Hello, and welcome to theCUBE pod episode 52. I'm John Furrier with Dave Vellante, Extracting the Subliminal of the Cubes. Look at the angle, Cube Research. Dave, one year in, 52 weeks of straight podcasting. One year? This is our anniversary? I think we missed one week, I think. Yeah. All right, we did it. I think we're in the top 10 in the overall tech industry. I'll have to go check the rankings on Apple iTunes. But yeah, we're rising right up the charts. Oh, it's good to be with you, John. All right, so, I mean, plenty of news. Obviously, it's a couple of things going on. I'm wearing my March Madness for Carolina, my daughter's senior there, UNC, big upsets in the March Madness, Bracketology. SBF is going to jail for 25 years. And that's the huge news. Obviously Databricks announces a potent open source LLM called DBRX. And Big Money Amazon pulled the trigger on their next tranche of their $4 billion investment in Anthropoc. I think they're adding another $2.75 billion into Anthropoc. Code here gets a $500 million. Jeremy Burton, keep alumni raises $115 million in Series B. Intel, Samsung, Qualcomm, all form this UXL foundation to take on NVIDIA's CUDA software. Okay, and also the AI officer role is in flux.  It's clear that an AI officer is coming down the pike. And of course, we've been a bunch of different events. We've been covering EC Connect, Adobe, all the different events, and Google Cloud's coming up. So we're about to slide into heavy-duty season for theCUBE. Obviously all the hot enterprise tech events will start trickling in. Like I said, we coupled a couple of months this week. We got Google Next, cloud events coming. Just a ton of activity. I think April, May, and June are going to be like literally nonstop for us and our team. We're going to be packed. And there's so much more action. I mean, every day it's like, I feel like I'm getting behind every day. It's like, oh damn, I got to dig into the model of experts. MOE, the new thing everyone's talking about, the MOE. I didn't have that on my bingo card, as they say this week in the news, but Databricks brings up this MOE, this model of experts concept to the LLMs, but faster, smaller, cheaper. This is the kind of a movie we've seen before with AI right now. And Databricks, I love the open source, open angle on this because this is going to be a boom for developers. You're going, we're going to see the beginning of a more of accelerated speed game. How people are using LLMs, using AI. I was just reading a paper just today on how academics are using the LLMs for peer review. You know why? Because it's more polite than peer review. They write in a coherent paper. So, you know, research mall, I mean, everything's being impacted, but it should be a great event season as we go into year two of the Q-Pod and also kind of year two of the AI hype day where the consumer will lead the market. You're going to start to see enterprise starting to put stakes in the ground. And it's going to be the year of, you know, put the stake, show the stake on the grill. If you get the sizzle, you better show the stake as they say. So it's going to be the year of open up the cover and let's see how the stake looks. Who's got the beef? That's going to be the question this year. Who's got the beef in AI? I was 16 months in, right? 16 months into the AI wave. A lot of sizzle, Dave, a lot of sizzle. Where's the stake? Well, I thought that Databricks announcement was pretty sizzly. They made a lot of claims.  I know it's 36 billion parameters, but that they use out of 132 billion. But that still feels pretty big to me, John. I mean, and you know, you just, I think as we were coming on the Q pod, you said somebody else just lead, lead problem. Samba Nova systems out of Palo Alto, just announced their COE version 0.2 outperforms TBRX and Databricks and Miestrel and Grock, okay? At a breakneck speed of 330 tokens per second. Again, this is just the breakthrough speeds without sacrificing precision is going to be the key. And it's going to come down to this whole, what will AI run on? That's the big enterprise battle. So, you know, remember we used to use the NASCAR analogy. She used to say horses on the track. I use the NASCAR analogy. All the cars are kind of in a cluster. Someone gets ahead, someone slingshots out front. I think we're going to see a lot more of this where it's going to be an arms race in AI like we've never seen before.  But you're starting to see a technical arms race and a financial arms race. You're seeing the investment community going all in even in spaces like observability. Jeremy Burton, we mentioned, Observe got $115 million in a Series B, which is a good Series B round, but that's a very tight market observability. So he's betting that they're going to be, they're going to have a war chest of funds to propel themselves into the position to be in the pack. Whatever analogy you want to use NASCAR, you know, Saturday moving day in the master is you want to be in the hunt. If you're not in the hunt, you're going to be left out. I think this is what we're seeing right now in AI. On Jeremy's raise, it looked like to me, like he didn't do the typical, you know, Jeremy's got wisdom, it's been around a while. He didn't do the typical try to maximize the valuation. I think he tried to maximize his balance sheet and his runway and create an attractive deal for investors. That's the way it looked to me. But I wanted to come back to DVRX, if we could for a second. Yeah, absolutely. I thought it was, I started to dig into it and I'm just sort of getting into, I was going to do it for my breaking analysis tomorrow, but it's like, tomorrow's good Friday. I kind of want to leave early and do the stations if I can. But so I got to pick an easier topic. But so the question is, so they've made a big deal out of being open source, but I was started to read the licenses. I don't usually read these open source licenses. There's some stuff in there, John, that is pretty interesting. Let me share some of it with you. Yeah. I found... On Twitter, by the way, they were commenting on this. So go ahead. Well, so that's how I saw it. And I was like, hmm, somebody said, it's not really open source. And I'm like, well, why not? So let's go read the license and see. So I found a few things that I think are worth discussing. And yeah, maybe it's okay, but I'd love your thoughts. So it says all distributions of DVRX or derivatives must be accompanied by a notice text file that contains the following notification, quote, DVRX is provided under, and subject to the Databricks open model license, copyright Databricks Inc, all rights reserved. So I'm like, that kind of caught my attention. That was one. And then it said, if on the release date of DVRX, if the monthly active users of the platform you're applying it to are more than 700 million, you can't use the model without Databricks permission. So that's by dance, I guess, and meta. It also says you can't use DVRX to improve other LLMs. So that's kind of restrictive. And in the sense, I found another clause that looks like they're maybe not hardcore about it.  And I can see why they do that. If an earlier version is deficient, they don't want it out there. But so I guess it's open in the sense that it's not a black box, the weights are open. But I don't think the training data is been revealed. And I think just, I don't know what other open source licenses look like. I got to dig into it, but these three or four things caught my attention. And I was like, huh, maybe it's like quasi open. What do you think about that? Well, first of all, it's a great call out. And I got that, so that same tweet might have been hitting us both on that one. But I think it's a really important point. This isn't a new era. If you looked at what Jensen Wong said at NVIDIA's GTC and all the covers we've been doing with AI, everything points to this new generative model is new. So if you combine, I call classic open source licenses, they've been under siege lately. You've seen what happened with MongoDB, you got Hashicorp, even Redis recently have changed their licenses all to get commercialized. And that's caused all kinds of forking. So just generally speaking, open source is upside down on the licensing anyway, right? So Matt has say he's been very vocal on this, on Info World, his column and on SiliconANGLE. So you have this pure open source model, commercial open source as it becomes more entrepreneurial and there's wealth to be created happening. And then enter this LLM stuff, which in and of itself is kind of like code. I mean, data, as I've always said, is like software. It's like code. So when you start looking at LLMs, you almost got to look at them like code bases. They're not code bases in the classic sense of software code, but they are code in the sense of they are data and it's got value. Like software used to have value in a proprietary sense. So how do you apply an open source concept to data that is constantly changing, right? So it's a very tough licensing call because one, it's never been done before. Then you got to sit down and figure out, one, what's the future going to look like for the data as it evolves? Number three, what's the best license to apply to it? So these are a lot of unknowns. So my first reaction was you play it down the middle of the fairway and see what happens. That's the way I see this going on. So I'm not too concerned with the license. I think it's more of a hedge from Databricks not to get into trouble and play it straight down the middle and say, let's see where it goes. I think it's an opportunity. I think what should happen is a team of people in the industry should form a little collective council or open source kind of vibe or, and just figure it out. I'm not deep enough on the licenses to understand the nuances between work on the data side.  So there's going to be a game of thrones going on and around this area. So again, the contributions are going to be like, we beat you and it's going to be the arms race. And then you got this license under the covers. And then by the way, who owns it? So what if they're mixing in data that's owned by somebody? So it's a can of worms to begin with in a good way. That's what innovation looks like. It's not pretty. It's not always tight. It's kind of ugly. And as they say, it's sausage making in the early days of these big waves. So I love what Databricks did. And I think the way they handle themselves over the past two couple of years, they've been good at managing some of these open models, especially how they handled all that stuff when opening up the data formats last year. So they have a track record. So I kind of trust Databricks on this one. Well, so yeah, and I couldn't find anything about the training dataset as to whether or not that's open, so I presume it's not been revealed. And this came out of the Mosaic ML acquisition, right? And they were, did you see the benchmarks? Did you see the, it looks like the graphs were kind of thrown together. Like, I was, you know, maybe I'm being a little bit too pedantic, but like the 71% graph for a mixed roll was under 70% or whatever it was. It was just, they weren't lined up. It was sort of maybe rushed a little bit, but I do a lot of crappy slides too. So I really can't. Well, Rob Hofen and Paul Gillin wrote that because Rob went to the dinner and had the briefing, but I think I read on Twitter, I was talking to Ali and Naveen on Twitter around some messaging back and forth when they announced it. The guy who did the graph spent all, holding all nighter, because he had to get him out. I've been there. I mean, I've been there. So I can't, that's, but I just- I got him some slack on that. But I don't totally, but it was just like, it just was interesting to see. But it's a, you know, just looking at it, some of the benchmarks, it's like two and a half times the size of mixed drill with about, you know, half the inference speed. So it's like, okay. And I don't know. I don't know how much to put into these, you know, the benchmarking. But then I was thinking, all right, well, what is, how does this fit with Databricks business? And I think it sits on top of Databricks.  