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 you know we've often said that the next 10 years in cloud computing won't be like the last ten cloud has firmly planted its footprint on the other side of the chasm with the momentum of the entire multi-trillion dollar tech business behind it both sellers and buyers are leaning in by adopting cloud technologies and many are building their own value layers on top of cloud in the coming years we expect innovation will continue to coalesce around the three big u.s clouds plus alibaba in apac with the ecosystem building value on top of the hardware saw tooling provided by the hyperscalers now importantly we don't see this as a race to the bottom rather our expectation is that the large public cloud players will continue to take cost out of their platforms through innovation automation and integration while other cloud providers and the ecosystem including traditional companies that buy it mine opportunities in their respective markets as matt baker of dell is fond of saying this is not a zero sum game welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll update you on our latest projections in the cloud market we'll share some new etr survey data with some surprising nuggets and drill into this the important cloud database landscape first we want to take a look at what people are talking about in cloud and what's been in the recent news with the exception of alibaba all the large cloud players have reported earnings google continues to focus on growth at the expense of its profitability google reported that it's cloud business which includes applications like google workspace grew 45 percent to five and a half billion dollars but it had an operating loss of 890 billion now since thomas curion joined google to run its cloud business google has increased head count in its cloud business from 25 000 25 000 people now it's up to 40 000 in an effort to catch up to the two leaders but playing catch up is expensive now to put this into perspective let's go back to aws's revenue in q1 2018 when the company did 5.4 billion so almost exactly the same size as google's current total cloud business and aws is growing faster at the time at 49 don't forget google includes in its cloud numbers a big chunk of high margin software aws at the time had an operating profit of 1.4 billion that quarter around 26 of its revenues so it was a highly profitable business about as profitable as cisco's overall business which again is a great business this is what happens when you're number three and didn't get your head out of your ads fast enough now in fairness google still gets high marks on the quality of its technology according to corey quinn of the duck bill group amazon and google cloud are what he called neck and neck with regard to reliability with microsoft azure trailing because of significant disruptions in the past these comments were made last week in a bloomberg article despite some recent high-profile outages on aws not surprisingly a microsoft spokesperson said that the company's cloud offers industry-leading reliability and that gives customers payment credits after some outages thank you turning to microsoft and cloud news microsoft's overall cloud business surpassed 22 billion in the december quarter up 32 percent year on year like google microsoft includes application software and sas offerings in its cloud numbers and gives little nuggets of guidance on its azure infrastructure as a service business by the way we estimate that azure comprises about 45 percent of microsoft's overall cloud business which we think hit a 40 billion run rate last quarter microsoft guided in its earning call that recent declines in the azure growth rates will reverse in q1 and that implies sequential growth for azure and finally it was announced that the ftc not the doj will review microsoft's announced 75 billion acquisition of activision blizzard it appears ftc chair lena khan wants to take this one on herself she of course has been very outspoken about the power of big tech companies and in recent a recent cnbc interview suggested that the u.s government's actions were a meaningful contributor back then to curbing microsoft's power in the 90s i personally found that dubious just ask netscape wordperfect novell lotus and spc the maker of harvard presentation graphics how effective the government was in curbing microsoft power generally my take is that the u s government has had a dismal record regulating tech companies most notably ibm and microsoft and it was market forces company hubris complacency and self-inflicted wounds not government intervention these were far more effective than the government now of course if companies are breaking the law they should be punished but the u.s government hasn't been very productive in its actions and the unintended consequences of regulation could be detrimental to the u.s competitiveness in the race with china but i digress lastly in the news amazon announced earnings thursday and the company's value increased by 191 billion dollars on friday that's a record valuation gain for u.s stocks aws amazon's profit engine grew 40 percent year on year for the quarter it closed the year at 62 billion dollars in revenue and at a 71 billion dollar revenue run rate aws is now larger than ibm which without kindrel is at a 67 billion dollar run rate just for context ibm's revenue in 2011 was 107 billion dollars now there's a conversation going on in the media and social that in order to continue this growth and compete with microsoft that aws has to get into the sas business and offer applications we don't think that's the right strategy for amp from for amazon in the near future rather we see them enabling developers to compete in that business finally amazon disclosed that 48 of its top 50 customers are using graviton 2 instances why is this important because aws is well ahead of the competition in custom silicon chips is and is on a price performance curve that is far better than alternatives especially those based on x86 this is one of the reasons why we think this business is not a race to the bottom aws is being followed by google microsoft and alibaba in terms of developing custom silicon and will continue to drive down their internal cost structures and deliver price performance equal to or better than the historical moore's law curves so that's the recent news for the big u.s cloud providers let's now take a look at how the year ended for the big four hyperscalers and look ahead to next year here's a table we've shown this view before it shows the revenue estimates for worldwide is and paths generated by aws microsoft alibaba and google now remember amazon and alibaba they share clean eye ass figures whereas microsoft and alphabet only give us these nuggets that we have to interpret and we correlate those tidbits with other data that we gather we're one of the few outlets that actually attempts to make these apples to apples comparisons there's a company called synergy research there's another firm that does this but i really can't map to their numbers their gcp figures look far too high and azure appears somewhat overestimated and they do include other stuff like hosted private cloud services but it's another data point that you can use okay back to the table we've slightly adjusted our gcp figures down based on interpreting some of alphabet's statements and other survey data only alibaba has yet to announce earnings so we'll stick to a 2021 market size of about 120 billion dollars that's a 41 growth rate relative to 2020 and we expect that figure to increase by 38 percent to 166 billion in 2022 now we'll discuss this a bit later but these four companies have created an opportunity for the ecosystem to build what we're calling super clouds on top of this infrastructure and we're seeing it happen it was increasingly obvious at aws re invent last year and we feel it will pick up momentum in the coming months and years a little bit more on that later now here's a graphical view of the quarterly revenue shares for these four companies notice that aws has reversed its share erosion and is trending up slightly aws has accelerated its growth rate four quarters in a row now it accounted for 52 percent of the big four hyperscaler revenue last year and that figure was nearly 54 in the fourth quarter azure finished the year with 32 percent of the hyper scale revenue in 2021 which dropped to 30 percent in q4 and you can see gcp and alibaba they're neck and neck fighting for the bronze medal by the way in our recent 2022 predictions post we said google cloud platform would surpass alibaba this year but given the recent trimming of our numbers google's got some work to do for that prediction to be correct okay just to put a bow on the wikibon market data let's look at the quarterly growth rates and you'll see the compression trends there this data tracks quarterly revenue growth rates back to 20 q1 2019 and you can see the steady downward trajectory and the reversal that aws experienced in q1 of last year now remember microsoft guided for sequential growth and azure so that orange line should trend back up and given gcp's much smaller and big go to market investments that we talked about we'd like to see an acceleration there as well the thing about aws is just remarkable that it's able to accelerate growth at a 71 billion run rate business and alibaba you know is a bit more opaque and likely still reeling from the crackdown of the chinese government we're admittedly not as close to the china market but we'll continue to watch from afar as that steep decline in growth rate is somewhat of a concern okay let's get into the survey data from etr and to do so we're going to take some time series views on some of the select cloud platforms that are showing spending momentum in the etr data set you know etr uses a metric we talked about this a lot called net score to measure that spending velocity of products and services netscore basically asks customers are you spending more less or the same on a platform and a vendor and then it subtracts the lesses from the moors and that yields a net score this chart shows net score for five cloud platforms going back to january 2020. note in the table that the table we've inserted inside that chart shows the net score and shared n the latter metric indicates the number of mentions in the data set and all the platforms we've listed here show strong presence in the survey that red dotted line at 40 percent that indicates spending is at an elevated level and you can see azure and aws and vmware cloud on aws as well as gcp are all nicely elevated and bounding off their october figures indicating continued cloud momentum overall but the big surprise in these figures is the steady climb and the steep bounce up from oracle which came in just under the 40 mark now one quarter is not necessarily a trend but going back to january 2020 the oracle peaks keep getting higher and higher so we definitely want to keep watching this now here's a look at some of the other cloud platforms in the etr survey the chart here shows the same time series and we've now brought in some of the big hybrid players notably vmware cloud which is vcf and other on-prem solutions red hat openstack which as we've reported in the past is still popular in telcos who want to build their own cloud we're also starting to see hpe with green lake and dell with apex show up more and ibm which years ago acquired soft layer which was really essentially a bare metal hosting company and over the years ibm cobbled together its own public cloud ibm is now racing after hybrid cloud using red hat openshift as the linchpin to that strategy now what this data tells us first of all these platforms they don't have the same presence in the data set as do the previous players vmware is the one possible exception but other than vmware these players don't have the spending velocity shown in the previous chart and most are below the red line hpe and dell are interesting and notable in that they're transitioning their early private cloud businesses to dell gr sorry hpe green lake and dell apex respectively and finally after years of kind of staring at their respective navels in in cloud and milking their legacy on-prem models they're finally building out cloud-like infrastructure for their customers they're leaning into cloud and marketing it in a more sensible and attractive fashion for customers so we would expect these figures are going to bounce around for a little while for those two as they settle into a groove and we'll watch that closely now ibm is in the process of a complete do-over arvin krishna inherited three generations of leadership with a professional services mindset now in the post gerschner gerstner era both sam palmisano and ginny rometty held on far too long to ibm's service heritage and protected the past from the future they missed the cloud opportunity and they forced the acquisition of red hat to position the company for the hybrid cloud remedy tried to shrink to grow but never got there krishna is moving faster and with the kindred spin is promising mid-single-digit growth which would be a welcome change ibm is a lot of work to do and we would expect its net score figures as well to bounce around as customers transition to the future all right let's take a look at all these different players in context these are all the clouds that we just talked about in a two-dimensional view the vertical axis is net score or spending momentum and the horizontal axis is market share or presence or pervasiveness in the data set a couple of call-outs that we'd like to make here first the data confirms what we've been saying what everybody's been saying aws and microsoft stand alone with a huge presence many tens of billions of dollars in revenue yet they are both well above the 40 line and show spending momentum and they're well ahead of gcp on both dimensions second vmware while much smaller is showing legitimate momentum which correlates to its public statements alibaba the alibaba in this survey really doesn't have enough sample to make hardcore conclusions um you can see hpe and dell and ibm you know similarly they got a little bit more presence in the data set but they clearly have some work to do what you're seeing there is their transitioning their legacy install bases oracle's the big surprise look what oracle was in the january survey and how they've shot up recently now we'll see if this this holds up let's posit some possibilities as to why it really starts with the fact that oracle is the king of mission critical apps now if you haven't seen video on twitter you have to check it out it's it's hilarious we're not going to run the video here but the link will be in our post but i'll give you the short version some really creative person they overlaid a data migration narrative on top of this one tooth guy who speaks in spanish gibberish but the setup is he's a pm he's a he's a a project manager at a bank and aws came into the bank this of course all hypothetical and said we can move all your apps to the cloud in 12 months and the guy says but wait we're running mission critical apps on exadata and aws says there's nothing special about exadata and he starts howling and slapping his knee and laughing and giggling and talking about the 23 year old senior engineer who says we're going to do this with microservices and he could tell he was he was 23 because he was wearing expensive sneakers and what a nightmare they encountered migrating their environment very very very funny video and anyone who's ever gone through a major migration of mission critical systems this is gonna hit home it's funny not funny the point is it's really painful to move off of oracle and oracle for all its haters and its faults is really the best environment for mission critical systems and customers know it so what's happening is oracle's building out the best cloud for oracle database and it has a lot of really