We've helped artists get billions of streams on Spotify, but the platform has changed over the years and not enough people are talking about how you should look at it today. So in this video, I'm going to go over the three areas that artists ask me about. I'm going to give you the sobering truth as someone who has helped multiple artists grow and has the benefit of connections to some of these platforms. But also, I'm going to end the video by telling you why I believe this is one of the best times ever for artists on Spotify. So here's the three areas that we're going to go through and it's worth watching through every single one of them because it's all going to connect. If you understand the market holistically like this, it'll be hard for you to get tricked by anybody who's offering a bad service and two for you to waste money on the wrong strategy for your Spotify streams. All right. So number one is bots. Number two is playlisting and number three is the Spotify tools. And I'll focus on one in particular, but let's start with bots. Many know who watched this channel that I'm not all opposed to bots. And I know most people are like, bots are so bad, bots are so bad, but let me give context because I feel like some people are actually taking my advice in the wrong way. So I want to be extremely clear by starting with this story. I'm talking with an artist probably last week. Yeah, it was last week and he's independent. He's early starting off and he signed some artists. Right. He signed other artists. This is an artist who signs some other artists and he's like, yeah, man, I was getting a decent amount of streams. I was doing like over $5,000 a month in revenue, but I got this check and when I got my check, it didn't account for all of my revenue. Spotify actually shorted me and immediately in my head, I'm like, oh, bots, bots, bots, but I don't know if he knows that bots might have been included in his streams. Right. I don't know what he's been doing. So I'm just listening. All right. And then he goes, yeah. So then I reported it to Spotify, basically trying to make it a case. And if I remember correctly, he even got a lawyer involved and he's making this case against Spotify. Spotify is basically like, nah, you know, we kind of gave you the right amount of money back and forth. And ultimately, he then hits his distributor. I'm not going to say the distributor's name because I actually think that they might have had some case. And I just don't want to put people's name out there like that. Right. But this is a real story. He hits the distributor. All of you would know it is one of the major distributors and they were like, we can't really do much about this because if we go against Spotify in this particular scenario, we're really going to risk tarnishing our relationship for all our other artists. And we can't just do this for this one random artist who you are. No offense. Sounds a little messed up, right? We're not going to do right by this one artist because it risked our relationship with all these other artists. Wrong. They were in the right based on hearing this scenario because Buddy was using bots. And Spotify is basically like, yo, look, man, your guy is using bots. You can try to stand up for him if you want to, but you know, it's against our policy. So that was really just the distributor being nice. Right. And then guess what?  He get all his accounts gets closed. He ends up on another distributor and you know what happened on that other distributor? He got kicked off that other distributor.  Ultimately, he's using bots and he becomes very open about it. And here we are today speaking about another strategy that he then again wants to spend like 20 K on bots. And I have to tell him, bruh, no, why are you trying to use bots? Still, I think he just told me about two years of struggle because of this. So this is where we get to the reality of the situation. Because I just started with, yo, I'm not all the way against bots. Let me make it extremely clear. I thought I've said this very clearly. Bots make sense for a very specific set of people. Right. Number one are people who are major label artists who already have a real fan base. And then there's a couple of scenarios where they're tweaking, using bots to elevate their streams. But you know why they're doing this? They're not doing it to make it seem like they have social proof.  They're also not doing it just to seem bigger than some other artists. In most cases, there's other incentives. There's the corporate structure of the music industry where you can play the games. The same reason I'll just leave it at this because I don't want to spend forever on bots in this video, but the same reason that artists will finesse and do things like bundles that account for more sales to have a higher ranking on the charts is similar to many of the reasons that they would do things like fake streams, even though they have a huge legitimate audience. I'll just leave that at that, right? Go to our other videos where we really go deeper into bots. I'll link it in the pinned comments in the description, something like that. All right, here's another thing about bots for those of you who want to do bots, because here's the other type of person who bots kind of make sense for it.  I don't want to focus on doing a real fan base. OK, I just want to try to get some streams and make my money back for those streams. There's better hustles in the world. But cool, bots can make sense for you. But if you are a real artist, you want to actually get a real fan base and you are independent or more, I don't even want to say independent because that's actually wrong. There's independent artists who actually do make sense and are using bots. And it makes sense within the consciousness of what they're doing. Can't tell you where they are. But that's not for me to speak on. So a better way of saying if you're an artist who doesn't have a real strong fan base, it makes zero sense for you to use bots. All right, cool. Very strong statement, very direct statement. If you are in that bucket, do not use bots. Here's the last thing that I want to add when it comes to using somebody for their services for bots. The real, the best bot people, they make hundreds of thousands of dollars per client per client. So if you aren't spending hundreds of thousands of dollars, then you probably aren't even working with the best bot people. Now, why would those bot people have clients that are spending hundreds of thousands of dollars on bots? Because again, they're working with major artists, major artists. Here's another thing. The best bot people, the people who are really doing this, like for real, for real, you don't know who they are. You just don't. Why imagine this? If I'm a great bot person, well, I'm going to be working with the best of the best, right? I'm going to be working with these clients that are paying hundreds of thousands of dollars per client. And if I'm a massive client, I'm going to be working with the best of the best bot people, right? Those two things would make sense. Why? One, from the client side, if I'm a major artist, a bigger client who has that kind of money, I want secrecy. I don't want my bot person to be out there in the world talking. I don't want my bot person to be someone who's just going to be in the street spreading my business because that puts me on front street. Yeah, you can have consumers speculate and people like, look, oh, this doesn't make sense. This person is probably using bots, but that actual bot person would never put that out there. Why? Because that validates any of the questioning. So me as a client, I wouldn't want that. And if I'm a bot person, right? I and I'm working with these type of people. Why would I ever put that relationship in jeopardy when I'm getting paid that type of money, right? And then comes working with smaller artists. Why would I work with smaller artists who have less to lose and are only going to have the budget to pay me only like a thousand dollars, twenty thousand dollars? It doesn't make sense for me to even risk it. Because why would I put my client at risk? Why would I work with a client who could put me at risk, therefore putting my business and my clients at risk? And why would I waste time doing more work by working with thousands of artists when I can make the exact same money working with like 50 artists? None of it makes sense. So you should be able to add that up. If you don't fit the bucket of having a lot of money or already having a strong fan base or having really strong connections to someone who happens to be using bots. And these are the best of the best people. Then you probably don't even have access to the right people in the first place. You're not just going to be able to go on IG or Google bots people and then look it up and hey, here's my bot servers and have the right people. All right. That's everything I got to say on bots. Now let's get to number two. And this is extremely important as well. Playlisting today has finally found a realistic positioning. Right. There was all this hype on playlisting and now the dust is settled. Here's the reality of playlisting. Over time, there has never really been a huge argument for playlisting in terms of real fans, never not a huge considerable argument where that should be your primary strategy. Does it mean that there haven't been some playlists here, some playlists there that have made sense? But outside of a rap caviar, that's been the only playlist throughout Spotify's history where you could almost look at that playlist and say that playlist can break an artist. And then you had the second tier. The second tier were playlists that just happened to be quality and it happened to be the right artist. And the best case scenario was, hey, this is a R&B playlist, for instance. And I'm an R&B artist and it's actually organic. And if I can get on this R&B playlist, hopefully my music is actually strong enough where people hear it and then they repeat it. And then you trigger Spotify's organic algorithm. That's the ultimate goal up out of that. Right. That was the second tier. That second tier, it didn't really get as many results as you would expect. Right. You would get on the editorial playlist. We've worked with editorial playlists, been on plenty of editorial playlists. They are great placements. They're valuable. They we we appreciate them. Why? Because it's free marketing, right? They're free editorial quality playlist placement is free. But never have we really seen it as a massive impact on a campaign where we say this song went completely viral all because of editorial playlists. And most artists end up being disappointed at the impact of editorial playlists, which leads me to the fact that editorial playlists aren't even the power anymore. I'm going to give you a gem on that in a second. But let's go to the third level. So we got the cultural impact of a rap caviar playlist, pretty much one of one. Then we have the second level, which is editorial and some like third party that happened to have a moment of time, literally a moment in time that has some similar level of impact of editorials. And then everything below that, which is vast majority of the market has very little impact, probably built up by fake streams and mostly has a negative impact on your actual business because they're going to put you in this playlist. And then after you on this playlist, because it's all mixed up, you're going to mess up your related artist. What do I mean by that? If I get on a playlist and then now I'm on some random playlist, everybody who actually does listen to my music. These are real people in this particular case, but these people aren't the type of people that typically listen to my music and the type of artists that are related to me on my profile. I will be able to see what my related look like. And now I have a bunch of randos who make no sense for my music, meaning Spotify doesn't even correctly know how to place me within their algorithm. So I'm not going to even be put in front of people who could be my fans because I'm being compared to these artists that are not related to me. That's a huge impact of a lot of bad playlists. But now