And I think their play is to sell DBUs, right?  And they'll probably do really well with this. So that's kind of how I looked at it. And it's, I guess it's a pretty good play. And it's, you know, it's, I think trained, I read it was trained on 12 trillion tokens, which is a lot more than like... Well, the large has got 130 billion, 32 billion parameters. And it's the design is around the MOE model of expert architecture. And it switches between which experts pieces to go with in the code. That's the key innovation. So it's designed to work in a very low compute footprint, which we've been talking about LLMs being optimized to say edge devices, whether that's cameras on an intersection or a human wearable device or a small machine in a retail store, it doesn't matter. It's a non data center device. So you started to see that. I think that's something that we said from the phone to the handheld, human wearable to sensor on a network to a light bulb. All this is going to be low footprint size. Well, it was trained on 12 trillion tokens, but I think you're right. It's the sort of mixture of experts is how they did it. But you compare that to like Falcon, I think was trained on three and a half trillion tokens, the Falcon 180. So it should be substantially better, right? But anyway, it's a good marketing. First of all, Databricks is great. They a lot of cash, a lot of smart people and they pull a lot of talents to the company. If you look at how they handle their projects, they're doing good on the talent side. But what this really proves to me is that continuing to validate the developer market right now is so hot right now. These companies want these developers using their tools. And we've seen in the cloud native wave that whichever developer adoption wave hits, those companies around those tool chains are extremely successful. So if you look at all the public companies that went on past decade, all were adopted either organically and or because they had a great product for developers. So as the infrastructure players like Databricks, Amazon, Google all try to win over that infrastructure. We saw Broadcom having chips and we have Nvidia wants to be this big data center. The infrastructure is building out and fast as it possibly can for the picks and shovels and the platforms and tools because the developers are hungry for the best.  Yeah, they're not dumbasses.  It's funny, John, right? Remember when we were talking to, we were in the private meeting with Jensen for a couple of hours last week. He said, well, imagine if you could let the AI go off for a week, which is kind of interesting. But at the same time, we know users want instant ratification. So I think Databricks recognizes that slow getting to an outcome slowly is a problem and they're addressing that and they're saying, run this on our stack. So it's interesting, the data business, like databases, it goes through all these cycles, it's got a query response. But what I think Jensen said that in our private meeting, as you pointed out, what's interesting is that the world that we lived in prior to Gen AI was programmed for us, even databases. You type a query, you get a response. And so that was kind of a static world. Now it's completely generating at runtime, generated response. But what I liked about his talk was, he said AI will fill two use cases. The prompt response model type of query and like chat GPT and get a generative response, that goes from basic text prompt to multimodal, some sort of runtime assembly response. And then he said, send it away to go reason. So there's two use cases, prompting and responding and generating at runtime and then go away and think. That's reasoning. So all the big conversations now in AI that's happening inside the ropes in the industry is this whole reasoning aspect is huge. And it's the number one thing that nobody's talking about that's the hottest thing. Ever saw about chat GPT and AI and inference is better than training, blah, blah, blah, blah, blah. The real action is reasoning. That's complicated. And that's where these GPU classes are gonna come in. That's why Zuckerberg is buying up all the GPUs at Metta because he wants to squirrel away all those resources. So what's gonna happen is we're gonna see another AWS emerge possibly. It could be Metta or AWS with Anthropi. They're already making that bet hard. You've seen them deploy the capital. So the infrastructure for AI is really the battle Royale right now. And it's super exciting because the developers are hungry. I mean, everybody's moving to AI. Everybody, consumers, developers, every single industry, every single company wants AI and they can't build out the infrastructure fast enough right now. AWS would be the next AWS you're saying. But so, yes, and I- They're trying, they're at risk. I mean, they could. Before we go there, I agree with you what you're saying about Jensen, but Databricks seems to be doing the opposite. It's like, we want fast outputs. We know that's a problem. And so we're gonna make it easy for people to get fast outputs, bring your data, bring your ML, bring your gen AI, we'll rag it, we'll vectorize it, we'll API it, we'll give you a model optionality. Do it all. Because that's where the action is right now. Right now, the action for developers is get your data going. But it's a smart play. And if you can squint through the benchmarking, which they love the benchmarks, you remember they in Snowflake used to go, the urinary Olympics. But that works for them. But I would caution users to just be careful about the benchmarks. I don't think they mean that much because as you pointed out, everybody's leapfrogging each other. Well, I mean, we said last year, Google necks that whoever wins the developers in the AI race will have a big cloud position because it's going to come down to trust. And developers don't want to work with someone who's not going to be innovating. So the leapfrogging will happen. I think that's table stakes and people should expect if you want to be in this business like data breaks and others, the arms race is on. And that's the market. So again, the prize is the developer uptake. So, and that'll translate directly into software and infrastructure customers. I do want to point out too, and we've talked about this, we've written about it in breaking analysis quite a bit. There's still a huge gap. When you look at the LLMs that are being adopted, open AI has a huge lead over virtually everybody, including Lama. And so, but there's a lot of money out there and everybody's going after them just like everybody's going after Nvidia. You mentioned this Intel-Samsung Qualcomm thing. Intel's now in both, right? Intel, isn't Intel in that AI alliance that Meta and IBM did. So Intel's playing its cards in both decks spreading its, which to me, look, if you really want to take it on Nvidia, don't split, don't fork these alliances, get together. And let me ask you a question. Let me ask you a question because this has come up this week in some of our meetings with people, Dave. This momentum around ecosystem land grabbing is happening. There seems to be a trend where everyone wants to have their quote AI Karitsu, their crew, their tribe. They want to put together their ecosystem. So, as we know in the enterprise, having an ecosystem means you have partnered in adoption because it's in everything's integrated these days. So not having an ecosystem is basically saying, we're not relevant. So is there ecosystem washing going on right now? Are people standing up, kind of ecosystem stories that aren't on solid ground? And what's your take on this? And if so, is that a feature or a bug? Is that just the way it is right now? And what will we need to look at to see for a successful ecosystem? Well, I think you're seeing history repeat itself. We saw this in the Unix days. And remember the original Unix was the original open systems, right? And you'd have different factions. Remember digital equipment led one and the sun led another one. I can't remember exactly what they were. And they ended up all going to Linux, right? I mean, which is the true open source. And so I think you see this all the time. These cobbles do form and these Karitsu's form and some of them make it, some of them don't. I guess my point is right now, everybody's going after Nvidia. Nvidia's got the mantle, they got the margins. Your margin is my opportunity, that's saying. But so it's kind of surprised me that these guys would fork it. And I guess they do it because you've got founding members and you've got laggards who come in later and they don't maybe have as much influence. But I don't know, you look at what the CNCF has done. I mean, that's worked.  But I think it's more of in any, you learn this in business school. I mean, anytime there's a market, the barriers to entry are as a key variable in assessing competitive advantage. And certainly Nvidia's got a huge bar, a barrier to enter. So they got the product, they got the software, we call it the moat. So anything that could increase the ladder to get into the market. And I think the plot to break up Nvidia's grip on the AI software business and hardware is to try to put an industry group together to have some sort of unified acceleration layer. It would be the equivalent of, to put our historian hat on to saying, IBM and owns the systems, network architecture operating system, the network operating system for proprietary mainframes. And let's create the OSI model, TCPIP, Ethernet. And I think you're seeing another kind of situation lining up where Nvidia is kind of like the monolithic mainframe. They even use that word in the event. But it's not proprietary as a vendor, but they do have a lock on the operating model with just the inherent barriers to entry. So in a way, there is a lock spec. It's called scale and software. So how do you break that? Open standards. Now the only way to connect that and try to get as much momentum. And of course, Nvidia will try to extend their lead during their time of prosperity by cutting deals with the metas of the world and Amazon's to have any product constraints control the supply. So the unified acceleration foundation, the UXL is a great initiative on paper to do that. But you know, Dave, we've seen the movie before where you can prop up these standards bodies. Sometimes that dog doesn't hunt. And so we'll see. I mean, it's going to be great to see. And by the way, do companies even have the time to participate in this? So my one API view on this is that I'm not sure I buy that. I think right now it's who's got the tools and de facto is more important than standing up and holding hands in public. But I think open source drivers. So what are you saying? I mean, you think that