from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante cloud repatriation is a term often used by technology companies the ones that don't operate a public cloud the marketing narrative most typically implies that customers have moved work to the public cloud and for a variety of reasons expense performance security etc are disillusioned with the cloud and as a result are repatriating workloads back to their safe comfy and cost-effective on-premises data center while we have no doubt this does sometimes happen the data suggests that this is a single digit de minimis phenomenon hello and welcome to this week's wikibon cube insights powered by etr some have written about the repatriation myth but in this breaking analysis we'll share hard data that we feel debunks the narrative and is currently being promoted by some we'll also take this opportunity to do our quarterly cloud revenue update and share with you our latest figures for the big four cloud vendors let's start by acknowledging that the definition of cloud is absolutely evolving and in this sense much of the vendor marketing is valid no longer is cloud just a distant set of remote services that lives up there in the cloud the cloud is increasingly becoming a ubiquitous sensing thinking acting set of resources that touches nearly every aspect of our lives the cloud is coming on prem and work is being done to connect clouds to each other and the cloud is extending to the near and far edge there's little question about that today's cloud is not just compute storage connectivity and spare capacity but increasingly it's a variety of services to analyze data and predict slash anticipate changes monitor and interpret streams of information apply machine intelligence to data to optimize business outcomes it's tooling to share data protect data visualize data and bring data to life supporting a whole new set of innovative applications notice there's a theme there data increasingly the cloud is where the high value data lives from a variety of sources and it's where organizations go to mine it because the cloud vendors have the best platforms for data and this is part of why the repatriation narrative is somewhat dubious actually a lot dubious because the volume of data in the cloud is growing at rates much faster than data on prem at least by a couple thousand basis points by our estimates annually so cloud data is where the action is and we'll talk about the edge in a moment but a new era of application development is emerging with containers at the center the concept of write wants run anywhere allows developers to take advantage of systems that run on-prem say a transaction system and tap data from multiple sources in various locations there might be multiple clouds or at the edge or wherever and combine that with immense cheap processing power that we've discussed extensively in previous breaking analysis episodes and you see this new breed of apps emerging that's powered by ai those are hitting the market so this is not a zero-sum game the cloud vendors have given the world an infrastructure gift by spending like crazy on capex more than a hundred billion last year on capex for example for the big four and in our view the players that don't own a cloud should stop being so defensive about it they should thank the hyperscalers and lay out a vision as to how they'll create a new abstraction layer on top of the public cloud and you know that's what they're doing and they'll certainly claim to be actively working on this vision but consider the pace of play between the hyperscalers and their traditional on-prem providers we believe the innovation gap is actually widening meaning the public cloud players are accelerating their innovation lead and will 100 compete for hybrid applications they have the resources the developer affinity they're doing custom silicon and have the expertise there and the tam expansion goals that loom large so while it's not a zero-sum game and hybrid is definitely real we think the cloud vendors continue to gain share most rapidly unless the hybrid crowd can move faster now of course there's the edge and that is a wild card but it seems that again the cloud players are very well positioned to innovate with custom silicon programmable infrastructure capex build-outs at the edge and new thinking around system architectures but let's get back to the core story here and take a look at cloud adoptions you hear many marketing messages that call into question the public cloud at its recent think conference ibm ceo arvind krishna said that only about 25 of workloads had moved into the public cloud and he made the statement that you know this might surprise you implying you might think it should be much higher than that well we're not surprised by that figure especially especially if you narrow it to mission critical work which ibm does in its annual report actually we think that's probably high for mission critical work moving to the cloud we think it's a lot lower than that but regardless we think there are other ways to measure cloud adoption and this chart here from david michelle's book c seeing digital shows the adoption rates for major technological innovations over the past century and the number of years how many years it took to get to 50 percent household adoption electricity took a long time as did telephones had that infrastructure that last mile build out radios and tvs were much faster given the lower infrastructure requirements pcs actually took a long time and the web around nine years from when the mosaic browser was introduced we took a stab at estimating the pace of adoption of public cloud and and within a decade it reached 50 percent adoption in top enterprises and today that figures easily north of 90 so as we said at the top cloud adoption is actually quite strong and that adoption is driving massive growth for the public cloud now we've updated our quarterly cloud figures and want to share them with you here are our latest estimates for the big four cloud players with only alibaba left to report now remember only aws and alibaba report clean or relatively clean i ass figures so we use survey data and financial analysis to estimate the actual numbers for microsoft in google it's a subset of what they report in q121 we estimate that the big 4is and pas revenue approached 27 billion that's q121 that figure represents about 40 growth relative to q1 2020. so our trailing 12-month calculation puts us at 94 billion so we're now on roughly 108 billion dollar run rate as you may recall we've predicted that figure will surpass 115 billion by year end when it's all said and done aws it remains the leader amongst the big four with just over half of the market that's down from around 63 percent for the full year of 2018. unquestionably as we've reported microsoft they're everywhere they're ubiquitous in the market and they continue to perform very well but anecdotally customers and partners in our community continue to report to us that the quality of the aws cloud is noticeably better in terms of reliability and overall security etc but it doesn't seem to change the trajectory of the share movements as microsoft's software dominance makes doing business with azure really easy now as of this recording alibaba has yet to report but we'll update these figures once their earnings are released let's dig into the growth rates associated with these revenue figures and make some specific comments there this chart here shows the growth trajectory for each of the big four google trails the pack in revenue but it's growing faster than the others from of course a smaller base google is being very aggressive on pricing and customer acquisition to that we say good google needs to grow faster in our view and they most certainly can afford to be aggressive as we said combined the big four are growing revenue at 40 on a trailing 12-month basis and that compares with low single-digit growth for on-prem infrastructure and we just don't see this picture changing in the near to midterm like storage growth revenue from the big public cloud players is expected to outpace spending on traditional on on-prem platforms by at least 2 000 basis