Narrator: From theCUBE studios in Palo Alto and Boston, it's theCUBE, covering IBM Think, brought to you by IBM. >> We're back. You're watching theCUBE and our coverage of IBM Think 2020, the digital IBM thinking. We're here with Jamie Thomas, who's the general manager of strategy and development for IBM Systems. Jamie, great to see you. >> It's great to see you as always. >> You have been knee deep in qubits, the last couple years. And we're going to talk quantum. We've talked quantum a lot in the past, but it's a really interesting field. We spoke to you last year at IBM Think about this topic. And a year in this industry is a long time, but so give us the update what's new in quantum land? >> Well, Dave first of all, I'd like to say that in this environment we find ourselves in, I think we can all appreciate why innovation of this nature is perhaps more important going forward, right? If we look at some of the opportunities to solve some of the unsolvable problems, or solve problems much more quickly, in the case of pharmaceutical research. But for us in IBM, it's been a really busy year. First of all, we worked to advance the technology, which is first and foremost in terms of this journey to quantum. We just brought online our 53 qubit computer, which also has a quantum volume of 32, which we can talk about. And we've continued to advance the software stack that's attached to the technology because you have to have both the software and the hardware thing, right rate and pace. We've advanced our new network, which you and I have spoken about, which are those individuals across the commercial enterprises, academic and startups, who are working with us to co-create around quantum to help us understand the use cases that really can be solved in the future with quantum. And we've also continued to advance our community, which is serving as well in this new digital world that we're finding ourselves in, in terms of reaching out to developers. Now, we have over 300,000 unique downloads of the programming model that represents the developers that we're touching out there every day with quantum. These developers have, in the last year, have run over 140 billion quantum circuits. So, our machines in the cloud are quite active, and the cloud model, of course, is serving us well. The data's, in addition, to all the other things that I mentioned. >> So Jamie, what metrics are you trying to optimize on? You mentioned 53 qubits I saw that actually came online, I think, last fall. So you're nearly six months in now, which is awesome. But what are you measuring? Are you measuring stability or coherence or error rates? Number of qubits? What are the things that you're trying to optimize on to measure progress? >> Well, that's a good question. So we have this metric that we've defined over the last year or two called quantum volume. And quantum volume 32, which is the capacity of our current machine really is a representation of many of the things that you mentioned. It represents the power of the quantum machine, if you will. It includes a definition of our ability to provide error correction, to maintain states, to really accomplish workloads with the computer. So there's a number of factors that go into quantum volume, which we think are important. Now, qubits and the number of qubits is just one such metric. It really depends on the coherence and the effect of error correction, to really get the value out of the machine, and that's a very important metric. >> Yeah, we love to boil things down to a single metric. It's more complicated than that >> Yeah, yeah. >> specifically with quantum. So, talk a little bit more about what clients are doing and I'm particularly interested in the ecosystem that you're forming around quantum. >> Well, as I said, the ecosystem is both the network, which are those that are really intently working with us to co-create because we found, through our long history in IBM, that co-creation is really important. And also these researchers and developers realize that some of our developers today are really researchers, but as you as you go forward you get many different types of developers that are part of this mix. But in terms of our ecosystem, we're really fundamentally focused on key problems around chemistry, material science, financial services. And over the last year, there's over 200 papers that have been written out there from our network that really embody their work with us on this journey. So we're looking at things like quadratic speed up of things like Monte Carlo simulation, which is used in the financial services arena today to quantify risk. There's papers out there around topics like trade settlements, which in the world today trade settlements is a very complex domain with very interconnected complex rules and trillions of dollars in the purview of trade settlement. So, it's just an example. Options pricing, so you see examples around options pricing from corporations like JPMC in the area of financial services. And likewise in chemistry, there's a lot of research out there focused on batteries. As you can imagine, getting everything to electric powered