what Intel, Samsung, Qualcomm are doing in addition to what IBM and Meta did with the AI Alliance is that you're saying that's good because let a thousand flowers bloom or do you think, I mean, I'm not hardcore about this, but it just seems to me, if Nvidia is the target, which we know it is, why not just bring all those resources together and say, okay, we're going open because collectively that's the only way we're ever going to catch up to Nvidia because they have such a huge moat and we'll. Yeah, I have no problem with that. I mean, I'm just saying it's like, again, if alliances have to be functional to work, like I've seen many alliances that look good on paper, but the market is so focused on other things that there's no time to make the alliance work. If the market changes. Well, one, there's a couple of factors. One, do they truly committed to the alliance? That's one, two, is the market moving too fast for the alliance to even get their eyes on the beachhead to be secure. So that's going to be another one. If they're motivated by the fact that together they're winning together, then that's something that could work. That's how OSI happened. The open systems interconnect, the OSI seven layer stack worked because the people were highly motivated, open standards just distributed computing, but the full stack wasn't standardized. It was only up to layer, I think four or five was really got the most traction. Layers one through three were really standard. And that's why, that's how we got inter networking and spine and leaf is a critical rack and stack topology. What was the target back then? Was it SNA, IBM SNA? And DECnet SNA and the whole mini computer and proprietary operating systems of Unix. Unix systems and proprietary network operating systems. They control the physical layer up to switching and transport. So, you know, layer two or three. Which was essentially the mainframe. Many computers are basically small mainframe. Cisco became a company because of open standards. And they once they locked in, you had routing, you had layers two and three, you had switching and routing was your goal, you'd done off to the races. Now you can connect buildings together and not get locked into the proprietary mainframe IBM or DEC, which by the way, if you had IBM you couldn't run DEC. So it's like a proprietary didn't interoperate. So interoperability was the big thing. So we'll, I mean, we'll see. I mean, I don't want to get into a rattle, but to me. No, I just, I'm trying to do a historical comparison. It comes down to money at the end of the day. If there's a shortage of GPUs and the main supplier NVIDIA is controlling them. And a lot of the big hyperscalers control the inventory because they have the customers. That's going to create market opportunity for capitalism. It's like, hey, there's a market. And if you got developers, which is really strong right now get developers building apps and you got entrepreneurs and motivated suppliers to create an alternative, then you got competition. That's a good thing. And I think that's natural. And that's the benefit of capitalism. And I think that for that reason, it's not bad to deform it. Let's just see if that dog will hunt. Like I said, if the market's moving too fast that's a challenge. And if people aren't playing together and sandbox properly, then that could be dysfunctional. So those are the two areas that we're going to watch closely. So what do you think of the Reddit IPO? It was hot, rocketed up. Kind of pulling back a little bit. I loved it back at birth. First of all, I love the fact that Reddit was a great company and form. Love the founders, love the whole story. Love why it exists. I think it was a great IPO. Even though they have no revenue, it's a power dynamic that has all the elements of going big. It has a huge audience. Let's not forget the GameStop generation. So there's plenty of people to activate that stock. So it's got built-in power dynamics in the customer base. Love that piece of it because that brings that whole new vibe in there. And it has the opportunity potentially to monetize given the scale of the audience. So a huge fan of what Reddit's doing. Love the fact that it went public. It also shows that the IPO market might be opening up which is great sign because I think the stock market S&P hit an all-time high yesterday, which is interesting. And just again, it's weird how this market is. You have startups falling out of the sky, but you know, you've got massive economic engine. Again, the productivity piece is coming in. So I just think it's going to be a good IPO years coming but I don't think we're going to have a dark time. I think it might be dark for some companies on the wrong side of history. But I think the Reddit IPO, if the election doesn't screw everything up, then maybe we have a window here. So this could change a lot, you know, a lot of activities. I'd like to see Databricks go, you know, dying to see those guys go public. Arctic Wolf is another one I had two years ago. I predicted they were, I think in 2021, I said they'd go public, but kind of missed that one. But I think that's another company that could go and they're going to line up. I mean, there's got to be a big pipeline of companies that want to go public. Cohesity is another one, you know, that with the Veritas acquisition or merger that's called, I mean, Sanjay definitely wants to take that company public.  You know, can you go with a half a billion, ARR or even revenue, right? Do you have to be a billion dollar company now? I remember it used to be a hundred million, yeah, they had to have a hundred million to do IPO. Now is it a billion? Is it a 10X in this AI? I think what you're going to see is you're going to see a fundamental change in how people are. The zero interest rate period is over. I think you're going to see a more pragmatic entrepreneurial story coming public. I think you're going to see real growth companies. There'll be some hype out there on the AI side, but I think in the next 18 months, we're going to figure that out, sure. But like in general, the vibe here in Silicon Valley is you can't run a business and think like get customers and at least have a strategy. If you're going to go for the growth, you better not just think of it like it's free money. You got to work on the fundamentals on the go-to-market aspect of the product market fit. I think that's the big observation that's happening here is that if you're in a series A or later, series A, B or C, if you don't have a solid field go-to-market growth plan, then that's not going to fly. You won't get funded. So those days of having that, not having that in place are over.  People have obviously freaked out about interest rates, 5% interest rates. But remember, during the dot-com run-up from whatever, 96, 97, 98, 99, even into 2000, interest rates were 7% to 8%. Now the difference was the Clinton administration who raised taxes were paying down debt. They were, they actually balanced the budget for one or two years. So now the difference is you got this massive 33, 34 trillion dollar debt and you're paying huge debt service. So that's the difference that compresses sort of the enthusiasm rightfully. So I think that has to be addressed. And of course, nobody's talking about it. And I don't want to really get into it, but that's kind of not the focus of our Q-pod. We like to talk tech, but yeah, so. I mean, we have a lot of, Adobe had their conference this week, they had that big blowout with pigment acquisition didn't go through. They're talking about Chinerva AI. You have this whole. I was invited to that, but I couldn't go. I wanted to go. I really like Adobe. I like what they're doing in AI. I think they're applying AI very intelligently. I heard the conference was really strong. I know I was talking to Andy Turai. He said it was pretty good. It was good. I followed the whole event, it was great. Followed all the keynotes, followed all the tweets. I obviously didn't want to go to Vegas. So I've tapped out, but we did cover it. We had a cube alumni there. We had cube collective resources there. We have a whole analysis piece coming out. So yeah, I mean, I think Adobe is really, they're trying hard. I mean, it was a good event, I'm sure, but because they have a great product, but Adobe's in the crosshairs right now. I think there, Dave, they had the big Figma deal went south on it. Remember that? Yeah, I do. I almost think that was a blessing in disguise though. Well, I mean, we'll see if they can cross the catapult. I mean, you know, there was a joint decision to end the merger. Remember that? I was, that's what I know. I felt like that, remember the Figma deal was announced, was consummated really prior to the chat GPT hype. And I think there was a period where they, that Figma wasn't worth what Adobe was offering. So I don't know. I think it'll- There was regulatory issues big time, so. For sure. And so that's why they both said, all right, forget it, let's tap out, but we need to add it again. I think Adobe with their assets could be a great Gen AI company. The question is, do they have the culture? That's what I was looking at and trying to read through the tea leaves there. Not sure yet how I feel about it, and more to dig in on that. But yeah, all good. And also, you know, saw the big news on Crypto Exchange King, Sam Bankman-Freed 25 years. You think that was the right call? I mean, to put him away for that long? I mean, not that I'm sympathetic to Sam Bankman-Freed, but you know, I think this, I think reasonable people could say, well. You know what I'm saying? It's a moment in time where you say, hey, you know, this is not how we do business. But my thinking is that they probably gave him the sentence and making him appeal out, something will go downstream, but it sends a message.  I mean, he basically didn't admit any wrongdoing, but you know, he'll be out in 2048, Dave. I think a lot of that was showed, I mean, he took the stand. He really showed no remorse. He's kind of a weird dude. You know, people say he's on the spectrum. So that couldn't have helped him. The tweets were pretty hilarious. SPF going to hold H-O-L-D in jail. Going to hold him. He's going, hold him. That's 25 years. Oh, it's beautiful. Which is, for those of you who don't know, it's like the crypto hotshot. Hold him. Don't sell, hold him. Someone else from SPF is going to come out of prison firing an all cylinders on the web five. Oh, that's brutal. The entertainment is strong on this tweet streams. So.  I mean, I know they could have sold and the money was locked up. And I'm sure that that affected a lot of people. I think what Madoff did was, well, Sam Bankman Freed was pretty bad what he did, but the outcomes for people with Madoff were a lot worse. And apparently anthropic investments doing extremely well. Yeah, there you go. Right. Hit the sell that off. So. Right. He hit assets, he could liquidate and people got their money back or at least, you know, well, I guess you could say that they would make a lot more now with crypto rising the way it has, but I don't know. It's a philosophical question, but he got what he got, deserved it. Not to change topics, but I do want to get your thoughts because I just finished watching the Apple dynasty about the Patriots. And. Yeah, what's that? Have you seen that yet? I have. What do you think about it? Did you think Belichick was? I thought it was a Belichick hit piece, you know, I think from hard. I mean, it's it's a isn't it wasn't it a craft production? I was surprised, John, that the players like Matthew Slater kind of threw Belichick under the bus. I wasn't surprised that Amondola. Amondola didn't like Belichick. But I thought Matthew Slater, who was the captain of the team and he really kind of threw Belichick under the bus. They made Belichick look really bad. I think people forget the great things that Belichick did. They made him look like a total tool the way his camera angles were. He was like leaning back, looking, looking fat and evil, like he was like, he's an old school coach, not in touch with the players. Brady's the king of all things.  