points for the foreseeable future now interestingly while aws is growing more slowly than the others from a much larger 54 billion run rate we actually saw sequential quarterly growth from aws and q1 which breaks a two-year trend from where aws's q1 growth rate dropped sequentially from q4 interesting now of course at aws we're watching the changing of the guards andy jassy becoming ceo of amazon adam silipsky boomeranging back to aws from a very successful stint at tableau and max peterson taking over for for aws public sector replacing teresa carlson who is now president and heading up go to market at splunk so lots of changes and we think this is actually a real positive for aws as it promotes from within we like that it taps previous amazon dna from tableau salesforce and it promotes the head of aws to run all of amazon a signal to us that amazon will dig its heels in and further resist calls to split aws from the mothership so let's dig in a little bit more to this repatriation mythbuster theme the revenue numbers don't tell the entire story so it's worth drilling down a bit more let's look at the demand side of the equation and pull in some etr survey data now to set this up we want to explain the fundamental method used by etr around its net score metric net score measures spending momentum and measures five factors as shown in this wheel chart that shows the breakdown of spending for the aws cloud it shows the percentage of customers within the platform that are either one adopting the platform new that's the lime green in this wheel chart two increasing spending by more than five percent that's the forest green three flat spending between plus or minus five percent that's the gray and four decreasing spend by six percent or more that's the pink and finally five replacing the platform that's the bright red now dare i say that the bright red is a proxy for or at least an indicator of repatriation sure why not let's say that now net score is derived by subtracting the reds from the greens anything above 40 percent we consider to be elevated aws is at 57 so very high not much sign of leaving the cloud nest there but we know it's nuanced and you can make an argument for corner cases of repatriation but come on the numbers just don't bear out that narrative let's compare aws with some of the other vendors to test this theory theory a bit more this chart lines up net score granularity for aws microsoft and google it compares that to ibm and oracle now other than aws and google these figures include the entire portfolio for each company but humor me and let's make an assumption that cloud defections are lower than the overall portfolio average because cloud has more momentum it's getting more spend spending so just stare at the red bars for a moment the three cloud players show one two and three percent replacement rates respectively but ibm and oracle while still in the single digits which is good show noticeably higher replacement rates and meaningfully lower new adoptions in the lime green as well the spend more category in the forest green is much higher within the cloud companies and the spend less in the pink is notably lower and you can see the sample sizes on the right-hand side of the chart we're talking about many hundreds over 1300 in the case of microsoft and if we look if we put hpe or dell in the charts it would say several hundred responses many hundreds it would look similar to ibm and oracle where you have higher reds a bigger fat middle of gray and lower greens it's just the way it is it shouldn't surprise anyone and it's you know these are respectable but it's just what happens with mature companies so if customers are repatriating there's little evidence here we believe what's really happening is that vendor marketing people are talking to customers who are purposefully spinning up test and dev work in the cloud with the intent of running a workload or portions of that workload on prem and when they move into production they're counting that as repatriation and they're taking liberties with the data to flood the market okay well that's fair game and all's fair in tech marketing but that's not repatriation that's experimentation or sandboxing or testing and deving it's not i'm leaving the cloud because it's too expensive or less secure or doesn't perform for me we're not saying that those things don't happen but it's certainly not visible in the numbers as a meaningful trend that should factor into buying decisions now we perfectly recognize that organizations can't just refactor their entire applications application portfolios into the cloud and migrate and we also recognize that lift and shift without a change in operating model is not the best strategy in real migrations they take a long time six months to two years i used to have these conversations all the time with my colleague stu miniman and i spoke to him recently about these trends and i wanted to see if six months at red hat and ibm had changed his thinking on all this and the answer was a clear no but he did throw a little red hat kool-aid at me saying saying that the way they think about the cloud blueprint is from a developer perspective start by containerizing apps and then the devs don't need to think about where the apps live whether they're in the cloud whether they're on prem where they're at the edge and red hat the story is brings a consistency of operations for developers and operators and admins and the security team etc or any plat on any platform but i don't have to lock in to a platform and bring that everywhere with me i can work with anyone's platform so that's a very strong story there and it's how arvin krishna plans to win what he calls the architectural battle for hybrid cloud okay so let's take a take a look at how the big cloud vendors stack up with the not so big cloud platforms and all those in between this chart shows one of our favorite views plotting net score or spending velocity on the vertical axis and market share or pervasiveness in the data set on the horizontal axis the red shaded area is what we call the hybrid zone and the dotted red lines that's where the elite live anything above 40 percent net score on the on on the vertical axis we consider elevated anything to the right of 20 on the horizontal axis implies a strong market presence and by those kpis it's really a two horse race between aws and microsoft now as we suggested google still has a lot of work to do and if they're out buying market share that's a start now you see alibaba shown in the upper left hand corner high spending momentum but from a small sample size as etr's china respondent level is obviously much lower than it is in the u.s and europe and the rest of apac now that shaded res red zone is interesting and gives credence to the other big non-cloud owning vendor narrative that is out there that is the world is hybrid and it's true over the past several quarters we've seen this hybrid zone performing well prominent examples include vmware cloud on aws vmware cloud which would include vcf vmware cloud foundation dell's cloud which is heavily based on vmware and red hat open shift which perhaps is the most interesting given its ubiquity as we were talking about before and you can see it's very highly elevated on the net score axis right there with all the public cloud guys red hat is essentially the switzerland of cloud which in our view puts it in a very strong position and then there's a pack of companies hovering around the 20 vertical axis level that are hybrid that by the way you see openstack there that's from a large telco presence in the data set but any rate you see hpe oracle and ibm ibm's position in the cloud just tells you how important red hat is to ibm and without that acquisition you know ibm would be far less interesting in this picture oracle is oracle and actually has one of the strongest hybrid stories in the industry within its own little or not so little world of the red stack hpe is also interesting and we'll see how the big green lake ii as a service pricing push will impact its momentum in the cloud category remember the definition of cloud here is whatever the customer says it is so if a cio says