profitable customers running on-prem that the company is migrating to oracle cloud infrastructure oci it's a safer bet than ripping it and putting it into somebody else's cloud that doesn't have all the specialized hardware and oracle knowledge because you can get the same integrated exadata hardware and software to run your database in the oracle cloud it's frankly an easier and much more logical migration path for a lot of customers and that's possibly what's happening here not to mention oracle jacks up the license price nearly doubles the license price if you run on other clouds so not only is oracle investing to optimize its cloud infrastructure it spends money on r d we've always talked about that really focused on mission critical applications but it's making it more cost effective by penalizing customers that run oracle elsewhere so this possibly explains why when the gartner magic quadrant for cloud databases comes out it's got oracle so well positioned you can see it there for yourself oracle's position is right there with aws and microsoft and ahead of google on the right-hand side is gartner's critical capabilities ratings for dbms and oracle leads in virtually all of the categories gartner track this is for operational dvms so it's kind of a narrow view it's like the red stack sweet spot now this graph it shows traditional transactions but gartner has oracle ahead of all vendors in stream processing operational intelligence real-time augmented transactions now you know gartner they're like old name framers and i say that lovingly so maybe they're a bit biased and they might be missing some of the emerging opportunities that for example like snowflake is pioneering but it's hard to deny that oracle for its business is making the right moves in cloud by optimizing for the red stack there's little question in our view when it comes to mission critical we think gartner's analysis is correct however there's this other really exciting landscape emerging in cloud data and we don't want it to be a blind spot snowflake calls it the data cloud jamactagani calls it data mesh others are using the term data fabric databricks calls it data lake house so so does oracle by the way and look the terminology is going to evolve and most of the action action that's happening is in the cloud quite frankly and this chart shows a select group of database and data warehouse companies and we've filtered the data for aws azure and gcp customers accounts so how are these accounts or companies that were showing how these vendors were showing doing in aws azure and gcp accounts and to make the cut you had to have a minimum of 50 mentions in the etr survey so unfortunately data bricks didn't make it just not enough presence in the data set quite quite yet but just to give you a sense snowflake is represented in this cut with 131 accounts aws 240 google 108 microsoft 407 huge [ __ ] 117 cloudera 52 just made the cut ibm 92 and oracle 208. again these are shared accounts filtered by customers running aws azure or gcp the chart shows a net score lime green is new ads forest green is spending more gray is flat spending the pink is spending less and the bright red is defection again you subtract the red from the green and you get net score and you can see that snowflake as we reported last week is tops in the data set with a net score in the 80s and virtually no red and even by the way single digit flat spend aws google and microsoft are all prominent in the data set as is [ __ ] and snowflake as i just mentioned and they're all elevated over the 40 mark cloudera yeah what can we say once they were a high flyer they're really not in the news anymore with anything compelling other than they just you know took the company private so maybe they can re-emerge at some point with a stronger story i hope so because as you can see they actually have some new additions and spending momentum in the green just a lot of customers holding steady and a bit too much red but they're in the positive territory at least with uh plus 17 percent unlike ibm and oracle and this is the flip side of the coin ibm they're knee-deep really chest deep in the middle of a major transformation we've said before arvind krishna's strategy and vision is at least achievable prune the portfolio i.e spin out kindrel sell watson health hold serve with the mainframe and deal with those product cycles shift the mix to software and use red hat to win the day in hybrid red hat is working for ibm's growing well into the double digits unfortunately it's not showing up in this chart with little database momentum in aws azure and gcp accounts zero new ads not enough acceleration and spending a big gray middle in nearly a quarter of the base in the red ibm's data and ai business only grew three percent this last quarter and the word database wasn't even mentioned once on ibm's earnings call this has to be a concern as you can see how important database is to aws microsoft google and the momentum it's giving companies like snowflake and [ __ ] and others which brings us to oracle with a net score of minus 12. so how do you square the momentum in oracle cloud spending and the strong ratings and databases from gartner with this picture good question and i would say the following first look at the profile people aren't adding oracle new a large portion of the base 25 is reducing spend by 6 or worse and there's a decent percentage of the base migrating off oracle with a big fat middle that's flat and this accounts for the poor net score overall but what etr doesn't track is how much is being spent rather it's an account based model and oracle is heavily weighted toward big spenders running mission critical applications and databases oracle's non-gaap operating margins are comparable to ibm's gross margins on a percentage basis so a very profitable company with a big license and maintenance in stall basin oracle has focused its r d investments into cloud erp database automation they've got vertical sas and they've got this integrated hardware and software story and this drives differentiation for the company but as you can see in this chart it has a legacy install base that is constantly trying to minimize its license costs okay here's a little bit of different view on the same data we expand the picture with the two dimensions of net score on the y-axis and market share or pervasiveness on the horizontal axis and the table insert is how the data gets plotted y and x respectively not much to add here other than to say the picture continues to look strong for those companies above the 40 line that are focused and their focus and have figured out a clear cloud strategy and aren't necessarily dealing with a big install base the exception of course is is microsoft and the ones below the line definitely have parts of their portfolio which have solid momentum but they're fighting the inertia of a large install base that moves very slowly again microsoft had the advantage of really azure and migrating those customers very quickly okay so let's wrap it up starting with the big three cloud players aws is accelerating and innovating great example is custom silicon with nitro and graviton and other chips that will help the company address concerns related to the race to the bottom it's not a race to zero aws we believe will let its developers go after the sas business and for the most part aws will offer solutions that address large vertical markets think call centers the edge remains a wild card for aws and all the cloud players really aws believes that in the fullness of time all workloads will run in the public cloud now it's hard for us to imagine the tesla autonomous vehicles running in the public cloud but maybe aws will redefine what it means by its cloud microsoft well they're everywhere and they're expanding further now into gaming and the metaverse when he became ceo in 2014 many people said that satya should ditch xbox just as an aside the joke among many oracle employees at the time was that safra katz would buy her kids and her nieces and her nephews and her kids friends everybody xbox game consoles for the holidays because microsoft lost money for everyone that they shipped well nadella has stuck with it and he sees an opportunity to expand through online gaming communities one of his first deals as ceo was minecraft now the acquisition of activision will make microsoft the world's number three gaming company by revenue behind only 10 cent and sony all this will be powered by azure and drive more compute storage ai and tooling now google for its part is battling to stay relevant in the conversation luckily it can afford the massive losses it endures in cloud because the company's advertising business is so profitable don't expect as many have speculated that google is going to bail on cloud that would be a huge mistake as the market is more than large enough for three players which brings us to the rest of the pack cloud ecosystems generally and aws specifically are exploding the idea of super cloud that is a layer of value that spans multiple clouds hides the underlying complexity and brings new value that the cloud players aren't delivering that's starting to bubble to the top and legacy players are staying close to their customers and fighting to keep them spending and it's working dell hpe cisco and smaller predominantly on-plan prem players like pure storage they continue to do pretty well they're just not as sexy as the big cloud players the real interesting activity it's really happening in the ecosystem of companies and firms within industries that are transforming to create their own digital businesses virtually all of them are running a portion of their offerings on the public cloud but often connecting to on-premises workloads and data think goldman sachs making that work and creating a great experience across all environments is a big opportunity and we're seeing it form right before our eyes don't miss it okay that's it for now thanks to my colleague stephanie chan who helped research this week's topics remember these episodes are all available as podcasts wherever you listen just search breaking analysis podcast check out etr's website at etr dot ai and also we publish a full report every week on wikibon.com and siliconangle.com you can get in touch with me email me at david.velante siliconangle.com you can dm me at divalante or comment on my linkedin post this is dave vellante for the cube insights powered by etr have a great week stay safe be well and we'll see you next time [Music] you

(upbeat music) >> Hello, and welcome to this startup showcase. It's great to be here and talk about some of the innovations we are doing at AWS, how we work with our partner community, especially our open source partners. My name is Deepak Singh. I run our compute services organization, which is a very vague way of saying that I run a number of things that are connected together through compute. Very specifically, I run a container services organization. So for those of you who are into containers, ECS, EKS, fargate, ECR, App Runner Those are all teams that are within my org. I also run the Amazon Linux and BottleRocketing. So anything AWS does with Linux, both externally and internally, as well as our high-performance computing team. And perhaps very relevant to this discussion, I run the Amazon open source program office. Serving at AWS for over 13 years, almost 14, involved with compute in various ways, including EC2. What that has done has given me a vantage point of seeing how our customers use the services that we build for them, how they leverage various partner solutions, and along the way, how AWS itself has gotten involved with opensource. And I'll try and talk to you about some of those factors and how they impact, how you consume our services. So why don't we get started? So for many of you, you know, one of the things, there's two ways to look at AWS and open-source and Amazon in general. One is the number of contributors you may have. And the number of repositories that contribute to. Those are just a couple of measures. There are people that I work with on a regular basis, who will remind you that, those are not perfect measures. Sometimes you could just contribute to one thing and have outsized impact because of the nature of that thing. But it address being what it is, increasingly we'll look at different ways in which we can help contribute and enhance open source 'cause we consume a lot of it as well. I'll talk about it very specifically from the space that I work in the container space in particular, where we've worked a lot with people in the Kubernetes community. We've worked a lot with people in the broader CNCF community, as well as, you know, small projects that our customers might have got started off with. For example, I want to like talking about is Argo CD from Intuit. We were very actively involved with helping them figure out what to do with it. And it was great to see how into it. And we worked, etc, came together to think about get-ups at the Kubernetes level. And while those are their projects, we've always been involved with them. So we try and figure out what's important to our customers, how we can help and then take because of that. Well, let's talk about a little bit more, here's some examples of the kinds of open source projects that Amazon and AWS contribute to. They arranged from the open JDK. I think we even now have our own implementation of Java, the Corretto open source project. We contribute to projects like rust, where we are very active in the rest foundation from a leadership role as well, the robot operating system, just to pick some, we collaborate with Facebook and actively involved with the pirates project. And there's many others. You can see all the logos in here where we participate either because they're important to us as AWS in the services that we run or they're important to our customers and the services that they consume or the open source projects they care about and how we get to those. How we get and make those decisions is often depends on the importance of that particular project. At that point in time, how much impact they're having to AWS customers, or sometimes very feel that us contributing to that project is super critical because it helps us build more robust services. I'll talk about it in a completely, you know, somewhat different basis. You may have heard of us talk about our new next generation of Amazon Linux 2022, which is based on fedora as its sub stream. One of the reasons we made this decision was it allows us to go and participate in the preneurial project and make sure that the upstream project is robust, stays robust. And that, that what that ends up being is that Amazon Linux 2022 will be a robust operating system with the kinds of capabilities that our customers are asking for. That's just one example of how we think about it. So for example, you know, the Python software foundation is something that we work with very closely because so many of our customers use Python. So we help run something like PyPy which is many, you know, if you're a Python developer, I happened to be a Ruby one, but lots of our customers use Python and helping the Python project be robust by making sure PyPy is available to everybody is something that we help provide credits for help support in other ways. So it's not just code. It can mean many different ways of contributing as well, but in the end code and operations is where we hang our happens. Good examples of this is projects that we will create an open source because it makes sense to make sure that we open source some of the core primitives or foundations that are part of our own services. A great example of that, whether this be things that we open source or things that we contribute to. And I'll talk about both and I'll talk about things near and dear to my heart. There's many examples I've picked the two that I like talking about. The first of these is firecracker. Many of you