Well, I didn't know what I didn't know because I wasn't living there because you were close to that. I didn't know how it was unraveling so much earlier in the process. I had no idea that Belichick really didn't want her around with the Garoppolo tire. So I had no idea that was the case there. So that's, that's the amazing thing about, you know, Brady, a couple of amazing things. I mean, first of all, don't forget, if Malcolm Butler doesn't intercept that pass, then Brady's just, you know, dynasty over, right? If, if there wasn't that strip sack against Maddie Ice in Atlanta, you know, I mean, football, they did an onside kick on that game and also, but by the way, but they also highlighted nothing was the whole Malcolm Butler sitting out in the Super Bowl. Well, again, that's, they really, hey, I don't forgive Belichick for that. He had his reasons. He didn't answer it. He was sort of all live. I've answered that.  So it was easier to make him look like a tool. It was a Brady, Brady piece for sure. But, but I mean, it was also interesting, Kraft saying the reason he kept Belichick was the masterful defensive scheme he did against LA that year, the Rams. And by the way, the Rams were a better team. They had a better offense and a better defense. And he outcoached, what's his name? The young guy. I mean, if you're, if you're the, if you're the owner, you keep Belichick, right?  But at some point you got to keep the coach. Well, in retrospect, no, but yeah. I mean, I don't think, I think at the time, and I'll tell you the sentiment around this region at the time was, okay, let Brady go. People were kind of pissed at Brady for leaving. And so, you know, a lot of people say, ah, he's dead to me. And then of course he just goes and wins the Super Bowl with Tim Pabetti. That was, that was, that was a dagger. But I mean, the first three Super Bowls, I mean, Brady's amazing, right? But, I mean, talk about team though. I mean, that, that team with Brusky and, and, and William McGinnis and, and those guys, Troy Brown. I mean, the consummate team, remember for Super Bowl, choosing to be introduced as a team. So, and, and Belichick, they bought into Belichick. And I think the last few years, the young kids didn't. They were like, screw this guy. He's old, he's out of touch. But I, I thought it was unfair. I thought they, they didn't do a really good job of talking about the dynasty. It was all about deflate gate and spy gate and all that other nonsense that was, you know, okay, they did it, whatever. But that's, but that's not why that team won. They, the team won because it was a consummate team. Everybody bought in Belichick was an awesome coach. He had great coaches around him that bought into his system. Of course they never became much outside of his system. But anyway, that's a shame to see Belichick, you know, I don't know, I think he's put his greatest coach of all time at risk these last few weeks. I think they could have done a better job. Belichick did get slammed. All right, well, we got the big NCAA brackets.  Oh, I had Kentucky going all the way. They lost in the first round to Oakland.  The Kansas loss was a big one for me. Yeah, that was, I'm rooting for the Carolina because Caroline, my daughter is a senior at UNC. They made it to the final game two years ago. Didn't even make the NCAA last year. NC State, I had, I should have picked them because I had my hunch was to pick them to the final four because they were hot. They won the five straight games in the ACC. There were on fire. Then they'd love how they just got into that last game, beat Oakland was really an awesome. So we'll see how they do against Marquette. Houston, I got Houston going, going deep. I had Houston going to the final four. Yeah, me too. And I have Purdue going there too. So Gonzaga Purdue is going to go down. So we'll see. I think NC State can have a good ride in. We'll see how Arizona does. They're a wild card now. Caleb Love on Arizona used to play for UNC. I don't know if you know this, but he left the team because he got into a scuffle with the RJ Davis. He went after RJ Davis's girlfriend. And there was a huge locker room problem at UNC around this whole issue. So he had to leave the team or left the team on that. That's why they had a terrible year last year. Huge dynamic. Caleb Love was a great player. I had. You can't go after the same group. You can't go after your teammate's girlfriend. That's like code. Oh, right. I think that was locker room problems, Dave. You think that's going to cause locker room problems? Oh, my God. That's crossing the line. That's crossing the line. That's a big time problem. You just don't do that to bros. I had Arizona because my daughter went there. So I like Arizona. If I had one daughter, I went to Ole Miss. So I always root for them and not that they were in this tournament, but football. They had a good football season. Arizona, go U of A. Bear down. So you got NC State and UNC. We got, speaking of North Carolina, we've got SAS Innovation Innovate Event coming up at the Aria Hotel next month. The week after Google Next, they're in Cary, North Carolina. What a great private company SAS is. Last year was our first time going to SAS Innovate. I was killer. We got MongoDB local on May 2nd. I got my, I have two kids graduating from college, Tyler and Caroline, Northeast and UNC. We got the Red Hat Summit, Boomi World, Alterics in Vegas. Delta Tech World. Delta Tech World, Informatica World, IBM Thinks, Just Go Live, Snowflake, Click, Databricks, HB Discover. HB Discover. I mean, the list goes on and on and on. It's going to be a fun run. Browstrike, Hall. Let me ask you a question, Dave. What are you looking for the next few months? As we look at the research, I saw your research agenda we're publishing on theCUBE. By the way, great job on theCUBE research, publishing that agenda, research agenda. But what are you looking at? What are we going to be talking about in the next few months? I mean, the real, the thing that I'm looking at is, and I said in my predictions, AI has to be, 2024 has to be the year of AI ROI, or it's going to constrict funding. And I think what I'm seeing in the data is that expectations for ROI are getting pushed. The macro, the spending is getting pushed into the second half. So I think people are realizing, they're getting a reality check. If you look at what they're doing with generative AI, it's pretty basic. I mean, it's amazing, but it's really not game changing yet. You hear some pockets of productivity boost, and I think the potential is there. But in terms of the real dollars that are going to loosen up the CFO purse strings, that's not happening yet. It's getting pushed out. And I think it's going to be a 2025 and even 2026 story that this thing really kicks in. And I think the big question is, okay, everybody wants to know, when does the bubble burst? Is it like the.com? And I think because funding levels are so much higher here, you know, we may get a soft landing for the AI bubble burst. It may not be that bad. We may hit productivity, you know, and the bubble bursting may not be as painful as it was in the past. At least I hope that's the case. Well, I'm going to tell you right now, it's an opening day in the MLB today. So you got Major League Baseball kicking off, March Madness with theCUBE season kicking in. The CUBE community is doing great. Want to have a shout out to our 14th year doing theCUBE. Can't believe it's been 14 years and a lot of still fun going on. And we got the Red Sox tickets this year again, right? You do baseball in March. It's crazy. We got a bunch of tickets this year. The years at early for baseball. It's early. It's definitely early and the Red Sox haven't done much on the off season. I'm really kind of disappointed. It's like two years in a row. They really have just like- Is it home game or is that Seattle? Yeah, they don't, nothing home in March. The first home games in April, opening day there in Seattle. All right, well, Dave, we'll call it a day. I know you wanted to take good Fridays to the stages of the cross and Devers had a solo home run. Red Sox got a one-nothing lead. So it's streaming right now. That's what you know. I think it was 2013. I think they were not projected to be last and they ended up winning the World Series. So you never know baseball. All right, well, Dave, I have a great weekend and a great pod, 52, one year in. I got to say, I think I like the feedback we're getting from people.  So we'll continue to do deep dive and bring in some guests next year. Have a great weekend. Thanks, John. You too. You have 52 in the books. Thanks for listening, everybody. Go to siliconangle.com and keep that net. Check out thecuberesearch.com. New site we just converted over for wikibon.com. That's all the research. That's the heavy lifting content. See you next time.


Our headliner for day one of the McLean conference is a lawyer with a PhD in economics. He's a professor of law and medicine at the University of Chicago. He clerked for the iconic Supreme Court Justice, Sandra De O'Connor. He's improved the lives of millions of people in the slums of India. He helped design and evaluate India's health insurance system. It's a new Pellani who will speak on the need to restructure health policy and innovation. Thank you very much for having me here today and sticking around. All right, so the title of this talk is obviously up there, but it really should read what is the value of health care innovation. Because that's what I'm going to stress. I'm going to use, I'm going to contrast the value of innovation. So medical technology, new drugs, new therapies with health insurance. Okay, so here are the two main points that I'm going to try to make in about 15 minutes. The first is that most people are worried about risk in the health care sector. And when we think about risk, we think about medical bills. And I want to kind of reframe that and help people see that in fact the main risk is sickness. And that once we see that sickness is the biggest risk that people face, that the solution to sickness is not health insurance, it's first medical innovation, new treatments. The work that you all do, not necessarily the work that health insurance does to pay the bills that are generated. The second thing that I want to point out is that even if we want to focus on financial risk, which is something that's a very big topic, for example, in the discussion that precedes the election that will come next year, the federal election that comes next year, the main source of financial risk is actually the price of health care. And the question is how should we tackle the problem of prices in health care? Should we negotiate that down or should we provide insurance? And on that I want to just issue a note