here comes the reality of it all. Spotify themselves have noticed that this whole play listing thing. It's not what we wanted to be when we started, right? We actually see better impact from release radar and Discover Weekly, those two playlists that don't have anything to do with meeting some magical person behind the scenes. And we've known some of these magical people have had connections to some of these magical people who are going to get me on this editorial playlist, right? These great people who actually are fans and really love music. Those play listing people are great. The ones who work at Spotify. But when they put you on there, you're not going to see that impact. And Spotify even started to see the impact is just not moving. And it's easier to scale the algorithmic playlists. So they saw more results in the algorithmic playlists. And you know what Spotify has been laying people off, right? A lot of it, they try to speak on as podcast related layoffs. But last November, they got a couple of playlist editors out of there as well. Some of people who had that influence. And that's what a lot of these big companies do. They'll do these big layoffs and then they'll throw in some other people that they don't want you to quite understand. And then they'll use that opportunity to lay off some other folks that have nothing to do with the primary narrative they're pushing. That way they don't scare people when they start questioning, well, how come you laid off all these people in the playlist in department? Or how come you laid off all these people in the XYZ? I'm just making something up right in that example, because once that arises, that leads to some other questions that need answers, right? But we know multiple people who had their playlisting person taken out of the game no longer as Spotify, because when you think about things like AI, if AI is becoming more and more of a thing, how valuable are these physical bodies? Do we really need that many people to be in charge of playlisting? When you think about Spotify, AI radio, I can't remember the name of it, the DJ or whatever. I can't think of the name right now. But that's another chip at the human capital of meeting a playlist. And this is where we get to number three and the most important of Spotify tools right now that is changing the game. And that is Discovery Mode. Discovery Mode is changing the game and people aren't saying it loud enough yet. So let's speak on that in the context of everything I just talked about in this video. I just had a client homie hit me saying, yo, Sean, we're at 50,000 daily streams on Spotify because of Discovery Mode. And he's not a new case. I've heard this story again and again of a massive boost in your streams and monthly listeners because of Discovery Mode. And why is this so important? What is Discovery Mode? Discovery Mode is something that you can have access to. Now, I know a lot of people are just hearing about it a little bit more, but we first heard about Discovery Mode probably a year and a half because of the conversations we've been able to hear about, right? But it wasn't for us to say back then and it was only it was super, super limited and went from internal Spotify, super selective. And then it became a few distributors had access. And now there's individual artists. When I say my client homie, I'm talking about their unsigned, completely independent, have no connections in the industry in that particular way. And, bam, they have access to Discovery Mode, massive boost in their streams because they said, hey, Spotify will collect 30 percent less off of our royalties. And in exchange for you taking that 30 percent from us, you are going to put us as one of the songs that becomes upnecks when people, you know, listen on Spotify, right? That's a pretty short and sweet way of saying what's going on with Discovery Mode, how it works. Great tool. Here's why it's so powerful. Yet again, this is way better. The results that I've seen from Discovery Mode is way better than anything I've ever seen from the editorial playlist. And you know what? You didn't have to talk to a person to get it done.  You didn't have to have a distributor to get it done. So that takes the distributor's power right there, because many of these distributors, their lone power today was, hey, yeah, we can just do what DistroKid does and TuneCore does and put you on Apple Music, Spotify and all the other DSPs. But we also got playlisting connections. So that's why you're coming to us.  Well, hey, DistroKid artists have Discovery Mode. We'll get deeper into how you can get Discovery Mode in a second. But imagine that you don't have to do any politicking. You don't need any of those types of connections to have access to Discovery Mode. There are so many artists that we work with who are doing Discovery Mode and none of them have any label. They don't have a manager and many of them are using the typical distributors. That puts the power back in your hands, especially when it comes to Spotify far more than it ever has. But look at this. This is what I love about Discovery Mode. A lot of y'all have been talking about that music, right? The music is great. All this marketing, having to run ads, doing this content. All of that stuff is repulsive. I just want to spend time on my music and get that thing listened to. I get it, right? Or the beautiful thing about Discovery Mode is it's just the music.  They're judging you strictly off the music. That's one of the best marketing tools out there when it comes to being an artist and marketing yourself as a artist. Specifically, your actual product. I don't know anyone better than that actually right now when I try to think of it. So Discovery Mode is a great opportunity for those artists who say, Hey, my music is the thing. I really can't do this content stuff. All right. And then you add the facts that you don't have to do any politicking to get access to it. That's great. It's a beautiful thing. But how do you get Discovery Mode? Let's be on that and then I'll give you my final strategy that you should be using to grow on Spotify going forward. All right, here's what Spotify says. You may be eligible for Discovery Mode. If your artist team meets all of the following requirements, you have at least three tracks that meet the track eligibility criteria below, which will go into that criteria. Secondly, you have at least 25,000 monthly listeners. We found that Discovery Mode works best with an audience of this size or above. And then thirdly, you have an active Spotify for artists team that's registered in one of the countries below Africa, Asia, Europe, North America, Oceania, which is Australia or New Zealand, or South America. All right. So, you know, if you're in one of those countries, but let's go back up to you have at least three tracks that meet the track eligibility criteria below. What are those criteria? A track is eligible if a participating licensor is the distributor of the track, which is currently a muse. Are you using CD Baby, CMD Shift Dance All Day? I actually never heard of that one. DistroKid, EMU Bands, Stem, United Masters, Venice Music and Vidya. All right. Now look, some of these distributors are a little bit more relationship based in terms of getting access to them. But CD Baby, easy, United Masters, easy, DistroKid, easy. Those are all a layup to begin using. So if you want access to Discovery Mode and you don't have any of these, you can easily get access to CD Baby, DistroKid or United Masters. Secondly, the track has been released on Spotify for at least 30 days. And then thirdly, the track has been streamed in radio or autoplay in the last seven days. Now, keep in mind, this is still relatively new in terms of the amount of artists that have access to it and the criteria. So they'll probably be evolving this over time, right? Changing some of the criteria. Maybe they take something out, maybe they add something in, but it's not that hard. It's not that hard. Here's the thing you need to pay attention to, right? Twenty five thousand monthly listeners. Now let's get back to strategy because your distributor, whoever you use. Bam, easy, go check that box. The country that you're in, easy, check that box. You don't live in the right country, go move, whatever. But you can do that, right? That's very easy to be able to check that box. The hardest thing that is a part of this criteria is getting twenty five thousand monthly listeners. So what does this bring things back to? One, if you're using bots, they're not going to count those monthly listeners. They know what's up. You're not going to get the score of Discovery Mode for that. Two, play listing. You can try to hack your way to getting twenty five thousand monthly listeners with play listing. But again, you're probably risking having bots and not even knowing it. And again, let's go back to the guy at the beginning of this video.  You don't want to put yourself in that type of relationship with the distributors. And now you got your name messed up. Nobody wants to work with you anymore because you've been on play lists that have been using bots or you've just been outright using bots. He was doing both because he thought he was being mindful, by the way, going under the radar based on Spotify's criteria they used to try to watch if you're using bots, but I'm not going to get into that. So what does this mean, though? If you need to have twenty five thousand monthly listeners, if you can get to at least twenty five thousand real monthly listeners, how will not be extremely powerful then to be able to use Discovery Mode to boost yourself up even more? Yes, that's one of the best things you can do for yourself right now. So let me walk you through my thought process and what we've been seeing artists do. One, we use the same methods, whether that's through content, whether that's through advertising, whether that's through influencers, the same typical methods that are tried and true and you don't hear people not talking about them anymore and they're still around because they actually work. They're not one of those trends people keep saying for a period of time and then they disappear. They're tried and true. They all work. And if you can at least get yourself to twenty five thousand monthly listeners and then follow the rest of the criteria as well, especially the individual track record criteria, that's the second tier in terms of these streams, right? But you follow that and at least get to that point. Once you're able to use Discovery Mode, you're going to get over another twenty thousand monthly listeners if you have solid product, right? They want to basically use this criteria to determine do you have a solid product or not. And then once you have a solid product, which is music, then it's no problem to be pushed out to the rest of the world, right? But they don't want to push bad music and mess up the Discovery Mode algorithm for everybody. So do great content. We've gotten hundreds of millions of streams literally just off of artists posting organic content using our methods or run ads. We've seen millions of streams come directly from ads. People triggering the algorithm, running ads and then influencer campaigns. Y'all know it. Influencers campaign can do to make a song go viral. Figure out how to get your streams up from that alone and get yourself in a position to be eligible for Discovery Mode. When you are eligible for Discovery Mode, then it should be hard for you to get under twenty five thousand monthly listeners again. Once you start activating that and if you stay consistent, that's it. All right. Get yourself in a position to be able to use Discovery Mode should be your strategy for Spotify specifically right now. Get yourself in a position and then use it if you think it makes sense. That's a whole another video in terms of why it might make sense numerically and how you should look at it as a long term strategy. I'll do that and put that here somewhere. Like we'll do a conversation about that.   Anyway, that's it for this video. Let me know your comments and thoughts in the comment section below. And if you want to see a video about something else, drop those comments as well. Peace.


