So, what I will do today is that I will give an introduction to this molecular motors. I will not do a lot of maths today I will keep that for next class, but I thought I will just introduce what motors are and in what contexts we want to study them ok. So, here is one of the most famous examples of a molecular motor. This is the kinesin motor which is walking on a microtubule. So, this orange thing over here is kinesin, this green track over here is the microtubule and this darker green sphere of which you can only see apart is the cargo that this motor is carrying. So, it is very nice in that. So, this is a protein basically, it walks along this microtubule track by using ATP to bind and unbind this microtubule binding domain. So, which are let us say its legs ok. So, it places its legs one after another and it walks and on its head it is attached again chemically to this cargo. In this case of vesicle it can attach to different types of cargo and it transports this cargo. Inside this vesicle you might have different proteins or whatever that needs to be transported from one end of the cell to another. So, it just carries this cargo along these tracks and it does so processively and repetitively many many times over the life cycle of the cell. So, it takes in what it does is that it takes in chemical energy in the form of ATP hydrolysis. So, these are active processes they require an input of energy. So, ATP binds it hydrolyzes forming ADP plus phosphate plus energy that energy is utilized into converted into this mechanical energy which results in this directed non-random motion. So, this is not like a random walk where this motor you know sort of does takes one step this way or the other another step the other way. These motors move along because these microtubules remember have a polarity it has a minus end and a plus end. So, there is a directionality to the railway track of this microtubules and then they move directly on this on the line railway track. So, this is one motor. There can be other types of motors for example, this RNA polymerase motor. So, again there is this nice video. So, here is my DNA. The DNA is wrapped around these histone proteins forming the nucleosome right. If you do not remember this you should go back and recall it it is also part of the quiz question. So, when this DNA needs to be transcribed you need to form the mRNA then this RNA polymerase will comes and binds to this DNA. It first opens up from the histones so that it can be read then this RNA polymerase comes and binds to this and then it moves along this DNA track while forming this mRNA by reading the sequence. And again this movement of this RNA polymerase is an energy driven process. It takes in chemical energy in the form of ATP it converts it to directed motion as it moves along the DNA backbone and it produces this RNA transcript. So, again this is an example of a motor. So, anything that takes in this chemical energy and converts into some sort of mechanical word is what I will call a biological motor and they can be of different types as we will see as we go along. But for this RNA polymerase which we have seen in the context of this DNA transcription that also fits our definition of the motor. So, for the first part what I will try to focus on are these translational motors families of translational motors. There can be other types although I will talk I will do not think I will talk about them today. Let me just say that rough or broadly you can say that I will categorize my motors into maybe three classes transcriptional motors like this kinesin or this RNA polymerase motor that we saw or these other motors which I will discuss kinesin dyneins, myosins whatever. So, these are motors that takes in chemical energy and it sort of uses that chemical energy to produce directed motion along different substrates in different contexts, but produce directed motion. Linear sort of motion along a track something like this. So, that is one sort of motor. Another sort of motor could be rotatory motors rotatory motors. And where have we seen a rotatory motor? The helical flagella for example, in the E. coli where the motor that rotates the helical flagellum that is an example of a rotatory motor. Again it takes in chemical energy, but it converts that not into linear motion, but into rotational motion. So, that is a different class of motors. Or you could think of stuff like polymerization motors for example, microtubules and actins themselves. They can take in energy, they polymerize and in the process of that polymerization they can do some work by exerting forces on different substrates. So, that is a different class of motor that is called polymerization motors. So, this would be actins, microtubules and so on. So, all of these are motors because broadly they fall under the class of active processes. So, these are systems, protein complexes that take in chemical energy in the form of ATP or GTP or something and converts into some sort of motion. So, for today what I will deal with is mostly these translational motors and in particular motors that walk on microtubules and on actin. So, here are three representative motors. So, this motor over here is what is called the is example of a myosin category of motors. So, myosins are motors that walk along actin filaments. So, myosins will walk only along actins, myosins these walk along actin filaments. If you see the structure there is the stock of the motor, then over here are these two head domains and over here there is something called the tail domain. The tail domain is the one that binds the cargo. So, the vesicle like the vesicle in the previous animation, the head domain is what binds the stock or the track along which the motor walks. Here in the middle is something that is it is a example for kinesin motor and kinesins as opposed to myosins they walk along microtubules, they walk along microtubule filaments and not only that they walk in a specific direction along microtubule filaments. The microtubule remember has a minus and a plus depending on whether alpha tubulin is exposed or beta tubulin is exposed it is a chemical polarity and then kinesins walk towards the plus end of microtubules always. Whenever you have a kinesin motor, they will walk towards the plus end of from the minus end towards the plus end. And again it is roughly similar, it has these head domains. So, the head domains are much smaller, the tail domains look different. So, structurally this is different from the myosin family, but functionally it is somewhat the structure is somewhat conserved in that you have this head domains which bind to the track, microtubule in this case, the tail domains which bind to the cargo. And finally, there is this other class which is dynines which is dynines these also walk along microtubule filaments. This also walk along microtubule filaments except they walk in the reverse direction. So, they walk from the plus end of microtubules towards the minus end as opposed to kinesins. The dynine structure is somewhat