we're buying cloud from hpe or ibm or cisco or dell or whomever we take her or his word for it and that's how it works cloud is in the eye of the buyer so you have the cloud expanding into the domain of on-premises and the on-prem guys finally getting their proverbial acts together with hybrid that they've been talking about since 2009 but it looks like it's finally becoming real and look it's true you're not going to migrate everything into the cloud but the cloud folks are in a very strong position they are on the growth flywheel as we've shown they each have adjacent businesses that are data based disruptive and dominant whether it's in retail or search or a huge software estate they are winning the data wars as well that seems to be pretty clear to us and they have a leg up in ai and i want to look at that can we all agree that ai is important i think we can machine intelligence is being infused into every application and today much of the ai work is being done in the cloud as modeling but in the future we see ai moving to the edge in real time and real-time inferencing is a dominant workload but today again 90 of it is building models and analyzing data a lot of that work happens in the cloud so who has the momentum in ai let's take a look here's that same xy graph with the net score against market share and look who has the dominant mind share and position and spending momentum microsoft aws and google you can see in the table insert in the lower right hand side they're the only three in the data set of 1 500 responses that have more than 100 n aws and microsoft have around 200 or even more in the case of microsoft and their net scores are all elevated above the 60 percent level remember that 40 percent that red line indicates the elevation mark the high elevation mark so the hyperscalers have both the market presence and the spend momentum so we think the rich get richer now they're not alone there are several companies above the 40 line databricks is bringing ai and data science to the world of data lakes with its managed services and it's executing very well salesforce is infusing infusing ai into its platform via einstein you got sap on there anaconda is kind of the gold standard that platform for data science and you can see c3 dot ai is tom siebel's company going after enterprise ai and data robot which like c3 ai is a small sample in the data set but they're highly elevated and they're simplifying machine learning now there's ibm watson it's actually doing okay i mean sure we'd like to see it higher given that ginny rometty essentially bet ibm's future on watson but it has a decent presence in the market and a respectable net score and ibm owns a cloud so okay at least it's a player not the dominance that many had hoped for when watson beat ken jennings in jeopardy back 10 years ago but it's okay and then is oracle they're now getting into the act like it always does they want they watched they waited they invested they spent money on r d and then boom they dove into the market and made a lot of noise and acted like they invented the concept oracle is infusing ai into its database with autonomous database and autonomous data warehouse and look that's what oracle does it takes best of breed industry concepts and technologies to make its products better you got to give oracle credit it invests in real tech and it runs the most mission critical apps in the world you can hate them if you want but they smoke everybody in that game all right let's take a look at another view of the cloud players and see how they stack up and where the big spenders live in the all-important fortune 500 this chart shows net score over time within the fortune 500 aws is particularly interesting because its net score overall is in the high 50s but in this large big spender category aws net score jumps noticeably to nearly 70 percent so there's a strong indication that aws the largest player also has momentum not just with small companies and startups but where it really counts from a revenue perspective in the largest companies so we think that's a very positive sign for aws all right let's wrap the realities of cloud repatriation are clear corner cases exist but it's not a trend to take to the bank although many public cloud users may think about repatriation most will not act on it those that do are the exception not the rule and the etr data shows that test and dev in the clouds is part of the cloud operating model even if the app will ultimately live on prem that's not repatriation that's just smart development practice and not every workload is will or should live in the cloud hybrid is real we agree and the big cloud players know it and they're positioning to bring their stacks on prem and to the edge and despite the risk of a lock-in and higher potential monthly bills and concerns over control the hyperscalers are well com positioned to compete in hybrid to win hybrid the legacy vendors must embrace the cloud and build on top of those giants and add value where the clouds aren't going to or can't or won't they got to find places where they can move faster than the hyperscalers and so far they haven't shown a clear propensity to do that hey that's how we see it what do you think okay well remember these episodes are all available as podcasts wherever you listen you do a search breaking analysis podcast and please subscribe to the series check out etr's website at dot plus we also publish a full report every week on wikibon.com and siliconangle.com a lot of ways to get in touch you can email me at david.velante at siliconangle.com or dm me at dvalante on twitter comment on our linkedin post i always appreciate that this is dave vellante for the cube insights powered by etr have a great week everybody stay safe be well and we'll see you next time you

>>Ply from Barcelona, Spain pits the cube covering Cisco live 2020 Ratu by Cisco and its ecosystem partners. >>Hey, welcome back live to Cisco live in 2020 in Barcelona. We're in Europe, Barcelona. I'm John Ferrara, Dave Alante. We've got a great guest here and the whole theme of the show is not about the infrastructure is about the applications and the applications being powered by an infrastructure powered by Cisco. We've got a great guest, senior vice president, general manager, team collaboration, Shri Travaasa of Cisco. You run all the big products, WebEx on steroids, new announcements. You had a really killer announcements, the pack booth. We'll get into that. Welcome to the cube. Thanks for coming. Thank you for having me. What's the quick news? You're on stage giving the keynote quickly share the news. We can get into it. So we are obviously >>coming out with a set of updates to our great portfolio. We reach out to about 300 million users across the enterprise today who use us for all the way from meetings to team collaboration to calling to powering meeting rooms. So in a sense, what we have as a products that, uh, is either in the meeting room or on the desktop or on a mobile phone. So any one of those methods and mechanisms. And in the past couple of years we've seen massive adoption of video, uh, whether it'd be on the mobile phone, whether it be in your desktop or in a meeting room itself. >>So video is the key. You had an announcement with Mike, uh, Microsoft teams explain that because don't they? Don't they compete with you? >>Yes, we, we, so the best way to describe it as is it's compatibility and competition. So it's competitive to compete, um, for the sake of our end users. So end user choice pretty much drives, uh, the types of integrations we do these days. You can't leave it to an it organization to do that integration. You've got to make sure these products work. So we integrate quite a bit with our competitors, spar, Slack, Microsoft teams, zoom. We do integrate with all of those guys. And the Microsoft teams integration, um, is prefaced on providing the best real time media experience into the Microsoft ecosystem. So if a customer is using office three 65 for document collaboration and chooses us for real time collaboration, they get >>the best experience comes from. So this has been a sleepy space for awhile and then all of a sudden you've