introduce the next speaker. Our next speaker is Sergei Froilov. Froilov and him, he is a data simulation and coupled model forecasting expert with a strong track record of formulating and implementing advanced computing algorithms that drive a system science modeling and observation workflows. When I read for his CVs, I thought it was really interesting that past contributions included his scientific support for negotiations of the US-Canada water sharing treaty and other advisory tasks. Now Sergei is in Boulder and he is working here at NOAA and working on the coupled re-analysis system with the UFS. Sergei, welcome. Thank you, Judas. It's pretty strange when people read your, you know, describe yourself resume blurbs aloud. Who's that talking about? I thought I need to rewrite mine after this. Thank you, Judas and Anish for inviting me to the summer school. Summer school was a highlight of my graduate careers and it's nice to contribute. Also putting this presentation together started to appreciate the teachings that all of our university colleagues are doing. It's definitely different ball games and doing research in the lab. Okay, so with that I'll share my screen. I will shift the focus from MGO and talk about something much more simpler than a couple data simulation. Okay, first I'll start by doing a brief introduction to what data simulation is and I'll take a historic perspective to it and I'll talk about relevance of data simulation to S2S forecasts. So the basis of data simulation is the ability to communicate observations quickly. So before the mention of telegraphs you couldn't actually observe the weather, carry it ahead of the weather and deliver it to a different place where it matters because you could only ride as fast as a horse, which is about 75 miles per day and we know the travel weather travels faster than that. So in 1840 the telegraph was invented and you could actually telegraph your weather report across the continent and the first weather maps were created. So this is an example of a weather map created in 1887 and you can see that there's really very few stations. There is a station in Kiev and Petersburg and there's one in Prague and there's none in Italy or Spain. So the weather observant network was very sparse back then. Can I just check with you guys? I see everybody else freezing. Are you guys hearing me? Yeah, we hear you well. Everything works well. Because everybody else's image froze so I just wanted to check. Okay. So this analysis map is really the first form of data simulation where you take sparse measurements and you're trying to make a map out of it. So in 1904 weather prediction was formulated as a two-part program. First you have to make an initial condition and then you take prognostic equations and predict them. So making initial conditions is a data simulation problem. And in 1922 they tried to do it by hand using human computers. They took six weeks to produce a six-hour forecast for two locations in Europe and they were off by two models, orders of magnitude. And that was the essence of numerical weather prediction for the next 100 years. Just making these things better and faster. So the first practical weather forecast was 1950 when the first computer became available. 1980s we see the first global atmospheric and ocean models. There were two separate things. 1990s was the first ensemble forecast and you guys heard about it on Monday. So 2004 the first coupled global model forecast was transitioned. It was NASA's NOAA CFS system and that was the birth of coupled data simulation because for the coupled model you have to produce initial conditions for both ocean and atmosphere and at the first time coupled data simulation became the same. And as a recent milestone 2018 all models in European center suite become fully coupled. So they no longer run uncoupled model unless it's for testing or something. So the last 20 years is really when coupled data simulation became a necessity and I'll talk about that, how we're going about it. So a little bit more about data simulation as an interpolation problem. So here is a hand-drawn map. It seems like it came from some course or an undergraduate course. So instructions are very simple. Connect the points of the map with equal temperatures. Separate regimes with warmer temperatures from colder temperatures. And it's pretty simple. You took all the area where the temperatures are 30 and color it in purple and you take areas in 40, color the blue and so on and it seems pretty simple. But for model initialization it's much more complex and here is why. So you have to assimilate about 100 million measurements a day so you can do it by hand. And you need to initialize 3D fields with billions of grid points. So you have to produce a grid analysis. You also need to convert from measured quantity such as brightness temperature on top of the atmosphere. That's what the satellite sees, right? To model prognostic variables such as potential temperature through the entire depth of the atmosphere. And finally you need to weight accuracy of different measurement systems against a forecast. So to do all of that you have to solve is an optimization problem or linear algebra problem depending on how you look at it. But before we go talk about all these words on the slide let's look at this figure first. So this is a simplified the weather forecast and the scalar prediction problem here. So on the x-axis is time. This is our simulation window. We collected our observations with green dots. And we're making a forecast from a previous time in the blue line here who made the forecast and it doesn't go through observations. We'll have errors. So what we want to do, we want to correct our initial condition for the forecast in such a way that it will fit observations as closely as possible. And as closely as possible is defined by our prior guess of what observation errors are and what the accuracy of the forecast are. And to go about it there are two equivalent formulations. One is a minimum, you know you can look at data simulation as a minimization problem. So you write out a quadratic function here with simplicity. So we have initial conditions. So a forecast from an initial condition and we have our analyzed initial condition. You take a difference from that and you weight it by this, that's actually is an inverse. I forgot to put an inverse here, by the inverse of the forecast error covariance. So that specifies how tightly you can, how far away from the initial condition you can move to generate analysis based on how accurate you think your forecast is. And on the other hand you have this miss feed between observations y and the forecast from your analysis initial condition. And you're specifying with your inverse of r, I forgot to put an inverse here. So r is your observation error covariance matrix and it specifies how much you trust your observations. So it's a quadratic derivation problem, there are methods to solve it and there's a whole class of algorithms doing it. Alternatively you can rewrite it as a linear algebra problem so you still have your y minus h of x a here and you have this big matrix inverse multiplication problem in front of it and you solve for it in one way or the other but there are two ways to go about it and there are practical implications of why you will go one way or the other. But the crucial part of it is you have to specify this r, observation error covariance and p, a forecast error covariance matrix. So you would think that specifying r will be easy, you just look up the specification for an instrument and tells you that it measures temperature with accuracy of 0.2 Kelvin. So it turns out that this r also incorporates representation error. So how well does your grid model can represent a point temperature measurement? So in practice, r is always tuned through trial and error. For p, we usually can specify it either as a parametric form, so maybe some the correlation scale of the Gaussian through some physical balances such as geostrophy and vertical balances or you can specify it through ensemble simulations.  And that's probably as much as we need to know about the algorithms themselves. So how does a data simulation problem connect to the S2S forecast? So you saw this slide a couple of times already, so you have your weather forecast and you started to be very, very good. And by day 20, it really is not very useful. And then there is subsaisonal time frame, which in these figures between week five and week eight, so second months of the forecast and then it leads into the seasonal forecast. So we know that weather forecast problem is a data simulation problem. So the quality of the weather forecast is in large part determined by initial condition. If you read through these details, it says that subsaisonal forecasts are determined by ability to monitor media engineering oscillation, land surface data, and other sources. So really subsaisonal forecast is our ability to initialize land, ocean, and some properties of the MGO. And for seasonal, you want to really initialize and so in CI is corrected. So all of these forecast problems have a large component of data simulation or ability to initialize a model state. So let's actually, you know, like everybody looks at this figure here and they're like, oh, you know, MGO has predictability in months too. So if you actually look at our ability to predict a week MGO, and here we're looking at anomaly correlation of, I think, RMM-1, and we have three models here. The scale of the ECM-DLVF model, the scale of the CFS-V2 model in blue, and the scale of the Navy ESPC. It's a new subsaisonal system that Navy has developed and it was transitioned a couple of years ago. It's in black. So you can see a couple of things here. First, like, I would like you to look at the scale of the initial time. So you could see that none of the models actually have a perfect ability to initialize MGO as an atmospheric state. So none of them get initialization of convection and clouds correctly. And wins. So there's still quite a bit of work to be done to do a better job initializing the atmosphere in the tropics. Secondly, there is a big gap between the best model, which is ECM-DLVF, and between the runner-ups. So there's a lot of work that can be done and better initialization of the atmosphere. If you look at the extended range, week three, and so on, you can see that the biggest difference is between the first generation of the coupled models, which is CFS, and the modern generation. So all modern models get the skill from MGO about the same skill, about right. But the previous generation was really struggling to do it right. So my take on it, that data simulation has a role to play at two times scale. So I already talked to you about the initial time. It's very obvious. The second part is you could really use data simulation to inform how you specify model error and parameterization, how you improve models. And this iteration between data simulation and model development is something that will improve weeks two, three, and four. And finally, to make a final connection about data simulation and MGO, if you look at this map, so it's correlation in between ROM-1 and MGO and OILAR index. And you can see that a lot of action is happening over maritime continent. We talked about it today in other lectures. But if you think about it, to initialize the state of the MGO over maritime continent, you