more complicated than the kinesins or the myosins and as a result its functionality also has some complications we will come to that later. But at the simplest level it is again the same, you have these head domains which will bind to the track, the microtubule in this case and the tail domains which will bind to the cargo. I call it examples of myosin kinesin and dynine because these are basically not a single motor, but these are a family of motors ok. So, for example, if I think of myosin here is the myosin super family of motors. So, these are all different proteins all different myosin like proteins occurring in different organisms or in even in the same organism in different cell types ok. So, there are myosin 1, myosin 2 and so on and even within one there are subclasses of myosin motors they might differ by amino acid residues, but they will all roughly do the same thing in that they will bind to an actin filament and they will walk along the actin filament. The actin the myosin have not written whether it is a plus walking motor or a minus walking motor because in myosins there are categories which walk towards the plus end of actin and categories which walk towards the minus end, but they are not different proteins as such unlike in the case of kinesins and dinins. So, even kinesin you would have a super family like this there are different types of kinesins of what is shown here is a particular kinesin kinesin 1. There can be other types of kinesins, but again and they will differ in the sort of cargos they can bind, they will differ in the organisms in which they are formed, but they will all bind to microtubules and they will all walk from the minus end to the plus end. Similarly, there will be different types of dinins, but again they will all bind the microtubules and walk from the plus end to the minus end. So, these are if you think about the diversity these are quite complicated objects, but as long as we are talking in terms of a modeling context I will often use kinesin as something that just you know I do not care about what particular cargo it binds and so on I will just consider it to be a motor that binds to microtubules and walks along the plus end. How do I say that these motors are different? One is of course, by looking at the structure and the minus end residue and so on. So, at a chemical level you can see that these this protein is different from this protein is different from that protein ok, but physically one can also show that these motors behave differently by using different experiments. For example, one common experiment that is used to characterize the properties of a motor or what I call these force velocity curves. So, you apply a sort of force. So, let us say this here is my kinesin let us say here is my microtubule on which I have this kinesin it is a very bad kinesin, but this kinesin motor binding here is my cargo. And what I can do is that I can it walks with some velocity some average velocity it will walk along the microtubule. What I could do is that I could pull back on this cargo with some force let us say the cargo is let me just draw it on the cargo. So, let us say I pull back on the cargo with some force generally these experiments are done using optical traps. So, you trap the cargo bead and you pull on it with some opposing force and you see how this velocity changes as a function of force. How does this velocity change as a function of the force that you apply? And if you look at it for different motors this curve will look different. So, these are all the force axes are all normalized by the force at the half maximal velocity because the forces that are produced by these different motors are very different, but I have just normalized it. So, that when f is equal to 0 whatever it is it is y axis is normalized with a maximum velocity. So, that all points on the y axis for these different motors all fall on one. So, this is data for three different motors one is this kinesin motor that we have been talking about. The second is the blue curve is this RNA polymerase motor that I showed and the third one is this phage packaging motor ok. The phage packaging motor is in viruses like bacteriophage you need to package your DNA inside this viral capsid and that again happens through a motor. So, that is the phage packaging motor. So, that is the one shown in the red curve. And of course, when force is very high all of these velocities will drop to 0. So, if you apply a counter you apply an opposing force eventually provided you apply a large enough force all of these motors will stop ok. And so, the velocity in each will drop to 0, but how it drops to 0 is very different for different classes of motors. So, kinesin so, for example, RNA polymerase has this very strong sigmoidal curve. So, for small forces it does not really affect the velocity that much then beyond a certain this critical force it sort of very quickly and very sharply drops to 0. Kinesin is somewhat of a more of a less steeper sigmoid whereas, this phage packaging motor it sort of drops linearly with force almost for a very long range. The more force you apply the more the slower the velocity and then it slowly drops to 0. If you were to do it for different motors you will see different sorts of curves that emerge and you can use this sort of force velocity curves to characterize. So, the internal working of all of these motors are different and what this force velocity curve shows you is sort of a macroscopic manifestation of the differences in this internal architecture of the internal machinery of this motor. So, it is a reflection of the fact that these different motors have different mechanisms ok. So, how do I how can I characterize motors for example, what are the different quantities that I can use. So, one is this thing that we have been talking about direction of motion right. So, for example, kinesin is plus and directed on microtubules whereas, dynines are minus and directed. I could talk about the speed of the motor. So, for the speed of course, depends on the depends on the force as we saw. So, these v is a function of f, but I could talk about the 0 force velocity right. So, in the absence of any opposing load how does this what is this velocity with which the motor moves. So, for kinesin for example, the step size is 8 nanometers and this 8 nanometer step size is nothing but the size of the underlying lattice basically. You remember this microtubule consists of this alpha beta dimers right and the size of this alpha beta dimers is 8 nanometers. What it means is that this kinesins bind hopped from one of these alpha beta tubulin subunits to the next one and therefore, the step size of these kinesin motors is 8 nanometers. The speed is some of the order of microns per second. So, some 6 microns per second for the case of kinesins. Again these are all sort of typical numbers in the sense that if you did different if you looked at different kinesin motors for example, you would maybe find some differences, but roughly of that order.