of caution that insurance, although it doesn't seem it, actually can make things worse and not always better. So I want to talk about those two themes. So let me start with the first one, which is about medical innovation and comparing medical innovation with health insurance. So let me start with the conventional view. I think this is the view that you'll hear in policy discussions. You'll often hear this, I think just in discussions with friends and things like that. People compare, complain about medical bills. And they think the big problem in the medical system is that treatment, medical treatment is expensive and that creates financial risk. And that's what worries people. And in response to that, they want things like health insurance to help protect them against those financial risks. The $20,000 bill from the hospital or treatments for drugs required for treatment for chronic care. So that's the standard view. And that's why you see in health care policy discussions and health care reform discussions, the focus always seems to be about health insurance. How can we protect people against medical bankruptcy? How can we give them more health insurance? Let's expand health insurance. And I want to argue that that view, or I will argue that view is wrong. There's a better understanding of health care risk. And in this better understanding, the key is to appreciate that the main risk that we face in health is sickness, not your medical bill. The medical bill is a consequence of sickness, but it's sickness that drives risk. And that's very, very important. Because once you see that, you'll see that health insurance doesn't actually ensure you against health risk. It ensures you against the medical bills that come after that. The thing that helps you with medical risk, the sickness, the thing that helps you with that is medical care. And the reason why people still think that there's risk is because the medical care has a cost, and that cost leads to a bill, and people focus on the bill instead of the sickness. Then they fail to appreciate the value that health care plays. Now, once you see it this way, I think the thing that you realize is what if you had a world where there was not a treatment for your medical care? Any particular sickness that you want to imagine that there was no health care? What could health insurance do for you? Not much, right? So in order for you to even have value from health insurance, you have to have the health care in the first place. And I think that's a very important insight when we think about policy. So the way that I always try to reframe this is to say, look, the primary risk in health is sickness. Medical technology takes what is a physical risk to you and converts it into a financial risk, which is a medical bill. Why is that important? Because you can't insure against the health risk, the actual sickness with insurance. But once it's a medical bill, you can insure against it. You can financially reallocate it to other people, spread it around, share the cost, which you can't do with sickness. And I think that's the most important way to look at this problem. Because once you do that, you then suddenly start thinking about medical technology differently, okay? So medical technology, most people, like if you do cost effectiveness work, you will typically say, okay, how do I value medical technology? How do I value a new therapy, a new type of surgery, a new drug? What you'll typically say is, okay, I'm going to put myself in the feet or in the shoes of somebody that's sick. And I'm going to ask, how much does my health improve? I'm sick, how much does my health improve once I have the medical care? And that's right. It is certainly true that health care has that value to a person that's sick. But what's interesting is that once you look at health care the way that I've suggested in the previous slide, you'll see that there's a second value of medical care. And that second value of medical care is a value that accrues not just to the sick person, but to the healthy person. And the reason is what health care technology does is it takes from the perspective of a healthy person, when they face the risk of sickness, they face something with some sort of physical cost. If I can then come along with technology and reduce the magnitude of that physical cost, or convert it from a physical cost, physical risk, to a financial risk, then all of a sudden there's additional value. Even a healthy person appreciates this new medical technology. It's like reducing the risk that you're going to face in the future, even before you're sick, okay? And that alteration of the gamble is a very, very important thing. In fact, I'm going to argue that it's not only like insurance, but it's worth more than insurance. And I'm going to show you some evidence on this. But before I do that, I want to give you a numerical example, because I think this concept is not intuitive. I think what I said on the last slide is fairly intuitive, but this is a little bit different. So let me give you an example with heart. And this is a stylistic example. I've made up some numbers, but it's not that far off. Okay, so heart, everybody I think in this room is familiar with highly active antiretroviral treatment. It's a treatment that helps you, that slows the progression or eliminates the progression for the most part from HIV to AIDS. Okay, now imagine a world back in, say, 1990. I'm going to assume that you live in a pre-heart world.  I don't need those facts to be true. We can modify this to make it more realistic. It won't make a difference to the point that I'm making.   Okay, that's pre-heart. By the way, insurance does nothing for you. If I gave you, if Medicaid covered AZT, it wouldn't have made a difference. It's not going to make you live longer. Okay, it's a great illustration of how health insurance by itself doesn't do anything. It needs the health care. Now let's suppose heart comes along. So now you're in the year 2000, let's say, or 96 heart comes out. So let's suppose that you're in the year 2000. Heart is going to allow you to live for a very long time, 25 plus years, even if you have HIV. Now that means that you don't have that $100,000 loss each year, right, if you get HIV. But you will have a bill. Now imagine if that bill was $100,000. Imagine if the people that produced heart charged you $100,000 a year. Would you prefer to live in 1990 or live in the year 2000? You prefer to live in the year 2000 because whereas before you guaranteed to die, face $100,000 a year monetary equivalent loss, there's nothing you could do about it. But once it's an insurable risk, once it's a $100,000 medical bill, before you even got HIV you could buy insurance that would deal with that. The government could help you with that and you could live, right? So that's the first value that comes from medical technology. It just converts a physical risk into a financial risk that you can use financial instruments for or that you can get help with from the government. Now here's another thing. Heart wasn't actually priced at $100,000. It was priced at something close to $15,000. Now that's a second benefit that medical care had, that heart had, which is that it took a risk that was $100,000 a year potential loss, and it reduced it to a $15,000 a year potential loss. That's on top of the fact that it made something that was a physical risk, into a financial risk that you can ensure. It also reduced the magnitude of that risk down to $15,000. And that's the second value that has both accruing not just to the sick person, but to the healthy person because it changed the nature of the risk. Now, what's really interesting about this is what role does insurance have at that point? Insurance takes you from, takes that $15,000 risk and helps you smooth it out, helps you get help from other people. So that you only have to pay an annual premium rather than play that $15,000 a year shock if you happen to get hard. But now you can compare what health insurance does to what heart did. Heart took you from $100,000 to $15,000. Health insurance takes you from $15,000 to your premiums. Okay, now, this is stylized fact. I could have just made up these numbers. In fact, I did make these numbers up, but they're not entirely unrealistic. I think what you would probably want to know is what about the data? What does the data say? So what we did, I did this with co-authors of mine, all actually associated with the University of Chicago, Bryce Lackawalla and Julian Rafe. Bryce is at USC and Julian is now at University of Illinois. Then what we did is we went to a database that just maintains of cost effectiveness studies. And in this database, we looked at about 1,600 studies that had enough data that we could work with. There's 1,600 studies of different treatments, both drug treatments, non-drug treatments, but that had cost effectiveness analysis done so that we could try to figure out what would be the benefit to a healthy person of getting access to these particular treatments. And then what we did is we said, okay, let's price these treatments out, let's value these treatments in the traditional way that cost effectiveness does. Then let's figure out what additional value they offer to the healthy person, this insurance value that I pointed out in the previous slide, this stuff. And then the last thing we're going to do is we're going to try to figure out how much value health insurance provides in terms of risk reduction. Once the pricing is taken care of, does that make sense? So here's what we found. We found that on average the risk reduction value of innovation first doubles the value of innovation as compared to what cost effective analysis says. Then on top of that, if you compare the risk reduction value that you get from health innovation on average across all these studies to the value of health insurance, that multiple is about 10x. That is to say, the risk reduction value of medical care among these technologies was 10x the risk reduction value of health insurance itself. So now I'm going to show you a table that's not going to make a lot of sense to you. But the important thing is if you're interested in the actual prices, this is a way that we actually depict this. We look at in the first column different types of people that have different risk tolerance. We take the typical value that we see in the literature, which is the middle row, which starts with three. We calculate what the value is for the median technology, for the 90th percentile technology, for the mean technology, using cost effective analysis. That's that first panel. Then we calculate the value, the risk reduction value, that you get from health care technology in this middle panel. Again, the median 90th percentile in the mean. And then we compare that to the value that you get from health insurance ensuring against whatever remaining financial risk there is. I circle two numbers. Those are numbers that are from the mean for the average risk averse guy in the population. And what we basically find is for the average technology in the cost effectiveness analysis registry, the value of that technology is worth about $883, the risk reduction value. But the actual financial risk reduction from health insurance is only worth about $45. That's about 20x multiple. It's a little bit smaller multiple if you look at medians. But the general idea is that you're not getting, a lot of the risk reduction