have heard about it, a firecracker for those of you who don't know is a very lightweight virtual machine manager, which allows you to run these micro VMs. And why was this important many years ago when we started Lambda and quite honestly, Fugate and foggy, it still runs quite a bit in that mode, we used to have to run on VMs like everything else and finding the right VM for the size of tasks that somebody asks for the size of function that somebody asks for is requires us to provision capacity ahead of time. And it also wastes a lot of capacity because Lambda function is small. You won't even if you find the smallest VM possible, those can be a little that can be challenging. And you know, there's a lot of resources that are being wasted. VM start at a particular speed because they have to do a whole bunch of things before the operating system spins up and the virtual machine spins up and we asked ourselves, can we do better? come up with something that allows us to create right size, very lightweight, very fast booting. What's your machines, micro virtual machine that we ended up calling them. That's what led to firecracker. And we open source the project. And today firecrackers use, not just by AWS Lambda or foggy, but by a number of other folks, there's companies like fly IO that are using it. We know people using firecracker to run Kubernetes on prem on bare metal as an example. So we've seen a lot of other folks embrace it and use it as the foundation for building their own serverless services, their own container services. And we think there's a lot of value and learnings that we can bring to the table because we get the experience of operating at scale, but other people can bring to the table cause they may have specific requirements that we may not find it as important from an AWS perspective. So that's firecracker an example of a project where we contribute because we feel it's fundamentally important to us as continually. We were found, you know, we've been involved with continuity from the beginning. Today, we are a whole team that does nothing else, but contribute to container D because container D underlies foggy. It underlies our Kubernetes offerings. And it's increasingly being used by customers directly by their placement. You know, where they're running container D instead of running a full on Docker or similar container engine, what it has allowed us to do is focus on what's important so that we can operate continuously at scale, keep it robust and secure, add capabilities to it that AWS customers need manifested often through foggy Kubernetes, but in the end, it's a win-win for everybody. It makes continuously better. If you want to use containers for yourself on AWS, that's a great way to you. You know, you still, you still benefit from all the work that we're doing. The decision we took was since it's so important to us and our customers, we wanted a team that lived in breathed container D and made sure a super robust and there's many, many examples like that. No, that we ended up participating in, either by taking a project that exists or open sourcing our own. Here's an example of some of the open source projects that we have done from an AWS on Amazon perspective. And there's quite a few when I was looking at this list, I was quite surprised, not quite surprised I've seen the reports before, but every time I do, I have to recount and say, that's a lot more than one would have thought, even though I'd been looking at it for such a long time, examples of this in my world alone are things like, you know, what work had to do with Amazon Linux BottleRocket, which is a container host operating system. That's been open-sourced from day one. Firecracker is something we talked about. We have a project called AWS peril cluster, which allows you to spin up high performance computing clusters on AWS using the kind of schedulers you may use to use like slum. And that's an open source project. We have plenty of source projects in the web development space, in the security space. And more recently things like the open 3d engine, which is something that we are very excited about and that'd be open sourced a few months ago. And so there's a number of these projects that cover everything from tooling to developer, application frameworks, all the way to database and analytics and machine learning. And you'll notice that in a few areas, containers, as an example, machine learning as an example, our default is to go with open source option is where we can open source. And it makes sense for us to do so where we feel the product community might benefit from it. That's our default stance. The CNCF, the cloud native computing foundation is something that we've been involved with quite a bit. You know, we contribute to Kubernetes, be contribute to Envoy. I talked about continuity a bit. We've also contributed projects like CDK 8, which marries the AWS cloud development kit with Kubernetes. It's now a sandbox project in Kubernetes, and those are some of the areas. CNCF is such a wide surface area. We don't contribute to everything, but we definitely participate actively in CNCF with projects like HCB that are critical to eat for us. We are very, very active in just how the project evolves, but also try and see which of the projects that are important to our customers who are running Kubernetes maybe by themselves or some other project on AWS. Envoy is a good example. Kubernetes itself is a good example because in the end, we want to make sure that people running Kubernetes on AWS, even if they are not using our services are successful and we can help them, or we can work on the projects that are important to them. That's kind of how we think about the world. And it's worked pretty well for us. We've done a bunch of work on the Kubernetes side to make sure that we can integrate and solve a customer problem. We've, you know, from everything from models to work that we have done with gravity on our arm processor to a virtual GPU plugin that allows you to share and media GPU resources to the elastic fabric adapter, which are the network device for high performance computing that it can use at Kubernetes on AWS, along with things that directly impact Kubernetes customers like the CDKs project. I talked about work that we do with the container networking interface to the Amazon control of a Kubernetes, which is an open source project that allows you to use other AWS services directly from Kubernetes clusters. Again, you notice success, Kubernetes, not EKS, which is a managed Kubernetes service, because if we want you to be successful with Kubernetes and AWS, whether using our managed service or running your own, or some third party service. Similarly, we worked with premetheus. We now have a managed premetheus service. And at reinvent last year, we announced the general availability of this thing called carpenter, which is a provisioning and auto-scaling engine for Kubernetes, which is also an open source project. But here's the beauty of carpenter. You don't have to be using EKS to use it. Anyone running Kubernetes on AWS can leverage it. We focus on the AWS provider, but we've built it in such a way that if you wanted to take carpenter and implemented on prem or another cloud provider, that'd be completely okay. That's how it's designed and what we anticipated people may want to do. I talked a little bit about BottleRocket it's our Linux-based open-source operating system. And the thing that we have done with BottleRocket is make sure that we focus on security and the needs of customers who want to run orchestrated container, very focused on that problem. So for example, BottleRocket only has essential software needed to run containers, se Linux. I just notice it says that's the lineups, but I'm sure that, you know, Lena Torvalds will be pretty happy. And seeing that SE linux is enabled by default, we use things like DM Verity, and it has a read only root file system, no shell, you can assess it. You can install it if you wanted to. We allowed it to create different bill types, variants as we call them, you can create a variant for a non AWS resource as well. If you have your own homegrown container orchestrator, you can create a variant for that. It's designed to be used in many different contexts and all of that is open sourced. And then we use the update framework to publish and secure repository and kind of how this transactional system way of updating the software. And it's something that we didn't invent, but we have embraced wholeheartedly. It's a bottle rockets, completely open source, you know, have partners like Aqua, where who develop security tools for containers. And for them, you know, something I bought in rocket is a natural partnership because people are running a container host operating system. You can use Aqua tooling to make sure that they have a secure Indiana environment. And we see many more examples like that. You may think so over us, it's all about AWS proprietary technology because Lambda is a proprietary service. But you know, if you look peek under the covers, that's not necessarily true. Lambda runs on top of firecracker, as we've talked about fact crackers and open-source projects. So the foundation of Lambda in many ways is open source. What it also allows people to do is because Lambda runs at such extreme scale. One of the things that firecracker is really good for is running at scale. So if you want to build your own firecracker base at scale service, you can have most of the confidence that as long as your workload fits the design parameters, a firecracker, the battle hardening the robustness is being proved out day-to-day by services at scale like Lambda and foggy. For those of you who don't know service support services, you know, in the end, our goal with serverless is to make sure that you don't think about all the infrastructure that your applications run on. We focus on business logic as much as you can. That's how we think about it. And serverless has become its own quote-unquote "Sort of environment." The number of partners and open-source frameworks and tools that are spun up around serverless. In which case mostly, I mean, Lambda, API gateway. So it says like that is pretty high. So, you know, number of open source projects like Zappa server serverless framework, there's so many that have come up that make it easier for our customers to consume AWS services like Lambda and API gateway. We've also done some of our own tooling and frameworks, a serverless application model, AWS jealous. If you're a Python developer, we have these open service runtimes for Lambda, rust dot other options. We have amount of number of tools that we opened source. So in general, you'll find that tooling that we do runtime will tend to be always be open-sourced. We will often take some of the guts of the things that we use to build our systems like firecracker and open-source them while the control plane, etc, AWS services may end up staying proprietary, which is the case in Lambda. Increasingly our customers build their applications and leverage the broader AWS partner network. The AWS partner network is a network of partnerships that we've built of trusted partners. when you go to the APN website and find a partner, they know that that partner meets a certain set of criteria that AWS has developed, and you can rely on those partners for your own business. So whether you're a little tiny business that wants some function fulfill that you don't have the resources for or large enterprise that wants all these applications that you've been using on prem for a long time, and want to keep leveraging them in the cloud, you can go to APN and find that partner and then bring their solution on as part of your cloud infrastructure and could even be a systems integrator, for example, to help you solve this specific development problem that you may have a need for. Increasingly, you know, one of the things we like to do is work with an apartment community that is full of open-source providers. So a great one, there's so many, and you have, we have a panel discussion with many other partners as well, who make it easier for you to build applications on AWS, all open source and built on open source. But I like to call it a couple of them. The first one of them is TIDELIFT. TIDELIFT, For those of you who don't know is a company that provides SAS based tools to curate track, manage open source catalogs. You know, they have a whole network of maintainers and providers. They help, if you're an independent open developer, or a smart team should probably get to know TIDELIFT. They provide you benefits and, you know, capabilities as a developer and maintainer that are pretty unique and really help. And I've seen a number of our open source community embraced TIDELIFT quite honestly, even before they were part of the APN. But as part of the partner network, they get to participate in things like ISP accelerate and they get to they're officially an advanced tier partner because they are, they migrated the SAS offering onto AWS. But in the end, if you're part of the open source supply chain, you're a maintainer, you are a developer. I would recommend working with TIDELIFT because their goal is making all of you who are developing open source solutions, especially on AWS, more successful. And that's why I enjoy this partnership with them. And I'm looking to do a lot more because I think as a company, we want to make sure that open source developers don't feel like they are not supported because all you have to do is read various forums. It's challenging often to be a maintainer, especially of a small project. So I think with helping with licensing license management, security identification remediation, helping these maintainers is a big part of what TIDELIFT to us and it was great to see them as part of a partner network. Another partner that I like to call sysdig. I actually got introduced to them many years ago when they first launched. And one of the things that happened where they were super interested in some of our serverless stuff. And we've been trying to figure out how we can work together because all of our customers are interested in the capabilities that cystic provides. And over the last few years, he found a number of areas where we can collaborate. So sysdig, I know them primarily in a security company. So people use cystic to secure the bills, detect, you know, do threat response, threat detection, completely continuously validate their posture, get this continuous analytics signal on how they're doing and monitor performance. At the end of it, it's a SAS platform. They have a very nice open source security stack. The one I'm most familiar with. And I think most of you are probably familiar with is Falco. You know, sysdig, a CNCF project has been super popular. It's just to go SSS what 3, 37, 40 million downloads by now. So that's pretty, pretty cool. And they have been a great partner because we've had to do make sure that their solution works at target, which is not a natural place for their software to run, but there was enough demand and interest from our customers that, you know, or both companies leaned in to make sure they can be successful. So last year sister got a security competency. We have a number of specific competencies that we for our partners, they have integration and security hub is great. partners are lean in the way cystic has onto making our customer successful. And working with us are the best partners that we have. And there's a number of open source companies out there built on open source where their entire portfolio is built on open source software or the active participants like we are that we love working with on a day to day basis. So, you know, I think the thing I would like to, as we wind this out in this presentation is, you know, AWS is constantly looking for partnerships because our partners enable our customers. They could be with companies like Redis with Mongo, confluent with