have to get a lot of things right. You have to get SSTs right. And if MGO is active, you have to get SST right under the clouds, which is a whole different problem. You also have to get the land right because you're transiting over very mountainous terrain with a lot of moisture in it. And you also have to get the whole atmospheric cone right, what the clouds are doing, what the convection, what the active convective state is. And honestly, we don't know how to do any of this very well. So it's almost a magic that we're doing as well as we're doing right now. So really to improve MGO skill, working on our ability to initialize over maritime continent is a key. So what I'm going to do in the next part of the talk, I will go through atmospheric ocean ice and land perspective on data simulation. And I want to talk about what are the actual observations available for each of these components? And how well do we do with assimilating them? Because our skill is really coming from our ability to take information from measurements and do make it into the initial state. Because there is no measurement measuring the saying, there's nothing you can do. Okay, so this is a kind of an overview slide of why a couple data simulation is difficult. So this is a little slide made for Navy model. So we have an atmospheric model, it's at 19 kilometers and 80 levels. European center model goes to nine kilometers, so it's about twice as refined. But the initialization is done at 100 kilometers, well, I'll correct that. You initialize a 19 kilometer model, but you compute your increment at 100 kilometers resolution. So for ice and the ocean, you might be resolving the ocean ice at 125th degree, but you're only incorporating observations at the one eighth of a degree information. And the details of how you're doing it are very different for atmosphere and for land and for ice and ocean. The other differences between the different mediums is the scales differ. So if you take Gulfstream and Jetstream as a comparison points, they have almost one to two orders of magnitudes in the representative scales. Observation data delays in atmosphere, you know, you might get things in order of one or two hours. And in the ocean, you might be waiting for 24 hours or more for the observation data to arrive. Observation coverage, you have almost complete observation coverage from polar orbiters in 12 hours. And for specific regions from gestational satellites, you can get a complete picture every five minutes. But for ocean, you have to wait for 10 days for all the outer flows to surface. And the model resolutions are very different as well. So how do we go about it in each of the media? So let's look at our atmospheric observing system. So the three work horses of atmospheric observing system, first is what we can call conventional data. So conventional data usually means a thermometer or a humidity probe that we physically stick in the middle of the atmosphere. And the ways for us to do it are using aircraft, ships, buoys, weather balloons, and sometimes we throw zones and we have a lot of ground stations. So that's our basic ways of sticking a probe in the atmosphere. From a remote sensing perspective, we have radar, which is pretty sparse. But the work horse of weather prediction from a satellite perspective, it is polar orbit in satellites. So what does polar orbit mean? It means it rotates around the globe. It goes pole to pole under some inclination. And every so often it will circle the entire globe through this really narrow swath of view. And we'll see an example of that. And another satellite is geostationary satellite. So if polar orbit flies maybe 200 kilometers, 150 kilometers above the Earth, geostationary satellites have to sit in a much higher orbit, a thousand kilometers up. So it actually is rotating together with Earth. So it always looked at the same spot on Earth. So it observes a large disk. And the benefit of it, you can take a picture every five minutes. The problem is that there's only a certain set of sensors you can deploy on the geostationary satellites. And because it's so much further away from Earth, it affects its resolution. The other type of satellites are GPS satellites. And I think it's pretty old picture. So this picture of satellite is pretty big. But there's a very large number of small sets. And they're about a shoebox size. And they orbit the Earth. And they measure the time that it takes to travel from different GPS satellites to the shoebox. And this time is affected by temperature and humidity along the path of this GPS transmission. And that's kind of a new thing. That's the extremely accurate measurement. And small sets are very affordable. So that's a different way of observing Earth. So let's talk about what sensors can we deploy on the satellites? So infrared is one type of sensor. So the downside of the infrared is you can only see above the cloud. You cannot see through the cloud. So the infrared was the first type of sensor and it kind of shows you where the clouds are. But that's about it. Well, it's not true. From geostationary satellites, that's what's available right now. So you can take a picture very quickly from geostationary satellites every five minutes. You have a picture. You could also have a lot of channels. You can have a thousand channels. So you can profile. If there's no clouds, you can profile atmosphere in very small increments. Microwave is a more powerful tool. It can see through the cloud, but we can only deploy them on polar orbiters right now. So you could only get a complete picture every 12 hours. I talked about conventional GPSRO. And the question is how much of each of observing systems contributes to our ability to sense the Earth. So microwave is a workhorse. It contributes. So the different colors means tropics is green, southern hemisphere is red, northern hemisphere is blue. So microwave is a workhorse of observing system, even though we don't get the pictures very often. They're the most informative ones. Infrared is very informative, especially in southern hemisphere. MVs is the atmospheric motion vector. So actually looking at the five minute snapshots of the clouds, you could see how fast the cloud is moving and convert it to the vector wind at the cloud height. Conventional observations are extremely powerful in northern hemisphere, but not very much elsewhere. And the same thing with aircraft. Aircraft are extremely useful observation in northern hemisphere. Okay, let's switch to the ocean. So here you could see the polar orbiters. You could kind of see how they only observe a swath of Earth at the time when they go north to south or south to north. For oceans, the picture is different. The observing system is surface dominated. So we're observing a sea surface height altimeter. So it's a pencil beam. It's a radar. So it shoots electromagnetic pulse and see how long it takes it to come back. And it measures how high the surface height is. Or we can look at the SST either using microwaves or infrareds. And we have a lot of our profilers, Argo profilers and surface drifting stations. And you can see the tau array lights up once in a while when it reports back. You can also see that the number of ocean observations really peaked after the 2000s once we start having Argo array. And before 2000, most of them were just surface based, which is sheep. So before about 1990, we know very little about subsurface of the ocean. Ice. The workhorse of ice observing system is microwave sensors. So this is a picture of an ice extent from a microwave sensor. I'm sure too here. The alternative for determining the ice edge is ice charts. The way people obtain ice charts, the United States buys a whole bunch of SAR imagery from Canada. There is a person sitting there every day with a mouse and they look at the SAR imagery and they see where the ice edge is and they draw it on the map with their mouse and they come up with an ice chart. And you can see that in some of the ice chart is much more accurate what we get from microwave. And the reason is because if you have water sitting on the surface of the ice for the microwave, it looks like water. But really it's ice with a water puddle on top of it. So the two of them work together. It's a nice thing that we do have microwave imagery going back to 1979. Thanks to US Navy who needed to sail submarines and wanted to know where the ice is. A new type of measurements that we're trying to incorporate in our models is ice drift. So it's measured very much the same way as cloud drift is. You're looking at the surface of the ice and you're seeing how the cracks in ice are moved when you're tracking features in ice. And you can see the ice motion because ice moves very slowly. You can only get one of those estimates every two days. The other one is ice height. So the different ways to measure because what you want to do is to measure ice thickness. The best way we can do it is by measuring ice height. And then you can compute height into free board of how high is ice above the seawater. And then from density of ice, you know how much of ice is underwater and you calculate the ice height into ice thickness. You can also measure ice thickness by the color of the ice. And it works better if the ice is seen. And you can also measure it from SAR sometimes as well. Finally, the final observer system is land. We can see the ice is the temperature of the land very well if there's no clouds. That's the first picture. We can also observe the snow extent, which is very important for the forecast scale, especially when it's full and there's a lot of snow accumulating and changing albedo. We have extensive network of ground stations in developed countries, less so on the maritime continent. And finally, we can sense soil moisture. And you hear a lot probably about the impact of soil moisture now as a ability to our way to predict on subsistence scales. So indeed, if you assimilate soil moisture, you improve your forecast. But so far, this improvement has been only seen up to day six. So we still have to figure out how to take that information about soil moisture and project it into subsistence scale. And WP systems usually do not assimilate biomass properties because biomass is really not part of the land models and WP. But there is a different class of land models that are kind of more climate change based and biomass is a big part. Okay, so that was kind of an overview of the type of things we can observe in a modern observing system. So now that we have fully coupled models, and now we'll be talking about fully coupled data simulation, let's see how we can exploit this system to bring more information to initialize our coupled models. So you already saw this slide on Monday. So I use this slide to describe the challenge of data simulation opportunity from algorithmic perspective for quite a while now. So what we have is we have the systems that have been developed since the 1980s, that can do atmosphere and ocean as well as pretty well, not perfect, but pretty well. What we're trying to do in coupled data simulation, we want to use this cross fluid correlations that can inform, if I observe the ocean, I want to improve my atmosphere right away. And the other opportunity is that a lot of satellite measurements are actually sensitive to both atmosphere, ocean and land, and we're not using that information very effectively right now. So that's another opportunity. But in this talk I want to ask... Excuse me, Sergei. So you have about two more minutes. I don't want to rush you, but just to let you know that... Yeah, it's near... Yeah, so that you have time for questions. So don't rush it, but just get the message across so we can open it up for questions. Oh, sorry. I missed time with my talk. I thought I had multiple... Apologies. Yes, I understand and I apologize for it. Okay. Well, there is about... I'm only halfway through my talk. I'm so sorry for... No, just take five more minutes and go to the initialization problem. That's really important for S2S. Yeah. Okay, so let's look at this slide. So we're looking at the correlation between SST and wind speed here. That's one way to judge the strengths of the correlation between ocean and atmosphere. And it really falls into three regimes in a global model. We are either looking at the boundary currents where the ocean is driving atmosphere, small perturbations in the ocean will drive atmospheric response.  