mentioned Slack, zoom comes out, big IPOs, high valuations, Microsoft kind of transitioning and gets, it's based to to teams. There's a lot of excitement all of a sudden. And I was thinking in the last year out, geez, I wonder if Cisco is asleep at the wheel, but today you had all these announcements, so obviously not asleep at the wheel. Describe what you see going on in the space and what excites you from a standpoint of what you've just announced. So I think >>over the past two years, rightfully so, there's been a ton of movement in this space and I think it's driven by, it's, it's important to talk about why it's driven by globalization of the workforce. So that globalization of the workforce has, has, has, has gotten caught steam in the past few years and you pretty much see folks being employed across the globe. Whoever has the skill gets employed in a sentence. And what we see within the confines of WebEx is an increase in user engagement. So the same user is using WebEx a lot more and we wonder why we're seeing basically cross time zone meetings go up and team collaboration as we know it is no longer across the table. It's actually across time zones, across geographies, across language boundaries. So you're seeing that happen and the power of team collaboration is not just bringing people together, it's the data in heading to within the conversation becomes the new currency. >>It's the new frontier. And you can do a whole bunch of analytics on that. You can provide information on that. You can basically bring what I would call uninterrupted work streams in the myths, which is, you know, how do you take a conversation, take a part of a set of action items out of it and basically take it all the way so that there's automation, there's least amount of transmission loss and transmission loss in a sense. So that's, that's what's causing, um, this, this industry to wake up because it's a productivity gain in knowledge worker population. >>I don't know why it's off the charts on these systems, you know, low denominator and it's so easy to justify. I mean to me this is the biggest way that people are kind of talking about, but not really specifically addressing it. And to me, I always like to look at the startup world because the startup world is ultimately the Canary in the coal mine. Cody cloud native was before cloud hit, the startups were in there wipe clean sheet of paper, all cloud. Now that's mainstream. I had a conversation with Mitchell, the founder of Hashi Corp and we were talking about the concept of virtual first. And his startup was all virtual. They didn't have an office, they could afford one, but their teams were remote. This is the new dynamic that works. And so I believe that this is going to be an enterprise requirement because this has been validated. >>You seeing people work virtually, development teams, marketing to any team, they're remote, they're at home. So this is a trend. This is real. And designing a product for virtual first versus saying, Oh, if your virtual uses Proctor was designed for this, this is really where it's coming to in my opinion. How are you guys addressing that? Because in that video is not easy. Totally not. You guys been doing video Cisco for a lot them. I know from the cable companies to make a deep packet inspection and managing packets, QoS and mean policy basis, the perfect storm for making video work better. So explain the whole virtual first and the video. Start by sharing a small little secret. I run this business and yet I'm a remote worker. Cisco's based in San, I live in Seattle. >>I live in a small town called mamasan. I'm, I'm a perfect example of who we are. It's all the. So without a doubt, what has also spurred this is the bandwidth to trust the globe, not just in the U S uh, I find that, you know, parts of Asia have very good connectivity. If you go into Korea, Singapore, it's just fantastic, right? If you go into the Western Europe, Scandinavian countries, it's just fabulous. So I think the, the fact of the matter is you, the act of working together across the table and the act of these collaboration tools bringing people together need to be the same. That's pretty much where we are all headed. We're all trying to achieve that Nirvana, making sure there's no dissonance when you bring people across video that's key. That requires not only the ability to see and hear people, but to be able to whiteboard, to be able to have a very rich and immersive conversation on biblical creation so that, you know, using like stickies on a whiteboard for example, how well can you do it? >>So those are the types of things that we are headed towards. Uh, and I w I would pretty much say you guys said it in your question. You have to design for a remote worker for a virtual work environment, which basically is all about optimizing for team collaboration and optimizing for information that's consistent across different communication types. Whether you pick up the phone, whether you are on a meeting in a persistent chat, all that transcription should look and feel the same. This is the convergence really of networking and software because software is where the action is, but the network controls the routes. So, you know, give you an example, we were doing a live broadcast in our studio in Palo Alto had Ken Jennings on from jeopardy and it was, I was so excited. It was a good interview. We had multiple guests on about AI and you know, and he was kind of our celebrity guests and he had terrible bandwidth with his house. >>I don't know, maybe his kids were playing games on it or he was downloading some Netflix, who knows, but he had a horrible visual. We couldn't control that. This is where the network optimization comes in. What are you guys doing there? You guys run the networks, you guys have access to some of the routes and looking for, you know, best route, best quality. So I think without a doubt, you know, the, your lowest common denominator leg in your network kind of decides the quality per se. Uh, but we, we continue to do things like a compression of bits on the wire so that you need the smallest amount of pipe. But at the end of the day for high Raz video, you still need a decent amount of bandwidth. And what ends up happening is it's not just bandwidth, it's uh, you know, understanding what kind of packet loss profile you have on that network. >>So what we are doing across nearly nearly every vendor today is figuring out how we can optimize for these Laci networks. So if you're talking to any collaboration engineer, um, the first interview question will inadvertently be, tell me your experience on Laci networks. What have you done, how many patents do you have? You know, that's kind of the, the discussion per se. So I think without a doubt the advent of 5g and its expansion will lead to Ken Jennings potentially having a much better experience. Right. Can you auto scale, not auto scale, but auto detect? Yes. That cause that's something that could be automated. And we, we automatically, we call it graceful degradation. So we start with aspiring for the 10 ADP. Then we'll bring it down to seven 2360 and no video. And that happens automatically and we let the end user know you're having a network blip and hence, uh, we have, we are degrading it or today's product. Yes. >>So years ago when you, there's video conferencing, you just have to show 15 minutes beforehand just to make sure everybody get on. Okay. So simplicity is another big adoption theme, whether it's one push phone calling or call me or whatever it is. At the same time, you've got to add functionality. You've had a transcription, you've had a translation, you've got the split screen. And when I stand up, the camera follows me. So are those counterpoints simplicity and functionality, how do you integrate those together? >>I think the, the, all of this is done in the quest to simplicity, right? Um, one of the key things we've done across the Cisco WebEx portfolio, we've been known as the stodgy characters. Um, you know guys who don't move fast, which is exactly the opposite, to be honest with you. We worked on making sure we get rid of, I'm going to use the word here, nerd knobs in the product optimized