Mae yna ddefnyddio i gael. Rwy'n gobeithio i'r oeddu i ddim yn gweithio i gael y bydd yma. Yn ymgylch, yng Nghymru, mae'n ddweud i gael y Llyfrgellu Fygrifol Fygrifol, mewn cyfael y Llyfrgellus Fygrifol, yn y rhan. Rwy'n gweithio i gael, mae'n gwirionedd y Prif Weinidog, mae'n gwirionedd y Cyfryd, mae'n gwirionedd ymgylch. Mae'n gweithio i'r cyfryd, mae'n gwirionedd y nifer. I'm sure many, all of us here today will agree that the sooner the world cuts its greenhouse gas emissions, the smaller the rapidly increasing resource requirements for adaptation will be. As we've already heard, the IPCC's fifth assessment has told us that greenhouse gas emissions are growing faster than ever and the world's carbon budget will soon be exhausted. According to the IPCC, if we keep emitting greenhouse gases at the present rate, the world is on track, unfortunately for an average warming between 3.7 and 4.8 degrees by the end of this century. And we need to bear might be starving statistics and uncertainties as we look out there here. And although the focus on developing countries is rightly on adaptation, many will agree that there is an opportunity for all countries to contribute to the collective effort to reduce emissions by creating and enabling by investments in low emissions and carbon resilience investments in sectors such as agriculture, energy transport and construction. And it's true that the IPCC's recent report does provide some good news. There is growing evidence that says that taking action to reduce greenhouse gas emissions can reduce, provide the development and climate benefits co-benefits are enlarged through the recent IPCC reports. But we actually know a great deal. We know enough now to act and to act effectively to reduce society's climate-related risks. And the power of CBA that we gathered at the eighth time to celebrate, in many respects, is that it harnesses the local capacity and resources bills on indigenous knowledge and recognises the potential of autonomous adaptation. And there's plenty of action that you've seen here in the parliament and that you yourself are part of. Some of the experience we've seen in Cairn's partners from, for example, the city of Cartagena in Colombia, which has created and adapted neighborhoods with communities and municipalities in business in the face of the Greek flood. How communities in Ethiopia, Uganda and Mozambique are reprogramming their district development budgets to more climate-resilient activities to benefit local libraries. How climate-resilient housing in Da Nang in Vietnam is saving lives and poverty in the face of typhoons. And here, with the government and the power of the CBA Cairn, it's very pleased to be launching reports on the economic impact assessment of climate change across key sectors, where the cost of a sector of adaptation has been explicitly pulled together. These are great examples and you will know many more. But I think we know, as the previous CBA conference has said, that it is still a very large-scale challenge and we know that finance, the focus of this conference, is critical to this. Of course, the international climate finance is expected to deliver a huge range of things and work at a range of scales and it will need to be challenged to drive transformation change at these various levels. And this has already happened with the GEF and the Adaptation Fund and a national law through the National Climate Change Fund, such as for now, in Rwenda. But as the CBA's annual climate finance landscape report tells us, despite increased commitments by Dems to adaptation, in 2012, according to CPI, 94% of $360 billion spent on climate finance was focused on mitigation. CBA data also tells us that most climate finance originates in the same country in which it was spent, so some 75% of finance that's raised with climate finance is delivering in that same country. And I believe that by demonstrating a national commitment and a trend in increasing of such resources, that developing countries are in a strong position to make the case of increased support for international climate finance to various institutions. And here in Nepal, we've seen our hosts have taken real action to do this at local areas and heard about the matters, and they're leading role in the LDC group with the demonstrations of commitment that the government in Nepal has made. So in conclusion, ladies and gentlemen, I would say that international climate finance needs to enable adequate resources for developing countries to pursue their transition to low carbon and climate resilience economies. These resources, of course, will need to be considered as catalytic and used in a way that shifts larger sums of investment from public sector and private sector to drive the broader transformation to climate resilient societies. This does mean that at all levels, from national, international or local level, long term planning for strong institutional capacity will be necessary to effectively access and deliver climate finance. I think many of you are part of that, and the CKM stand very much ready to continue to partner with you and others in making this happen. Ladies and gentlemen, thank you very much.


Good afternoon, everyone. Lisa Martin here with Rob's Touch A. We are coming to you live from the VMware Explorer at the Venetian Expo. This is day one of three days of Qth coverage. We have two sets, lots of content. It's also our 13th year of covering VMware customer events. We have one of the luminaries from Pure Storage. Finally, making his debut on the Qth, please welcome Cody Hosterman, Senior Director of Product Management at Pure. Great to have you, Cody. It's really great to be here. I've been watching the streams for many, many years and it's very fulfilling for me to be on here with you. So thank you very much. We appreciate it. So talk to us a little bit about Pure's big focus at VMware Explorer this year. I know there's some breaking news that broke this morning. Tell us about that.  The Wi-Fi password is multi-cloud, right? You know, send the, you should say like send a message in your Wi-Fi name, but they went with the password. I think it's very interesting. No, a major focus for us, a major announcement for us just today was our support of Azure VMware solution with our product Pure Cloud Block Store. You know, a first enterprise block external offering for VMware Cloud. We're super excited to get this in the market. It's a pretty cool day for us. I bet. Talk about also what that means in terms of the expansion and the evolution of Pure's relationship, partnership with VMware and what that signifies. Yeah, this is a really important step for us around our go-to-market partnership with both VMware and Microsoft, right? Is that we can, an important part of what we're introducing here is help to optimize cost and really make the Azure VMware solution environment fit our customer's needs, right? Because a lot of customers needed some changes, needed some additional storage capacities, and it wasn't working out for them today. And so basically what we can do is by introducing our product into the sales cycle, we can shorten it and sell with these VMware and Microsoft sellers that were currently blocked out of some of these opportunities. So we have a really nice partnership around helping them sell their services, which is really what our folks is in particular