Databricks customers. Your default reaction might be, "Hey, these are companies that maybe compete with AWS." but no, I mean, I think we are partners as well, like from somebody at the lower end of the spectrum where people run on top of the services that I own on Linux and containers are SE 2, For us, these partners are just as important customers as any AWS service or any third party, 20 external customer. And so it's not a zero sum game. We look forward to working with all these companies and open source projects from an AWS perspective, a big part of how, where my open source program spends its time is making it easy for our developers to contribute, to open source, making it easy for AWS teams to decide when to open source software or participate in open source projects. Over the last few years, we've made significant changes in how we reduce the friction. And I think you can see it in the results that I showed you earlier in this stock. And the last one is one of the most important things that I say and I'll keep saying that, that we do as AWS is carry the pager. There's a lot of open source projects out there, operationalizing them, running them at scale is not easy. It's not all for whatever reason. It may not have anything to do with the software itself. But our core competency is taking that and being really good at operating it and becoming experts at operating it. And then ideally taking that expertise and experience and operating that project, that software and contributing back upstream. Cause that makes it better for everybody. And I think you'll see us do a lot more of that going forward. We've been doing that for the last few years, you know, in the container space, we do it every day. And I'm excited about the possibilities. With that. Thank you very much. And I hope you enjoy the rest of the showcase. >> Okay. Welcome back. We have Deepak sing here. We just had the keynote closing keynote vice-president of compute services. Deepak. Great to a great keynote, great wisdom and insight from that session. A very notable highlights and cutting edge trends and product information. Thanks for sharing. >> No, anytime it's always good to be here. It's too bad that we still doing this virtually, but always good to talk to you, John. >> We'll get hopefully through this way pretty quickly, I want to jump right in. Cause we don't have a lot of time. I want to get some quick question. You've brought up a good things. Open source innovation. Okay. Going next level. You've seen the rise of super clouds and super apps developing at open source. You're seeing big companies contributing, you know, you mentioned Argo into it. You're seeing that dynamic where companies are forming around this. This is a rising tide. This is, this is actually real. It's not the old school of, okay, here's a project. And then someone manages support and commercialization of it. It's actually platform in cloud scale. This is next gen. >> Yeah. And actually I think it started a few years ago. We can talk about a company that, you know, you're very familiar with as part of this event, which is armory many years ago, Netflix spun off this project called Spinnaker. A Spinnaker is CISED you know, CSED system that was developed at Netflix for their own purposes, but they chose to open solicit. And since then, it's become very popular with customers who want to use it even on prem. And you have a company that spun up on it. I think what's making this world very unique is you have very large companies like Facebook that will build things for themselves like VITAS or Netflix with Spinnaker and open source them. And you can have a lot of discussion about why they chose to do so, etc. But increasingly that's becoming the default when Amazon or Netflix or Facebook or Mehta, I guess you call them these days, build something for themselves for their own needs. The first question we ask ourselves is, should it be opensource? And increasingly we are all saying yes. And here's what happens because of that. It gives an opportunity depending on how you open source it for innovation through commercial deployments, so that you get SaaS companies, you know, that are going to take that product and make it relevant and useful to a very broad number of customers. You build partnerships with cloud providers like AWS, because our customers love this open source project and they need help. And they may choose an AWS managed service, or they may end up working with this partner on a day-to-day basis. And we want to work with that partner because they're making our customers successful, which is one reason all of us are here. So you're having this set of innovation from large companies from, you know, whether they are just consumer companies like Metta infrastructure companies like us, or just random innovation that's happening in an open source project that which ends up in companies being spun up and that foster that innovative innovation and that flywheel that's happening right now. And I think you said that like, this is unique. I mean, you never saw this happen before from so many different directions. >> It really is a nice progression on the business model side as well. You mentioned Argo, which is a great organic thing that was Intuit developed. We just interviewed code fresh. They just presented here in the showcase as well. You seeing the formation around these projects develop now in the community at a different scale. I mean, look at code fresh. I mean, Intuit did it Argo and they're not just supporting it. They're building a platform. So you seeing the dynamics of tools and now emerging the platforms, you mentioned Lambda, okay. Which is proprietary for AWS and your talk powered by open source. So again, open source combined with cloud scale allows for new potential super applications or super clouds that are developing. This is a new phenomenon. This isn't just lift and shift and host on the cloud. This is actually a construction production developer workflow. >> Yeah. And you are seeing consumers, large companies, enterprises, startups, you know, it used to be that startups would be comfortable adopting some of these solutions, but now you see companies of all sizes doing so. And I said, it's not just software it's software, the services increasingly becoming the way these are given, delivered to customers. I actually think the innovation is just getting going, which is why we have this. We have so many partners here who are all in inventing and innovating on top of open source, whether it's developed by them or a broader community. >> Yeah. I liked, I liked the represent container. Do you guys have, did that drove that you've seen a lot of changes and again, with cloud scale and open source, you seeing the dynamics change, whether you're enabling that, and then you see kind of like real big change. So let's take snowflake, a big customer of AWS. They started out as a startup too, but they weren't a data warehouse. They were bringing data warehouse like functionality and then changing everything differently and making it consumable for the cloud. And hence they're huge. So that's a disruption into an incumbent leader or sector. Then you've got new capabilities emerging. What's your thoughts, Deepak? Can you share your vision on how you have the disruption to existing leaders, old guard, if you will, as you guys call them and then new capabilities as these new platforms emerge at a net new functionality, how do you see that emerging? >> Yeah. So I speak from my side of the world. I've lived in over the last few years, which has containers and serverless, right? There's a lot of, if you go to any enterprise and ask them, do you want to modernize the infrastructure? Do you want to take advantage of automated software delivery, continuous delivery infrastructure as code modern observability, all of them will say yes, but they also are still a large enterprise, which has these enterprise level requirements. I'm using the word enterprise a lot. And I usually it's a trigger word for me because so many customers have similar requirements, but I'm using it here as large company with a lot of existing software and existing practices. I think the innovation that's coming and I see a lot of companies doing that is saying, "Hey, we understand the problems you want to solve. We understand the world where you live in, which could be regulated." You want to use all these new modalities. How do we allow you to use all of them? Keep the advantages of switching to a Lambda or switching to, and a service running on far gate, but give you the same capabilities. And I think I'll bring up cystic here because we work so closely with them on Falco. As an example, I just talked about them in my keynote. They could have just said, "Oh no, we'll just support the SE2 and be done with it." They said, "No, we're going to make sure that serverless containers in particular are something that you're going to be really good at because our customers want to use them, but requires us to think differently. And then they ended up developing new things like Falco that are born in this new world, but understand the requirements of the old world. If you get what I'm saying. And I think that a real example. >> Yeah. Oh, well, I mean, first of all, they're smart. So that was pretty obvious for most people that know, sees that you can connect the dots on serverless, which is a great point, but not everyone can see that again, this is what's new and and systig was just found in his backyard. As I found out on my interview, a great, great founder, they would do a new thing. So it was a very easy to connect the dots there again, that's the trend. Well, I got to ask if they're doing that for serverless, you mentioned graviton in your speech and what came out of you mentioned graviton in your speech and what came out of re-invent this past year was all the innovation going on at the compute level with gravitron at many levels in the Silicon. How should companies and open source developers think about how to innovate with graviton? >> Yeah, I mean, you've seen examples from people blogging and tweeting about how fast their applications run and grab it on the price performance benefits that they get, whether it's on, you know, whether it's an observability or other places. something that AWS is going to embrace across a compute something that AWS is going to embrace across a compute portfolio. Obviously you can go find EC2 instances, the gravitron two instances and run on them and that'll be great. But we know that most of our customers, many of our customers are building new applications on serverless containers and serveless than even as containers increasingly with things like foggy, where they don't want to operate the underlying infrastructure. A big part of what we're doing is to make sure that graviton is available to you on every compute modality. You can run it on a C2 forever. You've been running, being able to use ECS and EKS and run and grab it on almost since launch. What do you want me to take it a step further? You elastic Beanstalk customers, elastic Beanstalk has been around for a decade, but you can now use it with graviton. people running ECS on for gate can now use graviton. Lambda customers can pick graviton as well. So we're taking this price performance benefits that you get So we're taking this price performance benefits that you get from graviton and basically putting it across the entire compute portfolio. What it means is every high level service that gets built on compute infrastructure. And you get the price performance benefits, you get the price performance benefits of the lower power consumption of arm processes. So I'm personally excited like crazy. And you know, this has graviton 2 graviton 3 is coming. >> That's incredible. It's an opportunity like serverless was it's pretty obvious. And I think hopefully everyone will jump on that final question as the time's ticking here. I want to get your thoughts quickly. If you look at what's happened with containers over the past say eight years since the original founding of the first Docker instance, if you will, to how that's evolved and then the introduction of Kubernetes and the cloud native wave we're seeing now, what is, how would you describe the relationship between the success Docker, seeing now with Kubernetes in the cloud native construct what's different and why is this combination so successful? >> Yeah. I often say that containers would have, let me rephrase that. what I say is that people would have adopted sort of the modern way of running applications, whether containers came around or not. But the fact that containers came around made that migration and that journey is so much more efficient for people. So right from, I still remember the first doc that Solomon gave Billy announced DACA and starting to use it on customers, starting to get interested all the way to the more sort of advanced orchestration that we have now for containers across the board. And there's so many examples of the way you can do that. Kubernetes being the most, most well-known one. Here's the thing that I think has changed. I think what Kubernetes or Docker, or the whole sort of modern way of building applications has done is it's taken people who would have taken years adopting these practices and by bringing it right to the fingertips and rebuilding it into the APIs. And in the case of Kubernetes building an entire sort of software world around it, the number of, I would say number of decisions people have to take has gone smaller in many ways. There's so many options, the number of decisions that become higher, but the com the speed at which they can get to a result and a production version of an application that works for them is way low. I have not seen anything like what I've seen in the last 6, 7, 8 years of how quickly the most you know, the most I would say is, you know, a company that you would think would never adopt modern technology has been able to go from, this is interesting to getting a production really quickly. And I think it's because the tooling makes it So, and the fact that you see the adoption that you see right and the fact that you see the adoption that you see right from the fact that you could do Docker run Docker, build Docker, you know, so easily back in the day, all the way to all the advanced orchestration you can do with container orchestrator is today. sort of taking all of that away as well. there's never been a better time to be a developer independent of whatever you're trying to build. And I think containers are a big central part of why that's happened. >> Like the recipe, the combination of cloud-scale, the timing of Kubernetes and the containerization concepts just explode as a beautiful thing. And it creates more opportunities and will challenges, which are opportunities that are net new, but it solves the automation piece that we're seeing this again, it's only makes things go faster. >> Yes. >> And that's the key trend. Deepak, thank you so much for coming on. We're seeing tons of open cloud innovations, thanks to the success of your team at AWS and being great participants in the community. We're seeing innovations from startups. You guys are helping enabling that. Of course, they want to live on their own and be successful and build their super clouds and super app. So thank you for spending the time with us. Appreciate. >> Yeah. Anytime. And thank you. And you know, this is a great event. So I look forward to people running software and building applications, using AWS services and all these wonderful partners that we have. >> Awesome, great stuff. Great startups, great next generation leaders emerging. When you see startups, when they get successful, they become the modern software applications platforms out there powering business and changing the world. This is the cube you're watching the AWS startup showcase. Season two episode one open cloud innovations on John Furrier your host, see you next time.