for the simple in a meeting, there are three things that matter. Three big use cases, scheduling, joining in, meeting quality. Those are the only three things matter. The rest doesn't matter, right? So if you look at our devices, if you look at everything, we have this consistent green button that shows up everywhere. Whether you bring up outlook, whether you bring up an iPhone calendar, whether you bring up a desktop in one of our devices, all of those things will have this consistent green bar. We don't, we never want the end user to miss it. See it hit it. It'll show up at the right time. Basically shows up between six minutes and the 40 minute Mark before the meeting. >>And by that in meeting quality, you mean the experience overall, how hard it is to share something or >>actually can you see that person? Can you hear that person, you know, things of that sort of, right. You know, how do you avoid echos in a meeting? Like, what if I turn on both audio multiple times in a particular echo, right. As I mentioned in our last interview, Sri about um, uh, the previous guests around, they want API APIs cause it was like API APIs. It's kind of a trend towards a thin, I won't say thin client cause that's some kind of an old, old word. But um, more efficient source code on the client side, not bloated >>software in the sense of having all these bells and whistles. I mean, I mean at some point you're going to use, right? It could be an advanced version. Maybe you have a tiered thing, but at the base set, how do you create software in this modern error so that you can have really fast software managing front end with the powerful backend. You think about, Hey Siri, you know, there's the front end, there's a back end. So you starting to see this kind of decoupling. How do you guys look at that as it changed the development thesis? Is that something that you guys are thinking about? What's your take on all that? >>Yeah, without a doubt. Right? So we, we, we constantly optimize media is a very different workload than for example, a commanding tool. Right? Yeah. Uh, and I don't mean to trivialize city or any other assistant media is hard when you're doing video. The app needs to have some intelligence to be able to disintegrate audio and video streams and content sharing, right? So these apps tend to have a bigger footprint on the desktop, on the mobile phone than other traditional apps. So there is a constant quest for that additional bit of optimization to reduce, you know, substantially reduce the juice you use out of the laptop. Uh, and with laptops becoming more and more powerful, mobile phones becoming more and more, more powerful, we are only able to bring more, more into that big tree. >>Yes. And the rich media is only getting more and more robust with video. Look at the gaming world. My kids got their rig set up, multiple monitors. I mean, it's a lifestyle experience, consumption of video. It's all, it put more pressure on you guys. It's hard. We know we do it. How, what's the, in your mind, what's your guiding principle for future innovation? Whether you're hiring, designing around video, what do you guys chasing that Nirvana? What is it? Is it the software, the hardware? It's a chips. >>I think it's a combination of them, right? If you look at Cisco, our inherent differentiation is we know, we know how to do software. We know a thing or two about networks. I mean no hardware. How do you bring these three together and there's a four to dimension, I'm going to call it quad. And it's security. You can't ignore security. You know, it's, it's something that you have to intrinsically think about. It's not a check by check box after you don't want somebody peeping Toms in their meeting. For example, everybody is simply >>back in the cams. Jeff Bezos has got hacked on video on his WhatsApp embedded malware. So are all kinds of weird things that come through. You don't know. >>I think it's, it's the amalgamation of all of these things. How do you maximize every single element of the pipe? Um, so we are working with, for example, our own DNA center methods and mechanisms by which we're saying based on our workload, how do we optimize the next look for our workload. When we find an issue within let's say WebEx, how do we automatically self heal the network? That is basically where we are headed. So we want to make sure we are constantly stack up and down the stairs, down the stack. And the other, you know you've talked about simplicity of use case. I'll give you an example. What we're doing with our devices now as it has face recognition, we don't store any, any images in the cloud. So as soon as you walk into a meeting room, we've got an IOT sensor that it recognizes your face. >>It says, Hey, let me pull up your meetings. It starts to track who all have joined your meeting. And then let's assume you forget to join the meeting. It wakes up and it says, would you like to join the meeting? Two of two of your colleagues have joined so you don't even have to hit the button. It is germaphobe friendly. So you don't have to touch. It binds you in basic automation. So that level of automation is coming in. So you're talking about the future. The future is about simplicity. That spans generations. So you're pretty much worn the human to come back and for the tech to fade away in the back of them. If you don't want them to be reliant on this app that you have to learn, right, it should be discernible, relatable, easy to use. >>Works like the movies in history. You're a rock star. I'm great to have you. In fact, now we know you live in Seattle. We're going to have you in our studio remotely and we're gonna make sure that bandwidth and that video is of highest quality., the SVP, senior vice president, general manager of the collaboration group of Cisco. Big part of the future of Cisco. This group is going to be really driving some of those network benefits. The applications are big part of the focus, changing the business models, business outcomes. This is the conversation is the cube coverage from Barcelona. We'll be right back after this short break.

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Welcome everyone to this special CUBE Conversation Around the CUBE segment, Unpacking AI, number two, sponsored by Juniper Networks. We've got a great lineup here to go around the CUBE and unpack AI. We have Ken Jennings, all-time Jeopardy champion with us. Celebrity, great story there, we'll dig into that. John Hinson, director of AI at Evotek and Charna Parkey, who's the applied scientist at Textio. Thanks for joining us here for Around the CUBE Unpacking AI, appreciate it. First question I want to get to, Ken, you're notable for being beaten by a machine on Jeopardy. Everyone knows that story, but it really brings out the question of AI and the role AI is playing in society around obsolescence. We've been hearing gloom and doom around AI replacing people's jobs, and it's not really that way. What's your take on AI and replacing people's jobs? >> You know, I'm not an economist, so I can't speak to how easy it's going to be to retrain and re-skill tens of millions of people once these clerical and food prep and driving and whatever jobs go away, but I can definitely speak to the personal feeling of being in that situation, kind of watching the machine take your job on the assembly line and realizing that the thing you thought made you special no longer exists. If IBM throws enough money at it, your skill essentially is now obsolete. And it was kind of a disconcerting feeling. I think that what people need is to feel like they matter, and that went away for me very quickly when I realized that a black rectangle can now beat me at a game show. >> Okay John, what's your take on AI replacing jobs? What's your view on this? >> I think, look, we're all going to have to adapt. There's a lot of changes coming. There's changes coming socially, economically, politically. I think it's a disservice to us all to get to too indulgent around the idea that these things are going to change. We have to absorb these things, we have to be really smart about how we approach them. We have to be very open-minded about how these things are going