in the public cloud. How can we help cloud vendors, VMware, sell their offerings with our product? I mean, Pure has been known for go-fast storage for quite a while now. How is this and how are the folks that are going to be using AVS, Microsoft's Azure VMware service? So we're solution. I always get, I always get it, always mixed up. There's an S in there. I'm just going to go AVS because I don't work for them. Exactly. But how are they going to be able to take advantage of the go-fast storage that Pure is so known for? And how does that really play within the Microsoft environment as well? Yeah, I think there's two main points here. And I could feather that into about a thousand other ones, but I'll keep it to two. Is really there's two things that we've done well historically across the board, right? So one is we build our product on the concept of data reduction, efficiency, deduplication, compression, pattern removal, thin provisioning. We built this to make flash cost-effective, right? I mean, this is what we built our company on. And, you know, surprise, surprise, these efficiencies also make a pretty big impact in the public cloud, right? How introducing our data reduction technologies to make efficient use of the back-end storage reduces the footprint, makes it more cost-effective and brings that into the cloud environment. The other prong here is just the VMware use case. About over three-quarters of our FlashArray customers are running VMware on one or more other FlashArray. It's a huge business for us. We've put a lot of energy into our co-engineering with VMware to make it a really streamlined and simple use case. And so all the stuff that we've built for on-premises VMware environments are now available with Azure VMware solution. Talk a little bit about the customer demand. Obviously, you're launching this now. I imagine customers were asking for it. You were talking about some of the challenges that they were having. How were customers involved in the actual evolution of this in the partnership? Yeah, so there's a couple pieces here. Like, certainly what started our interest in this, and the Microsoft's interest as well, is that they've been getting customer demand. Like, hey, we use pure on-prem. We want to take advantage of it in the public cloud. We like this feature. We like this use case. Whatever the case may be. There was building demand around that across the board. And, of course, there was losses, right? There were customers not able to take advantage of the product, right? We saw a couple different reasons around struggles around databases, cost optimization, and overall feature usage. And when we dug down into it, it was storage-related problems. And so we can open up that to those customers that were looking at AVS but couldn't for some other reason. And, of course, in the process of bringing it to market, we had customers in what Microsoft calls private preview, validating use cases. We had about 15 or 20 things. We really specifically wanted customers to give us feedback on. Is this right? Is this truly solving your problem? So we didn't want to launch something that was just a checkbox. We wanted to truly solve customer problems around their adoption with AVS. And so it's been a great partnership with our customers, with Microsoft as well. One of the notes I was reading about Cloud Block Store for AVS is that customers can achieve up to a 10x cost reduction. So we're talking about significant savings, potentially. Yeah, absolutely. Because there's really two pieces at play around this cost savings, right?  That's a part of it, but not honestly the main one. A big part of it is that a significant amount of on-premises VMware customers are storage heavy. 60% or so of external storage users are blocked. And there's some file as well. And then there's a smattering of other options on-premises. And so a significant amount of them are very storage heavy. And so those customers themselves were fairly blocked out around the particular solution itself. And so bringing that into AVS and opening up that market was really a key piece of what we were trying to do here. And it would seem that having same kit on-prem and in the cloud gives them advantages, especially when you look at mobility and data reduction and what you bring up into the cloud.  Exactly. And then that's kind of more pointed that I want to get as well on the cost optimization point around that 10x number is that if you need more storage without external storage like Pure, you have to provision more compute, right? That's you're paying for compute that you don't need. And so we can reduce the amount of compute that you need to the exact that you actually want and then add in the amount of storage that you need, really right-sizing the environment. And so where these large numbers around cost savings come into are use cases where you don't necessarily need all that compute right now, disaster recovery. You can provision zero compute and just use our product to store the data and then bring up the environment as needed. Analytics, DevTest, places where it's very elastic. We can reduce what you need of compute to very minimal amount, introducing significant cost savings and those particular use cases across the board. What's been some of the feedback today? Obviously that news broke this morning at 6 a.m. Pacific time. But what have you heard on the show floor, analysts, customers? It's been, it's been, there's been a lot of different pieces of feedback. I think one of my favorite quotes is that it was from a customer that commented on one of my LinkedIn posts. They're just saying, I have Pure on-prem. I'm really excited to be able to finally use it in the public cloud. That's really a cool thing for me to see across the board because I've been working our VMware ecosystem for a while. Similar thing with our channel partners. We've been comments like, I've been selling with Pure for a decade. I'm very excited that we finally sell with them really on the Azure VMware solution today too. So channel partner customers in VMware folks. I've been getting a lot of VMware folks coming up to me this at the booth and just randomly walking around. They're like, hey, this is like, we see what you're doing. You're really solving a problem. This is opening up a new market for us. And so they're excited to talk to their customers about it. So it's been fun to hear the feedback on this offering. Yeah, I think one of the things that I wonder about and you can help me with this is one of the things your customers, when I talk to them, they love is the Pure Evergreen. Is that part of this whole thing as well? Or how does that work? That's a great question because really Evergreen, there's two pieces that I see Evergreen come to play here. So one is the consumption model, the licensing model. So we have an Evergreen one offering that allows you to just purchase capacity from Pure. And that is applicable to flash arrays on-premise hardware, flash arrays, and as well as Pure Cloud Blockstore in Azure. And so that license can actually follow your data set. So if you're migrating, building new applications in the Azure VMware solution, either one, in particular the migration, you don't need to line that migration up with your storage refresh. You move it as needed, that license follows it. And if you empty that flash array on-premises because you've