batteries is an important topic. But today, the way we manufacture batteries can in fact create air pollution, in terms of the process, as well as we want batteries to have more retention in life to be more effective in energy conservation. So, how do we create batteries and still protect our environment, as we all would like to do? And so we've had a lot of research around things like the next generation of electric batteries, which is a key topic. But if you can think, you know Dave, there's so many topics here around chemistry, also pharmaceuticals that could be advanced with a quantum computer. Obviously, if you look at the COVID-19 news, our supercomputer that we installed at Oak Ridge National Laboratory for instance, is being used to analyze 8000 different compounds for specifically around COVID-19 and the possibilities of using those compounds to solve COVID-19, or influence it in a positive manner. You can think of the quantum computer when it comes online as an accelerator to a supercomputer like that, helping speed up this kind of research even faster than what we're able to do with something like the Summit supercomputer. Oak Ridge is one of our prominent clients with the quantum technology, and they certainly see it that way, right, as an accelerator to the capacity they already have. So a great example that I think is very germane in the time that we find ourselves in. >> How 'about startups in this ecosystem? Are you able to-- I mean there must be startups popping up all over the place for this opportunity. Are you working with any startups or incubating any startups? Can you talk about that? >> Oh yep. Absolutely. There's about a third of our network are in VC startups and there's a long list of them out there. They're focused on many different aspects of quantum computing. Many of 'em are focused on what I would call loosely, the programming model, looking at improving algorithms across different industries, making it easier for those that are, perhaps more skilled in domains, whether that is chemistry or financial services or mathematics, to use the power of the quantum computer. Many of those startups are leveraging our Qiskit, our quantum information science open programming model that we put out there so it's open. Many of the startups are using that programming model and then adding their own secret sauce, if you will, to understand how they can help bring on users in different ways. So it depends on their domain. You see some startups that are focused on the hardware as well, of course, looking at different hardware technologies that can be used to solve quantum. I would say I feel like more of them are focused on the software programming model. >> Well Jamie, it was interesting hear you talk about what some of the clients are doing. I mean obviously in pharmaceuticals, and battery manufacturers do a lot of advanced R and D, but you mentioned financial services, you know JPMC. It's almost like they're now doing advanced R and D trying to figure out how they can apply quantum to their business down the road. >> Absolutely, and we have a number of financial institutions that we've announced as part of the network. JPMC is just one of our premiere references who have written papers about it. But I would tell you that in the world of Monte Carlo simulation, options pricing, risk management, a small change can make a big difference in dollars. So we're talking about operations that in many cases they could achieve, but not achieve in the right amount of time. The ability to use quantum as an accelerator for these kind of operations is very important. And I can tell you, even in the last few weeks, we've had a number of briefings with financial companies for five hours on this topic. Looking at what could they do and learning from the work that's already done out there. I think this kind of advanced research is going to be very important. We also had new members that we announced at the beginning of the year at the CES show. Delta Airlines joined. First Transportation Company, Amgen joined, a pharmaceutical, an example of pharmaceuticals, as well as a number of other research organizations. Georgia Tech, University of New Mexico, Anthem Insurance, just an example of the industries that are looking to take advantage of this kind of technology as it matures. >> Well, and it strikes me too, that as you start to bring machine intelligence into the equation, it's a game changer. I mean, I've been saying that it's not Moore's Law driving the industry anymore, it's this combination of data, AI, and cloud for scale, but now-- Of course there are alternative processors going on, we're seeing that, but now as you bring in quantum that actually adds to that innovation cocktail, doesn't it? >> Yes, and as you recall when you and I spoke last year about this, there are certain domains today where you really cannot get as much effective gain out of classical computing. And clearly, chemistry is one of those domains because today, with classical computers, we're really unable to model even something as simple as a caffeine molecule, which we're all so very familiar with. I have my