to actually change us all. But ultimately, I think it's going to be positive at the end of the day. It's definitely going to be a little rough for a couple of years as we make all these adjustments, but I think what AI brings to the table is heads above kind of where we are today. >> Charna, your take around this, because the role of humans versus machines are pretty significant, they help each other. But is AI going to dominate over humans? >> Yeah, absolutely. I think there's a thing that we see over and over again in every bubble and collapse where, you know, in the automotive industry we certainly saw a bunch of jobs were lost, but a bunch of jobs were gained. And so we're just now actually getting into the phase where people are realizing that AI isn't just replacement, it has to be augmentation, right? We can't simply use images to replace recognition of people, we can't just use black box to give our FICO credit scores, it has to be inspectable. So there's a new field coming up now called explainable AI that actually is where we're moving towards and it's actually going to help society and create jobs. >> All right so let's stay on that next point for the next round, explainable AI. This points to a golden age. There's a debate around are we in a bubble or a golden age. A lot of people are negative right now on tech. You can see all the tech backlash. Amazon, the big tech companies like Apple and Facebook, there's a huge backlash around this so-called tech for society. Is this an indicator of a golden age coming? >> I think so, absolutely. We can take two examples of this. One would be where, you remember when Amazon built a hiring algorithm based upon their own resume data and they found that it was discriminating against women because they had only had men apply for it. Now with Textio we're building augmented writing across the audience and not from a single company and so companies like Johnson and Johnson are increasing the pipeline by more than nine percent which converts to 90,000 more women applying for their jobs. And so part of the difference there is one is explainable, one isn't, and one is using the right data set representing the audience that is consuming it and not a single company's hiring. So I think we're absolutely headed into more of a golden age, and I think these are some of the signs that people are starting to use it in the right way. >> John, what's your take? Obviously golden age doesn't look that to us right now. You see Facebook approving lies as ads, Twitter banning political ads. AI was supposed to solve all these problems. Is there light at the end of this dark tunnel we're on? >> Yeah, golden age for sure. I'm definitely a big believer in that. I think there's a new era amongst us on how we handle data in general. I think the most important thing we have here though is education around what this stuff is, how it works, how it's affecting our lives individually and at the corporate level. This is a new era of informing and augmenting literally everything we do. I see nothing but positives coming out of this. We have to be obviously very careful with our approaching all the biases that already exist today that are only going to be magnified with these types of algorithms at mass scale. But ultimately if we can get over that hurdle, which I believe collectively we all need to do together, I think we'd live in much better, less wasteful world just by approaching the data that's already at hand. >> Ken, what's your take on this? It's like a daily double question. Is it going to be a golden age? >> Laughs >> It's going to come sooner or later. We have to have catastrophe before, we have to have reality hit us in the face before we realize that tech is good, and shaping it? It's pretty ugly right now in some of the situations out there, especially in the political scene with the election in the US. You're seeing some negative things happening. What's your take on this? >> I'm much more skeptical than John and Charna. I feel like that kind of just blinkered, it's going to be great, is something you have to actually be in the tech industry and hearing all day to actually believe. I remember seeing kind of lay-person's exposure to Watson when Watson was on Jeopardy and hearing the questions reporters would ask and seeing the memes that would appear, and everyone's immediate reaction just to something as innocuous as a AI algorithm playing on a game show was to ask, is this Skynet from Terminator 2? Is this the computer from The Matrix? Is this HAL pushing us out of the airlock? Everybody immediately first goes to the tech is going to kill us. That's like everybody's first reaction, and it's weird. I don't know, you might say it's just because Hollywood has trained us to expect that plot development, but I almost think it's the other way around. Like that's a story we tell because we're deeply worried about our own meaning and obsolescence when we see how little these skills might be valued in 10, 20, 30 years. >> I can't tell you how much, by the way, Star Trek, Star Wars and Terminators probably affected the nomenclature of the technology. Everyone references Skynet. Oh my God, we're going to be taken over and killed by aliens and machines. This is a real fear. I thinks it's an initial reaction. You felt that Ken, so I've got to ask you, where do you think the crossover point is for people to internalize the benefits of say, AI for instance? Because people will say hey, look back at life before the iPhone, look at life before these tools were out there. Some will say society's gotten better, but yet there's this surveillance culture, things... And on and on. So what do you guys think the crossover point is for the reaction to change from oh my God, it's Skynet, gloom and doom to this actually could be good? >> It's incredibly tricky because as we've seen, the perception of AI both in and out of the industry changes as AI advances. As soon as machine learning can actually do a task, there's a tendency to say there's this no true Scotsman problem where we say well, that clearly can't be AI because I see how the trick worked. And yeah, humans lose at chess now. So when these small advances happen, the reaction is often oh, that's not really AI. And by the same token, it's not a game-changer when your email client can start to auto-complete your emails. That's a minor convenience to you. But you don't think oh, maybe Skynet is good. I really do think it's going to have to be, maybe the inflection point is when it starts to become so disruptive that actually public policy has to change. So we get serious about >> And public policy has started changing. >> whatever their reactions are. >> Charna, your thoughts. >> The public policy has started changing though. We just saw, I think it was in September, where California banned the use of AI in the body cameras, both real-time and after the fact. So I think that's part of the pivot point that we're actually seeing is that public policy is changing.