been fully moved into the cloud, that gets shipped back to Pure. We own it. It's your OPEX paying for the capacity. And so the licensing model makes it really flexible to make this migration happen at your pace at the amount that you want to actually move it to. That's one piece, kind of the economic model of it. The other side is the technical model, the technology that we introduced. Part of the Evergreen story is that your product is Evergreen, right? It gets better and better and better over time. We continue to invest in improving the product. Very recently, we moved from Altra SSD, which is a form of block storage with an Azure, into premium SDV2. And we moved our controllers from a D-series to E-series compute, improving our performance by 30% and dropping the cost of running our product by a third. Significant improvement that just came with a random purity upgrade. It was literally our code. It was six, I think it was purity 645, if I recall, right? It's a very innocuous number. We're releasing monthly on our purity offering. And so the Evergreen promise is not just about the mobility of licensing, but also just really just your subscribing to innovation to the platform. And that's a big part of our software-only model when it comes to CBS. So the solution that was announced this morning, when is that going to be generally available for the regions? So it is in public preview right now. So any customer can go on and try it out. GA usually happens. Microsoft process is usually about 90 days or so around it. And that can vary based on customer feedback. So we have some significant feedback. We want to go and dress and make some changes. That can delay it a little bit longer, but generally it's around 90 days. So we expect GA towards the end of the year. Nice. And is this kind of an expansion in the Microsoft partnership? I mean, or is this the first stop in a partnership that is continuing to grow? Oh, this is, it's certainly a major stop, but it's certainly not our first one, but it's definitely a big one for us. We've been offering Cloud Block Store in Azure. And there's many other parts of Pure's business, of course, that interact with Azure, right? Our Portworx team, you know, we spoke with the Venkat in the past and Flash Play as well, some EDA use cases. But at least from the software side and the Cloud Block Store side, this is certainly a step, but not the first one. We've been seeing a tremendous amount of growth in what we call the Cloud Storage Optimization offering, where customers are looking to invest in Azure in different ways, AWS as well. I want to spend my money on more strategic initiatives, AI, pipelining and past services around that, but I'm not getting more money in my IT budget to spend. And so what do you do? You have to find somewhere in your budget to save money and reallocate that cost. And so we've been seeing customers look at their bill. It's like, well, the two biggest places are compute and storage, right? Not functionally different than you see on premises, right? When it comes to the biggest cost centers. And so they're like, all right, we right size our compute and what can we do around right sizing storage? Well, there's not a lot you can do. You can drop tiers, you can sacrifice features. There's not much else. And so they did that, but didn't really save the money they needed to invest in these other places. And so that's where we got brought in. Our products is about optimization of storage footprint as a data reduction, T-dub, compression, all that type of stuff. And saving 30, 40% on their cloud storage bill what they can reinvest, right? And that money, you know, like sometimes we talk to Azure sellers and like, well, we don't want them to spend less. Like no one spends less. Like if you spend your money more wisely at home, you don't give it back to your company, right? You put it in your investment or you buy a TV or whatever. Same thing is that that money is being reallocated to far more strategic initiatives within Azure. And so we've been seeing some great success that makes Microsoft happy, us happy because we're selling to happy customers and these customers are spending their money more strategically. And so it's been a motion that we've been working on for a while and we announced a major customer that purchased that at our recent annual fiscal reporting as well. So we're seeing a lot of success in that area too. So continued flywheel of momentum at Pure Storage which we always see a year after year. What are some of the things that excite you about the, say the next, you know, six months, 12 months and in particular in your relationship with VMware as it evolves. Yeah, so I mean, there's a couple of things. I mean, like, I'm certainly excited about we finally have this AVS offering in public previews that customers can dive in really taking advantage of it and I'm excited just to see the partnership between our sales organizations and our joint customers really grow. So I think I truly excited about that. Certainly we're doing a lot with VMware. We've always made a point with VMware to co-engineer offerings, really build the vSphere platform and this external storage ecosystem further and further. We recently added NFS data stores to our offering for a unified experience around flash arrays of block and file and we're working with VMware to push that forward too. There's a lot of stuff that hasn't been happening around that, around enhancements on the NFS stack and VMware is really open to working with us on that. So there's some cool things coming with that. Overall, we've been spending a lot with NVMware fabrics in the virtual volume program with VMware and we're releasing NVMware fabric support for VVolts quite soon. We have a new version of a certificate management for VASA providers with VVolts a lot coming out just on the core storage side with VMware. So I'm excited about some of the conversations we had with VMware PM this week, what we can start moving on and the things we'll be shipping over the next six months. So there's a ton coming on core VMware. I was going to say you were one of the first to actually support VVolts on an array. I remember back to when that was there. We weren't the first. So there was kind of an early wave around it and then we were, I would call us the second wave where when we came in and we re-looked at the architecture, how can truly simplify it? I call us one of the leaders of that second wave of VVolts vendors that really simplified the platform and really brought it into the kind of the core thinking and strategy around our customers around storage. So I think we've been very vocal about it and frankly that's a fairly common thought that we were the first ones because frankly, my personal view, we're the first ones to do it right. Oh, there you go. Chots fired. Chots fired. That's a bike chop I'm just telling you right there. There we go. You are officially a CUBE alumni. We so appreciate you coming on and sharing the great news and the continued momentum that we see from Pure Storage. Yeah, I really appreciate the time. This has been a lot of fun. Thank you very much. Good, our pleasure. For our guests and for Rob Scudge, I'm Lisa Martin. You're watching theCUBE live from VMware Explorer 23. Stick around. Dave Vellante is here next for the great insights on day one of our coverage. We'll see you soon.