caffeine here with me today. (laughs) But you know, clearly, to the degree we can actually apply molecular modeling and the advantages that quantum brings to those fields, we'll be able to understand so much more about materials that affect all of us around the world, about energy, how to explore energy, and create energy without creating the carbon footprint and the bad outcomes associated with energy creation, and how to obviously deal with pharmaceutical creation much more effectively. There's a real promise in a lot of these different areas. >> I wonder if you could talk a little bit about some of the landscape and I'm really interested in what IBM brings to the table that's sort of different. You're seeing a lot of companies enter this space, some big and many small, what's the unique aspect that IBM brings to the table? You've mentioned co-creating before. Are you co-creating, coopertating with some of the other big guys? Maybe you could address that. >> Well, obviously this is a very hot topic, both within the technology industry and across government entities. I think that some of the key values we bring to the table is we are the only vendor right now that has a fleet of systems available in the cloud, and we've been out there for several years, enabling clients to take advantage of our capacity. We have both free access and premium access, which is what the network is paying for because they get access to the highest fidelity machines. Clearly, we understand intently, classical computing and the ability to leverage classical with quantum for advantage across many of these different industries, which I think is unique. We understand the cloud experience that we're bringing to play here with quantum since day one, and most importantly, I think we have strong relationships. We have, in many cases, we're still running the world. I see it every day coming through my clients' port vantage point. We understand financial services. We understand healthcare. We understand many of these important domains, and we're used to solving tough problems. So, we'll bring that experience with our clients and those industries to the table here and help them on this journey. >> You mentioned your experience in sort of traditional computing, basically if I understand it correctly, you're still using traditional silicon microprocessors to read and write the data that's coming out of quantum. I don't know if they're sitting physically side by side, but you've got this big cryogenic unit, cables coming in. That's the sort of standard for some time. It reminds me, can it go back to ENIAC? And now, which is really excites me because you look at the potential to miniaturize this over the next several decades, but is that right, you're sort of side by side with traditional computing approaches? >> Right, effectively what we do with quantum today does not happen without classical computers. The front end, you're coming in on classical computers. You're storing your data on classical computers, so that is the model that we're in today, and that will continue to happen. In terms of the quantum processor itself, it is a silicon based processor, but it's a superconducting technology, in our case, that runs inside that cryogenics unit at a very cold temperature. It is powered by next-generation electronics that we in IBM have innovated around and created our own electronic stack that actually sends microwave pulses into the processor that resides in the cryogenics unit. So when you think about the components of the system, you have to be innovating around the processor, the cryogenics unit, the custom electronic stack, and the software all at the same time. And yes, we're doing that in terms of being surrounded by this classical backplane that allows our Q network, as well as the developers around the world to actually communicate with these systems. >> The other thing that I really like about this conversation is it's not just R and D for the sake of R and D, you've actually, you're working with partners to, like you said, co-create, customers, financial services, airlines, manufacturing, et cetera. I wonder if you could maybe kind of address some of the things that you see happening in the sort of near to midterm, specifically as it relates to where people start. If I'm interested in this, what do I do? Do I need new skills? Do I need-- It's in the cloud, right? >> Yeah. >> So I can spit it up there, but where do people get started? >> Well they can certainly come to the Quantum Experience, which is our cloud experience and start to try out the system. So, we have both easy ways to get started with visual composition of circuits, as well as using the programming model that I mentioned, the Qiskit programming model. We've provided extensive YouTube videos out there already. So, developers who are interested in starting to learn about quantum can go out there and subscribe to our YouTube channel. We've got over 40 assets already recorded out there, and we continue to do those. We did one last week on quantum circuits for those that are more interested in that particular domain, but I think that's a part of this