comes from the value that health technology itself creates. Okay, so what's the policy implications of that? And if I stop the slide right here, I'd be okay, but I'll continue. The first thing is that maybe what we want to do is invest in innovation and not just in health insurance. That if we really are worried about risk, worried about the risk that people face, we should be focused on sickness and not just the medical bills. And even if we're worried about the medical, we should be focusing on innovation. And there are many ways that we could invest in innovation. Spend more money on the NIH. We could try to make private if this is what you want. Richard Epstein was here. I think he would argue for this. Let's make private innovation more profitable. And what you do for that is you could extend patent life for certain things, especially for more effective drugs. You can lower the hurdles to drug approval. You can subsidize clinical trials. There are many different ways. I'm not gonna plug any particular instrument. I'm just saying let's think about ways that we can promote innovation as a form of risk reduction. Now, there's a question that lingers. Well, shouldn't we still do some health insurance? And I think health insurance is a great idea for risk reduction. I don't think it's necessarily as valuable as medical innovation. But you have to be a little bit careful because it's not obvious to me at least whether or not health insurance encourages or discourages innovation. There are arguments on both sides. I'm on one hand, by having health insurance, you have guaranteed payment for treatments. And so if I'm a researcher, I say, hello, there's a future market out there for me because I know people have health insurance. But the flip side is that Tom Phillips and I have this paper out that says one of the things that we've noticed is that when people have access to health insurance, they're less likely to enroll in clinical trials. And so it makes it harder and harder for people to run the clinical trials required for you to get drug approval and that's marketing approval from the FDA. So those two things work in opposite directions. I don't know which one is necessarily bigger, but it's something worth considering is that these two things might be at odds. So let's see if I have time to make a second point, which is to think about what is the source of financial risk. So even if you took what I said before seriously and then you said, but I still wanna focus on that $15,000 risk that's left over from part, how do I address that? Well, I'm gonna talk a little bit about that and what drives that. And what's gonna drive that is prices and I wanna ask what we should do about prices. It's also a big topic today. So let me start out with a simple stylized fact. The US and the UK on a per capita basis consume about the same amount of quantity of medical care, okay? We have very similar numbers for nurses per capita, physicians per capita, office visits per capita, things like that. For sure there are some differences, but in terms of the big spending numbers, quantity-wise we're not that dissimilar, okay? Obviously we're much bigger, so I did this on a per capita basis. However, the US spends about double per capita what the UK does. So if we look at it on a GDP basis, we spend about 17%, they're spending 8.9% on a per capita basis. We spend about 8,500 per capita in the UK and this is 2010. So we're past this point. They spend about, much though, it's not exactly 3,100, that's at the bottom end, but it's about half as much, around 4,000. So we spend, consume the same quantity, but spend twice as much. So this leads, by the way, those figures come from one of my favorite articles by Ewa Reinhart and co-authors. It's called It's the Price is Stupid that says the reason why we spend more than Europe and I think that there's more nuance to this, but I love the basic point which is it's we pay double for the same stuff. We can do the same amount, we just pay double. So the question is, what do we do about that? Because it suggests that we're getting the same quantity, but we are facing twice as much risk in the United States, right? Not only a price is high, but remember, price is what leads to that financial risk that people want health insurance for. So what do we do about that? Now there are two policy options. One is we can lower prices, okay? And this is, you know, there are two ways to lower prices. Well, there are two ways to lower prices, but let me actually not go down the specific mechanism for lowering prices. But one thing is we could lower prices, you know, for example, by having the government negotiate prices with hospitals, with drug companies, we could have the government provide more healthcare so it has even more market power to be able to negotiate those prices. That's one way to do it, okay? And but the risk of that approach is that you're gonna have a risk to innovation. And so you see these debates coming out where people are saying, look, if we negotiate down prices, you know, if we negotiate down drug prices, then there's gonna be less medical drug innovation. If we negotiate down prices for hospitals or physicians, we're gonna get less amenities at hospitals or there are gonna be lower quality physicians that come into the marketplace. You know, people are gonna wanna turn to become lawyers or investment bankers, God forbid, rather than becoming doctors. So that's maybe one argument. So you say, okay, well, what else can I do? If I can't, if there's risks, downside risks to cutting prices as a way to control risk, maybe what I should do is provide people health insurance. Okay, so instead of eliminating the high prices, I'm gonna protect them against those high prices. Okay, that seems reasonable, but I wanna point out that health insurance is a double edged sword. It can sometimes worsen the problem. So let me give you two reasons why. And these are fairly even. I'm not gonna give you a lot of numbers, I'm just gonna give you the conceptual idea. And one I think is fairly obvious. Let's think about the incentives of a health insurance company. I'm a health insurance company. On January 1st, you give me your premiums. I care about my profits. During that year, you know what I wanna do? I wanna make sure that prices are low. Because the higher prices when you guys consume care, the less money I have left over at the end of the year, right? I wanna minimize costs. However, I don't exist only for one year. I exist year after year. Here's one thing I know. I know that if prices are high, you guys are gonna be worried about medical risk. And so next year, you're gonna come back to me and want more of my product. And those incentives push in opposite directions. One causes the insurance company within the policy year to wanna reduce prices, reduce costs. But the other one causes the insurance company to be like, we're okay with risk. In general, we think this increases our demand in the long run. We want that. So at least they should convince you that it's not obvious that health insurance companies are good at reducing costs. And if you ever work with health insurance companies and I have friends that have tried in the startup space, they're perplexed why the health insurance companies are not interested in lowering costs. It's just like a weird thing. But it's not so weird once you think about their long-term incentives. So let me offer you a second one, which again, I think is kind of intuitive. It's just, I wanna modify. Everybody knows that the downside of health insurance, health insurance is good, it helps reduce these risks that you face. But the thing that it can cause is moral hazard. And what I mean by that or what economists mean when they talk about that is they say, look, instead of paying $20,000 for say hospitalization or $200 for a drug, if you're facing a cost that's just the copay or 10% of that or even just $10, you'll consume more of it. And maybe you'll consume more than you need to. I know people think that's kind of weird, like why would people consume more medical care than they need to? But we do that for lots of things. And I can imagine that there would be more visits to the doctor, more visits to the hospital, the more days in the hospital, things like that. And we've estimated that empirically that in fact when you reduce the price of care by 10%, you increase the utilization of care about 2%. That's moral hazard. Now that's fine. That's not the thing that I'm really worried about. What I'm actually worried about is something that's more subtle than that. And here I think it's much easier to think about your cable bill. This is something that happens to be each year. Okay, I look at the cable bill and the cable bill changes. And then when the cable bill changes, I go, I don't need that many channels. I need to switch to another plan, things like that. Now, I do that with the cable bill, and not often as I should, but I do that because every time the cable bill goes up in price, I face that cost. Okay, so if the cable bill goes up by $10, by the way, my cable bill is about 60 bucks. So $10 is the reasonable amount. If it goes up by $10, I worry about that and I go and call the cable company up and I say I want a different plan, okay? But now let's suppose that you had insurance. And insurance paid for 90% of the cost. And so when your cable bill went up $10, you actually only paid $1. Would you call up, would you spend time on hold, trying to get the cable company? It's like over a year, that's 12 bucks, not worth my time. The inside here is that when you have insurance, you not only over consume, but you don't complain when prices rise quickly. Does that make sense? Because somebody else is bearing most of the cost increase, so you don't feel the need to complain. And what that means is that you're more tolerant of price increases, medical price inflation. If there's more medical price inflation, that's increasing medical risk over the long run, right? And so that's the second reason where it's not obvious to me that in fact health insurance is helping you reduce. It is reduce prices or keep down that financial risk. In the very, very short run, yes. But in the long run, not obvious to me. And I think that for a lot of people, this is intuitive, like medical price inflation has been quite high, prices of medical care have been much larger than they were way in the past when we had a lot less insurance. Okay, so again the policy implications. One is you can either negotiate prices, okay? You can have the government do it, do Medicare for all, Medicaid for all, whatever your preference, and have them negotiate. Exercise them to opposite prices. Alternatively you can just say, for whatever we're doing now, we're gonna do more cost effectiveness analysis and only pay people, pay providers for treatments that are cost effective or pay the amount that we pay would be proportional to the cost effectiveness. Sometimes this is called value-based insurance design. I think Stacey a little bit talked about this, to some extent. If you're not a government type and you're a free market type, then you could go the opposite way and say, hey, I actually want less insurance and actually I want more price transparency. Let's make all the prices transparent so people can compete, free markets. You can do that too, that's one policy option. But if you do those things and you somehow lower prices, maybe what you