway. We announced them in the most recent reinvent. But it is a challenging problem. I think people have an expectation for low latency communication and you know, if I'm playing Angry Birds in, you know, San Francisco and I go to London, I expect the, you know, to still be able to start that game from where I left it off. Right. And you know that what's true in consumer is equally true in enterprise. Right. And it's a funny thing that enterprise tends to trail consumer. Right. Like the, you know, think about messaging apps and you know like the cost of them on the enterprise side versus the value you get just by using say Facebook. Yeah, right. And the pace of innovation on the consumer and the simplicity of the experience and all the rest of those things. And you know, the, the fact that it's free. Right. For me as a consumer at least. Right.

When you think about your product and people are thinking about. We always talk about having product strategy. Right. And now we think about coming back to the data rise a little bit and even global in Asia. How do you think about data strategy? Like what's the tactical advice that you give to people when they like is it just sort of even having a conversational data strategy? Like when you get into sort of talking to. When you meet your customers, what is it that you want them to think about?

That's a really interesting question. I think. I frankly don't know. I think I'm not a deeply strategic guy. My basic notion is do what my customers want and just aggressively go and keep iterating. Because that way I don't have to think forward five years. I can think forward one scratch or maybe a little bit longer than that. If it comes to the question of something that's just fundamentally hard to do. But I think it's really just a question of understanding what your customers want from that side, from a data right side. I think it's really just a question of understanding what one might want to do and making sure that you handle the privacy considerations, particularly with, you know, the European Union, GDPR and all of that. But you know, which I think is, you know, not just an EU issue. Right. I mean you know, you want to be able to do that for any customer anywhere.

Well, maybe another way to comment a question is like, if you think about the capabilities, the range, like the, you know, the 1215 products you have, what is it that people. And we've gone from batch to real time, we've gone from global in nature and the depth of the pipeline and data lake. What is it that people don't appreciate sort of how they should use these products yet that would turn sort of that business advantage to them?

Yeah. So, you know, I think what's increasingly important, I think is bringing simplicity for people. And so I announced a bunch of stuff at the past reinvent our AWS's user conference in November. And one of I was pretty surprised that the thing that really most attracted people in the data space was serverless. This notion that I'm just gonna send requests, the server spin up, spin down, resize, whatever. And why would someone care about that? You know, it's just, I mean you can kind of build that kind of thing on your own if you had to. But I think the benefit is that we kind of are increasingly in the world. Like 10 years ago we were in the world where you had nine backend engineers to every front end engineer that's flipped over. Now you've got one backend engineer, two or three ops engineers, and the rest of them are front engineers, which is awesome if you're an apps person, because the front end is where you're providing value. So the degree to take the complexity.

Out already on the back end.

That's right. And the degree to which we can provide that simplification, I think is mass MA matters a great deal. And it's, you know, it's not just about aws. Right. There's so many app comp, sorry, API companies that are out there now. Right. Like if I previously, if I were creating an application, I'd have to burn like four engineers just on how to do payments and now I do an API call and that's amazing. Right. And you like to think of it.

As the application, the API application network there. Right. How do you, before coming with how they, how they tied everything together?

I think the next interesting thing is going to be how the SaaS companies themselves become API companies. Right. Like, you know, how do I integrate my expense reporting thing directly into somebody else's application so it's largely invisible and you know, it. You know, we think that having, you know, the, you know, the time in front, you know, the screen time in front of the customer is valuable. But it's really the data that's valuable and attention's a scarce resource and there are only so many applications I'm willing to learn. Right. I think we all probably installed 50 applications on our iPhones or whatever when we first got them, and now we're using four or five.

So when you think about that coming back to that, how do you add intelligent back into the software stack, right? The notion of having that transformation where we're talking about going from SaaS to that intelligent SaaS and how do you build that, mold that data mode in there are there things that the good practice that you're seeing people do right now?

So I think one thing is that the world moves to SaaS, right? Maybe, arguably has already, but there's an awful lot that's still sitting in enterprise applications sitting on premise. And, you know, people don't want to work and be, you know, those applications basically treat you like a clerk. Right. You know, you have to spend a month learning how to, you do order entry and, you know, I don't want to spend a month learning how to use order entry, you know, you know, or, you know, like doing time off or any of the things that I end up having to do. Right. I really want small apps that are in, you know, single purpose, you know, I want them to have a trivial interface. I want them to be gesture or voice oriented to the degree possible, and I want them not to bother me if they can do the thing themselves. Right. And so that all turns into AI and so forth on the back end. And also Automated integrations into APIs between applications so that they're not asking me to carry a piece of data from one app to another. I mean, you know, almost like the iPhone used to be, right. You know, it was transformative to say, like, oh, I just read an email or I press on something that it recognized as a phone number and I'm placing a call. Right. But that doesn't seem to exist in applications today, right, that deeper integration in part because I think everyone wants to own the screen.

They do and duck, really. I mean, I think it creates a really interesting opportunity even in enterprise app space.

Right. And particularly if you, you know, try to monetize the API call. That's right, right. Because. And that's sort of where serverless comes in. If you can go to something where I'm not charging you on a per month basis or on a per year subscription basis, I'm charging you for what you use. And you know, I think that's been an interesting Move. Right. I mean a large part of the innovation I think in the cloud was this move from three year tco, you know, pay provision to peak to pay by the hour. And if you look at something like Lambda, which is our serverless compute offering in AWS, you know, it's pay per request in 100 millisecond chunks. And if you think about a hundred millisecond that has the same ratio to an hour as an hour has to three years.

That's right.

Roughly. Right. And that's, you know, I think if you, you know, and that's the model, I mean that's, you know, so we talked a little bit about non consumption as a place to go. The other interesting thing to do from a business model perspective is over consumption. Right. People using something that they're not paying for, something they're not using over provisioning. Yeah. It's just useless. Right. And so for example, if I had a ETL tool, you know, and I pay for it and I'm running that ETL, my ETL jobs, you know, like eight hours a day, there's 16 hours where it's sitting idle. Why am I paying for it? And you know, I think SaaS vendors have the opportunity to interleave usage. You know, I mean, Yeah, I mean you have to provision to peak, just as I have to provision to peak. But my customer doesn't and you know, your customer doesn't, which is awesome.

Yeah, totally.

I mean the challenge becomes how to make money at those price points. But you know, that's all about volume. Right. One of the things we talk about a lot in awesome in Amazon is how to run high volume, low margin businesses. Right. And to, you know, really pay attention to the underlying efficiencies. And you know, that also builds you a moat. Yeah, right. Because the larger you are and the more you can take out of the efficiencies, you know, that only really comes with scale.

That's right.

And so, you know, that's another positive advantage. Just as data is, you guys have.

Brought the pricing to a level that's so much slower than everything else and made it on demand. That sort of. And, but it created volume out and.

You know, it's based in some sense on the intrinsic, sense of optimism that the demand is there. If I can find a price point and a product that people can afford.

Yeah, cool. Well, thank you so much for coming here and sharing your thoughts. I mean, round of applause for Anurag here.