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 aws is pointing the way to a revolution in system architecture much in the same way that aws defined the cloud operating model last decade we believe it is once again leading in future systems design the secret sauce underpinning these innovations is specialized designs that break the stranglehold of inefficient and bloated centralized processing and allows aws to accommodate a diversity of workloads that span cloud data center as well as the near and far edge hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll dig into the moves that aws has been making which we believe define the future of computing we'll also project what this means for customers partners and aws many competitors now let's take a look at aws's architectural journey the is revolution it started by giving easy access as we all know to virtual machines that could be deployed and decommissioned on demand amazon at the time used a highly customized version of zen that allowed multiple vms to run on one physical machine the hypervisor functions were controlled by x86 now according to werner vogels as much as 30 of the processing was wasted meaning it was supporting hypervisor functions and managing other parts of the system including the storage and networking these overheads led to aws developing custom asics that help to accelerate workloads now in 2013 aws began shipping custom chips and partnered with amd to announce ec2 c3 instances but as the as the aws cloud started to scale they really weren't satisfied with the performance gains that they were getting and they were hitting architectural barriers that prompted aws to start a partnership with anaperta labs this was back in 2014 and they launched then ec2 c4 instances in 2015. the asic in c4 optimized offload functions for storage and networking but still relied on intel xeon as the control point aws aws shelled out a reported 350 million dollars to acquire annapurna in 2015 which is a meager sum to acquire the secret sauce of its future system design this acquisition led to a modern version of project nitro in 2017 nitro nitro offload cards were first introduced in 2013 at this time aws introduced c5 instances and replaced zen with kvm and more tightly coupled the hypervisor with the asic vogels shared last year that this milestone offloaded the remaining components including the control plane the rest of the i o and enabled nearly a hundred percent of the processing to support customer workloads it also enabled a bare metal version of the compute that spawned the partnership the famous partnership with vmware to launch vmware cloud on aws then in 2018 aws took the next step and introduced graviton its custom designed arm-based chip this broke the dependency on x86 and launched a new era of architecture which now supports a wide variety of configurations to support data intensive workloads now these moves preceded other aws innovations including new chips optimized for machine learning and training and inferencing and all kinds of ai the bottom line is aws has architected an approach that offloaded the work currently done by the central processing unit in most general purpose workloads like in the data center it has set the stage in our view for the future allowing shared memory memory disaggregation and independent resources that can be configured to support workloads from the cloud all the way to the edge and nitro is the key to this architecture and to summarize aws nitro think of it as a set of custom hardware and software that runs on an arm-based platform from annapurna aws has moved the hypervisor the network the storage virtualization to dedicated hardware that frees up the cpu to run more efficiently this in our opinion is where the entire industry is headed so let's take a look at that this chart pulls data from the etr data set and lays out key players competing for the future of cloud data center and the edge now we've superimposed nvidia up top and intel they don't show up directly in the etr survey but they clearly are platform players in the mix we covered nvidia extensively in previous breaking analysis and won't go too deep there today but the data shows net scores on the vertical axis that's a measure of spending velocity and then it shows market share in the horizontal axis which is a measure of pervasiveness within the etr data set we're not going to dwell on the relative positions here rather let's comment on the players and start with aws we've laid out aws how they got here and we believe they are setting the direction for the future of the industry and aws is really pushing migration to its arm-based platforms pat morehead at the 6-5 summit spoke to dave brown who heads ec2 at aws and he talked extensively about migrating from x86 to aws's arm-based graviton 2. and he announced a new developer challenge to accelerate that migration to arm instances graviton instances and the end game for customers is a 40 better price performance so a customer running 100 server instances can do the same work with 60 servers now there's some work involved but for the by the customers to actually get there but the payoff if they can get 40 improvement in price performance is quite large imagine this aws currently offers 400 different ec2 instances last year as we reported sorry last year as we reported earlier this year nearly 50 percent of the new ec2 instances so nearly 50 percent of the new ec2 instances shipped in 2020 were arm based and aws is working hard to accelerate this pace it's very clear now let's talk about intel i'll just say it intel is finally responding in earnest and basically it's taking a page out of arm's playbook we're going to dig into that a bit today in 2015 intel paid 16.7 billion dollars for altera a maker of fpgas now also at the 6.5 summit nevin shenoy of intel presented details of what intel is calling an ipu it's infrastructure processing unit this is a departure from intel norms where everything is controlled by a central processing unit ipu's are essentially smart knicks as our dpus so don't get caught up in all the acronym soup as we've reported it's all about offloading work and disaggregating memory and evolving socs system-on-chip and sops system on package but just let this sink in a bit a bit for a moment intel's moves this past week it seems to us anyway are designed to create a platform that is nitro like and the basis of that platform is a 16.7 billion dollar acquisition just compare that to aws's 350 million dollar tuck-in of annapurna that is incredible now chenoy said in his presentation rough quote we've already deployed ipu's using fpgas in a in very high volume at microsoft azure and we've recently announced partnerships with baidu jd cloud and vmware so let's look at vmware vmware is the other you know really big platform player in this race in 2020 vmware announced project monterrey you might recall that it's based on the aforementioned fpgas from intel so vmware is in the mix and it chose to work with intel most likely for a variety of reasons one of the obvious ones is all the software that's running on on on vmware it's been built for x86 and there's a huge install base there the other is pat was heading vmware at the time and and you know when project monterey was conceived so i'll let you connect the dots if you like regardless vmware has a nitro like offering in our view its optionality however is limited by intel but at least it's in the game and appears to be ahead of the competition in this space aws notwithstanding because aws is clearly in the lead now what about microsoft and google suffice it to say that we strongly believe that despite the comments that intel made about shipping fpgas and volume to microsoft that both microsoft and google as well as alibaba will follow aws's lead and develop an arm-based platform like nitro we think they have to in order to keep pace with aws now what about the rest of the data center pack well dell has vmware so despite the split we don't expect any real changes there dell is going to leverage whatever vmware does and do it better than anyone else cisco is interesting in that it just revamped its ucs but we don't see any evidence that it has a nitro like plans in its roadmap same with hpe now both of these companies have history and capabilities around silicon cisco designs its own chips today for carrier class use cases and and hpe as we've reported probably has some remnants of the machine hanging around but both companies are very likely in our view to follow vmware's lead and go with an intel based design what about ibm well we really don't know we think the best thing ibm could do would be to move the ibm cloud of course to an arm-based nitro-like platform we think even the mainframe should move to arm as well i mean it's just too expensive to build a specialized mainframe cpu these days now oracle they're interesting if we were running oracle we would build an arm-based nitro-like database cloud where oracle the database runs cheaper faster and consumes less energy than any other platform that would would dare to run oracle and we'd go one step further and we would optimize for competitive databases in the oracle cloud so we would make oci run the table on all databases and be essentially the database cloud but you know back to sort of fpgas we're not overly excited about about the market amd is acquiring xi links for 35 billion dollars so i guess that's something to get excited about i guess but at least amd is using its inflated stock price to do the deal but we honestly we think that the arm ecosystem will will obliterate the fpga market by making it simpler and faster to move to soc with far better performance flexibility integration and mobility so again we're not too sanguine about intel's acquisition of altera and the moves that amd is making in in the long term now let's take a deeper look at intel's vision of the data center of the future here's a chart that intel showed depicting its vision of the future of the data center what you see is the ipu's which are intelligent nixed and they're embedded in the four blocks shown and they're communicating across a fabric now you have general purpose compute in the upper left and machine intelligent on the bottom left machine intelligence apps and up in the top right you see storage services and then the bottom right variation of alternative processors and this is intel's view of how to share resources and go from a world where everything is controlled by a central processing unit to a more independent set of resources that can work in parallel now gelsinger has talked about all the cool tech that this will allow intel to incorporate including pci and gen 5 and cxl memory interfaces and or cxl memory which are interfaces that enable memory sharing and disaggregation and 5g and 6g connectivity and so forth so that's intel's view of the future of the data center let's look at arm's vision of the future and compare them now there are definite similarities as you can see especially on the right hand side of this chart you've got the blocks of different process processor types these of course are programmable and you notice the high bandwidth memory the hbm3 plus the ddrs on the two sides kind of bookending the blocks that's shared across the entire system and it's connected by pcie gen 5 cxl or ccix multi-die socket so you know you may be looking to say okay two sets of block diagrams big deal well while there are similarities around disaggregation and i guess implied shared memory in the intel diagram and of course the use of advanced standards there are also some notable differences in particular arm is really already at the soc level whereas intel is talking about fpgas neoverse arms architecture is shipping in test mode and we'll have end market product by year end 2022 intel is talking about maybe 2024 we think that's aspirational or 2025 at best arm's road map is much more clear now intel said it will release more details in october so we'll pay attention then maybe we'll recalibrate at that point but it's clear to us that arm is way further along now the other major difference is volume intel is coming at this from a high data center perspective and you know presumably plans to push down market or out to the edge arm is coming at this from the edge low cost low power superior price performance arm is winning at the edge and based on the data that we shared earlier from aws it's clearly gaining ground in the enterprise history strongly suggests that the volume approach will win not only at the low end but eventually at the high end so we want to wrap by looking at what this means for customers and the partner ecosystem the first point we'd like to make is follow the consumer apps this capability the capabilities that we see in consumer apps like image processing and natural language processing and facial recognition and voice translation these inference capabilities that are going on today in mobile will find their way into the enterprise ecosystem ninety percent of the cost associated with machine learning in the cloud is around inference in the future most ai in the enterprise and most certainly at the edge will be inference it's not today because it's too expensive this is why aws is building custom chips for inferencing to drive costs down so it can increase adoption now the second point is we think that customers should start experimenting and see what you can do with arm-based platforms moore's law is accelerating at least the outcome of moore's law the doubling of performance every of the 18 to 24 months it's it's actually much higher than that now when you add up all the different components in these alternative processors just take a look at apple's a5 a15 chip and arm is in the lead in terms of performance price performance cost and energy