ought to be thinking about is, how do I address this innovation problem? And I want a couple of those things with things that make innovation easier. And that's where we get back to the last set of slides. Maybe you want to do things like NIH spending, lower barriers to drug approvals, allow companies to innovate within their organizations to reduce costs, things like that. Okay, to do that sort of innovation. Okay, let me stop there, but thank you very much for your time. Okay, I love the concept of insurance so I'll try to speak in defense of the insurance industry. For the second part of your presentation. Well, we might, you know, can we evaluate the two edges of insurance the same way you evaluate insurance versus innovation like in the first part, whether that principal agent problem actually worsens the insurance coverage of that. I mean, that's some research direction which will take quite some time in research funding. But for the first part, you know, is the result, the comparison result, heavily dependent on how you measure risk? You know, whether it's about variance or whether it's tail risk, because I would imagine insurance is a very good cover for tail risk, but innovation might not be a very good cover for tail risk, you know, for diseases that cost a lot to cure, but are rare. Yeah, so I think I'm at, so let me just restate the point to a re-understand. So health insurance might be really good for very rare but very big bills and maybe innovation is not as good at that. And I want to push back on both of those claims a little bit. I don't know the answer, but I'm a little skeptical. Here's the reason why. First of all is people in this country don't have high deductible, don't in general have high deductible health insurance plan that covers you when you're just above $5,000 in cost or $10,000 in cost. They have something a lot closer to first dollar insurance where the deductibles are in the order of a few hundred dollars or a thousand dollars. And so in that context, I don't think health insurance is only providing those, the high risk costs. And as it turns out, the biggest drivers of actual costs I think are not the tail risks, but that is to say the big drivers of health insurance costs are not the tail risks, it's the middle, like the lower cost treatments that we see more frequently. The answer to the medical innovation part is, I don't know. I mean, on the one hand, you might say HIV heart is a great example of tail risk. It was not a very common thing up until a point and death was as about as big an outcome as you could imagine. But I can see the arguments going in both directions. There's also chronic treatments that are actually not tail risks, but middle risk. What I would say to that is that there's some people that care about tail risks, some people that care about middle risk. Health insurance can cover both and health care can cover both. The question is whether you care about the tail or the middle, what is doing a more cost effective job of protecting you? If I had my druthers, I'd worry a little bit more about tail risk, but I would still think that medical care is critical. And the last point I leave you with is, without the medical care, health insurance is not even good for tail risk. There's no reason to go to the hospital for a $20,000 bill if the hospital can't do something for your problem. True. Thanks very much, and you have a great talk. Thanks again. Thank you. Mark, we'll let the...


Welcome to dealing with materials data, we are going to look at collection and analysis and interpretation of materials data, specifically we are going to look at how to use R to do these things. And this is the first module, this is the module to introduction to R and we are coming almost to the end of this module. So I want to do a few more data sets and case studies using R to look at the data, import the data, plot the data or manipulate the data and save the figures and save the data and so on and so forth. So that is what we are going to do in this session. So this is a session on case studies in importing and plotting data. Once you import the data, you can manipulate the data, you can manipulate the data both the numerical data and the strings. So I am going to show you an example of how we can do that and we can save the modified data. So sometimes after you have done the manipulation maybe you want to save this data and you can save it as a CSV file. Like I mentioned it is also possible to use a write and write it to a file but that is something that we are not going to look at at least at the moment. So if it is needed later we will do it. So that is the first one that we are going to do, take a data and do some manipulation on the data and save the modified data. And then the second case study is to consider another data set. It is the same as the elements data set except that it has now also the ENX modulus data. Once you have ENX modulus data, we want to know how to plot specific modulus that is modulus divided by density against the melting temperature. So previously we just took the two columns and plotted but now we want to do some algebraic manipulations on the columns and such modified data we want to plot. And you might be wondering why such plots are needed. These plots are very useful and they are known as property charts are Ashby plots and they are used in learning about the properties as well as in the selection of materials for engineering applications and some of you might have seen in properties and selection courses such plots and so you will see how to generate such plots for yourself. So we are going to use the melting point versus specific modulus plot and once we do that such a plot will also show you the extreme points. So we are going to plot and we are going to look how the points look like and specifically we are going to learn that iron is an extreme point and so then we are going to manipulate the plot in such a way that we leave out iron and look at what happens. And in the process there is some programming thing that one learns and I want to emphasize it at this point. Whenever R returns any error message you should read it and take the corresponding action or if it gives some warnings you have to learn what the warning is about. So this is what conversing with the machines means you have to read the messages you have to understand the messages and you have to respond to the messages. So it is exactly like knowing language if you say you know a specific language it means that if people ask you something or people tell you something you will be able to understand and respond to them. In a similar fashion when you say I know our programming language what is expected is that you are able to converse with the machine if it gives some messages be it error or warning messages you are able to read understand and respond. I know that the tendency for beginning programmers is to go look everything up on the internet and then copy paste the solutions it is sometimes useful and it can help you pick things fast. But if you want to be a good programmer at some point it is better to read the error messages and try to decode them yourself before taking help from the net. Of course net is a very good resource and you should utilize it but before doing that it is useful sometimes to try it out on your own because that way even when you get the solution from the net you will have a better understanding. So it is important to read the error and warning messages and you will see that when we do some of the manipulations it does give some messages and you should pay attention to those messages. The second case study that we are going to do is for specific strength and we will see that for things like Ashby plot sometimes the logarithmic scale is needed and you will also see why property charts or Ashby maps are useful and you are going to notice that the materials are grouped according to the kind of material it is and you will also see some grouping according to geometry for example all fibers and whiskers irrespective of what type of material they are are going to fall in one way and it also is very useful to notice the outliers and in this case the outlier happens to be a natural composite namely wood. So this is what we want to do in this session we are going to take some data in CSV format we are going to import them we are going to use ggplot for plotting and we are going to do with a little bit of manipulation and we are going to pay more attention to the data and try to learn about the data by doing this and so this is going to be our almost last session on introduction to R. So let us go do that. So this is the first one is to take the elements data that we have already done so let me open R and let me take these commands. So elements so we are going to read the CSV file from data and then we are going to order the elements according to melting point and we are going to write this data into new CSV file called elements melting point sorted. So that is the thing we want to do and so you can see x of course we can see x and you can see that it is sorted according to melting points you know previously it was first aluminum or something and you can see the original numbering it was 1 here and then 2 was nickel and 3 was gold and so on 4 was silver and so on but that ordering has been changed because now it is going by the increasing order of the melting point so it is 321, 426, 50, 660 etc and so we have written a new data file and that is there in the elements tms sorted so let us go there and see so there is this elements tms sorted so let us say okay. So according to melting point they are now listed and this data is available and to do this so I also have this name sorted let me remove this file okay so I am going to remove this file so in this directory there is no name sorted now but let us do sorting according to names and write that file okay so let us do that this here is where we are going to do okay so again we are going to read the data and we are going to write the data and we are going to so the difference between the previous one and this one is that previously we said okay elements according to melting point you have to order and here we are going to say that order according to the names element element right. So what does it mean to say order according to this as you will see it will alphabetically order and the file will then be written to a CSV file okay. So now let us say x and you can see aluminum, beryllium, cadmium, chromium, copper etc so this is now alphabetically ordered and this is what would be written here so element names sorted or CSV is available for us now and as you can see now it is sorted according to the alphabetical order. So in other words it is possible to take data import it and manipulate it and you can manipulate numerical data as well as strings so you can alphabetically order or order according to numbers increasing decreasing or ascending descending order and then you can write them so the write.csv is the equivalent of read.csv for writing csv files and you can also so help order will also tell you for example if you want to do not ascending order but descending order what should you do okay. So decreasing equal to false which would mean that increasing is true so you can order so should be sort order be increasing or decreasing okay so that is what this does so you can use the increasing command and so you can read the help file to get more