Sunlight shines on rippling water and the spokes of a bicycle wheel spin. We see all these products that are made over time that are cherished now because they have this personal handwriting. I think that's our challenge as designers. Chromatography. A pen is lifted. We always start with a blank page. It's dragged across a sheet of paper. Once you draw the first line, you know you will start like this process which takes maybe three years. Four years before it's ending. You should forget all that to be very creative and very open. Color is the starting point of our design process. You need to apply color of course. So the more minimalistic the canvas is, the better. Paintbrush is cleaned. We always try to have this balance in colors with a little bit of an edge. So people have to get used to it. We're always trying to push these boundaries. The color range we try to achieve with the material is based on study. I think that's also to do that we are husbands and wives by creating together the designs get more unique. And I think that's the combination which makes our relationship very strong. It's a way of working almost like an artist or a sculptor that we also do in our practice. So we make our own materials and we make our own colors and we make our own models. And by doing so, we get this layering and it will, we hope, provide this quality to a product. Quilted fabric samples are unpacked. The model making is very important. Stiff paper is cut. Instead of going from A to B as quick as possible in the design process, the model helps us to find new shapes, new possibilities. A diorama is built. If you create a model with your own hands, you see the difficulties. And sometimes you see new solutions which you never could find on paper. Quilted fabric is draped over the arm of a sofa and pressed into place. Our inspiration comes from our life as designers. Water is poured. We travel a lot. We work in all these different cultures. But special nature is providing us so many details. Because if you look, for example, to flowers, even though they're the brightest flowers, they're always perfect in balance. And this perfection in color and shape is what we really love and what we also like to achieve in our work. I'm Carol Beijing. My name is Stefan Scholten. And we work together at Scholten and Beijing's Studio for Design.


From the CUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. IBM's new CEO has an opportunity to reset the direction of the company. Outgoing CEO, Ginny Rometti, inherited a strategy that was put in place over two decades. It became fossilized in a lower margin, services led model that she helped architect. Ginny spent a large portion of her tenure shrinking the company so it could grow, but unfortunately she ran out of time. For decades, IBM has missed opportunities to aggressively invest in the key waves that are now powering the tech economy. Instead, IBM really tried to balance investing innovation with placating Wall Street. We believe IBM has an opportunity to return to the big blue status that set the standard for the tech industry, but several things have to change. Some quite dramatically. So we're going to talk about what it's going to take for IBM to succeed in this endeavor. Welcome to this special Wikibon CUBE insights powered by ETR. In this breaking analysis, we're going to address our view of the future of IBM and try to accomplish three things. First, I want to review IBM's most recent earnings, the very first one under new CEO, Arvind Krishna, and we'll discuss IBM's near-term prospects. Next, we'll look at how IBM got to where we are today. I want to review some of the epic decisions that it has made over the past several years and even decades. Finally, we'll look at some of the opportunities that we see for IBM to essentially remake itself and return to that tech titan that was revered by customers and feared by competitors. First, I want to look at the comments from new CEO, Arvind Krishna. And let's try to decode them a bit. Arvind in the first earnings call that he held, and in interviews as well, and also internal memos, he's given some clues as to how he's thinking. This slide addresses a few of the key points. Arvind has clearly stated that he's committed to growing the IBM company and of course, increasing its value. This is no surprise, every IBM CEO has been under pressure to do the same. We'll look at that further a little later on in this segment. Arvind also stated that he wants the company, he said it this way, to lead with a technical approach. Now, as we reported in January when Krishna was appointed to CEO, we're actually very encouraged that the IBM board chose a technical visionary to lead the company. Arvind's predecessors did not have the technical vision needed to make the bold decisions that we believe are now needed to power the company's future. As a technologist, we believe his decisions will be more focused on bigger technical bets that can pay bigger returns, potentially with more risk. Now, as a point of just tactical commentary, I want to point out that IBM noted that it was doing well coming into the March month, but software deals especially came to a halt as customers focused on managing the pandemic and other parts of the business were okay. Now, this chart pulls some of the data from IBM's quarter. And let me make a few comments here. Now, what was weird here, IBM cited modest revenue growth on this chart, this was pulled from their slides, but revenue was down 2% for the quarter relative to last year, so I guess that's modest growth. Cloud revenue for the past 12 months, the trailing 12 months was 22 billion and grew 23%. We're going to unpack that in a minute. Red Hat showed good growth, Stu Miniman and I talked about this last week, and IBM continues to generate solid free cash flow. Now, IBM, like many companies, they prudently suspended forward guidance. Some investors bristled at that, but I really have no problem with it. I mean, it's just way too much uncertainty right now. So I think that was a smart move by IBM and basically everybody's doing it. Now, let's take a look at IBM's business segments and break those down and make a few comments there. As you can see in this graph, IBM's 17 plus a billion dollar quarter comprises their four reported segments, cloud and cognitive software, which is, of course, its highest margin and highest growth business at 7%. You can see its gross margin is really, really nice, but it only comprises 30% of the pie. Services, global business services and GTS, global technology services are low growth or no growth businesses that are relatively low margin operations, but together they comprise more than 60% of IBM's revenue in the quarter and consistently throughout the last several years. Systems, by the way, grew