And Eastern Tropical Pacific where you have these reflective pros and b-waves. The second regime is tropics and subtropics. And specifically when the mixed layer depth is very shallow. When the mixed layer depth is very shallow, a small perturbation in your convective state can translate into your ocean state very rapidly. And finally, you have mid-latitudes. So mid-latitudes, you have the seasonal, large seasonal change in the depths of the mixed layer. And depending on this change, you're either sensitive to the atmosphere or you're not. And you can look or fly at some mechanisms behind these three things. Over ice, we have a great opportunity to assimilate things like ice temperature and ice velocity that's currently not assimilated. But both ice temperature has a strong correlation to atmospheric temperature. And ice velocity has a very strong correlation to both atmospheric velocity and to ocean velocities. And by developing these new assimilation methods, we can really close the observation gap in high latitudes. In the atmosphere, the key problem is that we're not assimilating a lot of channels that are sensitive to land surface and ocean surface because we really don't know how to specify it very well. And the challenge is to do it better. And the way that Alan Geer puts it, if you look at the SMIS imagery of the surface, you can see a lot of features on it. But we're only exploiting the things circled in purple and CIS as well. The rest of it, we're not exploiting. And that's an opportunity. My final slide is the importance of reanalysis and reforecasts. I think that Frederick Wittarff mentioned already that reanalysis and reforecast is really the workhorse of S2S prediction. So here I'm showing precipitation scores and they happen to be for the first week. But you can see that if you don't reprocess precipitation forecasts, the skill is not very good. But if you reprocess, if you use statistical processing techniques to enhance the skill of the forecast, you can get pretty good skill scores even in the week one. But our ability to reprocess really depends on how many years of statistical data we have. You need to have between 10 and 20 years to do it for week one. And you can imagine you need even more time to do it for weeks three, four, five and six. And you get this information by writing a longer reanalysis, not of 20 to 40 years, and then writing a reforecast from them. And that's the importance of reanalysis and reforecasts. And with that, I'll wrap up, saying that data simulation is an essential part of our system prediction enterprise, migration to coupled models, open new opportunity for research, and tightly coupled data simulation. And reanalysis and reforecast is a workhorse of S2S prediction. So thank you. I'm sorry for running late. Thank you so much, Sergei. And now apologies. We weren't clear that the 45 minutes includes 15 minutes discussions because we wanted the students to ask as many questions as possible. Thank you so much. I was really interested. And I know you have a very vast and deep knowledge about how difficult really it is to do a coupled data simulation.


Mr. Nona, welcome to CMC Markets and welcome to the January non-farm payrolls report with me, Michael Houston. This week's been an interesting week, if I'm honest, because I think unless we get a really dire report, then I don't think we're going to see that much of a market reaction. And let me explain why. About a week ago, the expectation was that we were probably going to get, or the market was hoping that we were going to get a rate cut in March from the Federal Reserve. Now, the regular listeners will know that I've always been skeptical about that. And it was actually quite nice for Jerome Powell to pretty much rule it out on Wednesday evening. Ultimately, he said the base case, the Fed's base case for a March rate cut, it wasn't a base case. So when you actually look at the data, I always thought the pricing was a little bit optimistic. And even if we get a really core report today, I should still think that it's unlikely that that pricing will move back towards March. I mean, the Fed has said it's data dependent. And certainly on the basis of this move that we've seen this week in two year yields, we can certainly see that there hasn't really been that much of a move. If you actually look at what's happened over the course of the past couple of days, in fact, equity markets have pretty much shrugged it off. On Wednesday, we saw the biggest one day decline in the S&P 500 since September. And yet here we are, almost 48 hours later, and the S&P is back towards record highs of only a few weeks ago. And the DAX itself is also rebounded quite strongly. So we're still very much, I think, and what can only be called as a bit of a Goldilocks scenario is in that the US economy continues to perform reasonably well. It's not in dire need of a rate cut. And all markets have done is they just pushed March out to May, which means that the Fed may cut rates in May, or they may not. I think it's very much data dependent. The latest estimate for Q1 GDP, the Atlanta Fed now cost estimate of Q1 GDP is 4.2%. Well, we just come off a Q4 number of 3.3. We're expecting 2%. So for me, I think that still makes it highly unlikely that the Fed is going to be in any rush cut rates. The only elephant in the room with respect to an early rate cut is obviously the election coming up in November. And the Fed, I don't think we'll want to start considering cutting rates in the second half of this year in the run-up to a presidential election. So we may find that we will probably see a rate cut sometime in May or June, but I certainly don't think we'll see one before that. And I think essentially, that's what Mark is surprising. And obviously, we saw the Bank of England yesterday. The Divergent votes there. The two votes for a hike and the one for a rate cut. And once again, you can sort of understand the Bank of England's dilemma when it comes to cutting rates. So I think what we can say is that ECB, Bank of England, the Federal Reserve, they've all gone some way to basically telling Mark, we're not going to be raising rates anymore. The bigger question is when that first rate cut comes. And that for me, March was never likely. The market is slowly starting to come to that same conclusion, which really sort of brings us on to today's payrolls report. And there are a number of factors at play here. To be honest with you, unless we get a number wildly outside consensus, I can still conceivably conceive that US markets will continue to push higher. I'm not particularly comfortable with that idea, given how rich evaluations are. But essentially, as with anything with technical analysis, you trade what you see. And at the moment, we've seen this week's volatility. Obviously, this is today's price action that was yesterday, that was Wednesday, and that was Tuesday. So we've had a choppy week. And I think this is what's likely going to continue to be the case for quite some time. The other thing that has concerned concerns me quite a bit. And I tweeted about it earlier this week. The top five US companies in the NASDAQ 100, top five, account four, or all the companies that were reporting this week account for over $10 trillion of market capitalization, $10.5 trillion. The total market cap of the NASDAQ 100 is $20.2 trillion. So essentially, you've got five or six companies in the NASDAQ accounting for more than half of the entire market cap of that index. So essentially, if any of these companies attach a big, big cold, and at the moment, judging by the numbers that we've seen so far this week, that doesn't look likely, then the uptrend that's been in place since October certainly has scoped to go further. And even if we do get a correction, you know, the 50-day moving average here, the trend line here, it's still likely to be reasonably supportive. So I've got the NASDAQ, that's rebounding quite nicely on the back of the meta numbers overnight, but also the Amazon numbers, Apple's slightly disappointing, that is probably going to open around about 2.5% lower. Meta, on the other hand, is expected to open 16% higher. It's already recovered from the record from the big losses of 2021. Let's zoom this chart out. This is the 2021 highs. This is the 65% decline. And now we're back above those previous record highs and likely to open all the way up here later today. I mean, that's just completely mind-boggling, eye-watering, whatever you want to call it. But ultimately, it's momentum. And markets very much are driven by momentum. And at the moment, momentum is positive. As far as foreign exchange is concerned, you know, Joe, what can I say? We're range bound, and I see no evidence whatsoever that we are likely to break out of the ranges that we've been in over the course of the past few weeks and months. So looking at euro a dollar, have you got a particular currency pair in mind, Joe? Because if you have, I'm more than happy to cover it. But certainly euro a dollar, very much finding support just below 108, quite a lot of bids through there. We can see that with respect to these series of lows in December, but also of the lows of earlier this week. But on the other hand, we've got resistance anywhere above 10920, 10930. So I think I don't think today's payrolls report is really going to shift the dial too much with respect to that. Now, dollar yen, I'm glad you said that, I used to trade dollar yen. That is probably the most interesting currency pair of all of them, because we have started to see a little bit of softening in the dollar. But we are currently finding support in and around these sorts of levels around about 146, 14580. So I think if we can break below 14580, then I think there's a fairly decent chance we can retest the 200-day moving average. But certainly on the basis of the fact that we are probably not going to see a rate cut until May, that should be mildly supportive of dollar yen while or above this area of support in and around 14580, 146. So on the other hand, we see a particularly weak payrolls number. That could well be dollar negative going forward. But certainly I think we are starting to sell in a bit of a range. Dollar yen looks to be toppy at the moment anywhere above 148 to 150, but it's also finding a few bids in and around the 140 level, which probably isn't particularly helpful to you. But for me at the moment, it does appear to be starting