information but this tells you how to manipulate data and order them and write them and so on so forth okay. So now let us look at the second data file like I said there is an elements 2.csv data file which has the extra information so in addition to this data it also has the ENX modulus and so we want to read this data and work with this data so this is what we are going to do okay so let us do this let us find out what this command is okay so we are going to read into elements the data from elements 2.csv and we are going to order and the ordering is according to the specific modulus because elements x modulus by elements density is the specific modulus so we are going to order the data according to this specific modulus this is just as an exercise so it is not essential that you have to order and we are going to say specific modulus is this quantity okay that is what we are going to show store and we are going to now use ggplot2 to plot this specific stiffness right that is the specific modulus versus melting point and we are going to save it as a PDF file and so the device off means that it will execute all these plotting commands and then close the PDF file and the plotting commands as usual so it is ggplot it says take the x data aesthetics is x is specific modulus y is melting point and color should be according to crystal structure and the geometry is that it is a scatter plot it is a point and you are going to label the points of course according to element and there is a justification for the label so that is what this plot commands are so this we have done several times and see when you do this now there is no plot that you can see here that is because the PDF file actually wrote the figure here right so this is what the data you have and as you can see the specific modulus versus melting point most of them fall in this column there is nothing beyond the 0.05 or 06 nothing beyond 0.05 actually except for iron which is really really an outlier so its specific modulus is like that is modulus divided by its density is very high it is falling somewhere beyond 0.15 so as compared to all the other elements iron is really an outlier so this you can see so in other words the modulus with respect to density if you normalize by the density value different elements have different densities and if you account for those differences then you see that the relative ratio is different for iron as compared to all these other materials and within of course given specific modulus now the melting temperature is different for different materials so you can see that there are many points cadmium, beryllium, titanium and tungsten they are all falling almost on the similar values but their melting points is different so this is what we see and so and there was no plot because we plotted it to the file. So let us now go back and do some more manipulation so of course you can get a plot by now after dev off if you put the command then you will get the plot here again showing that iron is really really an outlier here. So let us do the next one. Next one is that so we want to take a closer look at these values and because that is an outlier we really do not want beyond this point. So we are going to change the range when we are doing the x plot so that is what we are going to do now let us go back and do it here. So we are going to read so the reading and this thing has been done already in the data so probably we do not need up to this. So we have and we also do not need this let us start with ggplot and so what we want to do is that specific modulus melting point according to color but now we want to restrict ourselves to 0.05 we do not want to go beyond this and geometry is point and labeling is according to element. So everything else is the same as earlier as here except that we have introduced a new limit for the x range right. When you do that it gives you a warning message it says removed one rows containing missing values and removed one rows containing missing values for both the point and label that is because it had the iron point and it had the label iron and R is just telling you that when you did this rescaling of the x axis you actually missed one data point and this is very useful because sometimes if you do not pay attention and if you just give some x limit or y limit in the process if you leave out some data it is better to know that we have left out some data and that is what R is giving you as a warning. So in this case we know we wanted that data to be left out so it is okay for us but in some cases you should not inadvertently leave out data points and so it is always a good idea to read the warning messages and to know. Now because we have expanded the x axis in the relevant regime you can see that the data has spread out and previously we thought that you know all these points were at the same level but now you can see that cadmium and tungsten are actually slightly apart probably tungsten and gold are at the same level and so this kind of gives you a better picture and the more closer look at the data and ggplot just by adding one more layer allows you to do that. So that is what we have done and this is what is shown here also okay. So now we have done now let us take another data it is called specific strength and here is the data. So this data file I have generated by reading the data table that is given in one of the Wikipedia pages about specific strength and it has the material like concrete, rubber, copper, polypropylene etc. As you can see they are of different material types right this is a composite, this is an elastomeric, this is a metallic material, this is a polymeric material and some of them are fibrous, some of them are synthetic fibers and so on. So for example spider silk is a natural fiber, silicon carbide fiber is a synthetic fiber and glass material and so on and so forth okay. So and I have even put iron viscose as synthetic fibers and so on so forth. Then there is the density of these materials and the specific strength of these materials that is strength by density. As you know strength is the resistance that a material gives to permanent deformation or plastic deformation okay. So we want to know the specific strength how do these materials perform and as you can see the numbers change a lot I mean you have something that is going in thousands then you have numbers in hundreds then you have numbers in tens and then you might even have numbers in ones right. So there is a large range and by the way in these cases when the numbers were given in a range for some of these materials I have just taken the average to be the value just to make life easy for us when we are doing the plotting. And so here also you can see the numbers can change from point something to some large number like 8 point something. So there is two orders of magnitude here it is more it is about 3 orders of magnitude. So the numbers vary a lot so it is difficult to get them all on the same plot unless you do some manipulation which is to plot them on a logarithmic scale okay. So that is what we are going to do so let us start let us take this data and load them okay. So we are going to do the specific strength .csv file we are going to load and we are going to order that and store it as x and now if you say x yeah so it has all this data so it has the material type density and specific strength okay. Now we want to use ggplot and we are also going to use the library scales because we want to change the x y axis to a logarithmic scale okay. So let us do this and let us plot and see what happens. So ggplot we are using library scales as usual ggplot you have to tell what is the data what is the aesthetics this density versus specific strength we want to do. Now we are going to color according to material type metallic materials in one glass in another concrete in another and so on. And coordinate transformation both x and y we are taking logarithmic axis so we are plotting on logarithmic scale and the geometry is of course points and labeling has to be done according to the name of the material. So from the data it will take the name of the material and do the plotting right. So let us do this. So now you can see that okay so it is a logarithmic scale obviously so 1, 2 and up to 10. So here you saw that its density was not changing some 0.7 to something but here there was a large scale difference and so you can see that and according to material type the coloring is done right composites elastomeric glass metallic etc they are all so we can zoom and see okay. So you can see that there are different colors and the moment you put it in colors you can see that all these polymers for example are clustering together all these metallic materials for example are clustering together and all the fibers for example are clustering together I mean they are all of different type glass fiber basalt fiber silicon carbide spider silk and iron whisker so they are all of different type of materials but in terms of their structure or geometry or morphology they are different and you can see that they are also clustering together. So there is a effect of the material type there is an effect in which form that material is and both these come out very very nicely using these property charts and the other thing that comes out very very nicely is that wood is really an outlier so in terms of density very low densities but specific strengths comparable even more than I mean you know comparable to metals or more than and in terms of density there is a huge difference between these but they do have a very good specific strength. So this kind of information is very useful and it is also nice to have if you are trying to choose a material for a particular engineering problem and so that is what this plot does. So yeah so we are going to do some more plots so I want to that is a log plot now I am going to make it logarithm to the base 10 so it is possible to do that so everything is the same except that the transformation is to logarithm to the base 10 so you can do and you can get the data so you can see now it is 100,000 etc so it is on the logarithm to the base 10. So we can do the next exercise which is the same so once we have this let us do this and let us change the X limit so why do we want to do like we did earlier so we want to remove some of these outliers and take a closer look at the other data because of this balsa we are having this range and there is nothing much that is happening here so let us remove that and look at the close data and again it tells you that you know you have already missing a point which we already know and the scale is already present of course obviously it was already present so we do not have to worry about it but it is saying telling you that X was already present so it is adding another scale for the X according to what we gave so now you can see that things are now spread out a little bit and you can see again the greens clustering together the blues clustering together and so on so forth so it is really nice to have this kind of property charts. So that is the figure we have generated and that brings us to the end of this session. So what we have learned now is that you can read data and before plotting you can also manipulate the data and plot the manipulated data and do an analysis so this pretty much brings us to the end of the introduction to our session. We will have a summary session and complete this module. Thank you.