journey is making sure that we have all the assets out there digitally available for those around the world that want to interact with us. We have tremendous amount of education. We're also providing education to our business partners. One of our key network members, who I'll be speaking with later, I think today, is from Accenture. Accenture's an example of an organization that's helping their clients understand this quantum journey, and of course they're providing their own assets, if you will, but once again, taking advantage of the education that we're providing to them as a business partner. >> People talk about quantum being a decade away, but I think that's the wrong way to think about it, and I'd love your thoughts on this. It feels like, almost like the return coming out of COVID-19, it's going to come in waves, and there's parts that are going to be commercialized thoroughly and it's not binary. It's not like all of a sudden one day we're going to wake, "Hey, quantum is here!" It's really going to come in layers. Your thoughts? >> Yeah, I definitely agree with that. It's very important, that thought process because if you want to be competitive in your industry, you should think about getting started now. And that's why you see so many financial services, industrial firms, and others joining to really start experimentation around some of these domain areas to understand jointly how we evolve these algorithms to solve these problems. I think that the production level characteristics will curate the rate and pace of the industry. The industry, as we know, can drive things together faster. So together, we can make this a reality faster, and certainly none of us want to say it's going to be a decade, right. I mean, we're getting advantage today, in terms of the experimentation and the understanding of these problems, and we have to expedite that, I think, in the next few years. And certainly, with this arms race that we see, that's going to continue. One of the things I didn't mention is that IBM is also working with certain countries and we have significant agreements now with the countries of Germany and Japan to put quantum computers in an IBM facility in those countries. It's in collaboration with Fraunhofer Institute or miR Scientific Organization in Germany and with the University of Tokyo in Japan. So you can see that it's not only being pushed by industry, but it's also being pushed from the vantage of countries and bringing this research and technology to their countries. >> All right, Jamie, we're going to have to leave it there. Thanks so much for coming on theCUBE and give us the update. It's always great to see you. Hopefully, next time I see you, it'll be face to face. >> That's right, I hope so too. It's great to see you guys, thank you. Bye. >> All right, you're welcome. Keep it right there everybody. This is Dave Vellante for theCUBE. Be back right after this short break. (gentle music)

>> Hey, welcome back everybody. Jeff Rick here with the Cube. We are live in downtown San Jose at the historic Pagoda Lounge, part of Big Data SV, which is part of Strata + Hadoop Conference, which is part of Big Data Week because everything big data is pretty much in San Jose this week. So we're excited to be here. We're here with George Gilbert, our big data analyst from Wikibon, and a great guest, Gaurav Dhillon, Chairman and CEO of SnapLogic. Gaurav, great to see you. >> Pleasure to be here, Jeff. Thank you for having me. George, good to see you. >> You guys have been very busy since we last saw you about a year ago. >> We have. We had a pretty epic year. >> Yeah, give us an update, funding, and customers, and you guys have a little momentum. >> It's a good thing. It's a good thing, you know. A friend and a real mentor to us, Dan Wormenhoven, the Founder and CEO of NetApp for a very long time, longtime CEO of NetApp, he always likes to joke that growth cures all startup problems. And you know what, that's the truth. >> Jeff: Yes. >> So we had a scorching year, you know. 2016 was a year of continuing to strengthen our products, getting a bunch more customers. We got about 300 new customers. >> Jeff: 300 new customers? >> Yes, and as you know, we don't sell to small business. We sell to the enterprise. >> Right, right. >> So, this is the who's who of pharmaceuticals, continued strength in high-tech, continued strength in retail. You know, all the way from Subway Sandwich to folks like AstraZeneca and Amgen and Bristol-Myers Squibb. >> Right. >> So, some phenomenal growth for the company. But, you know, we look at it very simply. We want to double our company every year. We want to do it in a responsible way. In other words, we are growing our business in such a way that we can sail over to cash flow break-even at anytime. So responsibly doubling your business is a wonderful thing. >> So when you look at it, obviously, you guys are executing, you've got good products, people are buying. But what are some of the macro-trends that you're seeing talking to all these customers that are really helping push you guys along? >> Right, right. So what we see is, and it used to be the majority