consumption by moving some workloads onto graviton for example you'll see what types of cost savings you can drive for which applications and possibly generate new applications that you can deliver to your business put a couple engineers in the task and see what they can do in two or three weeks you might be surprised or you might say hey it's too early for us but you'll find out and you may strike gold we would suggest that you talk to your hybrid cloud provider as well and find out if they have a nitro we shared that vmware they've got a clear path as does dell because they're you know vmware cousins what about your other strategic suppliers what's their roadmap what's the time frame to move from where they are today to something that resembles nitro do they even think about that how do they think about that do they think it's important to get there so if if so or if not how are they thinking about reducing your costs and supporting your new workloads at scale now for isvs these consumer capabilities that we discussed earlier all these mobile and and automated systems and cars and and things like that biometrics another example they're going to find their way into your software and your competitors are porting to arm they're embedding these consumer-like capabilities into their apps are you we would strongly recommend that you take a look at that talk to your cloud suppliers and see what they can do to help you innovate run faster and cut costs okay that's it for now thanks to my collaborator david floyer who's been on this topic since early last decade thanks to the community for your comments and insights and hey thanks to patrick morehead and daniel newman for some timely interviews from your event nice job fellas remember i published each week on wikibon.com and siliconangle.com these episodes are all available as podcasts just search for breaking analysis podcasts you can always connect with me on twitter at d vallante or email me at david.velante at siliconangle.com i appreciate the comments on linkedin and clubhouse so follow us if you see us in a room jump in and let's riff on these topics and don't forget to check out etr.plus for all the survey data this is dave vellante for the cube insights powered by etr be well and we'll see you next time

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 nvidia wants to completely transform enterprise computing by making data centers run 10x faster at one tenth the cost and video's ceo jensen wang is crafting a strategy to re-architect today's on-prem data centers public clouds and edge computing installations with a vision that leverages the company's strong position in ai architectures the keys to this end-to-end strategy include a clarity of vision massive chip design skills a new arm-based architecture approach that integrates memory processors i o and networking and a compelling software consumption model even if nvidia is unsuccessful at acquiring arm we believe it will still be able to execute on this strategy by actively participating in the arm ecosystem however if its attempts to acquire arm are successful we believe it will transform nvidia from the world's most valuable chip company into the world's most valuable supplier of integrated computing architectures hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll explain why we believe nvidia is in the right position to power the world's computing centers and how it plans to disrupt the grip that x86 architectures have had on the data center for decades the data center market is in transition like the universe the cloud is expanding at an accelerated pace no longer is the cloud an opaque set of remote services i always say somewhere out there sitting in a mega data center no rather the cloud is extending to on-premises data centers data centers are moving into the cloud and they're connecting through adjacent locations that create hybrid interactions clouds are being meshed together across regions and eventually will stretch to the far edge this new definition or view of cloud will be hyper distributed and run by software kubernetes is changing the world of software development and enabling workloads to run anywhere open apis external applications expanding the digital supply chains and this expanding cloud they all increase the threat surface and vulnerability to the most sensitive information that resides within the data center and around the world zero trust has become a mandate we're also seeing ai being injected into every application and it's the technology area that we see with the most momentum coming out of the pandemic this new world will not be powered by general purpose x86 processors rather it will be supported by an ecosystem of arm-based providers in our opinion that are affecting an unprecedented increase in processor performance as we have been reporting and nvidia in our view is sitting in the poll position and is currently the favorite to dominate the next era of computing architecture for global data centers public clouds as well as the near and far edge let's talk about jensen wang's clarity of vision for this new world here's a chart that underscores some of the fundamental assumptions that he's leveraging to expand his market the first is that there's a lot of waste in the data center he claims that only half of the cpu cores deployed in the data center today actually support applications the other half are processing the infrastructure all around the applications that run the software defined data center and they're terribly under utilized nvidia's blue field three dpu the data processing unit was described in a blog post on siliconangle by analyst zias caravala as a complete mini server on a card i like that with software defined networking storage and security acceleration built in this product has the bandwidth and according to nvidia can replace 300 general purpose x86 cores jensen believes that every network chip will be intelligent programmable and capable of this type of acceleration to offload conventional cpus he believes that every server node will have this capability and enable every packed of every packet and every application to be monitored in real time all the time for intrusion and as servers move to the edge bluefield will be included as a core component in his view and this last statement by jensen is critical in our opinion he says ai is the most powerful force of our time whether you agree with that or not it's relevant because ai is everywhere an invidious position in ai and the architectures the company is building are the fundamental linchpin of its data center enterprise strategy so let's take a look at some etr spending data to see where ai fits on the priority list here's a set of data in a view that we often like to share the horizontal axis is market share or pervasiveness in the etr data but we want to call your attention to the vertical axis that's really really what really we want to pay attention today that's net score or spending momentum exiting the pandemic we've seen ai capture the number one position in the last two surveys and we think this dynamic will continue for quite some time as ai becomes the staple of digital transformations and automations an ai will be infused in every single dot you see on this chart nvidia's architectures it just so happens are tailor made for ai workloads and that is how it will enter these markets let's quantify what that means and lay out our view of how nvidia with the help of arm will go after the enterprise market here's some data from wikibon research that depicts the percent of worldwide spending on server infrastructure by workload type here are the key points first the market last year was around 78 billion dollars worldwide and is expected to approach 115 billion by the end of the decade this might even be a conservative figure and we've split the market into three broad workload categories the blue is ai and other related applications what david floyer calls matrix workloads the orange is general purpose think things like erp supply chain hcm collaboration basically oracle saps and microsoft work that's being supported today and of course many other software providers and the gray that's the area that jensen was referring to is about being wasted the offload work for networking and storage and all the software defined management in the data centers around the world okay you can see the squeeze that we think compute infrastructure is gonna gonna occur around that orange area that general-purpose workloads that we think is going to really get squeezed in the next several years on a percentage basis and on an absolute basis it's really not growing nearly as fast as the other two and video with arm in our view is well positioned to attack that blue area and the gray area those those workload offsets and the new emerging ai applications but even the orange as we've reported is under pressure as for example companies like aws and oracle they use arm-based designs to service general purpose workloads why are they doing that cost is the reason because x86 generally and intel specifically are not delivering the price performance and efficiency required to keep up with the demands to reduce data center costs and if intel doesn't respond which we believe it will but if it doesn't act arm we think will get 50 percent of the general purpose workloads by the end of the decade and with nvidia it will dominate the blue the ai and the gray the offload work when we say dominate we're talking like capture 90 percent of the available market if intel doesn't respond now intel they're not just going to sit back and let that happen pat gelsinger is well aware of this in moving intel to a new strategy but nvidia and arm are way ahead in the game in our view and as we've reported this is going to be a real challenge for intel to catch up now let's take a quick look at what nvidia is doing with relevant parts of its pretty massive portfolio here's a slide that shows nvidia's three chip strategy the company is shifting to arm-based architectures which we'll describe in more detail in a moment the slide shows at the top line nvidia's ampere architecture not to be confused with the company ampere computing nvidia is taking a gpu centric approach no surprise obvious reasons there that's their sort of stronghold but we think over time it may rethink this a little bit and lean more into npus the neural processing unit we look at what apple's doing what tesla are doing we see opportunities for companies like nvidia to really sort of go after that but we'll save that for another day nvidia has announced its grace cpu a nod to the famous computer scientist grace hopper grace is a new architecture that doesn't rely on x86 and much more efficiently uses memory resources we'll again describe this in more detail later and the bottom line there that roadmap line shows the bluefield dpu which we described is essentially a complete server on a card in this approach using arm will reduce the elapsed time to go from chip design to production by 50 we're talking about shaving years down to 18 months or less we don't have time to do a deep dive into nvidia's portfolio it's large but we want to share some things that we think are important and this next graphic is one of them this shows some of the details of nvidia's jetson architecture which is designed to accelerate those ai plus workloads that we showed earlier and the reason is that this is important in our view is because the same software supports from small to very large including edge systems and we think this type of architecture is very well suited for ai inference at the edge as well as core data center applications that use ai and as we've said before a lot of the action in ai is going to happen at the edge so this is a good example of leveraging an architecture across a wide spectrum of performance and cost now we want to take a moment to explain why the moved arm-based architectures is so critical to nvidia one of the biggest cost challenges for nvidia today is keeping the gpu utilized typical utilization of gpu is well below 20 percent here's why the left hand side of this chart shows essentially racks if you will of traditional compute and the bottlenecks that nvidia faces the processor and dram they're tied together in separate blocks imagine there are thousands thousands of cores in a rack and every time you need data that lives in another processor you have to send a request and go retrieve it it's very overhead intensive now technologies like rocky are designed to help but it doesn't solve the fundamental architectural bottleneck every gpu shown here also has its own dram and it has to communicate with the processors to get the data i.e they can't communicate with each other efficiently now the right hand side side shows where nvidia is headed start in the middle with system on chip socs cpus are packaged in with npus ipu's that's the image processing unit you know x dot dot dot x pu's the the alternative processors they're all connected with sram which is think of that as a high speed layer like an layer one cache the os for the system on a chip lives inside of this and that's where nvidia has this killer software model what they're doing is they're licensing the consumption of the operating system that's running this system on chip in this entire system and they're affecting a new and really compelling subscription model you know maybe they should just give away the chips and charge for the software like a razer blade model talk about disruptive now the outer layer is the the dpu and the shared dram and other resources like the ampere computing the company this time cpus ssds and other resources these are the processors that will manage the socs together this design is based on nvidia's three chip approach using bluefield dpu leveraging melanox that's