to show a little bit of signs of a little bit of softness. And consequently, we could well start to see a little bit of a drift flow. But this area of support, this cloud support here is likely to be a fairly tough nut to crack. But if we can break below that and into the cloud, then we could well drift back to 144 over the course of the next couple of weeks, perhaps. So that's dollar yen cable. Again, we can pretty much sum this cable move up by a really strong support, 125.90. I talk a lot about that in the chart forums on here. So if you're on the spread back platform, I usually do a daily update or even a twice weekly update on where I think the key levels are on cable. I've done the same thing on dollar yen as well on the spread platform or update where the key levels are. But at the moment, we're cable with 125.90, 128.20 on the wide of it. I still think that pound for pound, we're probably going to head higher, no pun intended. I think we can break above 128.20. The long term direction for cable, I think is higher while we're above this area around about 125.90. So certainly I'm in the camp of buying dips on cable. Euro dollar, as I say, is slightly more tricky. If anything, I think Euro sterling could well drift lower because I think of all the central banks that are probably going to cut, the ECB will probably cut first, perhaps as soon as April. But I certainly think that the Federal Reserve and Bank of England will come after the ECB. So I think that for me is the direction of travel, I think in terms of the main currency pairs. Next week, we've got the RBA. So I'm quickly going to cover that. Big support in and around 65 on Aussie dollar. We are starting to trend a little bit higher. And I think the RBA maybe may come out as slightly more dovish than it has been, which could be Aussie negative. But as we can see from this series of highs through here around about 66.30, you could find a few offers through there. And we'll probably continue to range trade between 65 and 66.30.40 on the wide of it. We've also got China CPI and China PPI next week. And if you think that as I do, China is exporting deflation, that could mean that inflationary pressures are likely to remain benign. And consequently, we could well see inflation slowly start to come down. What should I say, continue to come down with over the course of the next couple of months or so. So certainly I think we're going to see rate hike this year. There's just been a little bit of disappointment that the starting date has been pushed out. So let's look at today's payrolls report. So there are a number of factors at play here. We're looking for 185,000 on the headline number. There will also be a whole host of revisions, which we'll probably need to pay close attention to, but we'll probably take a while to shake out. The factors for a strong report, Goldman Sachs is calling for a number of around about 240,000, I think as his city group. And the factors behind that are lower jobless claims. They've been in and around 204,000, been as low as 187,000. So there's certainly no evidence that the US labor market is struggling. Jolt's job openings jumped quite sharply at the end of last year to over 9 million. So again, no evidence that vacancies are diminishing. But on the flip side of that, this week we've had a miss on the ADP report, which was a little bit on the weak side. So that was a little bit disappointing and could well weigh on the headline number. But overall, there is a general consensus. This particular payrolls report, as with all of the others, could go either way. So you've got a consensus of 185, I was listening to Bloomberg this morning and someone was saying that 100,000 number could put the prospect of a March rate cut back on the table. I don't think so. I think if you've got a negative number that could put the prospect of a March rate cut on the table. But I certainly don't think that 100,000 plus 100,000 number would put the prospect of a March rate cut on the table. So there was also poor weather in January, a lot of freezing weather in Texas and the southern states, which could mean that we get a weaker number. But that should correct itself in the February numbers in a month's time. So again, you've got to be a little bit sensitive about that. Unemployment could well age back up from 3.7 to 3.8%. I think one of the things that did surprise me about last month's report was the fact that the unemployment rate dropped so much. But a large part of that was down to a big drop in the participation rate, which to me was rather odd. It dropped from 62.8% to 62.5%. That made absolutely no sense to me. But then you've got a big jump in vacancies from the jolts. So why would people drop out of the workforce? So that could get revised higher in this afternoon's numbers. So that's certainly something to keep an eye on. Also, wages. Wage growth is still fairly resilient. 4.1% in December. No change expected in today's March numbers, in today's January numbers. And on the ADP payrolls report, wage growth was around about 7.2%. So again, there is evidence that inflation is proving to be particularly sticky and particularly services inflation. And actually, if you guys were looking at the ISM manufacturing numbers that came out this week, the prices paid component of that report showed a jump into positive territory, 52.9%. So again there, inflationary pressures do start to be building back up again. Now, whether that's a consequence of the disruption in the Red Sea, that could mean that while inflation is slowing, the process might not be as linear as perhaps central banks would like. And consequently, we could start to see an uptick. Certainly that's what the Bank of England is thinking is going to happen. We'll probably fall back to target in April when the energy price cap comes down. But then they're expecting to see an uptick to 2.75% by the end of this year. So you could see a bit of a rebound. And I think that's why central bankers are being so much more cautious about why they remain reluctant to telegraph with any degree of certainty about the timing of the first rate cut. So as I say, the numbers are as follows, 185 consensus. If we come in particularly low, anything close to 100,000, you might get a little bit of dollar weakness and a little bit of softness in yields. And that could prompt a bit of weakness in dolly yen and for euro dollar to test 109.20, 109.30, and the cable go back to 128. Anything above 200,000 pretty much blows the prospect of a March rate cut further away. It's currently around about 30%. So that could fall further and you could get a little bit of dollar weakness on the back of that. So a wide range of variations, a wide range of outcomes. On the wider scheme of things, I don't think we're going to see anything significant in terms of a move that's going to shift market expectations about a March rate cut. So we're five seconds to go and the numbers are due out now. So let's just wait and see as to what comes out. 353,000 jobs added in June, sorry, in January. I mean, that is just, that is humongous. So let's have a look at the, let's have a look at the other numbers on that. So we've got, I've just lost my screen now, 3.7% on the unemployment rate. So I think we could definitely say boom, because that is an absolute blowout number on the headline. So I think it really matters about the rest of it. 4.5% wages, boom again. And the labor force participation rate unchanged at 62%. Well, I think if anyone was holding a candle for March, I think they're about to be carried out because that is an absolute blowout number shows that the US economy has continued to main resilience and May at the very earliest. I'm looking at the revisions, US payrolls revised higher by an average of 27,000 a month in the second half of 2023. So US economy is going gangbusters. I think even a May rate cut could be a optimistic, shall we say. So that's really dollar positive and that's reflected, I think, what we're seeing with respect to dollar yen and it's certainly being reflected in yields. So you remember when I was saying this is going to move the dial that much? Did anyone have 353,000 on their non-farm payrolls cue card because I certainly didn't. And yeah, markets have now priced out 125 basis points of cuts this year. They've priced out 100 basis points of cuts this year and we're now pricing in fewer than 100 basis points of cuts this year. So the Bloomberg headline is a Fed swaps assign lower rods to March rate cut after the jobs data. Lower rods? Well, yeah, okay. No, yes, no something, something is all I can say to that particular headline number. That is a really solid number. So let's see what that's done to S&P futures. It's knocked it back slightly. You would expect it to have done so. Given the fact that yields have shot up. But you know, is it as bad? Is it as bad as that? Because the US economy is already showing signs that it's not being really affected by elevated levels of interest rates. And ultimately, prospect of a recession is becoming less and less. So it is the fact that the Fed is not going to need to cut rates necessarily a bad thing for equity markets. Because ultimately it will mean that the US economy is in much better shape. Employment levels are higher. The economy appears to be coping with current levels of rates. And consequently, that should mean consumers have more money in their pockets and more money essentially to spend on everyday goods and services. So just because the Fed is not going to be cutting rates in March or May or June, is that necessarily bad for the stock market? Certainly two year yields have continued to blow out. Let's look at the one day chart on this. I think that tells the story all by itself. So we've gone from 422 to 434 in the space of around about five minutes. So that is very much dollar positive. And certainly I think we're going to see a significant test of those support levels that I was talking about in the lead up to those numbers. So we're looking at 125.90 on cable. And we're looking at 107.80 on euro dollar. But certainly I think there's nothing in those numbers to suggest that we should be too concerned about a big, big sell-off in stock markets. Yes, certainly you're seeing a little bit of a pullback on the Nasdaq in the pre-market. And you can see that there. That was quite obvious. But does that mean that meta is not going to open 14 or 15 percent higher when US markets open later today? Does that mean that Amazon won't open higher when US markets open in just under an hour's time? I'm not sure. But I think what it does do is it basically just keeps, it will certainly keep I think the upside fairly limited today heading into the weekend. And the focus will now shift I think to the inflation numbers, the US CPI numbers later this month. I mean I think what was particularly noteworthy was the fact that we saw a big jump in wage growth in those numbers from 4.1 percent to 4.5 percent. I mean that was I think really notable. And December was revised up from 4.1 to 4.3. So that's going to worry the Fed. They're not, they're going to be in less rush to hike rates, hike rates, cut rates than they would have been before today's numbers. So unambiguously, decent set of numbers, and more to the point unambiguously dollar positive in the short to medium term. So let's keep an eye on those key support levels that I talked about earlier. Obviously it's also going to be