of our business. It's now getting to be 50/50. But still I would say, historically, the primary driver for 2016 of our business was a digital transformation at a boardroom level causing a rethinking of the appscape and people bringing in cloud applications like Workday. So, one of the big drivers of our growth is helping fit Workday into the new fabric in many enterprises: Vassar College, into Capital One, into finance and various other sectors. Where people bring in Workday, they want to make that work with what they have and what they're going to buy in the future, whether it's more applications or new types of data strategies. And that is the primary driver for growth. In the past, it was probably a secondary driver, this new world of data warehousing. We like to think of it as a post-modern era in the use of data and the use of analytics. But this year, it's trending to be probably 50/50 between apps and data. And that is a shift towards people deploying in the same way that they moved from on-premise apps to SAS apps, a move towards looking at data platforms in the cloud for all the benefits of racking and stacking and having the capability rather than being in the air-conditioning, HVAC, and power consumption business. And that has been phenomenal. We've seen great growth with some of the work from Microsoft Azure with the Insights products, AWS's Redshift is a fantastic growth area for us. And these sorts of technologies, we think are going to be of significant impact to the everyday, the work clothing types of analytics. Maybe the more exotic stuff will stay on prem, but a lot of the regular business-like stuff, you know, stuff in suits and ties is moving into the cloud at a rapid pace. >> And we just came off the Google Next show last week. And Google really is helping continue to push kind of ML and AI out front. And so, maybe it's not the blue suit analytics. >> Gaurav: Indeed, yes. >> But it does drive expectations. And you know, the expectations of what we can get, what we should get, what we should be moving towards is rapidly changing. >> Rapidly changing, for example, we saw at The New York Times, which as many of Google's flagship enterprise customers are media-related. >> Jeff: Right. >> No accident, they're so proficient themselves being in the consumer internet space. So as we encountered in places like The New York Times, is there's a shift away from a legacy data warehouse, which people like me and others in the last century, back in my time in Informatica, might have sold them towards a cloud-first strategy of using, in their case, Google products, Bigtable, et cetera. And also, they're doing that because they aspirationally want to get at consumer prices without having to have a campus and the expense of Google's big brain. They want to benefit from some of those things like TensorFlow, et cetera, through the machine learning and other developer capabilities that are now coming along with that in the cloud. And by the way, Microsoft has amazing machine learning capability in its Azure for Microsoft Research as well. >> So Gaurav, it's interesting to hear sort of the two drivers. We know PeopleSoft took off starting with HR first and then would add on financials and stumble a little bit with manufacturing. So, when someone wants to bring in Workday, is it purely an efficiency value prop? And then, how are you helping them tie into the existing fabric of applications? >> Look, I think you have to ask Dave or Aneel or ask them together more about that dynamic. What I know, as a friend of the firm and as somebody we collaborate with, and, you know, this is an interesting statistic, 20 percent of Workday's financial customers are using SnapLogic, 20 percent. Now, it's a nascent business for them and you and I were around in the last century of ERP. We saw the evolution of functional winners. Some made it into suites and some didn't. Siebel never did. PeopleSoft at least made a significant impact on a variety of other things. Yes, there was Bonn and other things that prevented their domination of manufacturing and, of course, the small company in Walldorf did a very good job on it too. But that said, what we find is it's very typical, in a sense, how people using TIBCO and Informatica in the last century are looking at SnapLogic. And it's no accident because we saw Workdays go to market motion, and in a sense, are following, trying to do the same thing Dave and Aneel have done, but we're trying to do the same thing, being a bunch of ex-Informatica guys. So here's what it is. When you look at your legacy installation, and you want to modernize it, what are your choices? You can do a big old upgrade because it's on-premise software. Or you can say, "You know what? "For 20% more, I could just get the new thing." And guess what? A lot of people want to get the new thing. And that's what you're going to see all the time. And that's what's happening with companies like SnapLogic and Workday is, you know, someone. Right here locally, Adobe, it's an icon in technology and certainly in San Jose that logo is very big. A few years ago, they decided to make the jump from legacy