the networking component the network enables shared dram across the cpus which will eventually be all arm based grace lives inside the system on a chip and also on the outside layers and of course the gpu lives inside the soc in a scaled-down version like for instance a rendering gpu and we show some gpus on the outer layer as well for ai workloads at least in the near term you know eventually we think they may reside solely in the system on chip but only time will tell okay so you as you can see nvidia is making some serious moves and by teaming up with arm and leaning into the arm ecosystem it plans to take the company to its next level so let's talk about how we think competition for the next era of compute stacks up here's that same xy graph that we love to show market share or pervasiveness on the horizontal tracking against next net score on the vertical net score again is spending velocity and we've cut the etr data to capture players that are that are big in compute and storage and networking we've plugged in a couple of the cloud players these are the guys that we feel are vying for data center leadership around compute aws is a very strong position we believe that more than half of its revenues comes from compute you know ec2 we're talking about more than 25 billion on a run rate basis that's huge the company designs its own silicon graviton 2 etc and is working with isvs to run general purpose workloads on arm-based graviton chips microsoft and google they're going to follow suit they're big consumers of compute they sell a lot but microsoft in particular you know they're likely to continue to work with oem partners to attack that on-prem data center opportunity but it's really intel that's the provider of compute to the likes of hpe and dell and cisco and the odms which are the odms are not shown here now hpe let's talk about them for a second they have architectures and i hate to bring it up but remember the machine i know it's the butt of many jokes especially from competitors it had been you know frankly hpe and hp they deserve some of that heat for all the fanfare and then that they they put out there and then quietly you know pulled the machine or put it out the pasture but hpe has a strong position in high performance computing and the work that it did on new computing architectures with the machine and shared memories that might be still kicking around somewhere inside of hp and could come in handy for some day in the future so hpe has some chops there plus hpe has been known hp historically has been known to design its own custom silicon so i would not count them out as an innovator in this race cisco is interesting because it not only has custom silicon designs but its entry into the compute business with ucs a decade ago was notable and they created a new way to think about integrating resources particularly compute and networking with partnerships to add in the storage piece initially it was within within emc prior to the dell acquisition but you know it continues with netapp and pure and others cisco invests they spend money investing in architectures and we expect the next generation of ucs oh ucs2 ucs 2.0 will mark another notable milestone in the company's data center business dell just had an amazing quarterly earnings report the company grew top line revenue by around 12 percent and it wasn't because of an easy compare to last year dells is simply executing despite continued softness in the legacy emc storage business laptop the laptop demand continued to soar in dell server business it's growing again but we don't see dell as an architectural innovator per se in compute rather we think the company will be content to partner with suppliers whether it's intel nvidia arm-based partners or all of the above dell we think will rely on its massive portfolio its excellent supply chain and execution ethos to compete now ibm is notable for historical reasons with its mainframe ibm created the first great compute monopoly before it unwind and wittingly handed it to intel along with microsoft we don't see ibm necessarily aspiring to retake that compute platform mantle that once once held with mainframes rather red hat in the march to hybrid cloud is the path that we think in our view is ibm's approach now let's get down to the elephants in the room intel nvidia and china inc china is of course relevant because of companies like alibaba and huawei and the chinese chinese government's desire to be self-sufficient in semiconductor technology and technology generally but our premise here is that the trends are favoring nvidia over intel in this picture because nvidia is making moves to further position itself for new workloads in the data center and compete for intel's stronghold intel is going to attempt to remake itself but it should have been doing this seven years ago what pat gelsinger is doing today intel is simply far behind and it's going to take at least a couple years for them to really start to to make inroads in this new model let's stay on the nvidia v intel comparison for a moment and take a snapshot of the two companies here's a quick chart that we put together with some basic kpis some of these figures are approximations or they're rounded so don't stress over it too much but you can see intel is an 80 billion dollar company 4x the size of nvidia but nvidia's market cap far exceeds that of intel why is that of course growth in our view it's justified due to that growth and nvidia's strategic positioning intel used to be the gross margin king but nvidia has much higher gross margins interesting now when it comes down to free cash flow intel is still dominant as it pertains to the balance sheet intel is way more capital intensive than nvidia and as it starts to build out its foundries that's going to eat into intel's cash position now what we did is we put together a little pro forma on the third column of nvidia plus arm circa let's say the end of 2022. we think they could get to a run rate that is about half the size of intel and that can propel the company's market cap to well over half a trillion dollars if they get any credit for arm they're paying 40 billion dollars for arm a company that's you know sub 2 billion the risk is that because of the arm because the arm deal is based on cash plus tons of stock it could put pressure on the market capitalization for some time arm has 90 percent gross margins because it pretty much has a pure license model so it helps the gross margin line a little bit for this in this pro forma and the balance sheet is a swag arm has said that it's not going to take on debt to do the transaction but we haven't had time to really dig into that and figure out how they're going to structure it so we took a took a swag in in what we would do with this low interest rate environment but but take that with a grain of salt we'll do more research in there the point is given the momentum and growth of nvidia its strategic position in ai is in its deep engineering they're aimed at all the right places and its potential to unlock huge value with arm on paper it looks like the horse to beat if it can execute all right let's wrap up here's a summary look the architectures on which nvidia is building its dominant ai business are evolving and nvidia is well positioned to drive a truck right to the enterprise in our view the power has shifted from intel to the arm ecosystem and nvidia is leaning in big time whereas intel it has to preserve its current business while recreating itself at the same time this is going to take a couple of years but intel potentially has the powerful backing of the us government too strategic to fail the wild card is will nvidia be successful in acquiring arm certain factions in the uk and eu are fighting the deal because they don't want the u.s dictating to whom arm can sell its technology for example the restrictions placed on huawei for many suppliers of arm-based chips based on u.s sanctions nvidia's competitors like broadcom qualcomm at all are nervous that if nvidia gets armed they will be at a competitive disadvantage they being invidious competitors and for sure china doesn't want nvidia controlling arm for obvious reasons and it will do what it can to block the deal and or put handcuffs on how business can be done in china we can see a scenario where the u.s government pressures the uk and eu regulators to let this deal go through look ai and semiconductors you can't get much more strategic than that for the u.s military and the u.s long-term competitiveness in exchange for maybe facilitating the deal the government pressures nvidia to guarantee some feed to the intel foundry business while at the same time imposing conditions that secure access to arm-based technology for nvidia's competitors and maybe as we've talked about before having them funnel business to intel's foundry actually we've talked about the us government enticing apple to do so but it could also entice nvidia's competitors to do so propping up intel's foundry business which is clearly starting from ground zero and is going to need help outside of intel's own semiconductor manufacturing internally look we don't have any inside information as to what's happening behind the scenes with the us government and so forth but on its earning call on its earnings call nvidia said they're working with regulators that are on track to complete the deal in early 2022. we'll see okay that's it for today thank you to david floyer who co-created this episode with me and remember i publish each week on wikibon.com and siliconangle.com these episodes they're all available as podcasts all you're going to do is search breaking analysis podcast and you can always connect with me on twitter at dvalante or email me at david.valante siliconangle.com i always appreciate the comments on linkedin and in the clubhouse please follow me so you can be notified when we start a room and riff on these topics and don't forget to check out etr.plus for all the survey data this is dave vellante for the cube insights powered by etr be well and we'll see you next time [Music] you

>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel. Along with it's ecosystem partners. >> Okay, welcome back everyone. It's theCUBE's live coverage here in Las Vegas for Amazon Web Series today, re:Invent 2019. It's theCUBE's seventh year covering re:Invent. Eight years they've been running this event. It gets bigger every year. It's been a great wave to ride on. I'm John Furrier, my cohost, Dave Vellante. We've been riding this wave, Dave, for years. It's so exciting, it gets bigger and more exciting. >> Lucky seven. >> This year more than ever. So much stuff is happening. It's been really exciting. I think there's a sea change happening, in terms of another wave coming. Quantum computing, big news here amongst other great tech. Our next guest is Bill Vass, VP of Technology, Storage Automation Management, part of the quantum announcement that went out. Bill, good to see you. >> Yeah, well, good to see you. Great to see you again. Thanks for having me on board. >> So, we love quantum, we talk about it all the time. My son loves it, everyone loves it. It's futuristic. It's going to crack everything. It's going to be the fastest thing in the world. Quantum supremacy. Andy referenced it in my one-on-one with him around quantum being important for Amazon. >> Yes, it is, it is. >> You guys launched it. Take us through the timing. Why, why now? >> Okay, so the Braket service, which is based on quantum notation made by Dirac, right? So we thought that was a good name for it. It provides for you the ability to do development in quantum algorithms using gate-based programming that's available, and then do simulation on classical computers, which is what we call our digital computers today now. (men chuckling) >> Yeah, it's a classic. >> These are classic computers all of a sudden right? And then, actually do execution of your algorithms on, today, three different quantum computers, one that's annealing and two-bit gate-based machines. And that gives you the ability to test them in parallel and separate from each other. In fact, last week, I was working with the team and we had two machines, an ion trap machine and an electromagnetic tunneling machine, solving the same problem and passing variables back and forth from each other, you could see the cloud watch metrics coming out, and the data was going to an S3 bucket on the output. And we do it all in a Jupiter notebook. So it was pretty amazing to see all that running together. I think it's probably the first time two different machines with two different technologies had worked together on a cloud computer, fully integrated with everything else, so it was pretty exciting. >> So, quantum supremacy has been a word kicked around. A lot of hand waving, IBM, Google. Depending on who you talk to, there's different versions. But at the end of the day, quantum is a leap in computing. >> Bill: Yes, it can be. >> It can be. It's still early days, it would be day zero. >> Yeah, well I think if you think of, we're about where computers were with tubes if you remember, if you go back that far, right, right? That's about where we are right now, where you got to kind of jiggle the tubes sometimes to get them running. >> A bug gets in there. Yeah, yeah, that bug can get in there, and all of those kind of things. >> Dave: You flip 'em off with a punch card. Yeah, yeah, so for example, a number of the machines, they run for four hours and then they come down for a half hour for calibration. And then they run for another four hours. So we're still sort of at that early stage, but you can do useful work on them. And more mature systems, like for example D-Wave, which is annealer, a little different than gate-based machines, is really quite mature, right? And so, I think as you go back