nicely on the strength of the Z15 product cycle. Z was up by 60% and dragged storage with it, but unfortunately power had a terrible quarter and hence the 4% growth, but decent margins compared to services at 50%. IBM's balance sheet looks pretty good. It took advantage of some low rates recently and took out another $4 billion in corporate debt. So it's okay. I'm not too concerned about its debt related to the Red Hat acquisition. Now I want to come back to cloud at 22 billion for the past 12 months and growing at 23%. What? You say that sounds very large. I don't understand. It's understandable that you don't understand, but let me explain with this next graphic. What this shows is the breakdown of IBM's cloud revenue by segment from fiscal year 19. As you can see, the cloud and cognitive segments or segment, which includes Red Hat, comprises only 20% of IBM's cloud business. I know, kind of strange. Professional services accounts for 2 thirds of IBM's cloud revenue with systems at 14%. So look, IBM's defining cloud differently than most people. I mean, actually this, what percent of the cloud business of AWS, Azure, and Google Cloud come from professional services and on-prem hardware? It's just, this just doesn't have real meaning. And I think, frankly, it hurts IBM's credibility as it hides the ball on cloud. Nobody really believes this number. So, I mean, there's really not much else I can say there. But look, why don't we bring in the customer angle and let's look at some ETR data. So what this chart shows is the results of an ETR survey. That survey ran, we've been reporting on this, ran from mid-March to early April. And more than 1,200 respondents and almost 800 IBM customers are in there. If this chart shows the percentage of customers is spending more on IBM products by various product segments that we chose with three survey samples. April last year, January 2020 and the most recent April 2020 survey. So the good news here is that container platforms, OpenShift, Ansible, the staples of Red Hat are showing strength, even though they're notably down from previous surveys. But that's the part of IBM's business that really is promising. AI and machine learning and cloud, they're right there in the mix and even outsourcing and consulting and really across the board. You can see a pretty meaningful and respectable number or percent of customers are actually planning on spending more. So that's good, especially considering that the survey was taken right during the middle of the COVID-19 pandemic. But if you look at the next chart, the next scores across IBM's portfolio, they're not so rosy. Remember, net score is a measure of spending momentum. It's derived by essentially subtracting the percent of customers that are spending less from those that are spending more. It's a nice simple metric. It's kind of like NPS and ETR surveys. Every quarter with the exact same methodology for consistency so we can do some comparisons over a time series, it's quite nice. And you can see here that Red Hat remains the strongest part of IBM's portfolio. But generally in my experience, as net scores start to dip below 25%, it kind of get into the red zone, the so-called danger zone. And you can see many parts of IBM's portfolio are showing softness as we measure in net score. And even though you see here, the outsourcing and consulting businesses are up relative to last year, if you slice the data by large companies, as we showed you with Sagar Kedakia last week, that services business is showing deceleration. Same thing we saw for Accenture, EY, Deloitte, et cetera. So here's the takeaway. Red Hat, of course, is where all the action is. And that's where IBM is going to invest, in our opinion. And we'll talk a little bit more about that and drill into that kind of investment scenario a bit later. But what I want to do now is I want to come back to Arvin Krishna. Because he has a chance to pull off a Satya and Adela like move. Them is different, but there are definite similarities. I mean, you have an iconic brand, a great company that's in many technology sectors. And yes, there are differences. IBM doesn't have the recurring software revenue that Microsoft had. It didn't have the monopoly in PCs. But let's move on. Arvin has cited four enduring platforms for IBM. Mainframes, services, middleware and the newest hybrid cloud. He says that IBM must win the architectural battle for hybrid cloud. Now, I'm going to really share later what we think that means. There's a lot in that statement, including the role of AI in the edge, both of which we'll address later on in this breaking analysis. But before we get there, I want to understand from a historical perspective where we think Arvin is going to take IBM. And to do that, we want to look back over the modern history of IBM, modern meaning of the post-mainframe dominance era, which really started in 1993 when Lou Gershner took over. Look, it's been well documented how Lou Gershner pivoted into services. He wrote his own narrative with the book who says elephants can't dance. And you know, look, you can't argue with his results. The graphic here shows IBM's rank in the Fortune 500. That's the green line over time. IBM was sixth under Gershner, and today it's number 38. The blue area chart on the insert, it shows IBM's market cap. You know, look, Gershner was a hero to Wall Street and IBM's performance under his tenure was pretty stellar. But his decision to pivot to services, set IBM on a path that to this day, marks the company's greatest strength, and in my view, its greatest vulnerability. Name a product other than mainframes in which IBM leads. I guess, middleware, I guess, web sphere, okay. But you know, IBM used to be the leader in the all-important database market. Semiconductors, storage, servers, even PCs back in the day. All right, so look, I don't want to beat on this too much, I can say it's been well-documented. And I said earlier, Ginny essentially inherited a portfolio that she had to unwind, and hence the steep revenue declines, as you see here. And because she had a jettison, the so-called non-strategic businesses. But the real issue is R&D, and how IBM has used its free cash. And this chart shows IBM's breakdown of cash use between 2007 and 2019. Blue is cash return to shareholders. Orange is research and development, and gray is capex. Now, I chose these years because I think we can all agree that this was the period of tech defined by cloud. And you can see, during those critical, early formative years, IBM consistently returned well over 50%, and often 60% plus of its free cash flow to shareholders in the form of dividends and stock buybacks. Now, while the orange appears to grow, it's because of what you see in this chart. The point is the absolute R&D spend really