middleware, TIBCO, Informatica, WebMethods, and they've replaced everything globally with SnapLogic. So in that same way, instead of trying to upgrade this version and that version and what about what we do in Japan, what do we do in Sweden, why don't you just find a platform as a service that lets you elevate your success and go towards a better product, more of a self-service better UX, millennial-friendly type of product? So that's what's happening out there. >> But even that three-letter company from Walldorf was on-stage last week. You can now get SAP on the Google Cloud Platform which I thought was pretty amazing. And the other piece I just love but there's still a few doubters out there on the SAS platform is now there's a really visual representation. >> Gaurav: There is. >> Of the dominance of that style going up in downtown San Francisco. It's 60 stories high, and it's taken over the landscape. So if there's ever any a doubt of enterprise adaptation of SAS, and if anything, I would wonder if kind of the proliferation of apps now within the SAS environment inside the enterprise starts to become a problem in and of its own self. Because now you have so many different apps that you're working on and working. God help if the internet goes down, right? >> It's true, and you know, and how do you make e pluribus unim, out of many one, right? So it's hilarious. It is almost at proliferation at this point. You know, our CFO tapped me the other day. He said, "Hey, you've got to check this out." "They're using a SAS application which they got "from a law firm to track stock options "inside the company." I'm like, "Wow, that is a job title and a vertical." So only high growth private venture backed companies need this, and typically it's high tech. And you have very capable SAS, even in the small grid squares in the enterprise. >> Jeff: Right, right. >> So, a sign, and I think that's probably another way to think about the work that we do at SnapLogic and others. >> Jeff: Right, right. >> Other people in the marketplace like us. What we do essentially is we give you the ERP of one. Because if you could choose things that make sense for you and they could work together in a very good way to give you very good fabric for your purposes, you've essentially bought a bespoke suit at rack prices. Right? Without that nine times multiplier of the last century of having to have just consultants without end, darkened the sky with consultants to make that happen. You know? So that, yes, SAS proliferation is happening. That is the opportunity, also the problem. For us, it's an opportunity where that glass is half-full we come in with SnapLogic and knit it together for you to give you fabric back. And people love that because the businesses can buy what they want, and the enterprise gets a comprehensive solution. >> Jeff: Right, right. >> Well, at the risk of taking a very short tangent, that comment about darkening the skies, if I recall, was the battle of the Persians threatening the 300 Greeks at the battle of Thermopylae. >> Gaurav: Yes. >> And they said, "We'll darken the skies with our arrows." And so the Greek. >> Gaurav: Come and get 'em. >> No, no. >> The famous line was, he said, "Give us your weapons." And the guy says, "Come and get 'em." (laughs) >> We got to that point, the Greek general says, "Well, we'll fight in the shade." (all laughing) But I wanted to ask you. >> This is the movie 300 as well, right? >> Yes. >> The famous line is, "Give us your weapons." He said, "Come and get 'em." (all laughing) >> But I'm thinking also of the use case where a customer brings in Workday and you help essentially instrument it so it can be a good citizen. So what does that make, or connect it so it can be a good citizen. How much easier does that mean or does that make fitting in other SAS apps or any other app into the fabric, application fabric? >> Right, right. Look, George. As you and I know, we both had some wonderful runs in the last century, and here we are doing version 2.0 in many ways, again, very similar to the Workday management. The enterprise is hip to the fact that there is a Switzerland nature to making things work together. So they want amazing products like Workday. They want amazing products like the SAP Cloud Suite, now with Concur, SuccessFactors in there. Some very cool things happening in the analytics world which you'll see at Sapphire and so on. So some very, very capable products coming from, I mean, Oracle's bought 80 SAS companies or 87 SAS companies. And so, what you're seeing is the enterprise understands that there's going to be red versus blue and a couple other stripes and colors and that they want their businesspeople to buy whatever works for them. But they want to make them work together. All right? So there is a natural sort of geographic or structural nature to this business where there is a need for Switzerland and there is a need for amazing technology, some of which can only come from large companies with big balance sheets and vertical understanding and a legacy of success. But if a customer like an AstraZeneca where you have a CIO like Dave Smoley who