` The state of Washington currently has a task force for AI who's making a set of recommendations for policy starting in December. But I think part of what we're missing is that we don't have enough digital natives in office to even attempt to, to your point Ken, predict what we're even going to be able to do with it, right? There is this fear because of misunderstanding, but we also don't have a respect of our political climate right now by a lot of our digital natives, and they need to be there to be making this policy. >> John, weigh in on this because you're director of AI, you're seeing positive, you have to deal with the uncertainty as well, the growth of machine learning. And just this week Google announced more TensorFlow for everybody. You're seeing Open Source. So there's a tech push, almost a democratization, going on with AI. So I think this crossover point might be sooner in front of us than people think. What's your thoughts? >> Yeah it's here right now. All these things can be essentially put into an environment. You can see these into products, or making business decisions or political decisions. These are all available right now. They're available today and its within 10 to 15 lines of code. It's all about the data sets, so you have to be really good stewards of the data that you're using to train your models. But I think the most important thing, back to the Skynet and all this science-fiction side, we have to collectively start telling the right stories. We need better stories than just this robots are going to take us over and destroy all of our jobs. I think more interesting stories really revolve around, what about public defenders who can have this informant augmentation algorithm that's going to help them get their job done? What about tailor-made medicine that's going to tell me exactly what the conditions are based off of a particular treatment plan instead of guessing? What about tailored education that's going to look at all of my strengths and weaknesses and present a plan for me? These are things that AI can do. Charna's exactly right, where if we don't get this into the right political atmosphere that's helping balance the capitalist side with the social side, we're going to be in trouble. So that's got to be embedded in every layer of enterprise as well as society in general. It's here, it's now, and it's real. >> Ken, before we move on to the ethics question, I want to get your thoughts on this because we have an Alexa at home. We had an Alexa at home; my wife made me get rid of it. We had an Apple device, what they're called... the Home pods, that's gone. I bought a Portal from Facebook because I always buy the earliest stuff, that's gone. We don't want listening devices in our house because in order to get that AI, you have to give up listening, and this has been an issue. What do you have to give to get? This has been a big question. What's your thoughts on all this? >> I was at an Amazon event where they were trumpeting how no technology had ever caught on faster than these personal digital assistants, and yet every time I'm in a use case, a household that's trying to use them, something goes terribly wrong. My friend had to rename his because the neighbor kids kept telling Alexa to do awful things. He renamed it computer, and now every time we use the word computer, the wall tells us something we don't want to know. >> (laughs) >> This is just anecdata, but maybe it speaks to something deeper, the fact that we don't necessarily like the feeling of being surveilled. IBM was always trying to push Watson as the star Trek computer that helpfully tells you exactly what you need to know in the right moment, but that's got downsides too. I feel like we're going to, if nothing else, we may start to value individual learning and knowledge less when we feel like a voice from the ceiling can deliver unto us the fact that we need. I think decision-making might suffer in that kind of a world. >> All right, this brings up ethics because I bring up the Amazon and the voice stuff because this is the new interface people want to have with machines. I didn't mention phones, Androids and Apple, they need to listen in order to make decisions. This brings up the ethics question around who sets the laws, what society should do about this, because we want the benefits of AI. John, you point out some of them. You got to give to get. Where are we on ethics? What's the opinion, what's the current view on this? John, we'll start with you on your ethics view on what needs to change now to move the ball faster. >> Data is gold. Data is gold at an exponential rate when you're talking about AI. There should be no situation where these companies get to collect data at no cost or no benefit to the end consumer. So ultimately we should have the option to opt out of any of these products and any of this type of surveillance wherever we can. Public safety is a little bit different situation, but on the commercial side, there is a lot of more expensive and even more difficult ways to train these models with a data set that isn't just basically grabbing everything our of your personal lives. I think that should be an option for consumers and that's one of those ethical check-marks. Again, ethics in general, the way that data's trained, the way that data's handled, the way models actually work, it has to be a primary reason for and approach of how you actually go about developing and delivering AI. That said, we cannot get over-indulgent in the fact that we can't do it because we're so fearful of the ethical outcomes. We have to find some middle ground and we have to find it quickly and collectively. >> Charna, what's your take on this? Ethics is super important to set the agenda for society to take advantage of all this. >> Yeah. I think we've got three ethical components here. We certainly have, as John mentioned, the data sets. However, it's also what behavior we're trying to change. So I believe the industry could benefit from a lot more behavioral science, so that we can understand whether or not the algorithms that we're building are changing behaviors that we actually want to change, right? And if we aren't, that's unethical. There is an entire field of ethics that needs to start getting put into our companies. We need an ethics board internally. A few companies are doing this already actually. I know a lot of the military companies do. I used to be in the defense industry, and so they've got a board of ethics before you can do things. The challenge is also though that as we're democratizing the algorithms themselves, people don't understand that you can't just get a set of data that represents the population. So this is true of image processing, where if we only used 100 images of a black woman, and we used 1,000 images of a white man because that was the distribution in our population, and then the algorithm could not detect the difference between skin tones for people of color, then we end up with situations where we end up in a police state where you put in an image of one black woman and it looks like ten of them and you can't distinguish between them. And yet, the confidence rate for the humans are actually higher, because they now have a machine backing their decision. And so they stop questioning, to your point, Ken, about what is the decision I'm making, they're like I'm so confident, this data told me so. And so there's a little bit of you need some expert in the loop and you also can't just have experts, because then you end up with Cambridge Analytica and all of the political things that happened there, not just in the US, but across 200 different elections and 30 different countries. And we are upset because it happened in the US, but this has been happening for years. So its just this ethical challenge of behavior change. It's not even AI and we do it all the time. Its why the cigarette industry is regulated (laughs). >> So Ken, what's your take on this? Obviously because society needs to have ethics. Who runs that? Companies? The law-makers? Someone's got to be responsible. >> I'm honestly a little pessimistic the general public will even demand this the way we're maybe hoping that they will. When I think about an example like Facebook, people just being able to, being willing to give away insane amounts of data through social media companies for the smallest of benefits: keeping in touch with people from high school they don't like. I mean, it really shows how little we value not being a product in this kind of situation. But I would like to see this kind of ethical decisions being made at the company-level. I feel like Google kind of surreptitiously moved away from it's little don't be evil mantra with the subtext that eh, maybe we'll be a little evil now. It just reminds me of Manhattan Project era thinking, where you could've gone to any of these nuclear scientists and said you're working on a real interesting puzzle here, it might advance the field, but like 200,000 civilians might die this summer. And I feel like they would've just looked at you and thought that's not really my bailiwick. I'm just trying to solve the fission problem. I would like to see these 10 companies actually having that kind of thinking internally. Not being so busy thinking if they can do something that they don't wonder if they should. >> That's a great point. This brings up the point of