not particularly good for gold. And gold is taking a little bit of a nosedive. But again here, you know, gold's very much in a range. And we have got a fairly decent trend line support coming in and around through these lows here. If I just stick that one in there, pull that through there. We'll probably go and see gold drift back towards the bottom end of this recent range. But it should stay above $2,000 an ounce in the short to medium term. Quick look at Brent crude. Obviously the strong dollar there is giving crude prices a little bit of a knock lower. And we do appear to have broken out of a little uptrend that we've been in since those lows back in December. So again, that should be reasonably positive for consumer spending and should act as a little bit of a lift when it comes to consumer spending power going forward. Does anyone have any questions on anything that I haven't thus far covered on this particular forum? I'm just trying to find the questions tab. Where are you? There you are. Okay. So based on those blowout numbers, any questions guys? What do I think and cause this rally inequities to run out of steam? Yeah, I mean, that's a really decent question. For me, I think that's a very hard question to answer because I think if you'd asked me a week ago whether or not this rally could continue, it would be something along the lines of the Fed is probably not going to be cutting rates in March. And we saw how explicitly say that it was a very high bar to a rate cut in March and the S&P posted its biggest one day fall in around about six months in September. And yet here we are 48 hours later. And that's that big fall on Wednesday is pretty much ancient history. So I think for me, it's really about not trying to worry about when something's going to run out of steam. It's more a case of follow the trend that's in place at the moment, trade with that until such times as there's evidence that it's coming to an end. And based on everything that I've shown you today with respect to equity markets, US equity markets in particular, we still remain very much in by the dip mode. So I can speculate to my heart's content as to why an equity market rally will run out of steam. But to my mind, it's sort of a distraction to the wider question is, how long is it likely to continue? And for me, this is about momentum. And at the moment, the momentum continues to favor buying the dips. So we've seen a we've seen a big sell off in European markets on the back of those numbers. But US markets have been a little bit weaker. But you wouldn't really know it. It's another arm, the rally that we saw yesterday. So you could argue that European markets haven't yet taken out their previous highs, the CAC 40, the DAX. And if you are looking for a market that might be a little bit toppy, you'd have to look at Europe. But when you look at the downside potential in Europe, the dips haven't been particularly, you know, they've been quite we drifted lower, we rebounded off the 50 day moving average.  And now we're back where we were just over two months ago, or just under two months ago. So, you know, for me, I don't think too much about what's going to cause the rally inequities to run out of steam. I just follow what the price action is doing, and then basically trade on the back of that, because that's essentially all you can do. If you worry too much about what might happen, you miss what is happening. And at the moment, what's happening is that markets are generally looking fairly resilient. The data in the US is really strong. In Europe, it's slightly better than expected. Consequently, there's no reason at the moment to suggest that markets are going to fall off a cliff. It's a slightly long winded answer and stuff, but I'm afraid it's the best I can do, I suppose. Has anyone else got any? I mean, I'm hoping that's okay. Any other questions, ladies and gents, on those blowout payroll numbers and what they mean for markets in general? So, we're seeing a little bit of weakness on the back of that, but I would be very surprised if that weakness is in any way prolonged. And at some point, we'll see buyers start to come back in cautiously. So, look at that dolly yen. Again, for you, Joe, yeah, it looks like we're re-testing those peaks again. Really nice, tasty move higher there. So, 148, 2030. That's going to be the next key resistance level there. Being interested in what the Bank of Japan have to say about their own tightening bias, because I think they will tighten at some point this year, bring rates back into positive territory, then minus 0.1, they can't stay there forever. But certainly, I think, in respect of US rate cuts has taken a little bit of a setback today. Not only in March, but May as well. Natural gas, oh, that old favorite. We just love natural gas. Well, it looks a bit bottomy at the moment, but we are now starting to head into very on a seasonal basis. What I would call is a time of probably fairly weak demand as spring starts to come. So, certainly on this basis, I would be cautious about being aggressively long of it. But having said that, we are very close to the December lows. So, I'd be interested to see whether we see a rebound back to around about 240 before we then drift lower again. If we look at where we were a year ago on US natural gas, so we've got February 2024, go back to February 2023, pretty much near the lows. And we spent most of the spring and summer months trading in and around or close to these lows. So, yeah, I mean, I wouldn't expect too much upside in US natural gas at this point in time. Any other questions? So, quickly next week, let me just give you a recap of what's coming up next week. We've got China CPI and PPI. They're both in deflationary territory. So, there's going to be a bit of a deflationary bias coming out of China. Chinese New Year starts on the 10th of Feb. So, we're probably not going to see much in the way of stimulus before the end of Chinese New Year. The triple R cut that was announced by Chinese authorities kicks in on the 5th of February on the Monday, five days before Chinese New Year. We've got services PMIs also on the Monday. Again, economic activity in Spain and Italy has been more resilient than it has been in France and Germany. So, maybe we could see an uptick there. US has proved to be much more resilient as has the UK. So, again, I don't think any of the data that we're going to see out on services next week is going to be particularly conducive to expectations of a rate cut. Could be wrong, but certainly I'll be paying close attention to the European ones because I think the sands are shifting in terms of a timing for an ECB rate cut. Talking in June, but I think we could get one as soon as April. On the earnings front, we've got BP's, fourth quarter numbers and four year numbers. They're due out on the 6th of Feb. It'll be interesting to see whether or not they decide to focus more on oil and natural gas and the big money spinners and start to shift slightly away from their performing oil transforming policy. Got photovoins, Q3 numbers. We've got Unilever's four year numbers and we've also got earnings numbers from Uber, Disney and Arm Holdings. So, again, big week, another big week for earnings. Not as big as the one just gone, but it'll be interesting to see whether or not the real resilience that we've seen carries over into next week. Okay, so in the absence of any other questions, ladies and gents, once again, thank you very much for listening in. It's always been a pleasure. Got my weekly video is available and my weekly head is available on the news and analysis section of the website. Otherwise, thanks very much for listening. Hope you all have a great weekend and see you all in the same time, same place next month. Thanks very much for listening.


Just look forward. Good afternoon everyone and welcome back to Supercomputing. We're live here with theCUBE in Dallas. I'm joined by my co-host David. Wonderful to be sharing the afternoon with you. And we are going to be kicking things off with a very thrilling discussion from two important thought leaders at Dell. Babush and Shreya, thank you so much for being on the show. Welcome, how are you doing? How does it feel to be at Supercomputing? Pretty good. We really enjoying the show and enjoying a lot of customer conversation ongoing. Are most of your customers here? Yes, most of our customers are mostly in the hide over there and a lot of discussions ongoing. Yeah, must be nice to see everybody show up. Are you enjoying the show so far Shreya? Yeah, I missed this for two years and so it's nice to be back and meeting people in person. Yeah, definitely, we all missed it. So it's been a very exciting week for Dell. Do you want to talk about what you're most excited about in the announcement portfolio that we saw yesterday? Absolutely. Go for it, Shreya. Yeah, so before we get into the portfolio side of the house, we really wanted to kind of share our thoughts in terms of what is it that's kind of moving HPC and Supercomputing. For a long time. Talk trends. For a long time HPC and Supercomputing has been driven by packing the racks, maximizing the performance. And as the work that Pavesh and I have been doing over the last couple of generations, we're seeing an emerging trend and that is the thermal dissipated power is actually exploding. And so the idea of packing the racks is now turning into how do you maximize your performance but are able to deliver the infrastructure in that limited kilowatts per rack that you have in your data center. So it's been interesting walking around the show, seeing how many businesses associated with cooling Greg are here. And it's funny to see they open up the cabinet and it's almost 19th century looking technology. It's pipes and pumps. And very industrial looking. Yeah, and I think so that's where the trends are more in the power and cooling. That is what everybody is trying to solve from an industry perspective. And what we did when we looked at our portfolio what we want to bring out in this timeframe for targeting more the HPC and AI space. There were a couple of vectors we had to look at. We had to look at cooling. We had to look at power where the trends are happening. We had to look at what are the data center needs showing up, be it in the colo space, be it in the HPC space, be it in the large install happening out there. So looking at those trends and then factoring in how do you build a node out? We said, okay, we need to diversify and build out an infrastructure. And that's what me and Shreya looked into not only looking at the silicon diversity showing up but more looking at, okay, there is this power, there is this cooling, there is silicon diversity. Now how do you start packing it up and bringing it to the marketplace? So kind of those are some of the trends that we captured and that's what you see kind of in the exhibit floor today even, right? And Dell Technologies supports both liquid cooling, air cooling, do you have a preference? Is it more just a customer based? It is going to be, and Shreya can allude to it, it's more workload and application focus. That is what we want to be thinking about and it's not going to be siloed into, okay, we're going to be just targeting air cooling. We wanted to target a breadth between air to liquid and that's how we built into our portfolio when we looked at our GPUs. To add to that, if we look at our customer