transformed Flextronics, is now doing the same thing at AstraZeneca bringing cloud apps, is able to use companies like SnapLogic and then deploy Workday appropriately, SAP appropriately, have his own custom development, some domestic, some overseas, all over the world, then you've got the ability again to get something very custom, and you can do that at a fraction of the cost of overconsulting or darkening the skies in the way that things were done in the last century. >> So, then tell us about maybe the convergence of the new age data warehousing, the data science pipeline, and then this bespoke collection of applications, not bespoke the way Oracle tried it 20 years ago where you had to upgrade every app tied into every other app on prem, but perhaps the integration, more from many to one because they're in the cloud. There's only one version of each. How do you tie those two worlds together? >> You know, it's like that old bromide, "Know when to hold 'em. "Know when to fold them." There is a tendency when programming becomes more approachable, you have more millennials who are able to pick up technology in a way. I mean, it's astounding what my children can do. So what you want to do is as a enterprise, you want to very carefully build those things that you want to build, make sure you don't overbuild. Or, say, if you have a development capability, then every problem looks like a development nail and you have a hammer called development. "Let's hire more Java programmers." That's not the answer. Conversely, you don't want to lose sight of the fact that to really be successful in this millennium, you have to have a core competence around technology. So you want to carefully assemble and build your capability. Now, nobody should ever outsource management. That's a bad idea. (chuckles) But what you want to do is you want to think about those things that you want to buy as a package. Is that a core competence? So, there are excellent products for finance, for human capital management, for travel expense management. Coupa just announced today their for managing your spend. Some of the work at Ariba, now the Ariba Cloud at SAP, are excellent products to help you do certain job titles really well. So you really shouldn't be building those things. But what you should be doing is doing the right element of build and buy. So now, what does that mean for the world of analytics? In my view, people building data platforms or using a lot of open source and a lot of DevOps labor and virtualization engineering and all that stuff may be less valuable over time because where the puck is going is where a lot of people should skate to is there is a nature of developing certain machine language and certain kind of AI capabilities that I think are going to be transformational for almost every industry. It is hard to imagine anything in a more mechanized back office, moving paper, manufacturing, that cannot go through a quantum of improvement through AI. There are obviously moral and certain humanity dystopia issues around that to be dealt with. But what people should be doing is I think building out the AI capabilities because those are very custom to that business. Those have to do with the business's core competence, its milieu of markets and competitors. But there should be, in a sense, stroking a purchase order in the direction of a SAS provider, a cloud data provider like Microsoft Azure or Redshift, and shrinking down their lift-and-shift bill and their data center bill by doing that. >> It's fascinating how long it took enterprises to figure out that. Just like they've been leveraging ADP for God knows how many years, you know, there's a lot of other SAS applications you can use to do your non-differentiated heavy lifting, but they're clearly all in now. So Gaurav, we're running low on time. I just want to say, when we get you here next year, what's top of your plate? What's top of priorities for 2017? Cause obviously you guys are knocking down things left and right. >> Thank you, Jeff. Look, priority for us is growth. We're a growth company. We grow responsibly. We've seen a return to quality on the part of investors, on the part of public and private investors. And you know, you'll see us continue to sort of go at that growth opportunity in a manner consistent with our core values of building product with incredible success. 99% of our customers are new to our products last quarter. >> Jeff: Ninety-nine percent? >> Yes sir. >> That says it all. >> And in the world of enterprise software where there's a lot of snake oil, I'm proud to say that we are building new product with old-fashioned values, and that's what you see from us. >> Well 99% customer retention, you can't beat that. >> Gaurav: Hard to beat! There's no way but down from there, right? (laughing) >> Exactly. Alright Gaurav, well, thanks. >> Pleasure. >> For taking a few minutes out of your busy day. >> Thank you, Jeff. >> And I really appreciate the time. >> Thank you, Jeff, thank you, George. >> Alright, he's George Gilbert. I'm Jeff Rick. You're watching the Cube from the historic Pagoda Lounge in downtown San Jose. Thanks for watching.