didn't change too much. It pretty much hovered, if you look back, around five and a half to $6 billion annually. The percentage grew because IBM's revenue declined. Meanwhile, IBM's competitors were spending on R&D and capex, what were they doing? Well, they were building out the cloud. Now, let me give you some perspective on this. In 2007, IBM spent $6.2 billion on R&D. Microsoft spent $7 billion that same year. Intel 5.8 billion, Amazon spent $800 million. That's it. Google spent $2.1 billion that year. In that same year, IBM returned nearly $21 billion to shareholders. In 2012, IBM spent $6.3 billion on R&D. Microsoft that year, $9.8 billion, Intel $10 billion, Amazon $4.6 billion, less than IBM, Google $6.1 billion, about the same as IBM. That year, IBM returned almost $16 billion to shareholders. Today, IBM spends about the same $6 billion on R&D. About the same as Cisco and Oracle. Meanwhile, Microsoft and Amazon are spending nearly $17 billion each, sorry, Amazon 23 billion. And IBM could only return $7 billion to shareholders last year. So, while IBM was returning cash to its shareholders, its competitors were investing in the future and are now reaping the rewards. Now, IBM suspended its stock buybacks after the Red Hat deal, which is good in my opinion. Buybacks have been a poor use of cash for IBM, in my view. Recently, IBM raised its dividend by a penny. It did this so it could say that it has increased its dividend 25 years in a row. Okay, great. Not expensive, so I'm glad that investors were disappointed with that move. But since 2007, IBM has returned more than $175 billion to shareholders. And somehow, Arvin has to figure out how to tell Wall Street to expect less while he invests in the future. So let's talk about that a little bit. Now, as I've reported before, here is the opportunity. This chart shows data from ETR. It plots the cloud landscape and is a proxy for multi-cloud and hybrid cloud. It plots net score or spending momentum on the Y-axis and market share, which really isn't market share as we've talked about. It's a measure of pervasiveness in the data set. That's plotted on the X-axis. So the point is, IBM has presence. It's pervasive in the marketplace. Red Hat and OpenShift, they have relevance. They have momentum with higher net scores. Arvin's opportunity is to really plug OpenShift into IBM's large install base and increase Red Hat's pervasiveness. Well, at the same time, lifting IBM's momentum. This, in my view, as Stu Miniman and I reported last week at the Red Hat Summit, puts IBM in a leading position to go after multi and hybrid cloud and the edge. So let's break that down a little bit further. When Arvin talks about winning the architectural battle for hybrid cloud, what does he mean by that? Here's our interpretation. We think IBM can create the de facto standard for cloud and hybrid cloud. And this includes on-prem, public cloud, cross-clouds or multi-cloud, and importantly, the edge. Here's the opportunity. It's to have OpenShift run natively, natively everywhere on-premises in the AWS cloud and the Azure cloud, GCP, Alibaba and the IBM cloud and the Oracle cloud everywhere natively so it can take advantage of the respective services within all those clouds. Same thing for on-prem, same thing for edge opportunities. And I'll talk a little bit more about that in a moment. But what we're talking about here is the entire IT stack running natively, if I haven't made that point, on OpenShift, the control plane, the security plane, the transport, the data management plane, the network plane, the recovery plane, every plane, a red hat led stack with a management of resources is 100% identical everywhere, the same cloud experience. That's how IBM's defining cloud. Okay, I'll give them a mulligan on that one. IBM can be the independent broker of this open source standard covering as many use cases and workloads as possible. Here's the rub. This is going to require an enormous amount of R&D. Just think about all the startups that are building cloud native services and imagine IBM building or buying to fill out that IT stack. Now I don't have enough time to go too deep to all other areas but I do want to address the edge, the opportunity there and weave in AI beyond what I said above, which I want to stress, the points I made above about hybrid multi-cloud include edge. The edge is a huge opportunity but IBM and many other if not most other traditional players we think are kind of missing the boat on that. I'll talk about that in a minute. Here's the opportunity. AI inference is going to run at the edge in real time. This is going to be incredibly challenging. We think about this, a car running inference AI generates a billion pixels per second today. In five years it'll be 15 times that. The pressure for real time analysis at the edge is going to be enormous and it'll require a new architecture with new processing models that are likely going to be ARM based in our opinion. IBM has the opportunity to build end to end solutions powered by Red Hat to automate the data pipeline from factory to data center to cloud and everywhere. Anywhere there's instruments IBM has an opportunity to automate them. Now rather than toss traditional Intel based IT hardware over the fence to the edge which is what IBM and most people are doing right now IBM can develop specialized systems and make new silicon investments that can power the edge with very low cost and efficient systems that process data in real time. All right, look, I'm out of time but some other things I want you to consider. IBM transitioning to a recurring revenue model interestingly back to the future, right? IBM used to have a massive rental revenue stream before it converted that base to sales. But if Arvin can recreate a culture of innovation and win the day with developers via its Red Hat relationships, as I said recently he will be CEO of the decade. But he has to transform the portfolio by investing more in R&D. He's got to convince the board to stop pouring money back to investors for a number of years, not just a couple of quarters and do whatever they have to do to protect the company from corporate raiders. This is not easy, but with the right leader, IBM a company that has shown resilience through the decades I think it can be done. All right, well thanks for watching this episode of the Wikibon Cube Insights powered by ETR. This is Dave Vellante and don't forget these episodes are available as podcasts wherever you listen. I publish weekly on siliconangle.com where you'll find all the news. I publish on wikibon.com, which is our research site. Please comment on my LinkedIn post. Check out ETR.plus, that's where all the data lives. And thanks for watching everybody. This is Dave Vellante for Breaking Analysis. We'll see you next time.