who is responsible. Almost as if who is less evil than the other person? Google, they don't do evil, but they're less evil than Amazon and Facebook and others. Who is responsible? The companies or the law-makers? Because if you look up some of the hearings in Washington, D.C., some of the law-makers we see up there, they don't know how the internet works, and it's pretty obvious that this is a problem. >> Yeah, well that's why Jack Dorsey of Twitter posted yesterday that he banned not just political ads, but also issue ads. This isn't something that they're making him do, but he understands that when you're using AI to target people, that it's not okay. At some point, while Mark is sitting on (laughs) this committee and giving his testimony, he's essentially asking to be regulated because he can't regulate himself. He's like well, everyone's doing it, so I'm going to do it too. That's not an okay excuse. We see this in the labor market though actually, where there's existing laws that prevent discrimination. It's actually the company's responsibility to make sure that the products that they purchase from any vendor isn't introducing discrimination into that process. So its not even the vendor that's held responsible, it's the company and their use of it. We saw in the NYPD actually that one of those image recognition systems came up and someone said well, he looked like, I forget the name of what the actor was, but some actor's name is what the perpetrator looked like and so they used an image of the actor to try and find the person who actually assaulted someone else. And that's, it's also the user problem that I'm super concerned about. >> So John, what's your take on this? Because these are companies are in business to make money, for profit, they're not the government. And who's the role, what should the government do? AI has to move forward. >> Yeah, we're all responsible. The companies are responsible. The companies that we work with, I have yet to interact with customers, or with our customers here, that have some insidious goal, that they're trying to outsmart their customers. They're not. Everyone's looking to do the best and deliver the most relevant products in the marketplace. The government, they absolutely... The political structure we have, it has to be really intelligent and it's got to get up-skilled in this space and it needs to do it quickly, both at the economy level, as well as for our defense. But the individuals, all of us as individuals, we are already subjected to this type of artificial intelligence in our everyday lives. Look at streaming, streaming media. Right now every single one of us goes out through a streaming source, and we're getting recommendations on what we should watch next. And we're already adapting to these things, I am. I'm like stop showing me all the stuff you know I want to watch, that's not interesting to me. I want to find something I don't know I want to watch, right? So we all have to get educated, we're all responsible for these things. And again, I see a much more positive side of this. I'm not trying to get into the fear-mongering side of all the things that could go wrong, I want to focus on the good stories, the positive stories. If I'm in a courtroom and I lose a court case because I couldn't afford the best attorney and I have the bias of a judge, I would certainly like artificial intelligence to make a determination that allows me to drive an appeal, as one example. Things like that are really creative in the world that we need to do. Tampering down this wild speculation we have on the markets. I mean, we are all victims of really bad data decisions right now, almost the worst data decisions. For me, I see this as a way to actually improve all those things. Fraud fees will be reduced. That helps everybody, right? Less speculation and these wild swings, these are all helpful things. >> Well Ken, John and Charna, thank- (audio feedback) >> Go ahead, finish. Get that word in. >> Sorry. I think that point you were making though John, is we are still a capitalist society, but we're no longer a shareholder capitalist society, we are a stakeholder capitalist society and the stakeholder is the society itself. It is us, it what we want to see. And so yes, I still want money. Obviously there are things that I want to buy, but I also care about well-being. I think it's that little shift that we're seeing that is actually you and I holding our own teams accountable for what they do. >> Yeah, culture first is a whole new shift going on in these companies that's a for-profit, mission-based. Ken, John, Charna, thanks for coming on Around the CUBE, Unpacking AI. Let's go around the CUBE Ken, John and Charna in that order, and just real quickly, unpacking AI, what's your final word? >> (laughs) I really... I'm interested in John's take that there's a democratization coming provided these tools will be available to everyone. I would certainly love to believe that. It seems like in the past, we've seen no, that access to these kind of powerful, paradigm-changing tools tend to be concentrated among a very small group of people and the benefits accrue to a very small group of people. But I hope that doesn't happen here. You know, I'm optimistic as well. I like the utopian side where we all have this amazing access to information and so many new problems can get solved with amazing amounts of data that we never could've touched before. Though you know, I think about that. I try to let that help me sleep at night, and not the fact that, you know... every public figure I see on TV is kind of out of touch about technology and only one candidate suggests the universal basic income, and it's kind of a crackpot idea. Those are the kind of things that keep me up at night. >> All right, John, final word. >> I think it's beautiful, AI's beautiful. We're on the cusp of a whole new world, it's nothing but positivity I see. We have to be careful. We're all nervous about it. None of us know how to approach these things, but as human beings, we've been here before. We're here all the time. And I believe that we can all collectively get a better lives for ourselves, for the environment, for everything that's out there. It's here, it's now, it's definitely real. I encourage everyone to hurry up on their own education. Every company, every layer of government to start really embracing these things and start paying attention. It's catching us all a little bit by surprise, but once you see it in production, you see it real, you'll be impressed. >> Okay, Charna, final word. >> I think one thing I want to leave people with is what we incentivize is what we end up optimizing for. This is the same for human behavior. You're training a new employee, you put incentives on the way that they sell, and that's, they game the system. AI's specifically find the optimum route, that is their job. So if we don't understand more complex cost functions, more complex representative ways of training, we're going to end up in a space, before we know it, that we can't get out of. And especially if we're using uninspectable AI. We really need to move towards augmentation. There are some companies that are implementing this now that you may not even know. Zillow, for example, is using AI to give you a cost for your home just by the photos and the words that you describe it, but they're also purchasing houses without a human in the loop in certain markets, based upon an inspection later by a human. And so there are these big bets that we're making within these massive corporations, but if you're going to do it as an individual, take a Coursera class on AI and take a Coursera class on ethics so that you can understand what the pitfalls are going to be, because that cost function is incredibly important. >> Okay, that's a wrap. Looks like we have a winner here. Charna, you got 18, John 16. Ken came in with 12, beaten again! (both laugh) Okay, Ken, seriously, great to have you guys on, a pleasure to meet everyone. Thanks for sharing on Around the CUBE Unpacking AI, panel number two. Thank you. >> Thanks a lot. >> Thank you. >> Thanks. I've been defeated by artificial intelligence again! (all laugh) (upbeat music)