and forth between these machines, the gate-based machines and annealers, you can really get a sense for what's capable today with Braket and that's what we want to do is get people to actually be able to try them out. Now, quantum supremacy is a fancy word for we did something you can't do on a classical computer, right? That's on a quantum computer for the first time. And quantum computers have the potential to exceed the processing power, especially on things like factoring and other things like that, or on Hamiltonian simulations for molecules, and those kids of things, because a quantum computer operates the way a molecule operates, right, in a lot of ways using quantum mechanics and things like that. And so, it's a fancy term for that. We don't really focus on that at Amazon. We focus on solving customer's problems. And the problem we're solving with Braket is to get them to learn it as it's evolving, and be ready for it, and continue to develop the environment. And then also offer a lot of choice. Amazon's always been big on choice. And if you look at our processing portfolio, we have AMD, Intel x86, great partners, great products from them. We have Nvidia, great partner, great products from them. But we also have our Graviton 1 and Graviton 2, and our new GPU-type chip. And those are great products, too, I've been doing a lot on those, as well. And the customer should have that choice, and with quantum computers, we're trying to do the same thing. We will have annealers, we will have ion trap machines, we will have electromagnetic machines, and others available on Braket. >> Can I ask a question on quantum if we can go back a bit? So you mentioned vacuum tubes, which was kind of funny. But the challenge there was with that, it was cooling and reliability, system downtime. What are the technical challenges with regard to quantum in terms of making it stable? >> Yeah, so some of it is on classical computers, as we call them, they have error-correction code built in. So you have, whether you know it or not, there's alpha particles that are flipping bits on your memory at all times, right? And if you don't have ECC, you'd get crashes constantly on your machine. And so, we've built in ECC, so we're trying to build the quantum computers with the proper error correction, right, to handle these things, 'cause nothing runs perfectly, you just think it's perfect because we're doing all the error correction under the covers, right? And so that needs to evolve on quantum computing. The ability to reproduce them in volume from an engineering perspective. Again, standard lithography has a yield rate, right? I mean, sometimes the yield is 40%, sometimes it's 20%, sometimes it's a really good fab and it's 80%, right? And so, you have a yield rate, as well. So, being able to do that. These machines also generally operate in a cryogenic world, that's a little bit more complicated, right? And they're also heavily affected by electromagnetic radiation, other things like that, so you have to sort of faraday cage them in some cases, and other things like that. So there's a lot that goes on there. So it's managing a physical environment like cryogenics is challenging to do well, having the fabrication to reproduce it in a new way is hard. The physics is actually, I shudder to say well understood. I would say the way the physics works is well understood, how it works is not, right? No one really knows how entanglement works, they just knows what it does, and that's understood really well, right? And so, so a lot of it is now, why we're excited about it, it's an engineering problem to solve, and we're pretty good at engineering. >> Talk about the practicality. Andy Jassy was on the record with me, quoted, said, "Quantum is very important to Amazon." >> Yes it is. >> You agree with that. He also said, "It's years out." You said that. He said, "But we want to make it practical "for customers." >> We do, we do. >> John: What is the practical thing? Is it just kicking the tires? Is it some of the things you mentioned? What's the core goal? >> So, in my opinion, we're at a point in the evolution of these quantum machines, and certainly with the work we're doing with Cal Tech and others, that the number of available cubits are starting to increase at an astronomic rate, a Moore's Law kind of of rate, right? Whether it's, no matter which machine you're looking at out there, and there's about 200 different companies building quantum computers now, and so, and they're all good technology. They've all got challenges, as well, as reproducibility, and those kind of things. And so now's a good time to start learning how to do this gate-based programming knowing that it's coming, because quantum computers, they won't replace a classical computer, so don't think that. Because there is no quantum ram, you can't run 200 petabytes of data through a quantum computer today, and those kind of things. What it can do is factoring very well, or it can do probability equations very well. It'll have affects on Monte Carlo simulations. It'll have affects specifically in material sciences where you can simulate molecules for the first time that you just can't do on classical computers. And when I say you can't do on classical computers, my quantum team always corrects me. They're like, "Well, no one has proven "that there's an algorithm you can run "on a classical computer that will do that yet," right? (men chuckle) So there may be times when you say, "Okay, I did this on a quantum computer," and you can only do it on a quantum computer. But then someone's very smart mathematician says, "Oh, I figured out how to do it on a regular computer. "You don't need a quantum computer for that." And that's constantly evolving, as well, in parallel, right? And so, and that's what's that argument between IBM and Google on quantum supremacy is that. And that's an unfortunate distraction in my opinion. What Google did was quite impressive, and if you're in the quantum world, you should be very happy with what they did. They had a very low error rate with a large number of cubits, and that's a big deal. >> Well, I just want to ask you, this industry is an arms race. But, with something like quantum where you've got 200 companies actually investing in it so early days, is collaboration maybe a model here? I mean, what do think? You mentioned Cal Tech. >> It certainly is for us because, like I said, we're going to have multiple quantum computers available, just like we collaborate with Intel, and AMD, and the other partners in that space, as well. That's sort of the nice thing about being a cloud service provider is we can give customers choice, and we can have our own innovation, plus their innovations available to customers, right? Innovation doesn't just happen in one place, right? We got a lot of smart people at Amazon, we don't invent everything, right? (Dave chuckles) >> So I got to ask you, obviously, we can take cube quantum and call it cubits, not to be confused with theCUBE video highlights. Joking aside, classical computers, will there be a classical cloud? Because this is kind of a futuristic-- >> Or you mean a quantum cloud? >> Quantum cloud, well then you get the classic cloud, you got the quantum cloud. >> Well no, they'll be together. So I think a quantum computer will be used like we used to use a math coprocessor if you like, or FPGAs are used today, right? So, you'll go along and you'll have your problem. And I'll give you a real, practical example. So let's say you had a machine with 125 cubits, okay? You could just start doing some really nice optimization algorithms on that. So imagine there's this company that ships stuff around a lot, I wonder who that could be? And they need to optimize continuously their delivery for a truck, right? And that changes all the time. Well that algorithm, if you're doing hundreds of deliveries in a truck, it's very complicated. That traveling salesman algorithm is a NP-hard problem when you do it, right? And so, what would be the fastest best path? But you got to take into account weather and traffic, so that's changing. So you might have a classical computer do those algorithms overnight for all the delivery trucks and then send them out to the trucks. The next morning they're driving around. But it takes a lot of computing power to do that, right? Well, a quantum computer can do that kind of problemistic or deterministic equation like that, not deterministic, a best-fit algorithm like that, much faster. And so, you could have it every second providing that. So your classical computer is sending out the manifests, interacting with the person, it's got the website on it. And then, it gets to the part where here's the problem to calculate, we call it a shot when you're on a quantum computer, it runs it in a few seconds that would take an hour or more. >> It's a fast job, yeah. >> And it comes right back with the result. And then it continues with it's thing, passes it to the driver. Another update occurs, (buzzing) and it's just going on all the time. So those kind of things are very practical and coming. >> I've got to ask for the younger generations, my sons super interested as I mentioned before you came on, quantum attracts the younger, smart kids coming into the workforce, engineering talent. What's the best path for someone who has an either advanced degree, or no degree, to get involved in quantum? Is there a certain advice you'd give someone? >> So the reality is, I mean, obviously having taken quantum mechanics in school and understanding the physics behind it to an extent, as much as you can understand the physics behind it, right? I think the other areas, there are programs at universities focused on quantum computing, there's a bunch of them. So, they can go into that direction. But even just regular computer science, or regular mechanical and electrical engineering are all neat. Mechanical around the cooling, and all that other stuff. Electrical, these are electrically-based machines, just like a classical computer is. And being able to code at low level is another area that's tremendously valuable right now. >> Got it. >> You mentioned best fit is coming, that use case. I mean, can you give us a sense of a timeframe? And people will say, "Oh, 10, 15, 20 years." But you're talking much sooner. >> Oh, I don't, I think it's sooner than that, I do. And it's hard for me to predict exactly when we'll have it. You can already do, with some of the annealing machines, like D- Wave, some of the best fit today, right? So it's a matter of people want to use a quantum computer because they need to do something fast, they don't care how much it costs, they need to do something fast. Or it's too expensive to do it on a classical computer, or you just can't do it at all on a classical computer. Today, there isn't much of that last one, you can't do it at all, but that's coming. As you get to around 52, 50, 52 cubits, it's very hard to simulate that on a classical computer. You're starting to reach the edge of what you can practically do on a classical computer. At about 125 cubits, you probably are at a point where you can't just simulate it anymore. >> But you're talking years, not decades, for this use case? >> Yeah, I think you're definitely talking years. I think, and you know, it's interesting, if you'd asked me two years ago how long it would take, I would've said decades. So that's how fast things are advancing right now, and I think that-- >> Yeah, and the computers just getting faster and faster. >> Yeah, but the ability to fabricate, the understanding, there's a number of architectures that are very well proven, it's just a matter of getting the error rates down, stability in place, the repeatable manufacturing in place, there's a lot of engineering problems. And engineering problems are good, we know how to do engineering problems, right? And we actually understand the physics, or at least we understand how the physics works. I won't claim that, what is it, "Spooky action at a distance," is what Einstein said for entanglement, right? And that's a core piece of this, right? And so, those are challenges, right? And that's part of the mystery of the quantum computer, I guess. >> So you're having fun? >> I am having fun, yeah. >> I mean, this is pretty intoxicating, technical problems, it's fun. >> It is. It is a lot of fun. Of course, the whole portfolio that I run over at AWS is just really a fun portfolio, between robotics, and autonomous systems, and IOT, and the advanced storage stuff that we do, and all the edge computing, and all the monitor and management systems, and all the real-time streaming. So like Kinesis Video, that's the back end for the Amazon ghost stores, and working with all that. It's a lot of fun, it really is, it's good. >> Well, Bill, we need an hour to get into that, so we may have to come up and see you, do a special story. >> Oh, definitely! >> We'd love to come up and dig in, and get a special feature program with you at some point. >> Yeah, happy to do that, happy to do that. >> Talk some robotics, some IOT, autonomous systems. >> Yeah, you can see all of it around here, we got it up and running around here, Dave. >> What a portfolio. >> Congratulations. >> Alright, thank you so much. >> Great news on the quantum. Quantum is here, quantum cloud is happening. Of course, theCUBE is going quantum. We've got a lot of cubits here. Lot of CUBE highlights, go to SiliconAngle.com. We got all the data here, we're sharing it with you. I'm John Furrier with Dave Vellante talking quantum. Want to give a shout out to Amazon Web Services and Intel for setting up this stage for us. Thanks to our sponsors, we wouldn't be able to make this happen if it wasn't for them. Thank you very much, and thanks for watching. We'll be back with more coverage after this short break. (upbeat music)