landscape, we see that there's a peak between 35 to 45 kilowatts per rack, we see another peak at 60, we see another peak at 80 and we've got select very specialized customers above 100 kilowatts per rack. And so if we take that 35 to 45 kilowatts per rack, you can pack maybe three or four of these chassis, right? And so to what Bhavesh is saying, we're really trying to provide the flexibility for what our customers can deliver in their data centers, whether it be at the 35 end, where air cooling may make complete sense as you get above 45 and above, maybe that's the time to pivot to a liquid cool solution. So you said that there are situations where you could have 90 kilowatts being consumed by a rack of equipment. So I live in California where we are very, very closely attuned to things like the price for a kilowatt hour of electricity. Seriously. And I'm kind of an electric car nerd. So for the folks who really aren't as attuned, 90 kilowatts, that's like over 100 horsepower. So think about 100 horsepower worth of energy being used for compute in one of these racks. It's insane. So you can kind of imagine, a layperson can kind of imagine the variables that go into this equation of, how do we bring the power in, get the maximum bang for per kilowatt hour? But are there any kind of interesting, odd twists in your equations that you find when you're trying to figure out, do you have a? Yeah, and a lot of these trends when we look at it, okay, it's not, we think about it more from a power density that we want to try to go and solve. We're mindful about all the, from an energy perspective where the energy prices are moving. So what we do is we try to be optimizing right at the node level and how are we going to do our liquid cooling and air cooled infrastructure. So it's trying to, how do you keep a balance with it? That's what we are thinking about. And thinking about it is not just delivering or consuming the power that is maybe not needed for that particular node itself. So that's what we are thinking about. The other way we optimize when we build this infrastructure out is we're thinking about, okay, how are we going to deliver it at the rack level and more keeping in mind as to how this liquid cooling plumbing will happen? Where is it coming into the data center? Is it coming in the bottom of the floor? Are we going to do it on the left-hand side of your rack or the right-hand side? It's a big thing. It's like it becomes, okay, yeah, it doesn't matter which side you put it on, but there is a piece of it going into our decision as to how we are going to build that node out. So there are multiple factors coming in. And besides the power and cooling which we all touched upon, what you and me also look at is where this whole GPU and accelerators are moving into. So we're not just looking at the current set of GPUs and where they're moving from a power perspective. We're looking at this whole silicon diversity that is happening out there. So we've been looking at multiple accelerators that are multiple companies out there. And we can tell you there'll be over 330 to 50 silicon companies out there that we are actually engaged in looking into. So our decision in building this particular portfolio out was being mindful about what the maturity curve is from a software point of view, from a hardware point of view, and what can we deliver what the customer really needs in it, yeah. It's a balancing act. It is a balancing act. Let's stay in that zone a little bit. What other trends, Shreya, let's go to you on this one. What other trends are you seeing in the acceleration landscape? Yeah, I think to your point, the balancing act is actually a very interesting paradigm. One of the things that Pavesh and I constantly think about and we call it the Goldilocks syndrome, which is at that 90 and 100, right? Density matters. A lot. But what we've done is we have really figured out what that optimal point is because we don't want to be the thinnest most possible. You lose a lot of power redundancy. You lose a lot of IO capability. You lose a lot of storage capability. And so from our portfolio perspective, we've really tried to think about the Goldilocks syndrome and where that sweet spot is. I love that. I love the thought of you all just standing around server racks, having a little bit of porridge and determining exactly the thickness that you want in terms of the density trade off there. Yeah, I love that though. I mean, it's very digestible. Are you seeing anything else? No, I think so. That's pretty much what Shreya summed it up when we think about what we are thinking about where the technology features are moving and what we are thinking in terms of our portfolio. So it is, yeah.  You put power into a CPU or a GPU and you're getting something out. And a lot of what we get out is heat. Is there a measure, is there an objective measure of efficiency in these devices that we look at? Because you could think of a 100 watt light bulb. An incandescent light bulb is going to give out a certain amount of light and a certain amount of heat. A 100 watt equivalent LED in terms of the lumens that it's putting out in terms of light. A lot more light for the power going in, a lot less heat. We have LED lights around us, thankfully, instead of incandescent lights. But what is, when you put power into a CPU or a GPU, how do you measure that efficiency? Because it's sort of funny, because it's like, it's not moving. So it's not like measuring putting power into a vehicle and measuring forward motion and heat. You're measuring this sort of esoteric thing, this processing thing that you can't see or touch. But I mean, how much per watt of power, how do you measure it, I guess? Help us out from the base up understanding. Because most people have never been in a data center before. Maybe they've put their hand behind the fan in a personal computer, or they've had a laptop feel warm on their lap. But we're talking about massive amounts of heat being generated. Can you kind of explain the fundamentals of that? So the way we think about it is, there's a performance per dollar metric. There's a performance per dollar per watt metric, and that's where the power kind of comes in. But on the flip side, we have something called PUE power utilization efficiency from a data center aspect. And so we try to marry up those concepts together and really try to find that sweet spot. Is there anything in the way of harvesting that heat to do other worthwhile work?  Water heated. Re-circulating too. Courtesy of Intel AMD. Yeah, yeah, yeah, you're so cool. I like it. So that's the circulation of, or recycling of that thermal heat that you're talking about, absolutely. And we see that our customers in the Europe region, actually a lot more advanced in terms of taking that power and doing something that's valuable with it, right? Cooking croissant and making lattes, probably, right? Or heating your home. Or heating the pool, croissants. That would be a good use. It's more on the PUE aspect of it. It's more thinking about how are we more energy efficient in our design even. So we're more thinking about what's the best efficiency we can get, but what's the amount of heat capture we can get? How are we just kind of wasting any heat out there? So that's always the goal when designing these platforms. So that's something that we had kept in mind with a lot of our power and cooling experts within Dell. When thinking about, okay, is it, how much can we get, can we capture? If we are not capturing anything, then what are we kind of re-circulating it back in order to get much better efficiency when we think about it at a rack level and for the other equipment, which is going to be purely air-cooled out there and what can we do about it? So. Do you think both of these technologies are going to continue to work in tandem, air cooling and liquid cooling? Yeah, so we're not going to see. Yeah, we don't kind of, when we think about our portfolio and what we see the trends moving in the future, I think so, air cooling is definitely going to be there. There'll be a huge amount of usage for customers looking into air cooling. Air cooling is not going to go away. Liquid cooling is definitely something that a lot of customers are looking into adopting. PUE becomes a bigger factor for it. How much can I heat capture with it? That's a bigger equation that is coming into the picture and that's where we said, okay, we have a transition happening and that's what you see in our portfolio now. Yeah, Intel is, excuse me, Dell is agnostic when it comes to things like Intel, AMD, Broadcom, Nvidia, so you can look at this landscape and I think make a fair judgment. When we talk about GPU versus CPU in terms of efficiency, do you see that as something that will live on into the future for some applications, meaning look, GPU is the answer? Or is it simply a question of leveraging what we think of as CPU cores differently? Is this going to be, is this going to ebb and flow back and forth, Shreya, are things going to change? Because right now, a lot of what's announced recently in the high performance compute area leverages GPUs, but we're right in the season of AMD and Intel coming out with NextGen processor architectures. Great point, yeah. Any thoughts? Yeah, so what I'll tell you is that it is all application dependent. If you rewind a couple of generations, you'll see that the journey for GPU just started, right? And so there is an ROI, a minimum threshold ROI, that customers have to realize in order to move their workloads from CPU base to GPU base. As the technology evolves and matures, you'll have more and more applications that will fit within that bucket. Does that mean that everything will fit in that bucket? I don't believe so. But as the technology will continue to mature on the CPU side, but also on the GPU side. And so depending on where the customer is in their journey, it's the same for air versus liquid. Liquid is not an if, but it's a when. And when the environment, the data center environment is ready to support that. And when you have that ROI that goes with it is when it makes sense to transition to one way or the other. That's awesome. All right, last question for you both in a succinct phrase, if possible. I won't character count. What do you hope that we get to talk about next year when we have you back on theCUBE? Shreya, we'll start with you. That's a good one. I'm going to let Povash go first. Go for it. What do you think, Povash? Next year I think so, what you'll see more because I'm in the CTIA group, more talking about where cash coherency is moving. So that's what I'll just leave it at that and we'll talk about it. All right, more tantalizing. I was going to say a little window in there, yep. And I think to kind of add to that, I'm excited to see what the future holds with CPUs, GPUs, smartnecks and the integration of these technologies and where that all is headed and how that helps ultimately, you know, our customers being able to solve these really, really large and complex problems. The problems our globe faces. Wow, well, it was absolutely fantastic to have you both on the show. Time just flew, David, wonderful questions. As always, thank you all for tuning in to theCUBE here live from Dallas where we are broadcasting all about supercomputing, high performance computing and everything that a hardware nerd like I loves. My name is Savannah Peterson, we'll see you again soon.